Method and device for predicting bus arrival time, electronic equipment, medium and program product

Abstract

The invention provides a method and device for predicting bus arrival time, electronic equipment, a medium and a program product, and relates to the field of data processing, in particular to the fields of intelligent transportation, smart cities, map big data and the like. According to the implementation scheme, predetermined arrival time information and reference arrival time information of a target bus for a to-be-predicted shift are obtained, and the predetermined arrival time information comprises predetermined time when the target bus arrives at all stations in the to-be-predicted shift; the reference arrival time information comprises the real time when the target bus arrives at at least part of stations in all stations in a historical shift corresponding to the to-be-predicted shift; correcting the predetermined arrival time information based on the reference arrival time information to obtain predicted arrival time information for the to-be-predicted shift; and determining the time when the target bus arrives at the target station based on the predicted arrival time information and the information of the target station provided by the user.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, in particular to the fields of intelligent transportation, smart cities, map big data, and specifically to a method, device, electronic device, computer-readable storage medium, and computer program product for predicting the arrival time of a public transportation vehicle. Background Art

[0002] In recent years, the share of public transportation has increased year by year, and the trajectory information of public transportation has become increasingly important. With the rise and development of data sharing and machine learning technologies, these technologies have gradually begun to be deeply integrated with the application field of public transportation trajectory information prediction in order to obtain wider applications.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0004] The present disclosure provides a method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for predicting the arrival time of a public transportation vehicle.

[0005] According to one aspect of the present disclosure, a method for predicting a bus arrival time is provided, comprising: obtaining scheduled arrival time information and reference arrival time information of a target bus for a to-be-predicted shift, wherein the scheduled arrival time information includes the scheduled time for the target bus to arrive at all stops in the to-be-predicted shift, and the reference arrival time information includes the real time for the target bus to arrive at at least some of all stops in a historical shift corresponding to the to-be-predicted shift; correcting the scheduled arrival time information based on the reference arrival time information to obtain predicted arrival time information for the to-be-predicted shift; and determining the time for the target bus to arrive at a target stop based on the predicted arrival time information and information about the target stop provided by a user.

[0006] According to another aspect of the present disclosure, a device for predicting the arrival time of a public transport vehicle is provided, comprising: a time information acquisition module, configured to acquire scheduled arrival time information and reference arrival time information of a target public transport vehicle for a shift to be predicted, wherein the scheduled arrival time information includes the scheduled time for the target public transport vehicle to arrive at all stations in the shift to be predicted, and the reference arrival time information includes the real time for the target public transport vehicle to arrive at at least some of all stations in the historical shift corresponding to the shift to be predicted; an arrival time correction module, configured to correct the scheduled arrival time information based on the reference arrival time information to obtain the predicted arrival time information for the shift to be predicted; and an arrival time determination module, configured to determine the time for the target public transport vehicle to arrive at the target station based on the predicted arrival time information and information of the target station provided by a user.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for predicting the arrival time of a public transport vehicle as provided above in the present disclosure.

[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method for predicting the arrival time of a public transportation vehicle provided above in the present disclosure.

[0009] According to another aspect of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the computer program implements the method for predicting the arrival time of a public transportation vehicle provided above in the present disclosure.

[0010] According to one or more embodiments of the present disclosure, the vehicle arrival time may be estimated without the real-time trajectory of the vehicle.

[0011] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1A schematic diagram showing an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0014] Figure 2 A flow chart showing a method for predicting the arrival time of a public transport vehicle according to an embodiment of the present disclosure is shown;

[0015] Figure 3A and Figure 3B A schematic diagram showing determination of reference arrival time information based on a historical positioning data set according to an embodiment of the present disclosure is shown;

[0016] Figure 4 A schematic diagram showing a method of determining the departure interval information according to an embodiment of the present disclosure is shown;

[0017] Figure 5 A schematic diagram showing correction of predicted arrival time information according to an embodiment of the present disclosure is shown;

[0018] Figure 6 A flow chart showing how to correct the predicted arrival time according to the vehicle's driving speed according to an embodiment of the present disclosure is shown;

[0019] Figure 7 A schematic diagram showing a method for map navigation according to an embodiment of the present disclosure is shown;

[0020] Figure 8 A structural block diagram of a device for predicting the arrival time of a public transport vehicle according to an embodiment of the present disclosure is shown;

[0021] Fig. 9 A structural block diagram of a device for predicting the arrival time of a public transport vehicle according to another embodiment of the present disclosure is shown;

[0022] Fig.10 A structural block diagram of a device for map navigation according to an embodiment of the present disclosure is shown;

[0023] Fig.11 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0024] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0025] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0026] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0027] In the related technology, the current method of providing real-time vehicle arrival information mainly relies on electronic bus stop signs or bus GPS information. However, since the real-time track information of many vehicles is difficult to obtain, when there is neither electronic bus stop signs nor real-time vehicle track information, it is difficult for users to estimate the waiting time for the bus.

[0028] To this end, the embodiments of the present disclosure provide a method for estimating the vehicle arrival time through departure schedule mining and online correction without the real-time trajectory of the vehicle, which can provide better technical services for users' travel.

[0029] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0030] Figure 1 FIG. 1 is a schematic diagram of an exemplary system 100 in which various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

[0031] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable the method for predicting the arrival time of a public transportation vehicle provided by an embodiment of the present disclosure to be executed.

[0032] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0033] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0034] The user may use client devices 101, 102, 103, 104, 105 and / or 106 to input the desired bus information or target station information, etc. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure may support any number of client devices.

[0035] Client devices 101, 102, 103, 104, 105 and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, game systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as GOOGLE Chrome OS); or include various mobile operating systems, such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular phones, smart phones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Game systems may include various handheld game devices, Internet-enabled game devices, etc. Client devices are capable of executing various different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and may use various communication protocols.

[0036] The network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0037] Server 120 may include one or more general purpose computers, dedicated server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0038] The computing units in the server 120 may run one or more operating systems including any of the above operating systems and any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0039] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and / or 106. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and / or 106.

[0040] In some embodiments, the server 120 may be a server of a distributed system, or a server combined with a blockchain. The server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in a cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0041] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The databases 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0042] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0043] Figure 1The system 100 may be configured and operated in various ways to enable the application of various methods and apparatuses described in the present disclosure.

[0044] Various aspects of predicting the arrival time of a public transport vehicle according to an embodiment of the present disclosure will be described in detail below.

[0045] Figure 2 A flowchart of a method 200 for predicting the arrival time of a public transportation vehicle according to an embodiment of the present disclosure is shown.

[0046] like Figure 2 As shown, method 200 includes step S201, step S202 and step S203.

[0047] In step S201, the scheduled arrival time information and reference arrival time information of the target bus for the to-be-predicted shift are obtained, wherein the scheduled arrival time information includes the scheduled time for the target bus to arrive at all stops in the to-be-predicted shift, and the reference arrival time information includes the actual time for the target bus to arrive at at least some of all stops in the historical shift corresponding to the to-be-predicted shift.

[0048] In the example, the target bus may refer to a bus line in the bus system that is of interest to the passengers or bus operators who are waiting to take the target bus. The to-be-predicted bus may refer to a specific bus line whose arrival time the passengers or bus operators want to predict. The scheduled arrival information may be obtained, for example, by the time pre-set by the bus company according to the operation plan and timetable of the bus. These times may be based on an ideal state, that is, they may be calculated without considering factors such as traffic congestion, weather effects, and vehicle failures. The reference arrival time information may refer to the real time record of the bus arriving at at least part of the station in the actual operation in the past. Accordingly, the to-be-predicted bus line is consistent in time with the corresponding historical bus line, such as the first bus of a certain line every day. These data reflect the actual arrival time of the bus after being affected by factors such as traffic congestion, road construction, passenger boarding and alighting time, road conditions, and weather conditions in actual operation. Since the bus is generally affected by various factors during actual operation, the scheduled arrival time information and the reference arrival time information are usually not necessarily completely consistent.

[0049] In step S202, the scheduled arrival time information is corrected based on the reference arrival time information to obtain the predicted arrival time information for the flight to be predicted.

[0050] In the example, correcting the scheduled arrival time information based on the reference arrival time information may involve optimizing and adjusting the scheduled timetable using historical real data to predict the actual arrival time of the bus vehicles of a specific shift at each station. The scheduled arrival time information may provide a preliminary prediction based on the scheduled timetable, but this prediction may not take into account various changes in actual operation. The reference arrival time information may provide historical real data that may reflect changes and trends in actual operation. Through this correction step S202, a more accurate arrival time may be provided for the shift to be predicted. This method can help improve the accuracy of bus predictions because it can take into account the changing factors in the actual operation of the bus.

[0051] In step S203, based on the predicted arrival time information and the information of the target station provided by the user, the arrival time of the target public transport vehicle at the target station is determined.

[0052] In the example, users often need to know the specific arrival time of a bus at a certain station in order to plan their trips, such as in daily commuting, planned travel, emergency situations, and traffic transfers. In these scenarios, for example, the information of the target station provided by the user can be first obtained, and the information of the target station can be the station name, station code, or location information, etc. The corresponding target station can be found in the public transportation system based on the information provided by the user. Then, the predicted time for the target bus to arrive at the target station can be retrieved from the corrected predicted arrival time information. This can include extracting prediction data related to the target station from a database or a real-time information system. If the predicted arrival time information provides the time when the vehicle arrives at each station, the time to arrive at the target station can be directly extracted. If the information is incomplete, it may be necessary to calculate the estimated time for the target bus to arrive at the target station based on the current position, speed, and remaining distance of the vehicle, as well as historical data and real-time traffic conditions.

[0053] Therefore, in the method 200 for predicting the arrival time of a bus according to the embodiment of the present disclosure, since the reference arrival time information including the historical real arrival time indirectly reflects the real-time vehicle trajectory information of the bus in the past historical operation, the method for predicting the arrival time information of the bus can be based on the reference arrival time information to correct the scheduled arrival time, thereby significantly improving the accuracy and reliability of the prediction of the arrival time of the bus, thereby optimizing the travel experience of passengers and reducing waiting and delays. At the same time, this method can not only quickly respond to changes in traffic conditions through real-time updates and dynamic adjustments, but also provide data-driven decision support for bus operators.

[0054] In some embodiments, Figure 2The step of obtaining the reference arrival time information of the target bus for the to-be-predicted shift in step S201 shown may include: obtaining a historical positioning data set formed by at least one user along the route trajectory of the target bus in a historical shift corresponding to the to-be-predicted shift, wherein the historical positioning data set has spatiotemporal consistency with the route trajectory for the historical shift, so that the historical positioning data set indicates the real time when the target bus arrives at at least some of all the stops in the historical shift; and determining the reference arrival time information based on the historical positioning data set.

[0055] In the example, the positioning data of several users can be obtained through applications or software such as map apps or navigation apps, and the positioning data can include the location information and time information of the users at several locations. Accordingly, the historical positioning data set can refer to the location and time data set formed based on the movement of the several users along the target bus route trajectory. Spatiotemporal consistency can refer to the historical positioning data in the historical positioning data set matching the actual bus route trajectory for the to-be-predicted shift in time and space. The historical positioning data in the historical positioning data set can match both a part of the actual bus route trajectory and the entire actual bus route trajectory. For example, when the movement trajectory of a certain user overlaps with the trajectory of a certain bus route in whole or in part, it can be considered that the user may be riding the bus on this route. In this case, the time of arrival at the bus stop of the part of the user's positioning data that overlaps with the trajectory of the bus route can be recorded as the reference arrival time. If similar positioning data of more users can be obtained, the data source of the positioning data can be further enriched to make it more accurate and reliable. For example, the positioning data formed by three users together can overlap with the trajectory of a certain bus route. It can be understood that the historical positioning data set is consistent with the route trajectory of the historical shift in time and space, which can ensure that the historical positioning data can accurately reflect the real time when the target bus arrives at at least some stations in the historical shift. Therefore, more accurate reference arrival time information can be obtained.

[0056] Therefore, by collecting a set of historical positioning data of at least one user that is consistent with the spatial and temporal trajectory of the historical bus route, the accuracy and authenticity of the reference arrival time information is ensured, so that the estimated time for the target bus to arrive at at least some of the stations in the predicted bus route can be accurately predicted based on these data, thereby improving the efficiency of the bus system and the travel experience of passengers.

[0057] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0058] Figure 3A and Figure 3B A schematic diagram of determining reference arrival time information based on a historical positioning data set according to an embodiment of the present disclosure is shown.

[0059] like Figure 3A As shown, a historical positioning data set formed by a certain user along the route trajectory of a target bus is shown, which includes the collected historical positioning data of the user arriving at bus stops 302, 303, 306, and 308. Figure 3B It shows the complete route between the target bus stops. Figure 3B The solid points 302', 303', 306', 308' shown in FIG. Figure 3A The bus stops 302, 303, 306, and 308 shown in FIG. 3 correspond to each other. Figure 3B The bus stop 302' shown in FIG. Figure 3A The bus stop 302 shown in FIG. 1 is the same bus stop. Figure 3B 303' shown in Figure 3A The bus stop 303 shown in is the same bus stop, Figure 3B The bus stop 306' shown in FIG. Figure 3A The bus stop 306 shown in FIG. 1 is the same bus stop. Figure 3B The bus stop 308' shown in Figure 3A The bus stop 308 shown in FIG. 1 is the same bus stop. When the user base of the map app or navigation app is large, similar information can be obtained through other users. Figure 3B The historical positioning data corresponding to the bus stops 301, 304, 305, 307, and 309 shown in FIG.

[0060] In such Figure 3A and Figure 3B In the example shown, Figure 3A The historical positioning trajectory of the user along the route trajectory of the target bus is Figure 3B The target bus route trajectory has temporal and spatial consistency, that is, it matches each other in time and space, which can indicate that the user has taken the target bus. Therefore, through the time data of the user's historical positioning at bus stops 302, 303, 306, and 308, the accurate reference arrival time of the target vehicle at bus stops 302', 303', 306', and 308' can be obtained.

[0061] Understandably, Figure 3A and Figure 3B The user's historical location data and bus track route are only schematically shown, but the embodiments of the present disclosure are not limited thereto.

[0062] In some embodiments, Figure 2The step of obtaining the scheduled arrival time information of the target bus for the to-be-predicted shift in step S201 may include: obtaining at least one of the departure time information or the departure interval information of the target bus for the to-be-predicted shift, and the driving speed information of the target bus; and determining the scheduled time for the target bus to arrive at all stops in the to-be-predicted shift based on at least one of the departure time information or the departure interval information and the driving speed information.

[0063] In the example, the departure time information may refer to the specific time when the bus departs from the starting station or a specific station. This time may be fixed or adjusted according to real-time traffic conditions and operational needs. The departure interval information may refer to the time interval between two consecutive buses departing from the same station. This interval may be uniform or may vary over time. The departure time information and departure interval information may be basic data in bus operations, which are essential to ensure the efficient operation of the bus system and meet the needs of passengers. Therefore, they may also be obtained from the official data of the bus system. Specifically, the departure time information or departure interval information of the target bus and the driving speed information of the vehicle may be obtained first. These data may come from the bus dispatching system, GPS positioning equipment or other real-time monitoring systems. Using these basic information, combined with historical trajectory data, it is possible to provide a relatively accurate scheduled time for the bus to be predicted to arrive at all stations, taking into account the running speed of the target bus.

[0064] In an example, the travel speed information of the target public transport vehicle may be based on a real-time speed value or an empirical speed value. The empirical speed value may be obtained in combination with historical road conditions.

[0065] Therefore, by comprehensively using known departure time information and / or departure interval information and combining it with real-time travel speed data to determine the scheduled arrival time of all bus stops, it is possible to provide support for users' travel plans, while improving the service quality and operational efficiency of the bus system and enhancing the adaptability and robustness of the bus system.

[0066] In some embodiments, in response to the target bus traveling in the bus-only lane, the traveling speed information of the target bus may be determined based on the inherent speed limit of the bus-only lane.

[0067] In the example, the target bus speed information may refer to the speed of the bus that the user wants to obtain during its travel, which plays a key role in the subsequent prediction of the arrival time of the bus at each station. The bus lane may be an independent right-of-way lane specially set up for buses, which may set a minimum or maximum speed limit for buses, and at the same time guarantee the exclusive or priority right of way for buses during special time periods, reducing delays caused by traffic congestion and other reasons.

[0068] Therefore, for the application scenario of traveling on a dedicated bus lane, the driving speed information of the target bus can be simply and accurately estimated by the inherent speed limit of the dedicated bus lane, thereby improving the operability of applying the method disclosed in this scenario.

[0069] In some embodiments, the target bus's travel speed information can be determined based on the weighted sum of the real-time traffic conditions and the historical traffic conditions of the target bus's route operation section during the time period corresponding to the predicted shift, wherein the sum of the first weight of the real-time traffic conditions and the second weight of the historical traffic conditions is 1.

[0070] In the example, the real-time traffic conditions can reflect the actual traffic conditions of each section on the route where the current target bus is traveling. This can be obtained in a variety of ways, such as the traffic flow, speed and other information collected by the monitoring equipment (such as cameras, electronic induction coils, etc.) set up by the traffic management department on the road, or the road congestion level and other conditions fed back by some map software based on the positioning of the user's mobile phone. For example, if the real-time traffic conditions show that a traffic accident has occurred on a certain section of the road and caused congestion, the speed of the bus will inevitably be affected and reduced when it travels to that section of the road. The historical traffic conditions can be a record of traffic conditions on the same route in the same or similar time period in the past. It can be based on the accumulated data in the past. For example, in the past several working days from 8 to 9 in the morning, a certain intersection where a bus line passes often slows down due to the large number of vehicles going to school in nearby schools. Historical data can be combined with real-time data reflecting the current situation as a reference to assist in judging the possible driving speed of the vehicle.

[0071] In the example, real-time traffic conditions and historical traffic conditions can be assigned corresponding weights according to their respective importance, and then the two are added together after calculation according to the weights to obtain the final value used to determine the driving speed information. The sum of the first weight of the real-time traffic conditions and the second weight of the historical traffic conditions is 1, which can ensure that these two factors are jointly and completely involved in the determination of the driving speed information, and there will be no problem of unreasonable sum of weights leading to large deviations in the results. For example, if you pay more attention to the current real-time traffic conditions, you can assign a higher weight to the real-time traffic conditions, such as 0.8, then the second weight corresponding to the historical traffic conditions is 0.2.

[0072] Therefore, by integrating real-time traffic conditions with historical traffic conditions and taking weighted summation, we can fully take into account the sudden changes in current traffic and combine past regular experience, so that the final determined target bus speed information is more reasonable and reliable, and then more accurately predict the time for buses to arrive at each station, thereby improving the accuracy of the prediction.

[0073] In some embodiments, the real-time traffic condition can be determined based on the traffic condition of the driving vehicle navigation on the line operation section, the real-time movement trajectory of other buses except the target bus on the line operation section, or the positioning trajectory of at least one user on the line operation section.

[0074] In the example, the navigation data of vehicles driving on the bus route can be obtained by cooperating with the map service provider. These data may include vehicle speed, congestion, accident information, etc. For example, the current No. 702 bus is running on Section 5, and the time of entering and leaving Section 5 is 10:34 and 10:56 respectively. Then the real-time traffic conditions can be processed in real time based on the navigation data of a certain vehicle, such as a private car, truck, or bus, which is running on Section 5 during the current time period to extract traffic condition information. The extracted traffic condition information may include the degree of traffic congestion being severe, the passing time being 20 minutes (10 minutes later than usual), the average speed being 20 km / h (20 km / h slower than usual), etc.

[0075] In the example, the real-time traffic conditions can be determined by collecting the real-time position and speed information of other buses on the same route through the GPS positioning system on the bus. By analyzing the driving trajectories of these buses, the traffic conditions of the running section of the route are determined, such as whether there is congestion and whether the vehicle can travel at the predetermined speed. For example, the current No. 702 bus is running on Section 8, and it is known that the No. 804 bus also passes through Section 8, that is, the movement trajectories of the No. 702 bus and the No. 804 bus overlap on Section 8. Then the real-time traffic conditions of the No. 702 bus can be determined based on the real-time movement trajectory of the No. 804 bus running on Section 8.

[0076] In the example, the real-time traffic conditions can also be determined by collecting the location data of at least one user on the route section through the navigation application or location sharing service used by the user, analyzing the user location trajectory data, identifying the peak hours and crowded areas, and predicting the operation status of the public transport vehicles in specific time periods and areas. For example, the user can be a passenger on the target bus No. 702, or a passenger on the No. 804 bus traveling on the same section as the target bus, or a driver or passenger on a motor vehicle traveling on the current section.

[0077] Therefore, by combining vehicle navigation data, the movement trajectories of other buses and user positioning trajectories, comprehensive line operation section road condition information can be provided, which can improve the accuracy of bus arrival time prediction.

[0078] In some embodiments, obtaining the departure interval information of the target bus for the to-be-predicted schedule may include: inputting the route information of the target bus into a trained prediction model to determine the departure interval information through the prediction model, wherein the route information may include first information associated with the inherent operating state of the target bus and second information associated with the dynamic operating adjustment of the target bus.

[0079] In the example, route information can be collected in various ways, including the bus company's operational database, vehicle-mounted GPS system, traffic monitoring camera, etc. The prediction model can be a machine learning model trained with a large number of samples, which can predict the departure interval based on the training set and the test set. The route information may include first information and second information. The first information may be associated with the inherent operating status, for example, it may include the city, region, date / hour (such as divided according to working days, holidays, morning and evening peaks) where the bus is located, the number of transfers / stations (such as the transfer transfer relationship with other routes), the number of basic information stations / kilometers (such as short lines, long lines, and the density of the number of stations), etc.; the second information may be associated with dynamic operation adjustments, for example, it may include positioning quantity (such as how many people are positioned on the current route), heat (such as the regional passenger flow heat grid), etc. The route information of the target bus can be collected through the database system related to each bus.

[0080] Therefore, by inputting route information into a trained prediction model, the prediction model can be used to more accurately predict the departure intervals of public buses to adapt to various dynamic changes, such as traffic conditions, weather changes, etc.

[0081] In some embodiments, inputting the route information of the target bus into a trained prediction model to determine the departure interval information through the prediction model may include: providing the prediction model with multiple reference regularity information for predicting the departure interval, the multiple reference regularity information indicating multiple different departure interval time periods.

[0082] In the example, the reference regularity information includes reference regularity information of multiple different departure interval time periods, which is used to assist the prediction model to make more accurate predictions, and can also be called a priori departure interval grading regularity, for example, the first grading of the departure interval is 6 to 8 minutes, the second grading is 8 to 12 minutes, etc.

[0083] Therefore, by considering a variety of reference regular information, the prediction model can be prompted during the process of predicting the departure interval information by the prediction model, so as to avoid the prediction model erroneously predicting a result that is obviously inconsistent with the regularity.

[0084] Figure 4 A schematic diagram of determining departure interval information according to an embodiment of the present disclosure is shown.

[0085] like Figure 4 As shown, the route information 401 of the public transport vehicle to be predicted can be input into the trained prediction model 410, and a plurality of reference regularity information 420 for predicting the departure interval can be provided to the prediction model 410, thereby determining the departure interval information 403 of the bus service to be predicted.

[0086] In an example, the prediction model 410 may be a deep neural network (DNN).

[0087] In some embodiments, obtaining the departure interval information of the target bus for the to-be-predicted shift may further include: determining preliminary estimated time information for the target bus to arrive at all stations based on the departure interval information obtained by the prediction model and the inherent first bus departure time information; determining whether the difference between the preliminary estimated time information and the reference arrival time information is greater than a predetermined standard; in response to determining that the difference is greater than the predetermined standard, determining factors causing the difference to be greater than the predetermined standard; and updating the prediction strategy of the prediction model according to the factors to obtain updated departure interval information.

[0088] In the example, the departure interval information can reflect the time interval of the bus departure in accordance with the operation plan. According to the departure interval and the inherent departure time of the first bus, the time when the subsequent vehicles are expected to arrive at each station can be calculated in turn, so as to determine the preliminary estimated time information of the target bus arriving at all stations. For example, if the first bus departs at 6 o'clock and the departure interval is 15 minutes, then in theory, the preliminary estimated time for the second bus to arrive at each station is based on the time when the first bus arrives at each station and pushes back 15 minutes in sequence. Similarly, the approximate time of each bus arriving at each station during the entire operation process can be obtained. The reference arrival time information contains the real time when the target bus arrives at at least some of the stations in the historical schedule corresponding to the schedule to be predicted. It is based on the real data recorded based on the actual operation in the past. By comparing the preliminary estimated time information with it, the deviation between the estimated time obtained by the prediction model and the real arrival time in the actual historical operation can be measured.

[0089] In the example, the predetermined standard can refer to a time difference limit determined based on the accuracy requirements of bus operations, the acceptable waiting time error for passengers, and other factors. For example, the predetermined standard can be set to 5 minutes. If the difference between the preliminary estimated time and the reference arrival time exceeds 5 minutes, it means that there is a significant problem with the accuracy of the preliminary estimate, and further analysis and adjustment are required; if the difference is less than or equal to 5 minutes, it means that the current preliminary estimate is within an acceptable range. In addition, there are many factors that may cause the difference to be greater than the predetermined standard. From the perspective of the prediction model, it may be that the line information input into the prediction model is inaccurate, such as the actual spacing between the stations and the planned spacing, the conventional speed setting of the vehicle is not in line with the actual situation, etc.; it may also be that the actual road conditions are not included in the model prediction range, such as temporary road construction causing the route to detour. From the perspective of the external environment, it may be that there are new regular changes in traffic flow in the recent period, or that sudden factors such as weather have a greater impact on traffic, etc. In this case, the prediction strategy of the prediction model can be updated to obtain updated departure interval information. For example, a new shopping mall is added to the route section where the target bus is located, resulting in a sudden increase in passengers getting on and off the bus station, a significant increase in the stop time, and congestion on the surrounding roads, resulting in a reduction in the speed of the bus. This factor is not taken into account in the process of predicting the departure interval information by the prediction model. Based on this, the prediction strategy of the prediction model can be updated by combining the mall business hours, passenger flow characteristics, re-estimated stop time, congestion data and other information, and the prediction model can be reused to predict the departure interval information to obtain updated departure interval information and improve the prediction accuracy.

[0090] Therefore, by determining whether the difference is greater than the predetermined standard and updating the prediction strategy, the prediction model can be more in line with the actual road conditions and emergencies, and updated departure interval information can be obtained. Furthermore, the subsequent arrival time of the bus at each station can be more accurately estimated, thereby improving the accuracy of the bus operation time estimate.

[0091] In some embodiments, Figure 2 The step S202 shown corrects the scheduled arrival time information based on the reference arrival time information to obtain the predicted arrival time information for the flight to be predicted, which may include: determining at least one station where the difference between the scheduled arrival time information and the reference arrival time information reaches a predetermined threshold; and updating the scheduled arrival time corresponding to the at least one station in the scheduled arrival time information to the reference arrival time corresponding to the at least one station in the reference arrival time information.

[0092] In the example, by comparing the reference arrival time of each station with the scheduled arrival time, the time difference between the reference arrival time and the scheduled arrival time of each station can be calculated. If the time difference exceeds the set threshold, such as 5 minutes, it can be considered that the scheduled arrival time needs to be updated. In practice, by setting the threshold reasonably, unnecessary timetable updates can also be reduced. For stations that need to be updated, the scheduled arrival time can be adjusted to the latest reference arrival time to reflect the actual operation situation. For stations that are missing reference arrival times, the scheduled arrival time can be maintained unchanged or estimated using neighboring station data.

[0093] Therefore, by updating the scheduled arrival time with reference to the arrival time information, it is possible to ensure that the accuracy of the arrival time is close to the real data to cope with time deviations in actual operations and improve prediction accuracy.

[0094] Figure 5 A schematic diagram showing correction of predicted arrival time information according to an embodiment of the present disclosure is shown.

[0095] like Figure 5 As shown, the three horizontal lines represent the same bus line, the solid points and hollow points represent bus stops, and the bus stops at both ends of the vertical arrow represent the same bus stop. For ease of explanation, three corresponding stops are omitted in the bus line corresponding to the first horizontal line. In the first horizontal line, the time above bus stops 502, 503, and 504 represents the reference arrival time of the target bus vehicle arriving at the stop, which are 8:12, 8:23, and 8:37, respectively, that is, the real arrival time; in the second horizontal line, the time above bus stops 511, 512, 513, 514, 515, and 516 represents the scheduled arrival time of the target bus vehicle arriving at the stop, which are 8:00, 8:10, 8:17, 8:33, 8:42, and 8:52, respectively. The difference between the reference arrival time and the scheduled arrival time of each stop can be calculated and compared with the predetermined threshold. If the time difference reaches a predetermined threshold, for example, the threshold can be set to 4 minutes, that is, the absolute value of the difference is greater than or equal to 4 minutes, the scheduled arrival time of the station can be updated to the reference arrival time. Figure 5For example, bus stop 502 and bus stop 512 represent the same stop, wherein the reference arrival time of the target bus to the stop is 8:12, and the scheduled arrival time is 8:10, and the difference between the two is 2 minutes, which is less than the threshold of 4 minutes, then the scheduled arrival time 8:10 of the stop does not need to be updated. For another example, bus stop 503 and bus stop 513 represent the same stop, wherein the reference arrival time of the target bus to the stop is 8:23, and the scheduled arrival time is 8:17, and the difference between the two is 5 minutes, which is greater than the threshold of 4 minutes, then the scheduled arrival time 8:17 of the stop needs to be updated to the reference arrival time 8:23. Similarly, bus stops 504 and bus stops 514 representing the same stop also undergo the above update process, and the scheduled arrival time 8:33 is updated to the reference arrival time 8:37. Thus, an updated arrival schedule of bus stops 521, 522, 523, 524, 525, and 526 indicated by the third horizontal line can be obtained, as shown by 521 to 526 in the figure.

[0096] Understandably, Figure 5 The examples are only provided for the purpose of illustration, and the embodiments of the present disclosure are not limited thereto.

[0097] Figure 6 A flow chart of correcting the predicted arrival time according to the vehicle driving speed according to an embodiment of the present disclosure is shown.

[0098] like Figure 6 As shown, the process 600 of correcting the predicted arrival time according to the vehicle speed according to the embodiment of the present disclosure may include step S601, step S602 and step S603. Figure 2 The method 200 shown may further include: Figure 6 Process 600 is shown.

[0099] In step S601, it may be determined whether the target public transport vehicle has experienced an event that affects the travel speed before arriving at the target station.

[0100] In step S602, in response to determining that the target public transport vehicle has experienced an event that affects the travel speed before arriving at the target station, a delay time corresponding to the event may be estimated.

[0101] In the example, the real-time traffic conditions of the roads that the target bus passes through can be monitored in real time to determine whether an event that affects the driving speed has occurred. Once an event that affects the driving speed is determined to have occurred, the specific impact of the event on the bus's driving speed can be evaluated. For example, when it is determined that a traffic accident or road congestion has occurred on the road ahead, the amount by which the bus's driving speed will be reduced can be estimated based on the severity of the accident and the traffic congestion. Based on the results of the event impact assessment, the delay time of the bus caused by the event can be estimated.

[0102] In step S603, it may be determined to add the delay time to the predicted arrival time information.

[0103] For example, if it is estimated that the bus will slow down to 50% of its normal speed of 20km / h due to a traffic accident, and the length of the accident section is 2 kilometers, it can be calculated that the additional time required for the bus to pass through the section is 6 minutes. The estimated delay time can be added to the original predicted arrival time to obtain the revised predicted arrival time. For example, if the original predicted arrival time is 10:10 and the estimated delay time is 6 minutes, then the revised predicted arrival time is 10:16.

[0104] Therefore, by evaluating events that affect bus speed, it is possible to respond to real-time road conditions in a timely manner to more accurately predict the arrival time of the bus, thereby reducing passenger waiting time.

[0105] According to an embodiment of the present disclosure, a map navigation method is also provided.

[0106] Figure 7 A flowchart of a map navigation method 700 according to an embodiment of the present disclosure is shown.

[0107] In step S701, a navigation request of a user and information about the current location of the user are obtained, where the navigation request indicates a route of a public transportation vehicle that the user is interested in.

[0108] In the example, the navigation request can be the destination entered by the user in the map navigation application, such as community A, and the user's current precise location, such as community B, can be automatically obtained through the user's GPS; the map navigation application can identify the bus route that the user is interested in based on the received destination and the current precise location, for example, it can identify that the No. 705 bus that stops at bus stop C near community A may be the bus that the user is interested in.

[0109] In step S702, based on the current location information of the user, the station closest to the user in the bus route is determined.

[0110] In the example, the user's current location information is used to determine the bus stop closest to the user through map data and bus route information. For example, the user is currently in Community A and is preparing to take Bus No. 705. It is known that Bus No. 705 passes through Community A, with Bus Stop C (800m), Bus Stop D (300m) and Bus Stop E (100m) within a 1KM diameter. Therefore, it can be determined that Bus Stop E, which is 100m away from Community A, is the bus stop closest to the user on Bus No. 705.

[0111] In step S703, according to the above method for predicting the arrival time of a public transport vehicle (for example, in combination with Figure 2 The map data processing method 200) predicts the time when at least one bus will arrive at a station.

[0112] In step S704, the predicted time is provided to the user.

[0113] In the example, for example, based on real-time traffic conditions and historical data, it can be predicted that the next bus No. 705 will arrive at bus stop F at 8:15, and the user can see the above information on the map navigation application.

[0114] In this way, users can obtain real-time bus arrival time predictions to better plan their travel, enhancing the practicality and experience of the service.

[0115] According to an embodiment of the present disclosure, a device for predicting the arrival time of a public transport vehicle is also provided.

[0116] Figure 8 A structural block diagram of an apparatus 800 for predicting the arrival time of a public transport vehicle according to an embodiment of the present disclosure is shown.

[0117] like Figure 8 As shown, the device 800 for predicting the arrival time of a public transport vehicle comprises a time information acquisition module 801, an arrival time correction module 802 and an arrival time determination module 803. The time information acquisition module 801 is configured to acquire the scheduled arrival time information and reference arrival time information of the target public transport vehicle for the shift to be predicted, wherein the scheduled arrival time information includes the scheduled time for the target public transport vehicle to arrive at all the stations in the shift to be predicted, and the reference arrival time information includes the real time for the target public transport vehicle to arrive at at least part of all the stations in the historical shift corresponding to the shift to be predicted; the arrival time correction module 802 is configured to correct the scheduled arrival time information based on the reference arrival time information to obtain the predicted arrival time information for the shift to be predicted; and the arrival time determination module 803 is configured to determine the time for the target public transport vehicle to arrive at the target station based on the predicted arrival time information and the information of the target station provided by the user.

[0118] The operations of the above-mentioned time information acquisition module 801, the arrival time correction module 802 and the arrival time determination module 803 may correspond to the following respectively: Figure 2 Therefore, the details of each aspect are not repeated here.

[0119] Fig. 9 A structural block diagram of an apparatus 900 for predicting the arrival time of a public transport vehicle according to another embodiment of the present disclosure is shown.

[0120] like Fig. 9 As shown, the apparatus 900 for predicting the arrival time of a public transport vehicle may include a time information acquisition module 901, an arrival time correction module 902, and an arrival time determination module 903. The operations of the above modules may be similar to those of Figure 8 The operations of the time information acquisition module 801, the arrival time correction module 802 and the arrival time determination module 803 are the same. In addition, the above modules may also include further submodules.

[0121] In some embodiments, the time information acquisition module 901 may include a reference information acquisition module 9011. The reference information acquisition module 9011 may include: a real time acquisition module 9011a, configured to acquire a historical positioning data set formed by at least one user along the route track of the target bus in the historical shift corresponding to the shift to be predicted, wherein the historical positioning data set has temporal and spatial consistency with the route track for the historical shift, so that the historical positioning data set indicates the real time when the target bus arrives at at least part of all the stations in the historical shift; a reference time determination module 9011b, configured to determine the reference arrival time information based on the historical positioning data set.

[0122] In some embodiments, the time information acquisition module 901 may include a scheduled information acquisition module 9012. The scheduled information acquisition module 9012 may include: a vehicle information acquisition module 9012a, configured to acquire at least one of the departure time information or departure interval information of the target public transport for the to-be-predicted shift, and the driving speed information of the target public transport; and a scheduled time determination module 9012b, configured to determine the scheduled time for the target public transport to arrive at all stations in the to-be-predicted shift based on at least one of the departure time information or departure interval information and the driving speed information.

[0123] In some embodiments, the vehicle information acquisition module 9012a may include a departure interval acquisition module 9012a-1. The departure interval acquisition module 9012a-1 may include a prediction model application module 9012a-11, which is configured to input the route information of the target bus into the trained prediction model to determine the departure interval information through the prediction model, wherein the route information includes first information associated with the inherent operation state of the target bus and second information associated with the dynamic operation adjustment of the target bus.

[0124] In some embodiments, the prediction model application module 9012a-11 may further include a reference rule providing module 9012a-111, which is configured to provide the prediction model with multiple reference rule information for predicting the departure interval, wherein the multiple reference rule information indicates multiple different departure interval time periods.

[0125] In some embodiments, the departure interval acquisition module 9012a-1 may include: a preliminary estimation module 9012a-12 is configured to determine the preliminary estimated time information of the target bus arriving at all stations based on the departure interval information obtained by the prediction model and the inherent first bus departure time information; a difference determination module 9012a-13 is configured to determine whether the difference between the preliminary estimated time information and the reference arrival time information is greater than a predetermined standard; a factor determination module 9012a-14 is configured to determine the factors that cause the difference to be greater than the predetermined standard in response to determining that the difference is greater than the predetermined standard; a strategy update module 9012a-15 is configured to update the prediction strategy of the prediction model according to the factors to obtain updated departure interval information.

[0126] In some embodiments, the arrival time correction module 902 may include: a difference station determination module 9021, configured to determine at least one station where the difference between the scheduled arrival time information and the reference arrival time information reaches a predetermined threshold; an arrival time update module 9022, configured to update the scheduled arrival time corresponding to at least one station in the scheduled arrival time information to the reference arrival time corresponding to at least one station in the reference arrival time information.

[0127] In some embodiments, the device 900 may include the following modules arranged after the arrival time determination module 903: an event determination module 904, configured to determine whether the target bus has experienced an event that affects the driving speed before arriving at the target station; a delay estimation module 905, configured to estimate the delay time corresponding to the event in response to determining that the target bus has experienced an event that affects the driving speed before arriving at the target station; and a delay increase module 906, configured to increase the delay time in the predicted arrival time information.

[0128] Fig.10 A structural block diagram of a device 1000 for map navigation according to an embodiment of the present disclosure is shown.

[0129] like Fig.10As shown, the device 1000 for map navigation includes a user information acquisition module 1001, a nearest station determination module 1002, an arrival time prediction module 1003 and a predicted time providing module 1004. The user information acquisition module 1001 is configured to acquire a user's navigation request and the user's current location information, wherein the navigation request indicates the route of the bus that the user is interested in; the nearest station determination module 1002 is configured to determine the station closest to the user in the route of the bus based on the user's current location information; the arrival time prediction module 1003 is configured to predict the time when at least one bus arrives at the station according to the above-mentioned device for predicting the arrival time of the bus; and the predicted time providing module 1004 is configured to provide the predicted time to the user.

[0130] According to an embodiment of the present disclosure, an electronic device is also provided, comprising at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

[0131] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is also provided, wherein the computer instructions are used to enable a computer to execute the method as described above.

[0132] According to an embodiment of the present disclosure, a computer program product is also provided, including a computer program, wherein the computer program implements the method as described above when executed by a processor.

[0133] refer to Fig.11 , a block diagram of an electronic device 1100 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0134] like Fig.11As shown, the electronic device 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1102 or a computer program loaded from a storage unit 1108 into a random access memory (RAM) 1103. In the RAM 1103, various programs and data required for the operation of the electronic device 1100 can also be stored. The computing unit 1101, the ROM 1102, and the RAM 1103 are connected to each other via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0135] Multiple components in the electronic device 1100 are connected to the I / O interface 1105, including: an input unit 1106, an output unit 1107, a storage unit 1108, and a communication unit 1109. The input unit 1106 can be any type of device that can input information to the electronic device 1100. The input unit 1106 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 1107 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1108 can include but is not limited to a disk and an optical disk. The communication unit 1109 allows the electronic device 1100 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device and / or the like.

[0136] The computing unit 1101 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1101 performs the various methods and processes described above. For example, in some embodiments, the method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1108. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1100 via ROM 1102 and / or a communication unit 1109. When the computer program is loaded into RAM 1103 and executed by the computing unit 1101, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 1101 may be configured to perform the method described above in any other appropriate manner (e.g., by means of firmware).

[0137] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0139] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0140] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0141] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0142] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0143] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0144] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0145] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A method for predicting the arrival time of a public transport vehicle, comprising: Obtaining scheduled arrival time information and reference arrival time information of the target public transport for the to-be-predicted shift, wherein the scheduled arrival time information includes the scheduled time for the target public transport to arrive at all stops in the to-be-predicted shift, and the reference arrival time information includes the actual time for the target public transport to arrive at at least some of the stops in the historical shift corresponding to the to-be-predicted shift; Correcting the scheduled arrival time information based on the reference arrival time information to obtain predicted arrival time information for the flight to be predicted; and Based on the predicted arrival time information and the information of the target station provided by the user, the time when the target public transport vehicle arrives at the target station is determined.

2. The method according to claim 1, wherein: The step of obtaining the reference arrival time information of the target public transport vehicle for the to-be-predicted bus service comprises: Acquire a historical positioning data set formed by at least one user along the route track of the target bus in a historical shift corresponding to the shift to be predicted, wherein the historical positioning data set has spatiotemporal consistency with the route track for the historical shift, so that the historical positioning data set indicates the real time when the target bus arrives at at least part of all the stops in the historical shift; and The reference arrival time information is determined based on the historical positioning data set.

3. The method according to claim 1 or 2, wherein: The step of obtaining the scheduled arrival time information of the target public transport vehicle for the to-be-predicted bus service comprises: Obtaining at least one of the departure time information or the departure interval information of the target public transport for the to-be-predicted bus service, and the travel speed information of the target public transport; and Based on at least one of the departure time information or the departure interval information and the travel speed information, a scheduled time for the target public transport vehicle to arrive at all the stations in the to-be-predicted schedule is determined.

4. The method according to claim 3, wherein: In response to the target bus traveling on a bus-only lane, the traveling speed information of the target bus is determined based on an inherent speed limit of the bus-only lane.

5. The method according to claim 3, wherein: The travel speed information of the target bus is determined based on the weighted sum of the real-time traffic condition and the historical traffic condition of the route section of the target bus during the time period corresponding to the predicted shift, wherein the sum of the first weight of the real-time traffic condition and the second weight of the historical traffic condition is 1.

6. The method according to claim 5, wherein: The real-time traffic condition is determined based on the traffic condition of the driving vehicle navigation on the line running section, the real-time movement trajectory of other buses except the target bus on the line running section, or the positioning trajectory of at least one user on the line running section.

7. The method according to claim 3, wherein: The step of obtaining the departure interval information of the target public transport vehicle for the to-be-predicted bus schedule includes: The route information of the target public transport vehicle is input into a trained prediction model to determine the departure interval information through the prediction model, wherein the route information includes first information associated with the inherent operation state of the target public transport vehicle and second information associated with the dynamic operation adjustment of the target public transport vehicle.

8. The method according to claim 7, wherein: The step of inputting the route information of the target public transport vehicle into a trained prediction model to determine the departure interval information through the prediction model includes: A plurality of reference regularity information for predicting the departure interval is provided to the prediction model, wherein the plurality of reference regularity information indicates a plurality of different departure interval time periods.

9. The method according to claim 7, wherein: The step of obtaining the departure interval information of the target public transport vehicle for the to-be-predicted bus schedule further includes: Based on the departure interval information and the inherent first bus departure time information obtained by the prediction model, determine the preliminary estimated time information of the target public bus arriving at all the stations; determining whether a difference between the preliminary estimated time information and the reference arrival time information is greater than a predetermined standard; In response to determining that the difference is greater than the predetermined standard, determining a factor causing the difference to be greater than the predetermined standard; and The prediction strategy of the prediction model is updated according to the factors to obtain updated departure interval information.

10. The method according to any one of claims 1 to 9, wherein: The correcting the scheduled arrival time information based on the reference arrival time information to obtain the predicted arrival time information for the flight to be predicted includes: determining at least one station at which a difference between the scheduled arrival time information and the reference arrival time information reaches a predetermined threshold; and The scheduled arrival time corresponding to the at least one station in the scheduled arrival time information is updated to the reference arrival time corresponding to the at least one station in the reference arrival time information.

11. The method according to any one of claims 1 to 10, wherein: After obtaining the predicted arrival time information for the flight to be predicted, the method further includes: Determining whether the target public transport vehicle has experienced an event that affects the travel speed before arriving at the target station; In response to determining that the target public transport vehicle experienced the event that affects the travel speed before arriving at the target station, estimating a delay time corresponding to the event; and The delay time is added to the predicted arrival time information.

12. A map navigation method, comprising: Acquire a navigation request of a user and the current location information of the user, wherein the navigation request indicates a route of a public transportation vehicle that the user is interested in; Based on the current location information of the user, determining the station on the route of the public transport vehicle closest to the user; Predicting the time when at least one of the public transport vehicles arrives at the station according to the method of any one of claims 1 to 11; and The predicted time is provided to the user.

13. A device for predicting the arrival time of a public transport vehicle, comprising: A time information acquisition module is configured to acquire the scheduled arrival time information and reference arrival time information of the target public transport for the to-be-predicted shift, wherein the scheduled arrival time information includes the scheduled time for the target public transport to arrive at all the stops in the to-be-predicted shift, and the reference arrival time information includes the actual time for the target public transport to arrive at at least some of the stops in the historical shift corresponding to the to-be-predicted shift; an arrival time correction module, configured to correct the scheduled arrival time information based on the reference arrival time information to obtain the predicted arrival time information for the to-be-predicted flight; and The arrival time determination module is configured to determine the time when the target public transport vehicle arrives at the target station based on the predicted arrival time information and the information of the target station provided by the user.

14. The device according to claim 13, wherein: The time information acquisition module includes a reference information acquisition module, and the reference information acquisition module includes: a real time acquisition module, configured to acquire a historical positioning data set formed by at least one user along the route track of the target public transport vehicle in a historical shift corresponding to the shift to be predicted, wherein the historical positioning data set has temporal and spatial consistency with the route track for the historical shift, so that the historical positioning data set indicates the real time when the target public transport vehicle arrives at least part of all the stops in the historical shift; and The reference time determination module is configured to determine the reference arrival time information based on the historical positioning data set.

15. The device according to claim 13 or 14, wherein: The time information acquisition module includes a predetermined information acquisition module, and the predetermined information acquisition module includes: a vehicle information acquisition module configured to acquire at least one of the departure time information or the departure interval information of the target public transport for the to-be-predicted bus service, and the travel speed information of the target public transport; and The scheduled time determination module is configured to determine the scheduled time for the target public transport vehicle to arrive at all the stations in the to-be-predicted shift based on at least one of the departure time information or the departure interval information and the travel speed information.

16. The device according to claim 15, wherein: In response to the target bus traveling on a bus-only lane, the traveling speed information of the target bus is determined based on an inherent speed limit of the bus-only lane.

17. The device according to claim 15, wherein: The travel speed information of the target bus is determined based on the weighted sum of the real-time traffic condition and the historical traffic condition of the route section of the target bus during the time period corresponding to the predicted shift, wherein the sum of the first weight of the real-time traffic condition and the second weight of the historical traffic condition is 1.

18. The device according to claim 17, wherein: The real-time traffic condition is determined based on the traffic condition of the driving vehicle navigation on the line running section, the real-time movement trajectory of other buses except the target bus on the line running section, or the positioning trajectory of at least one user on the line running section.

19. The device according to claim 15, wherein: The vehicle information acquisition module includes a vehicle departure interval acquisition module, and the vehicle departure interval acquisition module includes: The prediction model application module is configured to input the route information of the target public transport vehicle into a trained prediction model to determine the departure interval information through the prediction model, wherein the route information includes first information associated with the inherent operating state of the target public transport vehicle and second information associated with the dynamic operating adjustment of the target public transport vehicle.

20. The device according to claim 19, wherein The prediction model application module includes: The reference rule providing module is configured to provide the prediction model with a plurality of reference rule information for predicting the departure interval, wherein the plurality of reference rule information indicates a plurality of different departure interval time periods.

21. The device according to claim 19, wherein The departure interval acquisition module also includes: A preliminary estimation module is configured to determine preliminary estimated time information of the target public transport vehicle arriving at all the stations based on the departure interval information and the inherent first bus departure time information obtained by the prediction model; a difference determination module, configured to determine whether a difference between the preliminary estimated time information and the reference arrival time information is greater than a predetermined standard; a factor determination module configured to, in response to determining that the difference is greater than the predetermined standard, determine a factor causing the difference to be greater than the predetermined standard; and The strategy updating module is configured to update the prediction strategy of the prediction model according to the factors to obtain updated departure interval information.

22. The device according to any one of claims 13 to 21, wherein: The arrival time correction module comprises: a difference station determination module, configured to determine at least one station where the difference between the scheduled arrival time information and the reference arrival time information reaches a predetermined threshold; and The arrival time updating module is configured to update the scheduled arrival time corresponding to the at least one station in the scheduled arrival time information to the reference arrival time corresponding to the at least one station in the reference arrival time information.

23. The device according to any one of claims 13 to 22, wherein: The device further comprises the following modules arranged after the arrival time determination module: An event determination module is configured to determine whether the target public transport vehicle experiences an event that affects the travel speed before arriving at the target station; A delay estimation module, configured to estimate a delay time corresponding to the event in response to determining that the target public transport vehicle has experienced the event affecting the travel speed before arriving at the target station; as well as The delay adding module is configured to add the delay time to the predicted arrival time information.

24. A map navigation device, comprising: A user information acquisition module, configured to acquire a user's navigation request and the user's current location information, wherein the navigation request indicates a route of a public transportation vehicle that the user is interested in; A nearest station determination module, configured to determine the station closest to the user in the route of the public transport vehicle based on the current location information of the user; An arrival time prediction module, configured to predict the arrival time of at least one of the public buses at the station according to the apparatus of any one of claims 13 to 23; and The predicted time providing module is configured to provide the predicted time to the user.

25. An electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-12.

26. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to make a computer execute the method according to any one of claims 1-12.

27. A computer program product comprising a computer program, wherein: The computer program implements the method according to any one of claims 1 to 12 when executed by a processor.

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