Method and device for searching the fastest travel path for terminal vehicles

Abstract

The present invention discloses a method and device for searching the fastest travel path for terminal vehicles. The method includes storing and obtaining a corresponding list of alternative routes based on a directed graph, using an actor corresponding to an intersection; dynamically updating a list of dense quantity fluctuation time series records and a list of feature combination count records based on incoming vehicle messages and / or outgoing vehicle messages; obtaining a vehicle path planning instruction, searching for multiple alternative routes between a starting intersection and a destination intersection from the corresponding list of alternative routes, and simulating the multiple alternative routes in sequence after arranging them in ascending order of path length; sequentially triggering all actor instances included in each alternative route in a forward or reverse direction based on information in the vehicle path planning instruction, and calculating the estimated travel time of the vehicle on the alternative route; and selecting the alternative route corresponding to the shortest estimated travel time as the fastest travel route. The present invention can select the fastest travel route through the corresponding actor instances, thereby improving the efficiency of terminal loading and unloading operations.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method and device for searching the fastest travel path for vehicles at a terminal. Background Art

[0002] Currently, automated transformations at traditional container terminals are widely introducing driverless container trucks and retrofitting manned trucks with GPS positioning terminals. The goal is to use these devices to optimize vehicle routing and driving efficiency. Existing path planning methods, particularly those for driverless vehicles, often rely on dynamic planning based on real-time positioning data.

[0003] Traditional manned vehicles at terminals, especially container trucks without positioning equipment, have a high degree of autonomy and arbitrariness. Furthermore, after automation, manned and unmanned vehicles need to operate together, resulting in a very complex traffic environment. Consequently, existing path planning methods are unable to meet the path planning needs of terminals.

[0004] Based on this, a new technical solution is needed. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides a method and apparatus for searching the fastest travel path for vehicles at a terminal, so as to at least solve the problem that existing path planning methods cannot meet the path planning requirements of the terminal.

[0006] The embodiments of the present invention provide the following technical solutions:

[0007] An embodiment of the present invention provides a method for searching the fastest travel path for vehicles at a terminal, comprising:

[0008] S1. Build a directed graph under a distributed architecture:

[0009] Map all intersections and lanes of the terminal to corresponding Actor instances;

[0010] Defining the spatiotemporal deduction attributes of each of the Actor instances, wherein the spatiotemporal deduction attributes at least include a list of dense quantity fluctuation time series records and a list of feature combination count records;

[0011] The directed graph is constructed using the Actor instance corresponding to the intersection and the Actor instance corresponding to the lane as vertices and directed edges, respectively, and each Actor instance stores a list of alternative paths to other intersections;

[0012] S2. The Actor instance subscribes to the vehicle entry and exit messages pushed by the external system or other Actor instances, and dynamically updates the dense amount of fluctuation time series record list and the feature combination count record list based on the vehicle entry or vehicle exit message;

[0013] S3. Fastest driving route search:

[0014] Obtaining a vehicle route planning instruction, wherein the vehicle route planning instruction at least includes a designated departure time, a planned departure time, a starting intersection, and a destination intersection;

[0015] Searching for multiple alternative paths between the starting intersection and the destination intersection from the alternative path list and arranging the paths in ascending order of path length;

[0016] Simulating multiple alternative paths in sequence, triggering all the Actor instances included in each alternative path in sequence from the Actor instance corresponding to the starting intersection according to the vehicle path planning instruction, and calculating the expected driving time of the vehicle on the alternative path based on the dense quantity fluctuation time series record list and the feature combination count record list corresponding to each Actor instance on the alternative path and the vehicle characteristics, wherein the planned departure time is used as the vehicle's entry time at the starting intersection;

[0017] The alternative path corresponding to the shortest estimated travel time of the path is used as the fastest travel path.

[0018] Furthermore, the traffic density fluctuation time series record list records the changes in traffic density in a timestamp sequence, and the feature combination count record list is used to record the driving time and number of occurrences under different passing characteristics and traffic density combinations; the alternative path list includes multiple alternative paths from the current intersection to other intersections.

[0019] Furthermore, step S1 further includes:

[0020] Defining vertex attributes of the intersection, wherein the vertex attributes include an entry lane ID array, an exit lane ID array, and an intersection travel length dictionary table;

[0021] Defining directed edge attributes of the lane, wherein the directed edge attributes include an entry intersection ID, an exit intersection ID, a lane type, and a lane travel length;

[0022] The spatiotemporal deduction attributes of the intersection and the lane are defined, wherein the spatiotemporal deduction attributes further include an incoming vehicle message dictionary table, wherein the incoming vehicle message dictionary table is accessed by a key being a vehicle ID and a value being an incoming vehicle message key-value pair; the dense quantity fluctuation time series record list is arranged in chronological order;

[0023] The spatiotemporal deduction attribute corresponding to the intersection further includes the list of alternative paths, where the multiple alternative paths are obtained by the Actor instance corresponding to the intersection based on the K-shortest path algorithm;

[0024] The dense quantity fluctuation time series record list includes dense quantity change time and vehicle ID array within the corresponding time; the feature combination count record list also includes passing features, and the passing features include vehicle features and vehicle passing environment features.

[0025] Furthermore, the method for searching the fastest travel path for terminal vehicles further includes:

[0026] A method for defining each of the Actor instances includes calculating the traffic density using the entry time, calculating the estimated driving time of the corresponding Actor using the traffic density and characteristics, calculating the estimated exit time using the entry vehicle message, and calculating the estimated entry time using the vehicle exit message.

[0027] Furthermore, the method for calculating the traffic density using the entry time includes:

[0028] Determining whether the dense volume fluctuation time series record list contains a record whose dense volume change time is equal to or earlier than the entry time and is closest to the entry time;

[0029] If yes, the number of elements corresponding to the vehicle ID array corresponding to the record in the dense volume fluctuation time series record list is recorded as the dense volume of traffic;

[0030] If not, record the traffic density as zero; or

[0031] Calculating the estimated driving time of the corresponding Actor using the traffic density and the passing characteristics includes:

[0032] Determining whether the feature combination counting record list contains a record corresponding to the traffic density and the passing feature;

[0033] If yes, the driving duration that occurs the most times in the record is used as the estimated driving duration of the Actor, or the average of multiple driving durations that occur the most times and are the same in the record is used as the estimated driving duration of the Actor;

[0034] If not, the estimated driving time of the Actor is calculated using the driving length and speed limit corresponding to the Actor; or

[0035] The method of calculating the estimated exit time using the incoming vehicle information includes:

[0036] Acquire an entry time based on the entry vehicle message;

[0037] Calculate the expected exit time based on the entry time and the expected driving time of the Actor; or

[0038] The method of calculating the estimated entry time using the vehicle exit message includes:

[0039] Acquiring a departure time based on the vehicle departure message;

[0040] The estimated entry time is calculated based on the exit time and the estimated driving time of the Actor.

[0041] Furthermore, the calculating the expected entry time based on the exit time and the expected driving time of the Actor includes:

[0042] After obtaining the exit time, using the exit time instead of the entry time to calculate the traffic density;

[0043] and calculating an estimated travel time of the Actor based on the traffic density and the passing characteristics, and obtaining an alternative estimated entry time by subtracting the estimated travel time of the Actor from the exit time;

[0044] The traffic density is continuously calculated based on the alternative estimated entry time, and when it is the same as the traffic density calculated using the exit time instead of the entry time, the alternative estimated entry time is the correct estimated entry time.

[0045] Furthermore, step S3 includes:

[0046] Refresh the dense quantity fluctuation time series record list of the corresponding Actor instance based on the actual or expected vehicle entry message or the vehicle exit message;

[0047] The feature combination count record list of the corresponding Actor instance is refreshed based on the actual vehicle entering message or the vehicle exiting message.

[0048] Furthermore, refreshing the dense quantity fluctuation time series record list of the corresponding Actor based on the actual or expected incoming vehicle message includes:

[0049] Acquire a vehicle ID, entry time, and travel path based on the entry vehicle message, and calculate the estimated exit time based on the entry vehicle message;

[0050] Traversing the dense quantity fluctuation time series record list and canceling the passing traces at the last refresh, wherein canceling the passing traces at the last refresh includes deleting the vehicle ID added at the last refresh, and / or when the vehicle ID arrays in two consecutive records are the same, only retaining the previous record;

[0051] When the entry vehicle message does not include a revoked flag, a new passing trace is added to the dense volume fluctuation time series record list. The adding of the new passing trace includes: if there is no record corresponding to the entry time or the expected exit time in the dense volume fluctuation time series record list, the entry time and the expected exit time are used as the dense volume change time and a new record is added, and the vehicle ID array of the new record is filled with the content of the vehicle ID array corresponding to the previous record with a similar time; or if there is a record whose dense volume change time is later than or equal to the entry time and earlier than the expected exit time, the vehicle ID is added to the vehicle ID array corresponding to the record;

[0052] When there is a subsequent Actor instance in the Actor instance, the entry time is updated to the estimated exit time, and the updated entry vehicle message is pushed to the subsequent Actor instance; or

[0053] The list of dense quantity fluctuation time series records of refreshing the corresponding Actor instance based on the actual or expected vehicle exit message includes:

[0054] Acquire vehicle ID and departure time based on the vehicle departure message;

[0055] If there is no record in the dense volume fluctuation time series record list in which the dense volume change time is equal to the exit time, a new record is added using the exit time as the dense volume change time, and the vehicle ID array of the new record is filled with the vehicle ID array of the previous record;

[0056] If there is a record in the dense volume fluctuation time series record list whose dense volume change time is later than or equal to the exit time, then remove the vehicle ID in the vehicle ID array corresponding to the record; or

[0057] The feature combination count record list of the corresponding Actor instance refreshed based on the actual incoming vehicle message includes:

[0058] Acquire a vehicle ID based on the incoming vehicle message, and use the vehicle ID as a key to record the incoming vehicle message into the incoming vehicle message dictionary table; or

[0059] The feature combination count record list of the corresponding Actor instance refreshed based on the actual vehicle exit message includes:

[0060] Acquire a vehicle ID based on the vehicle exit message, and obtain the incoming vehicle message from the incoming vehicle message dictionary table using the vehicle ID;

[0061] Acquire an entry time based on the vehicle entry message, and acquire an exit time based on the vehicle exit message, so as to obtain a driving duration;

[0062] Based on the incoming vehicle information and the passing environment, a passing feature is obtained, and based on the entering time, a traffic density is calculated;

[0063] The driving duration and the number of occurrences under different passing characteristics and traffic density are calculated and updated.

[0064] Furthermore, the vehicle route planning instruction also includes a vehicle ID, a designated arrival time, and a planned arrival time;

[0065] The simulating the alternative path includes:

[0066] When determining the designated departure time and the planned departure time and triggering all the Actor instances included in each alternative path in sequence from the Actor instance corresponding to the starting intersection in a forward path, the planned departure time is used as the entry time, the alternative path is used as the driving path of the vehicle, and the driving path, the vehicle ID, and the vehicle characteristics are used to form the entering vehicle message;

[0067] Calculate the estimated exit time corresponding to the Actor instance in the alternative path based on the incoming vehicle message, and determine whether the Actor instance corresponding to the destination intersection is simulated;

[0068] If so, subtracting the planned departure time from the departure time to obtain the estimated travel time of the alternative route;

[0069] If not, the entry time in the entry vehicle message is replaced with the estimated exit time, and used as the subsequent Actor's estimated entry vehicle message until the Actor instance corresponding to the destination intersection is simulated; or

[0070] When determining the designated arrival time and the planned arrival time, and sequentially triggering all the Actor instances included in each of the alternative paths from the Actor instance corresponding to the destination intersection in a reverse path, the planned arrival time is used as the exit time of the destination intersection, the alternative path is used as the driving path of the vehicle, and the driving path, the vehicle ID, and the vehicle characteristics are used to form the vehicle exit message;

[0071] Calculating the estimated entry time based on the vehicle exit message, and determining whether the vehicle has been simulated to the starting intersection;

[0072] If so, the estimated travel time of the path is obtained by subtracting the estimated entry time of the Actor instance corresponding to the starting intersection from the planned arrival time;

[0073] If not, the exit time in the vehicle exit message is replaced with the estimated entry time and used as the vehicle exit message estimated by the subsequent Actor until the Actor instance corresponding to the starting intersection is simulated.

[0074] Furthermore, the method for obtaining the shortest estimated travel time of the path includes:

[0075] After simulating one of the alternative routes, caching the estimated travel time of the corresponding route;

[0076] Determine whether the estimated travel time of the current alternative route is greater than the estimated travel time of the shortest route among all previously simulated alternative routes; if not, continue simulating the current alternative route; if so, terminate the simulation of the current alternative route;

[0077] Determine whether the simulation has reached the last alternative path, or whether the estimated travel time of the current alternative path is longer than the estimated travel time of the previous alternative path; if not, continue simulating the next alternative path; if so, terminate the simulation and find the shortest estimated travel time among all the simulated alternative paths;

[0078] Further, after obtaining the shortest estimated travel time of the route;

[0079] If a designated departure time is determined, the estimated arrival time is calculated based on the planned departure time and the shortest estimated travel time of the route;

[0080] If a designated arrival time is determined, the estimated departure time is calculated based on the planned arrival time and the shortest estimated travel time of the path.

[0081] The present invention also provides a device for searching the fastest travel path for terminal vehicles, comprising:

[0082] A construction unit is configured to map all intersections and all lanes of the terminal to corresponding Actor instances; define spatiotemporal deduction attributes of each Actor instance, wherein the spatiotemporal deduction attributes include at least a list of dense quantity fluctuation time series records and a list of feature combination count records; construct the directed graph using the Actor instances corresponding to the intersections and the Actor instances corresponding to the lanes as vertices and directed edges, respectively, and each Actor instance stores a list of alternative paths to other intersections;

[0083] a refresh unit, configured to dynamically refresh the dense quantity fluctuation time series record list and the feature combination count record list according to the vehicle entry message and / or vehicle exit message pushed by each Actor instance after the Actor instance subscribes to the vehicle entry message and / or vehicle exit message pushed by the external system or other Actor instances;

[0084] A path search and deduction unit is used to obtain a vehicle path planning instruction, wherein the vehicle path planning instruction at least includes a designated departure time, a planned departure time, a starting intersection, and a destination intersection; searching for multiple alternative paths between the starting intersection and the destination intersection from the alternative path list, and arranging them in ascending order according to path length; simulating multiple alternative paths in sequence, triggering all the Actor instances included in each alternative path in sequence from the Actor instance corresponding to the starting intersection according to the vehicle path planning instruction, and calculating the expected driving time of the vehicle on the alternative path based on the dense quantity fluctuation time series record list and the feature combination count record list and vehicle characteristics corresponding to each Actor instance on the alternative path, wherein the planned departure time is used as the vehicle's entry time at the starting intersection; and taking the alternative path corresponding to the shortest expected path driving time as the fastest driving path.

[0085] Compared with the prior art, the at least one technical solution adopted in the embodiment of the present invention can achieve the following beneficial effects:

[0086] The present invention provides a method for searching the fastest travel path for terminal vehicles. The method can simulate vehicle travel on alternative routes based on spatiotemporal deduction, and derive an estimated travel time based on the spatiotemporal changes in traffic encountered during travel. The method then selects the one with the shortest estimated travel time from multiple alternative vehicle travel paths as the fastest vehicle travel path. The method can also specify departure or arrival times during the simulation to support more accurate task allocation by external systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0088] Figure 1 This is a flow chart of a method for searching the fastest travel path for terminal vehicles according to an embodiment of the present invention;

[0089] Figure 2 A detailed flow chart of a method for searching the fastest travel path for terminal vehicles according to an embodiment of the present invention;

[0090] Figure 3 This is an example diagram of a refresh intensive quantity fluctuation time series record list according to an embodiment of the present invention;

[0091] Figure 4 Schematic diagram of an intersection Actor or lane Actor instance according to an embodiment of the present invention. DETAILED DESCRIPTION

[0092] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0093] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the features in the following embodiments and embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application.

[0094] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this application, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspect described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0095] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0096] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples, however, one skilled in the art will appreciate that the examples can be practiced without these specific details.

[0097] Most existing path planning methods are designed for unmanned vehicles and are not fully applicable to traditional terminals. Traditional terminals need to provide a unified path planning method for all types of vehicles in the entire terminal to foresee the temporal and spatial changes of traffic on the driving path as much as possible.

[0098] Precise task allocation ensures that the queue order of vehicles at loading / unloading points closely matches the order of loading and unloading operations, minimizing lateness or early arrival. If the queue order and the operational order are misaligned, at best, the displaced vehicles will need to swap tasks (often limited to unladen container trucks), or at worst, vehicles will need to move out to make way for vehicles with earlier tasks. This is particularly true for ship loading and unloading operations at the dockside. If loading and unloading equipment is waiting for vehicles to arrive, this inevitably leads to a pause in loading and unloading operations, impacting terminal efficiency, particularly ship loading and unloading efficiency, which is closely linked to terminal operating profitability.

[0099] In light of this, the inventors conducted in-depth research and improved exploration into Actor technology, directed graphs, traffic density, passing characteristics, and path search planning. They discovered that by building a directed graph under a distributed architecture, the intersections and lanes of the terminal are mapped to Actor instances, and the K-shortest path algorithm is used to obtain multiple alternative paths. The list of time series records of traffic density fluctuations and the list of feature combination count records are dynamically updated based on actual or expected vehicle entry and exit messages. When planning vehicle paths, alternative paths are simulated in ascending order of path length, triggering Actor instances to calculate the expected travel time of the path, and ultimately selecting the fastest travel path. Departure or arrival times can also be specified to support external systems in achieving more accurate task allocation, thereby improving the efficiency of terminal loading and unloading operations.

[0100] The following is a brief description of the technology used in the present invention:

[0101] The Actor distributed parallel computing model originated from a conceptual model of parallel computing proposed by Carl Hewitt in 1973. The method for searching the fastest travel path for terminal vehicles proposed in this invention can be built on, but is not limited to, the Dapr open source platform.

[0102] The virtual actor computing unit (or "actor") used in this invention refers to a computing unit with independent computation and state (equivalent to a process, without shared memory). It can be deployed in a distributed computing hosting service cluster and registered with the cluster's hosting service instance. The hosting service is responsible for addressing, activation, execution, release, and fault recovery. Actors are transparent to the outside world; the outside world can access a specific actor simply by using a specified actor keyword (or ID) without knowing which server the actor is active on. Furthermore, actors can process incoming and outgoing messages using a publish-subscribe model, enabling event-driven and parallel computing. This means that computational processes can be split and pushed to multiple actors for parallel computation, and the final computation results can be aggregated and converted based on the feedback messages. When an actor receives a message, it can modify its internal state, execute the corresponding process, and respond by sending feedback messages. It can also utilize its own timers and reminders to activate itself periodically, execute pre-set processes, and influence the outside world. Generally, an activated actor is not released immediately after its execution completes. It can continue to respond to and provide feedback to the outside world, process real-time messages, and manipulate its own data. Its own data can be retained in memory and used until there are no new accesses or messages. At this point, the actor and its data are released, significantly reducing operational pressure on the database and enabling high-performance computing.

[0103] The database used in the present invention is a relational database, preferably the PostgreSQL open source database, but other databases may also be used instead.

[0104] The following describes the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0105] like Figures 1 to 4 As shown, an embodiment of the present invention provides a method for searching the fastest travel path for vehicles at a terminal, comprising:

[0106] S1. Build a directed graph under a distributed architecture:

[0107] Map all intersections and lanes of the terminal to corresponding Actor instances;

[0108] Define the spatiotemporal deduction properties of each Actor instance. These properties include a time series record list of traffic density fluctuations and a feature combination count record list. The time series record list of traffic density fluctuations records changes in a timestamp sequence, while the feature combination count record list is used to record the driving time and occurrence frequency under different passing features and traffic density combinations.

[0109] Build a directed graph using the Actor instances corresponding to the intersection and the Actor instances corresponding to the lane as vertices and directed edges respectively;

[0110] Based on the directed graph, the Actor corresponding to the intersection stores and obtains the corresponding alternative path list, which includes multiple alternative paths from the current intersection to other intersections.

[0111] Among them, each Actor instance represents an intersection or a lane in the terminal. For example, the Actor instance corresponding to the intersection is used to represent the intersection in the terminal, and the Actor instance corresponding to the lane represents the lane in the terminal. As the basic computing unit in the distributed architecture, the Actor instance is responsible for processing vehicle driving information and path planning tasks related to itself.

[0112] Among them, the time series record list of traffic density fluctuations records in detail the specific values ​​of traffic density at intersections or lanes at different time points. The timestamp is accurate to the second and can clearly reflect the changing trend of traffic density over time.

[0113] For example, the record shows that at 08:30:00, the traffic density at intersection A is 10 vehicles, and at 08:30:05, the traffic density increases to 12 vehicles, and so on.

[0114] The feature combination count record includes features such as vehicle type (e.g., manned or unmanned), empty or loaded indicator (empty or loaded), and environmental characteristics of the vehicle passing through the intersection or lane (e.g., intersection ID, lane ID, direction of travel). This list counts the duration of each feature combination and the number of times each combination occurred, along with the number of times each vehicle passed through the intersection or lane.

[0115] For example, at intersection A, when the vehicle types are manned vehicles and empty vehicles, and the traffic volume is 5 vehicles, the record of driving time of 15 seconds appears 20 times.

[0116] The following is a detailed explanation of how to build a directed graph under a distributed architecture, using the Actor instances corresponding to the intersections as vertices and the Actor instances corresponding to the lanes as directed edges:

[0117] The connection relationship between the intersections and lanes of the terminal is represented as a combination of vertices and edges. The direction of the directed edge represents the driving direction of the vehicle on the lane, thereby constructing a traffic network model of the entire terminal.

[0118] For example, intersection A is connected to intersection B through lane AB. In the directed graph, the Actor instance of intersection A is the vertex, and the Actor instance of lane AB is the directed edge, pointing from the vertex of intersection A to the vertex of intersection B.

[0119] Based on the established directed graph, the Actor instance corresponding to the intersection stores and retrieves the corresponding list of alternative paths. This list includes multiple alternative paths from the current intersection to other intersections. The Actor instance corresponding to the intersection uses a K-shortest path algorithm (such as Yen's algorithm) to calculate and store multiple alternative paths from the current intersection to other intersections. These alternative paths are sorted by length, allowing for quick selection of shorter paths for simulation during path planning.

[0120] For example, there are three alternative routes from intersection A to intersection C, with lengths of 500 meters, 600 meters, and 700 meters respectively. These three routes will be sorted in the alternative route list in ascending order of length.

[0121] like Figure 4 As shown, the present invention maps the intersection and lane entities of the terminal into Actor instances on a distributed parallel computing model. The intersection Actor instance is marked with an intersection ID, and the lane Actor instance is marked with a lane ID. Each instance consists of properties, methods, and events. Properties and public methods can be remotely called by the outside world, and events are triggered by subscribing to messages published and pushed by the outside world. The intersection Actor instance can be associated with one or more entry lane Actor instances, and also with one or more exit lane Actor instances. A lane Actor instance is generally associated with one entry intersection Actor instance and one exit intersection Actor instance. If there is an entry gate at one end of the lane, the other end is the exit intersection Actor instance. If there is an exit gate at one end of the lane, the other end is the entry intersection Actor instance.

[0122] Step S1 constructs a basic model and defines spatiotemporal deduction attributes to record and reflect changes in traffic flow and situations under different driving characteristics, thereby facilitating subsequent deduction of alternative routes and calculating the estimated travel time of the alternative routes.

[0123] S2. Each Actor instance subscribes to the vehicle entry and vehicle exit messages pushed by the external system or other Actor instances, and dynamically updates the dense quantity fluctuation time series record list and feature combination count record list based on the vehicle entry and / or vehicle exit messages.

[0124] Each Actor instance subscribes to vehicle entry and exit messages pushed by external systems (such as the Terminal Operating System (TOS)) or other Actor instances. These messages contain key vehicle driving information, such as vehicle ID, entry or exit time, vehicle characteristics, and driving path.

[0125] For example, when an Actor instance receives a message about an actual or predicted vehicle entry, it first uses the message to calculate the estimated exit time. It then dynamically updates the list of dense volume fluctuation time series records based on information such as the entry time and vehicle ID in the message. Specifically, the actual or predicted vehicle's passage through this Actor is first revoked, and then a judgment is made. If the message does not contain a "revoked" flag (indicating that the vehicle is actually traveling or has confirmed its planned route), the actual or predicted vehicle's passage through this Actor instance is added.

[0126] The following is a detailed explanation of the cancellation method and the addition method:

[0127] The cancellation method is: traverse the record list from early to late, and remove the vehicle ID from the recorded vehicle ID array; if the vehicle ID array contents of the two records are the same, retain the previous record and delete the next record to streamline the record list.

[0128] The adding method is: when it is determined that the dense quantity fluctuation time series record list does not contain a record whose dense quantity change time is equal to the entry time, then use the entry time as the dense quantity change time to add a record, and the vehicle ID array of the record is filled with the vehicle ID array content of the previous record (that is, earlier than the entry time and closest to the entry time); when it is determined that the dense quantity fluctuation time series record list does not contain a record whose dense quantity change time is equal to the expected exit time, then use the expected exit time as the timestamp to add a record, and the vehicle ID array of the record is filled with the vehicle ID array content of the previous record (that is, earlier than the expected exit time and closest to the expected exit time); finally, find the records whose dense quantity change time is later than or equal to the entry time and earlier than (but not equal to) the expected exit time from the dense quantity fluctuation time series record list, and add the vehicle ID to their vehicle ID array.

[0129] Similarly, when an actual vehicle exit message is received, the list of dense volume fluctuation time series records is dynamically updated based on the exit time and vehicle ID information in the message. Specifically, if the dense volume fluctuation time series record list does not contain a record with a dense volume change time equal to the exit time, a record is added using the exit time as the dense volume change time, and the vehicle ID array of this record is filled with the vehicle ID array content of the previous record (that is, the record that is earlier than the exit time and closest to the exit time). Finally, the dense volume fluctuation time series record list is found with a dense volume change time later than or equal to the exit time, and the vehicle ID of this vehicle is removed from the vehicle ID array.

[0130] For example, when an Actor instance receives a message about an actual vehicle entering the vehicle, it uses the vehicle ID in the message as the key to record the message in the dictionary table for incoming vehicle messages. Similarly, when an actual vehicle exits the vehicle, the feature combination count record list is dynamically updated based on information such as the exit time and vehicle ID in the message.

[0131] Specifically, the vehicle ID is used to find the incoming vehicle message from the dictionary table of incoming vehicle messages; the entry time is taken from the incoming vehicle message, and the driving time is subtracted from the exit time to obtain the driving duration; the passing feature is extracted from the incoming vehicle message and the passing environment; the traffic density is calculated using the entry time; the combination of driving duration, passing feature, and traffic density is used to search in the feature combination count record list: if the record is found, the record count value is increased by 1; if the record is not found, a record is added with this combination content, and the count value is 1.

[0132] In step S2, when the Actor instance receives the actual or expected incoming vehicle message pushed by the external system, the Actor instance corresponding to the lane or intersection entered not only refreshes its own spatiotemporal deduction attributes, but also calculates the expected exit time. Then, based on the driving path in the incoming vehicle message, the expected exit time is used as the entry time in the expected incoming vehicle message of the subsequent Actor instance of the Actor instance corresponding to the lane or intersection entered. By pushing the expected incoming vehicle message to the subsequent Actor instance, each Actor instance on the driving path can refresh its own spatiotemporal deduction attributes one by one according to the expected incoming vehicle message pushed by the previous Actor instance, such as refreshing the dense quantity fluctuation time series record list, and finally making the spatiotemporal deduction attributes of the Actor instance close to the actual state, so as to accurately calculate the fastest driving path.

[0133] For vehicles already in motion, the external system only needs to push messages about the actual vehicle entering the path to the Actor instance corresponding to the intersection or lane it entered, and messages about the actual vehicle exiting the path to the Actor instance corresponding to the intersection or lane it exited. If the planned path is confirmed, the external system only needs to push messages about the expected vehicle entering the path to the Actor instance corresponding to the starting intersection or lane. Other Actors along the path refresh their spatiotemporal deduction properties based on the expected vehicle entering message pushed by the previous Actor instance.

[0134] Step S2 can timely reflect the dynamic changes of the terminal traffic, so that the system can quickly adapt to the changes in the terminal traffic conditions, thereby improving the accuracy and timeliness of subsequent path planning.

[0135] S3. Fastest driving route search:

[0136] Obtain a vehicle path planning instruction with at least vehicle characteristics, a starting intersection, and a destination intersection, and search for multiple alternative paths from the starting intersection to the destination intersection from the alternative path list of the Actor instance corresponding to the starting intersection, and simulate multiple alternative paths in sequence after arranging them in ascending order of path length;

[0137] Trigger all Actor instances included in each alternative path in sequence, and calculate the estimated driving time of the vehicle on the alternative path based on the dense quantity fluctuation time series record list and feature combination count record list corresponding to each Actor instance on the alternative path and the vehicle characteristics;

[0138] The alternative path corresponding to the shortest estimated travel time is taken as the fastest travel path.

[0139] Vehicle routing instructions can be provided by an external system (such as a terminal's dispatching system) or input by the user. For example, the system receives a routing request from a manned heavy vehicle to go from intersection A to intersection C.

[0140] The following example illustrates "searching for multiple alternative paths from the starting intersection to the destination intersection from the alternative path list of the Actor instance corresponding to the starting intersection, and arranging them in ascending order of path length."

[0141] For example, three paths are selected from the list of alternative paths from intersection A to intersection C, with lengths of 500 meters, 600 meters, and 700 meters respectively. After being arranged in ascending order of length, they are 500 meters, 600 meters, and 700 meters.

[0142] Step S3 plans and screens routes, calculates the estimated travel time of routes, and screens the shortest alternative routes based on the estimated travel time, thereby quickly planning the fastest route from the starting intersection to the destination intersection for the vehicle, improving the vehicle's travel efficiency. By optimizing the vehicle's travel route, the vehicle's travel time at the terminal is reduced, thereby improving the terminal's overall operational efficiency.

[0143] In some embodiments, step S1 further includes defining vertex attributes of the intersection, the vertex attributes including an entry lane ID array, an exit lane ID array, and an intersection travel length dictionary table; and defining directed edge attributes of the lane, the directed edge attributes including an entry intersection ID, an exit intersection ID, a lane type, and a lane travel length.

[0144] The lane ID array records the lane IDs of all lanes entering the intersection.

[0145] For example, if intersection A has two entry lanes, with IDs lane1 and lane2, the entry lane ID array is [lane1, lane2]. This helps determine which lanes a vehicle can enter the intersection from, providing entry information for path planning.

[0146] The exit lane ID array records the lane IDs of all lanes exiting the intersection.

[0147] For example, intersection A has two exit lanes, whose IDs are lane3 and lane4 respectively. The exit lane ID array is [lane3, lane4], which clarifies which lanes the vehicle can exit from the intersection through and provides exit information for path planning.

[0148] The intersection travel distance dictionary table uses the entry and exit lane IDs as keys and the travel distance within the intersection as values. For example, at intersection A, if the travel distance from lane 1 is 50 meters and the vehicle exits lane 3, the corresponding key-value pair in the dictionary table is (lane1 & lane3, 50), providing a basis for calculating the vehicle's travel time and distance within the intersection.

[0149] Among them, lane types include main roads and work lanes, and the main roads generally have a higher speed limit, such as 30km / h, while the work lanes have a lower speed limit, such as 15km / h.

[0150] Among them, the lane travel length records the length of the lane.

[0151] The spatiotemporal deduction attributes of intersections and lanes are defined. The spatiotemporal deduction attributes also include a dictionary table of incoming vehicle messages. The dictionary table of incoming vehicle messages uses the vehicle ID as the key and the incoming vehicle message as the value to access the key-value pair. The list of dense quantity fluctuation time series records is arranged in chronological order. The spatiotemporal deduction attributes corresponding to the intersection also include a list of alternative paths. Multiple alternative paths are obtained by the Actor instance corresponding to the intersection based on the K-shortest path algorithm.

[0152] The dense quantity fluctuation time series record list includes dense quantity change time and vehicle ID array within the corresponding time; the feature combination count record list also includes passing features, which include vehicle features and vehicle passing environment features.

[0153] The traffic density refers to the actual / estimated number of vehicles traveling or parked at the intersection / lane at the moment the vehicle enters the intersection / lane, excluding the vehicle itself. If there are no other vehicles, the traffic density is zero, indicating that vehicles can pass through the intersection / lane at the speed limit without hindrance. The traffic density can be calculated by counting vehicle entry and exit messages: entering vehicles are counted as +1, and exiting vehicles are counted as -1.

[0154] Among them, the time series record list of traffic density fluctuations refers to the record of actual / expected traffic flow changes at intersections / lanes in seconds. It only needs to record the data when the traffic density fluctuates. The content includes but is not limited to: the time of density change, the vehicle ID array, and the traffic density is the number of elements in the vehicle ID array; the content of the vehicle ID array, adding the vehicle ID when the vehicle actually / expected enters, and removing the vehicle ID when the vehicle actually / expected exits; because vehicles do not necessarily arrive on time, when an Actor has an actual / expected entry or exit, it must not only refresh its own time series record, but also notify the next Actor on the driving path to iteratively refresh their respective time series records one by one for calibration.

[0155] Feature combination counting records extract travel time and pass features from actual entry and exit messages and the passing environment. For different combinations of travel time, pass features, and traffic density, the number of vehicle passes is counted separately. The resulting records include, but are not limited to, travel time, pass features, traffic density, and count values. Since count values ​​are accumulated over time, the count values ​​of all records must be periodically deducted by the smallest count value (for example, once a year) to avoid overflow.

[0156] Among them, passing features refer to some features of the moving vehicle and the passing environment that affect the driving time. The passing features of the intersection include but are not limited to: intersection ID, entry lane ID, exit lane ID, vehicle features, and the passing features of the lane include but are not limited to: lane ID, lane type (main road / work lane), and vehicle features; the passing features can be extracted from the incoming vehicle message. For example: in the passing features of the intersection, the entry lane ID is the ID value found in the driving path of the incoming vehicle message that is before the intersection ID, and the exit lane ID is the ID value found in the driving path of the incoming vehicle message that is after the intersection ID.

[0157] In the present invention, the method for obtaining the list of alternative paths is as follows:

[0158] Obtain weighted directed graph data from the database, with intersections as vertices, lanes as directed edges, and lane travel time calculated by dividing the lane travel length by the speed limit as edge weights (this data is also used to initialize the directed graph properties of the intersection actor and lane actor when they are activated).

[0159] Set the K value, that is, specify how many alternative paths to find. You can use the total number of dock intersections, or other empirical values, as long as it can reduce unnecessary calculations;

[0160] Use the K-shortest path algorithm on the weighted directed graph to find K alternative paths to other intersections, specifically Yen's algorithm;

[0161] The K alternative routes to other intersections are organized into an alternative route list and stored in the database for retrieval the next time it is activated.

[0162] This embodiment comprehensively models the terminal's traffic network by defining detailed attributes for intersections and lanes, including vertex attributes, directed edge attributes, and spatiotemporal deduction attributes. This includes not only static physical connectivity (such as how intersections and lanes are connected), but also dynamic traffic information (such as traffic density and vehicle characteristics), providing richer data support for subsequent path planning. Furthermore, the K-shortest path algorithm can quickly select shorter paths for simulation, improving path planning efficiency. Furthermore, by combining data from spatiotemporal deduction attributes (such as traffic density, travel time, and vehicle characteristics), it can more accurately predict vehicle travel on different paths, thereby determining the optimal travel path.

[0163] This embodiment uses data from spatiotemporal deduction attributes (such as the dictionary of incoming vehicle messages, the time-series record list of dense volume fluctuations, and the record list of feature combination counts) to provide detailed records for traffic analysis. Analysis of this data provides a deep understanding of the dynamics of terminal traffic, including trends in traffic flow and the behavioral characteristics of different vehicle types, thus supporting optimized terminal traffic management.

[0164] This embodiment takes into account different lane types (main roads and work lanes) as well as various vehicle characteristics (such as manned vehicles, unmanned vehicles, empty vehicles, loaded vehicles, etc.) and passing environment characteristics, enabling the system to adapt to the complex traffic scenarios of the terminal.

[0165] In some embodiments, the method for searching for the fastest travel path for terminal vehicles further includes:

[0166] Define methods for each Actor instance, including using entry time to calculate traffic density, using traffic density and features to calculate the corresponding Actor's estimated driving time, using incoming vehicle messages to calculate the estimated exit time, and using vehicle exit messages to calculate the estimated entry time.

[0167] The Actor's estimated driving time refers to the estimated driving time of a vehicle on the lane or intersection corresponding to the Actor instance.

[0168] The entry time refers to the time of entering the lane or intersection corresponding to the Actor instance; the exit time refers to the time of exiting the lane or intersection corresponding to the Actor instance.

[0169] The following are the methods in each Actor:

[0170] (1) Methods for measuring traffic density using entry time include:

[0171] Determine whether there is a record in the dense quantity fluctuation time series record list with a dense quantity change time equal to or earlier than the entry time and closest to the entry time;

[0172] For example, if the entry time is 08:30:05 and there are records with times 08:30:00 and 08:29:55 in the list, the record with time 08:30:00 is selected.

[0173] If yes, the number of elements corresponding to the vehicle ID array recorded in the dense volume fluctuation time series record list is recorded as the traffic flow dense volume.

[0174] For example, if the vehicle ID array in the record has 10 elements, then the traffic density at this time is 10 vehicles.

[0175] If not, the traffic density is recorded as zero, which means there are no other vehicles at the intersection or lane.

[0176] (2) The estimated driving time of the corresponding Actor calculated by using the traffic density and feature measurement includes:

[0177] Determine whether the feature combination counting record list contains records corresponding to the traffic density and passing characteristics.

[0178] Among them, the passing features include vehicle features such as vehicle type and empty weight mark, as well as vehicle passing environment features such as intersection ID and lane ID.

[0179] If yes, the driving duration with the most occurrences in the record is used as the Actor's estimated driving duration, or the average of the driving durations with the most occurrences in the record and the same number of occurrences is used as the Actor's estimated driving duration;

[0180] If not, the Actor's corresponding travel length and corresponding speed limit are used to calculate the Actor's estimated travel time.

[0181] Specifically, when the Actor corresponding to the intersection uses the traffic density and the feature to calculate the corresponding Actor's estimated travel time, the method for searching the record from the feature combination count record list is as follows:

[0182] Find the record that matches the traffic density, intersection ID, entry lane ID, exit lane ID, vehicle type (occupied / unoccupied / foreign vehicle), and empty weight flag (empty / loaded). If found, do not proceed to the next step.

[0183] Expand the search range and find records that match the traffic density, intersection ID, entry lane ID, exit lane ID, and empty / load mark (empty / loaded). If any records are found, do not proceed to the next steps.

[0184] Continue to expand the search range to find records that match the traffic density, intersection ID, and empty / load mark (empty / loaded). If you find any, do not proceed to the next step.

[0185] Continue to expand the search range and search the feature combination count record list of all intersections to find the record that matches the traffic density and empty / load mark (empty / loaded). If found, do not proceed to the subsequent steps.

[0186] Continue to expand the search range and search the feature combination count record list of all intersections to find the record that matches the traffic density. If found, do not continue with the subsequent steps.

[0187] Get the records of the feature combination count record list of all intersections.

[0188] When the Actor corresponding to the lane uses the traffic density and features to calculate the corresponding Actor's estimated driving time, the method for finding the record from the feature combination count record list is as follows:

[0189] Find the records that match the traffic density, lane ID, lane type (main road / operation lane), vehicle type (manned / unmanned / foreign vehicle), and empty weight mark (empty / loaded). If found, do not proceed to the next steps.

[0190] Expand the search scope and find records that match the traffic density, lane ID, lane type (main road / operation lane), and empty / load mark (empty / loaded). If found, do not proceed to the next steps.

[0191] Continue to expand the search scope and search the feature combination count record list of all lanes to find the record that matches the traffic density, lane type (main road / operation lane), and empty / load mark (empty / loaded). If found, do not proceed to the subsequent steps.

[0192] Continue to expand the search range and search the feature combination count record list of all lanes to find the record that matches the traffic density and lane type (main road / operation lane). If found, do not proceed to the subsequent steps.

[0193] Continue to enlarge the search range and search in the feature combination count record list of all lanes to find the record that is consistent with the lane type (main road / work lane).

[0194] (3) Information used to calculate the estimated time of exit of vehicles entering the vehicle includes:

[0195] Obtaining the entry time based on the entry vehicle message;

[0196] Extracting passing features from the incoming vehicle message and the passing environment;

[0197] Use entry time to measure traffic density;

[0198] Use traffic density and passing features to calculate the expected driving time of the Actor;

[0199] The estimated exit time is calculated based on the entry time and the estimated driving time of the Actor.

[0200] Specifically, the estimated exit time is obtained by adding the entry time to the Actor's estimated travel time.

[0201] For example, if the entry time is 08:30:00 and the Actor's estimated travel time is 30 seconds, the estimated exit time is 08:30:30.

[0202] (4) Calculating the estimated entry time using vehicle exit information includes:

[0203] Obtaining the departure time based on the vehicle departure message;

[0204] The estimated entry time is calculated based on the exit time and the estimated driving time of the Actor.

[0205] For example, subtract the initial estimated Actor travel time from the exit time to get a preliminary estimated entry time.

[0206] (4.1) Calculating the estimated entry time based on the exit time and the actor's estimated travel time includes:

[0207] After obtaining the exit time, the exit time is used instead of the entry time to calculate the traffic density;

[0208] Based on the traffic density and the characteristics, the actor's estimated driving time is calculated, and the alternative estimated entry time is obtained by subtracting the actor's estimated driving time from the exit time.

[0209] The traffic density is continued to be calculated based on the alternative estimated entry time, and when it is the same as the traffic density calculated using the exit time instead of the entry time, the alternative estimated entry time is the correct estimated entry time.

[0210] The initial estimated entry time is used to calculate the current traffic density and compared with the traffic density used to calculate the initial estimated travel time of the actor. If the two are the same, the initial estimated entry time is confirmed to be the correct estimated entry time. If they are different, the new traffic density is used to calculate the actor's estimated travel time and the estimated entry time is calculated again. This process is repeated until the two traffic density measurements are consistent.

[0211] In this embodiment, by defining the above methods, the Actor instance can accurately process the vehicle's entry and exit information, and calculate key parameters of the driving process in real time, such as the traffic density and the Actor's estimated driving time, to provide the vehicle with accurate driving time prediction; and these methods also provide the necessary calculation basis for path planning, so that the system can flexibly calculate the estimated driving time under different paths based on real-time traffic conditions and vehicle characteristics, thereby selecting the optimal driving path for the vehicle.

[0212] In some embodiments, step S3 includes refreshing the intensive quantity fluctuation time series record list of the corresponding Actor instance based on the actual or expected vehicle entering message or vehicle exiting message; refreshing the feature combination count record list of the corresponding Actor instance based on the actual vehicle entering message or vehicle exiting message.

[0213] The expected incoming vehicle message or the expected outgoing vehicle message is generally the expected incoming vehicle message or the expected outgoing vehicle message sent by the upstream Actor instance of the current Actor instance.

[0214] The following is a detailed description of how to refresh the space-time deduction attributes:

[0215] (1) Based on the actual or expected incoming vehicle message, the list of dense fluctuation time series records of the corresponding Actor is refreshed, including:

[0216] Obtain vehicle ID, entry time, and travel path based on incoming vehicle messages, and calculate estimated exit time based on incoming vehicle messages;

[0217] Traverse the dense quantity fluctuation time series record list and cancel the passing traces at the last refresh. Canceling the passing traces at the last refresh includes removing the vehicle ID added at the last refresh, and when the vehicle ID arrays in two consecutive records are the same, only retain the previous record;

[0218] When the entry vehicle message does not include a revoked flag, a new passing trace is added to the dense quantity fluctuation time series record list. Adding a new passing trace includes: if there is no record corresponding to the entry time or the expected exit time in the dense quantity fluctuation time series record list, then the entry time and the expected exit time are used as the dense quantity change time and a new record is added, and the vehicle ID array of the new record is filled with the vehicle ID array content corresponding to the previous record of similar time; or if the dense quantity change time is later than or equal to the entry time and earlier than the expected exit time, the vehicle ID is added to the vehicle ID array corresponding to the record;

[0219] When there are subsequent Actor instances in the Actor instance, the entry time is updated to the estimated exit time, and the updated entry vehicle message is pushed to the subsequent Actor instances.

[0220] By revoking the last refreshed trace and adding a new one, we ensure that the dense volume fluctuation time series record list accurately reflects the current vehicle traffic situation, avoiding redundant records and the accumulation of erroneous information. Furthermore, when subsequent Actor instances are added, the entry time and vehicle information are updated and pushed to subsequent Actors. This allows Actor instances along the entire driving path to perform spatiotemporal deduction based on the latest information, ensuring the consistency and accuracy of the simulated driving process.

[0221] This method ensures that the data in the dense traffic fluctuation time series record list is synchronized with the actual traffic conditions. The system can obtain the latest traffic flow information in real time, thereby improving the timeliness and accuracy of path planning. By timely updating and streamlining records, the record list is kept concise and efficient, which helps to improve the operating efficiency of the system and reduce unnecessary computing and storage overhead.

[0222] (2) Based on the actual or expected vehicle exit message, the list of dense fluctuation time series records of the corresponding Actor instance is also updated:

[0223] Obtain vehicle ID and departure time based on vehicle departure message;

[0224] If there is no record in the dense quantity fluctuation time series record list whose dense quantity change time is equal to the exit time, a new record is added with the exit time as the dense quantity change time, and the vehicle ID array of the new record is filled with the vehicle ID array of the previous record;

[0225] If there is a record in the dense quantity fluctuation time series record list whose dense quantity change time is later than or equal to the exit time, the vehicle ID in the vehicle ID array corresponding to the record is removed.

[0226] This method can correct the traffic density records at subsequent time points by removing the vehicle IDs corresponding to the records whose density change time is later than or equal to the exit time, so that the records can accurately reflect the actual traffic conditions of the vehicles.

[0227] This method ensures that the dense volume fluctuation time series record list can fully and accurately reflect the traffic flow changes from vehicle entry to exit, providing a complete data basis for systematic traffic analysis and prediction, and improving the accuracy of analysis and planning.

[0228] (3) Based on the actual incoming vehicle message, the feature combination count record list of the corresponding Actor instance is refreshed, including:

[0229] The vehicle ID is obtained based on the incoming vehicle message, and the incoming vehicle message is recorded in the incoming vehicle message dictionary table using the vehicle ID as the key.

[0230] This method records incoming vehicle messages in an incoming vehicle message dictionary table, facilitating subsequent rapid query and management of the vehicle information. It also provides basic data for updating the feature combination counting record list, and quickly locates and obtains the corresponding incoming vehicle messages through the vehicle ID, thereby improving the system's data processing efficiency and saving time for subsequent route planning and traffic analysis.

[0231] (4) Based on the actual vehicle exit message, the feature combination count record list of the corresponding Actor instance is refreshed, including:

[0232] Obtain the vehicle ID based on the vehicle exit message, and use the vehicle ID to obtain the incoming vehicle message from the incoming vehicle message dictionary table;

[0233] The entry time is obtained based on the vehicle entry message, and the exit time is obtained based on the vehicle exit message to obtain the driving duration;

[0234] Based on the incoming vehicle information and the passing environment, the passing characteristics are obtained, and the traffic density is calculated based on the entry time;

[0235] Calculate and update the driving duration and occurrence frequency under different passing characteristics and traffic density.

[0236] This method combines incoming vehicle messages and outgoing vehicle messages to obtain complete vehicle driving information, including driving duration and passing characteristics, so as to record the key data of a vehicle's complete driving cycle in the terminal transportation network. Then, based on the complete driving information, the corresponding records in the feature combination counting record list are updated to reflect the latest statistical information of driving duration under different feature combinations and traffic density.

[0237] The external system can implement the following functions based on step S3:

[0238] When a vehicle enters an intersection / lane, only the actual vehicle entry message needs to be pushed to the intersection / lane actor. The actor will automatically push the message to trigger subsequent actors on the driving path to refresh and calibrate their timing records one by one. The automatically pushed message is the expected vehicle entry message, and there is no need to push the expected vehicle exit message.

[0239] When determining a vehicle's driving path, only the expected incoming vehicle message needs to be pushed to the first intersection / lane actor. This actor will automatically push the message to trigger subsequent actors on the driving path to refresh their time series records one by one. The automatically pushed message only refers to the expected incoming vehicle message, and there is no need to push the expected vehicle exit message.

[0240] When revoking a vehicle's driving path, it is only necessary to push a message about the expected incoming vehicle containing the revoked sign to the first intersection / lane Actor. The Actor will push the message to automatically trigger subsequent Actors on the driving path to sort out their time series records one by one and remove the vehicle ID from the vehicle ID array of these time series records.

[0241] In some of these embodiments, the vehicle routing instructions also include a vehicle ID.

[0242] After obtaining a list of alternative paths according to the starting intersection and the destination intersection and simulating the alternative paths from shortest to longest, the method for simulating the alternative paths includes the following steps:

[0243] Extract the Actor instance sequence corresponding to the intersection or lane to be passed from the alternative simulation path;

[0244] When determining the designated departure time and the planned departure time and triggering all the Actor instances included in each alternative path in sequence from the Actor instance corresponding to the starting intersection in a forward path, the planned departure time is used as the entry time, the alternative path is used as the driving path of the vehicle, and the driving path, the vehicle ID, and the vehicle characteristics are used to form the entering vehicle message;

[0245] Calculate the estimated exit time of the Actor instance in the alternative path based on the incoming vehicle message, and determine whether the Actor instance corresponding to the destination intersection is simulated;

[0246] If so, subtract the planned departure time from the departure time to get the estimated travel time of the alternative route;

[0247] If not, the entry time in the entering vehicle message is replaced with the estimated exit time of the current Actor instance, and used as the estimated entering vehicle message of the subsequent Actor until the Actor corresponding to the destination intersection is simulated.

[0248] The planned departure time is the actual departure time of the vehicle, and the planned departure time may be equal to or earlier than the designated departure time.

[0249] In this embodiment, when a designated departure time is determined, the system begins simulation using the Actor instance corresponding to the starting intersection, following a forward path. The planned departure time is used as the entry time for the starting intersection, and the alternative path, vehicle ID, and vehicle characteristics are combined to form an entry vehicle message, providing complete initial conditions for the simulated driving.

[0250] The system calculates the estimated exit time for each actor instance in the candidate path based on incoming vehicle information, and gradually determines whether the actor instance corresponding to the destination intersection has been simulated. This forward simulation method can gradually deduce the vehicle's driving process on the path and accurately predict the exit time for each intersection or lane.

[0251] Among them, if the Actor instance corresponding to the destination intersection is successfully simulated, the estimated driving time of the alternative path can be obtained by subtracting the planned departure time from the exit time, providing key data for evaluating the advantages and disadvantages of different paths.

[0252] If the Actor instance corresponding to the destination intersection is not simulated, the entry time in the incoming vehicle message is replaced with the estimated exit time of the current Actor instance, and this is used as the estimated entry vehicle message for subsequent Actors to continue the simulation. This step-by-step deduction method ensures the consistency and integrity of the simulation process until the estimated travel time for the entire route is obtained.

[0253] Further, when determining the designated arrival time and the planned arrival time and sequentially triggering all the Actor instances included in each of the alternative paths from the Actor instance corresponding to the destination intersection in a reverse path, the planned arrival time is used as the exit time of the destination intersection, the alternative path is used as the driving path of the vehicle, and the driving path, the vehicle ID, and the vehicle characteristics are used to form the vehicle exit message;

[0254] Calculate the estimated entry time based on the vehicle exit message and determine whether the simulation reaches the starting intersection;

[0255] If so, the estimated travel time of the path is obtained by subtracting the estimated entry time of the Actor instance corresponding to the starting intersection from the planned arrival time;

[0256] If not, the exit time in the vehicle exit message is replaced with the estimated entry time of the current Actor instance, and used as the vehicle exit message of the subsequent Actor until the Actor instance corresponding to the starting intersection is simulated.

[0257] The planned arrival time can be earlier than or equal to the designated arrival time.

[0258] Among them, each Actor instance can calculate the estimated driving time of the Actor at the corresponding intersection or lane based on the estimated entry time, so as to obtain the estimated driving time of the path on each alternative path.

[0259] Among them, each Actor can also calculate the estimated driving time of the Actor in the lane or intersection corresponding to the Actor based on the traffic density and feature measurement.

[0260] This application can deduce the estimated driving time of alternative paths based on the properties, methods and events defined within each Actor. The external system can also deduce multiple alternative paths based on the specified arrival time, specified departure time, planned arrival time, planned departure time, destination intersection and starting intersection to determine which alternative path is the fastest driving path at the specified arrival time or specified departure time.

[0261] In this embodiment, when determining a designated arrival time, the system begins with the Actor instance corresponding to the destination intersection and simulates the reverse path. The planned arrival time is used as the exit time from the destination intersection, and the alternative paths, vehicle ID, and vehicle characteristics are combined to form a vehicle exit message, providing accurate target conditions for reverse deduction.

[0262] Among them, the reverse simulation can calculate the vehicle's driving process from the end point to the starting point, meeting the user's strict requirements on arrival time.

[0263] Among them, if the Actor instance corresponding to the starting intersection is successfully simulated, the estimated driving time of the path can be obtained by subtracting the estimated entry time of the Actor instance corresponding to the starting intersection from the planned arrival time.

[0264] Among them, if the Actor instance corresponding to the starting intersection is not simulated, the exit time in the vehicle exit message is replaced with the estimated entry time of the current Actor instance, and the simulation continues as the vehicle exit message of the subsequent Actor, ensuring the integrity and accuracy of the reverse reasoning, and finally determining the path that meets the arrival time constraint.

[0265] In both forward and reverse simulations, this embodiment gradually triggers Actor instances and calculates time. This system comprehensively considers factors such as traffic conditions, traffic density, and vehicle characteristics at each section of the route, thereby improving the accuracy and reliability of route planning. This time- and event-driven simulation approach ensures that the planned route is more consistent with actual traffic conditions, providing a more reliable driving plan for the vehicle.

[0266] Furthermore, methods for obtaining the shortest estimated travel time include:

[0267] After simulating an alternative route, cache the corresponding estimated travel time for subsequent comparisons;

[0268] Determine whether the estimated travel time of the current alternative route is greater than the estimated travel time of the shortest route among all previously simulated alternative routes; if not, continue simulating the current alternative route; if so, terminate the simulation of the current alternative route;

[0269] Determine whether the simulation has reached the last alternative path, or whether the estimated driving time of the current alternative path is longer than the estimated driving time of the previous alternative path; if not, continue to simulate the next alternative path; if so, terminate the simulation, and find the shortest estimated driving time of all the simulated alternative paths, and the corresponding alternative path is the path with the shortest estimated driving time.

[0270] This embodiment avoids unnecessary computational overhead by comparing estimated travel times of routes in real time during simulation and promptly terminating simulations of routes with no potential. For example, if partial results for the current simulated route indicate that its travel time is unlikely to be optimal, the system will prematurely terminate the simulation of that route and test other possible routes. This ensures that the system finds the route with the shortest estimated travel time among all possible routes, thereby providing the vehicle with the optimal travel plan.

[0271] Furthermore, after obtaining the estimated travel time of the shortest path;

[0272] If a specific departure time is determined, the estimated arrival time is calculated based on the planned departure time and the estimated travel time of the shortest route;

[0273] If a specific arrival time is determined, the estimated departure time is calculated based on the planned arrival time and the estimated travel time along the shortest route.

[0274] By providing accurate estimated arrival and departure times, this system can help terminal operators better coordinate various operational activities. For example, loading and unloading equipment and personnel can be prepared in advance based on the vehicle's estimated arrival time, ensuring efficient loading and unloading operations. Furthermore, vehicles can also rationally arrange subsequent trips based on their estimated departure times, improving overall operational efficiency.

[0275] The present invention also provides a device for searching the fastest travel path for terminal vehicles, comprising a building unit, a refreshing unit and a path search and deduction unit. Among them, the construction unit is used to map all intersections and all lanes of the terminal to corresponding Actor instances respectively; define the spatiotemporal deduction attributes of each Actor instance, and the spatiotemporal deduction attributes include a dense volume fluctuation time series record list and a feature combination count record list. The dense volume fluctuation time series record list records the changes in traffic density in a timestamp sequence, and the feature combination count record list is used to record the driving time and number of occurrences under different passing characteristics and traffic density combinations; use the Actor instances corresponding to the intersections and the Actor instances corresponding to the lanes as vertices and directed edges respectively to build a directed graph; based on the directed graph, the Actor corresponding to the intersection stores and obtains the corresponding alternative path list, and the alternative path list includes multiple alternative paths from the current intersection to other intersections; the refresh unit is used to dynamically refresh the dense volume fluctuation time series record list and the feature combination count record list according to the incoming vehicle message and / or vehicle exit message after each Actor instance subscribes to the incoming vehicle message and vehicle exit message pushed by the external system or other Actor instances; the path search deduction unit is used to obtain vehicle path planning instructions, and the vehicle path planning instructions at least include specified Departure time, designated arrival time, planned departure time, planned arrival time, starting intersection and destination intersection; search for multiple alternative paths between the starting intersection and the destination intersection from the alternative path list of the corresponding Actor instance, and simulate multiple alternative paths in sequence after arranging them in ascending order of path length; when determining the designated departure time and planned departure time, trigger all Actor instances included in each alternative path in sequence from the Actor instance corresponding to the starting intersection in a forward path, and calculate the estimated driving time of the vehicle on the alternative path based on the dense quantity fluctuation time series record list and feature combination count record list and vehicle characteristics corresponding to each Actor instance on the alternative path; or when determining the designated arrival time and planned arrival time, trigger all Actor instances included in each alternative path in sequence from the Actor instance corresponding to the destination intersection in a reverse path, and calculate the estimated driving time of the vehicle on the alternative path based on the dense quantity fluctuation time series record list and feature combination count record list and vehicle characteristics corresponding to each Actor instance on the alternative path; the alternative path corresponding to the shortest path estimated driving time is taken as the fastest driving path.

[0276] This paper proposes a method and device for searching for the fastest travel routes for terminal vehicles based on spatiotemporal deduction. This method utilizes a distributed parallel computing model and spatiotemporal deduction methods to plan the fastest travel routes for terminal vehicles (including internal and external container trucks in both traditional and automated terminals), thereby improving terminal loading and unloading efficiency and avoiding operational downtime. The system adapts to complex traffic environments, accurately allocates tasks, and improves terminal operational efficiency and economic benefits. It also offers native scalability and high performance, enabling it to support on-site production operations at large terminals.

[0277] Specifically, this method maps terminal intersections and lanes into Actor instances and constructs a directed graph. It then uses the K-shortest path algorithm to obtain multiple alternative routes. It then dynamically updates relevant records based on vehicle characteristics and real-time messages, simulates driving to estimate travel times, and ultimately determines the optimal route. Departure and arrival times can be specified to meet precise scheduling requirements. Its advantages lie in its wide applicability and adaptability to practical needs. It can fully utilize advanced equipment to optimize vehicle travel routes and travel times, making it more suitable for mixed-operation scenarios than traditional methods.

[0278] The present invention is applicable to both traditional container terminals and automated container terminals. It can perform unified route planning for all vehicles in the terminal, including not only internal container trucks but also external container trucks, and supports mixed operations of unmanned and manned vehicles.

[0279] When searching for the fastest travel route, the present invention can specify both the departure time and the arrival time. This allows the queueing order of vehicles at the loading / unloading point to be kept consistent with the order of loading and unloading operations as much as possible, avoiding lateness or early arrival, thus avoiding pauses in loading and unloading operations, and ensuring the efficiency of terminal loading and unloading operations, especially the efficiency of ship loading and unloading operations, which is closely related to the terminal's operating benefits.

[0280] The concept of the present invention is based on the Actor distributed parallel computing model, which can realize large-scale real-time data processing and high-performance computing, which is sufficient to support large-scale traditional or automated container terminal on-site production operation scenarios.

[0281] The concepts of the present invention are defined as follows:

[0282] 1. An intersection is a point where lanes meet, connecting one or more entry lanes and one or more exit lanes. Vehicles must pass through an intersection from one of the entry lanes to one of the exit lanes according to traffic regulations. The number of intersections is usually one or two digits.

[0283] 2. Lanes are roads for one-way traffic, consisting of one or more roads going in the same direction. They are divided into two types: main roads and work lanes. The speed limit on main roads is generally 30 km / h, and the speed limit on work lanes is generally 15 km / h. The speed limit at intersections can refer to the speed limit on work lanes.

[0284] 3. A vehicle's driving path refers to the sequence of intersection / lane IDs that a vehicle passes through during its journey from its starting point to its destination. The starting point and destination of a driving path are generally located at the work point, i.e., on the lane. The first intersection passed from the starting point is the starting intersection, and the last intersection before reaching the destination is the destination intersection. The starting and destination intersections are required intersections on all alternative routes. Planning the path between these two intersections is equivalent to planning the vehicle's path from the starting point to the destination.

[0285] 4. An alternative path is one of the K shortest paths between the vehicle's starting intersection and its destination intersection. This can be found using the K-shortest path algorithm. Each path is represented by a sequence of intersection / lane IDs it passes through. The first ID in the sequence is the starting intersection ID, and the last ID is the destination intersection ID. Intersection IDs and lane IDs are interleaved.

[0286] 5. Vehicle characteristics, which are characteristics of the vehicle that affect driving time, include but are not limited to: vehicle type (manned / unmanned / foreign vehicle) and empty / load indicator (empty / loaded);

[0287] 6. Entering vehicle messages are event messages indicating actual or anticipated vehicle entry into an intersection or lane. These messages include, but are not limited to, vehicle ID, entry time, vehicle characteristics, and travel path. Entering vehicle messages can be used to confirm a travel path, deduce a travel path, or cancel a travel path. The latter will include a "Cancelled" flag in the message.

[0288] 7. Vehicle exit messages are event messages indicating actual or expected vehicle exits from intersections or lanes. These messages include, but are not limited to, the vehicle ID and the time the vehicle exited. If used to predict a driving route, the message will also include vehicle characteristics and the driving route.

[0289] 8. Travel time, which refers to the time it takes for a vehicle to pass through an intersection / lane, is measured in seconds. This time, it can be calculated from the entry and exit times in entry and exit messages: the exit time minus the entry time. Under different traffic characteristics, travel time is positively correlated with traffic density, which is statistically significant.

[0290] 9. Vehicle routing instructions refer to the necessary information required from an external system when finding the fastest vehicle route using this method, including but not limited to: vehicle ID, vehicle characteristics, starting intersection ID, destination intersection ID, designated departure time / designated arrival time, and planned departure time / planned arrival time.

[0291] The present invention is based on a spatiotemporal deduction method. It records and analyzes spatiotemporal changes in traffic flow through a dense quantity fluctuation time series record list and a feature combination count record list. It can simulate the travel of vehicles on alternative paths and, when searching for the fastest travel path, can specify both the departure time and the arrival time.

[0292] The present invention is applicable to both traditional container terminals and automated container terminals, and can perform unified route planning for all vehicles in the terminal, including external container trucks in addition to internal container trucks, and supports mixed operations of unmanned vehicles and manned vehicles. When searching for the fastest driving route, the present invention can specify both departure and arrival times, so that the queuing order of vehicles at the loading / unloading point and the order of loading and unloading operations can be kept as consistent as possible, avoiding lateness or early arrival, thus avoiding pauses in loading and unloading operations, and ensuring the efficiency of terminal loading and unloading operations, especially the efficiency of ship loading and unloading operations that are closely related to the terminal's operating benefits. Based on the Actor distributed parallel computing model, the present invention can realize large-scale real-time data processing and high-performance computing, which is sufficient to support on-site production operation scenarios in large-scale traditional or automated container terminals.

[0293] In this specification, references to the same or similar parts between the various embodiments can be made to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the product embodiments described later, since they correspond to the methods, the description is relatively simple, and the relevant parts can be referred to the partial description of the system embodiment.

[0294] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for searching the fastest travel path for terminal vehicles, characterized in that: include: S1. Build a directed graph under a distributed architecture: Map all intersections and lanes of the terminal to corresponding Actor instances; Defining the spatiotemporal deduction attributes of each of the Actor instances, wherein the spatiotemporal deduction attributes at least include a list of dense quantity fluctuation time series records and a list of feature combination count records; The directed graph is constructed using the Actor instance corresponding to the intersection and the Actor instance corresponding to the lane as vertices and directed edges, respectively, and each Actor instance stores a list of alternative paths to other intersections; S2. The Actor instance subscribes to the vehicle entry and exit messages pushed by the external system or other Actor instances, and dynamically updates the dense amount of fluctuation time series record list and the feature combination count record list based on the vehicle entry or vehicle exit message; S3. Fastest driving route search: Obtaining a vehicle route planning instruction, wherein the vehicle route planning instruction at least includes a designated departure time, a planned departure time, a starting intersection, and a destination intersection; Searching for multiple alternative paths between the starting intersection and the destination intersection from the alternative path list and arranging the paths in ascending order of path length; Simulating multiple alternative paths in sequence, triggering all the Actor instances included in each alternative path in sequence from the Actor instance corresponding to the starting intersection according to the vehicle path planning instruction, and calculating the expected driving time of the vehicle on the alternative path based on the dense quantity fluctuation time series record list and the feature combination count record list corresponding to each Actor instance on the alternative path and the vehicle characteristics, wherein the planned departure time is used as the vehicle's entry time at the starting intersection; The alternative path corresponding to the shortest estimated travel time of the path is used as the fastest travel path.

2. The method for searching the fastest travel path for terminal vehicles according to claim 1, characterized in that: The traffic density fluctuation time series record list records the traffic density changes in a timestamp sequence, and the feature combination count record list is used to record the driving time and number of occurrences under different passing features and traffic density combinations; The alternative path list includes multiple alternative paths from the current intersection to other intersections; Step S1 further includes: Defining vertex attributes of the intersection, wherein the vertex attributes include an entry lane ID array, an exit lane ID array, and an intersection travel length dictionary table; Defining directed edge attributes of the lane, wherein the directed edge attributes include an entry intersection ID, an exit intersection ID, a lane type, and a lane travel length; The spatiotemporal deduction attributes of the intersection and the lane are defined, wherein the spatiotemporal deduction attributes further include an incoming vehicle message dictionary table, wherein the incoming vehicle message dictionary table is accessed by a key being a vehicle ID and a value being an incoming vehicle message key-value pair; the dense quantity fluctuation time series record list is arranged in chronological order; The spatiotemporal deduction attribute corresponding to the intersection further includes the list of alternative paths, where the multiple alternative paths are obtained by the Actor instance corresponding to the intersection based on the K-shortest path algorithm; The dense quantity fluctuation time series record list includes dense quantity change time and vehicle ID array within the corresponding time; the feature combination count record list also includes passing features, and the passing features include vehicle features and vehicle passing environment features.

3. The method for searching the fastest travel path for terminal vehicles according to claim 2, characterized in that: Also includes: A method for defining each of the Actor instances includes calculating the traffic density using the entry time, calculating the estimated driving time of the corresponding Actor using the traffic density and characteristics, calculating the estimated exit time using the entry vehicle message, and calculating the estimated entry time using the vehicle exit message.

4. The method for searching the fastest travel path for terminal vehicles according to claim 3, characterized in that: The method for calculating the dense traffic volume using the entry time includes: Determining whether the dense volume fluctuation time series record list contains a record whose dense volume change time is equal to or earlier than the entry time and is closest to the entry time; If yes, the number of elements corresponding to the vehicle ID array corresponding to the record in the dense volume fluctuation time series record list is recorded as the dense volume of traffic; If not, record the traffic density as zero; or Calculating the estimated driving time of the corresponding Actor using the traffic density and the passing characteristics includes: Determining whether the feature combination counting record list contains a record corresponding to the traffic density and the passing feature; If yes, the driving duration that occurs the most times in the record is used as the estimated driving duration of the Actor, or the average of multiple driving durations that occur the most times and are the same in the record is used as the estimated driving duration of the Actor; If not, the estimated driving time of the Actor is calculated using the driving length and speed limit corresponding to the Actor; or The method of calculating the estimated exit time using the incoming vehicle information includes: Acquire an entry time based on the entry vehicle message; Calculate the expected exit time based on the entry time and the expected driving time of the Actor; or The method of calculating the estimated entry time using the vehicle exit message includes: Acquiring a departure time based on the vehicle departure message; The estimated entry time is calculated based on the exit time and the estimated driving time of the Actor.

5. The method for searching the fastest travel path for terminal vehicles according to claim 4, characterized in that: The calculating of the estimated entry time based on the exit time and the estimated driving time of the Actor includes: After obtaining the exit time, using the exit time instead of the entry time to calculate the traffic density; and calculating an estimated travel time of the Actor based on the traffic density and the passing characteristics, and obtaining an alternative estimated entry time by subtracting the estimated travel time of the Actor from the exit time; The traffic density is continuously calculated based on the alternative estimated entry time, and when it is the same as the traffic density calculated using the exit time instead of the entry time, the alternative estimated entry time is the correct estimated entry time.

6. The method for searching the fastest travel path for terminal vehicles according to claim 5, characterized in that: Step S3 includes: Refresh the dense quantity fluctuation time series record list of the corresponding Actor instance based on the actual or expected vehicle entry message or the vehicle exit message; The feature combination count record list of the corresponding Actor instance is refreshed based on the actual vehicle entering message or the vehicle exiting message.

7. The method for searching the fastest travel path for terminal vehicles according to claim 6, characterized in that: The updating of the dense quantity fluctuation time series record list of the corresponding Actor based on the actual or expected incoming vehicle message includes: Acquire a vehicle ID, entry time, and travel path based on the entry vehicle message, and calculate the estimated exit time based on the entry vehicle message; Traversing the dense quantity fluctuation time series record list and canceling the passing traces at the last refresh, wherein canceling the passing traces at the last refresh includes deleting the vehicle ID added at the last refresh, and / or when the vehicle ID arrays in two consecutive records are the same, only retaining the previous record; When the entry vehicle message does not include a revoked flag, a new passing trace is added to the dense volume fluctuation time series record list. The adding of the new passing trace includes: if there is no record corresponding to the entry time or the expected exit time in the dense volume fluctuation time series record list, the entry time and the expected exit time are used as the dense volume change time and a new record is added, and the vehicle ID array of the new record is filled with the content of the vehicle ID array corresponding to the previous record with a similar time; or if there is a record whose dense volume change time is later than or equal to the entry time and earlier than the expected exit time, the vehicle ID is added to the vehicle ID array corresponding to the record; When there is a subsequent Actor instance in the Actor instance, the entry time is updated to the estimated exit time, and the updated entry vehicle message is pushed to the subsequent Actor instance; or The list of dense quantity fluctuation time series records of refreshing the corresponding Actor instance based on the actual or expected vehicle exit message includes: Acquire vehicle ID and departure time based on the vehicle departure message; If there is no record in the dense volume fluctuation time series record list in which the dense volume change time is equal to the exit time, a new record is added using the exit time as the dense volume change time, and the vehicle ID array of the new record is filled with the vehicle ID array of the previous record; If there is a record in the dense quantity fluctuation time series record list whose dense quantity change time is later than or equal to the exit time, then remove the vehicle ID in the vehicle ID array corresponding to the record; or The feature combination count record list of the corresponding Actor instance refreshed based on the actual incoming vehicle message includes: Acquire a vehicle ID based on the incoming vehicle message, and use the vehicle ID as a key to record the incoming vehicle message into the incoming vehicle message dictionary table; or The feature combination count record list of the corresponding Actor instance refreshed based on the actual vehicle exit message includes: Acquire a vehicle ID based on the vehicle exit message, and obtain the incoming vehicle message from the incoming vehicle message dictionary table using the vehicle ID; Acquire an entry time based on the vehicle entry message, and acquire an exit time based on the vehicle exit message, so as to obtain a driving duration; Based on the incoming vehicle information and the passing environment, a passing feature is obtained, and based on the entering time, a traffic density is calculated; The driving duration and the number of occurrences under different passing characteristics and traffic density are calculated and updated.

8. The method for searching the fastest travel path for terminal vehicles according to claim 1 or 7, characterized in that: The vehicle routing instructions also include vehicle ID, designated arrival time, and planned arrival time; When determining the designated departure time and the planned departure time and triggering all the Actor instances included in each alternative path in sequence from the Actor instance corresponding to the starting intersection in a forward path, the planned departure time is used as the entry time, the alternative path is used as the driving path of the vehicle, and the driving path, the vehicle ID, and the vehicle characteristics are used to form the entering vehicle message; Calculate the estimated exit time corresponding to the Actor instance in the alternative path based on the incoming vehicle message, and determine whether the Actor instance corresponding to the destination intersection is simulated; If so, subtracting the planned departure time from the departure time to obtain the estimated travel time of the alternative route; If not, the entry time in the entering vehicle message is replaced with the estimated exit time, and used as the subsequent expected entering vehicle message of the Actor until the Actor instance corresponding to the destination intersection is simulated; or When determining the designated arrival time and the planned arrival time, and sequentially triggering all the Actor instances included in each of the alternative paths from the Actor instance corresponding to the destination intersection in a reverse path, the planned arrival time is used as the exit time of the destination intersection, the alternative path is used as the driving path of the vehicle, and the driving path, the vehicle ID, and the vehicle characteristics are used to form a vehicle exit message; Calculating an estimated entry time based on the vehicle exit message, and determining whether the vehicle has reached the starting intersection; If so, the estimated travel time of the path is obtained by subtracting the estimated entry time of the Actor instance corresponding to the starting intersection from the planned arrival time; If not, the exit time in the vehicle exit message is replaced with the estimated entry time and used as the vehicle exit message estimated by the subsequent Actor until the Actor instance corresponding to the starting intersection is simulated.

9. The method for searching the fastest travel path for terminal vehicles according to claim 8, characterized in that: The method for obtaining the shortest estimated travel time of the path includes: After simulating one of the alternative routes, caching the estimated travel time of the corresponding route; Determine whether the estimated travel time of the current alternative route is greater than the estimated travel time of the shortest route among all the alternative routes previously simulated; if not, continue simulating the current alternative route; if so, terminate the simulation of the current alternative route; Determine whether the simulation has reached the last alternative path, or whether the estimated travel time of the current alternative path is longer than the estimated travel time of the previous alternative path; if not, continue simulating the next alternative path; if so, terminate the simulation and find the shortest estimated travel time of the alternative paths among all the simulated paths.

10. The method for searching the fastest travel path for terminal vehicles according to claim 9, characterized in that: After obtaining the shortest estimated travel time of the route; If a designated departure time is determined, the estimated arrival time is calculated based on the planned departure time and the shortest estimated travel time of the route; If a designated arrival time is determined, the estimated departure time is calculated based on the planned arrival time and the shortest estimated travel time of the path.

11. A device for searching the fastest travel path for vehicles at a terminal, characterized in that: include: A construction unit is configured to map all intersections and all lanes of the terminal to corresponding Actor instances; define spatiotemporal deduction attributes of each Actor instance, wherein the spatiotemporal deduction attributes include at least a list of dense quantity fluctuation time series records and a list of feature combination count records; construct a directed graph using the Actor instances corresponding to the intersections and the Actor instances corresponding to the lanes as vertices and directed edges, respectively, and each Actor instance stores a list of alternative paths to other intersections; a refresh unit, configured to dynamically refresh the dense quantity fluctuation time series record list and the feature combination count record list according to the vehicle entry message and / or vehicle exit message pushed by each Actor instance after the Actor instance subscribes to the vehicle entry message and / or vehicle exit message pushed by the external system or other Actor instances; A path search and deduction unit is used to obtain a vehicle path planning instruction, wherein the vehicle path planning instruction at least includes a designated departure time, a planned departure time, a starting intersection, and a destination intersection; searching for multiple alternative paths between the starting intersection and the destination intersection from the alternative path list, and arranging them in ascending order according to path length; simulating multiple alternative paths in sequence, triggering all the Actor instances included in each alternative path in sequence from the Actor instance corresponding to the starting intersection according to the vehicle path planning instruction, and calculating the expected driving time of the vehicle on the alternative path based on the dense quantity fluctuation time series record list and the feature combination count record list and vehicle characteristics corresponding to each Actor instance on the alternative path, wherein the planned departure time is used as the vehicle's entry time at the starting intersection; and taking the alternative path corresponding to the shortest expected path driving time as the fastest driving path.

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