Responsive bus intelligent scheduling and path planning method and system and electronic equipment

Abstract

The invention discloses a response type bus intelligent scheduling and route planning method and system, which can generate an alternative route set by analyzing historical travel data, and then, after a to-be-tripped order before the current moment is obtained, whether the to-be-tripped order meets the on-the-way condition or not is judged in real time for a departed vehicle; the orders on the way are distributed to the corresponding departed vehicles; and for unresponded orders, a set coverage problem can be generated based on the alternative route set and the unresponded order set, solving is carried out based on a mixed integer programming calculation model, a departure path is generated for the unresponded orders, and idle vehicles are allocated for departure. According to the method, the to-be-tripped order of the passenger is responded based on the running vehicle and the idle vehicle, so that the waiting time of the passenger is effectively shortened, the route planning rationality can better meet the actual demand, and the flexibility and the running efficiency of the public transportation system are greatly improved.

Description

Technical Field

[0001] The present invention relates to the fields of intelligent transportation and information technology services, and particularly to a responsive bus intelligent scheduling and path planning method, system and electronic device. Background Art

[0002] Demand-responsive public transportation, also known as responsive bus, is a type of bus mode without fixed operating routes, and stops at passenger demand locations to pick up and drop off passengers according to online and / or offline travel requests. It can be defined as a bus service with variable paths. That is to say, it can select bus routes based on the specific needs of passengers to provide transportation services for several passengers to share rides. Or, in other words, it operates by responding to the travel needs of passengers. Each operation needs to formulate a scheduling plan according to the actual passenger needs and perform vehicle path planning to achieve the bus service of dynamic carpooling. Responsive bus combines the characteristics of personal transportation and public transportation and can make up for the deficiencies of traditional public transportation.

[0003] Based on the above characteristics of responsive bus that need to consider the travel needs of several shared-ride passengers for dynamic variable path planning, how to perform reasonable path planning for responsive bus is a core problem. Moreover, the route planning and scheduling of responsive bus belong to the NP-complete problem, and there is no way to solve this problem through an algorithm with polynomial time for the time being.

[0004] Based on the above, it is very necessary to provide a path planning method and system for responsive bus, so that responsive bus can perform reasonable and effective dynamic path planning considering the actual needs of several shared-ride passengers, and can meet the actual work needs in terms of the number of paths and path rationality, thus playing an obvious role in improving the passenger experience and reducing system resource consumption, and then significantly improving the market value of responsive bus. Summary of the Invention

[0005] In view of this, the present invention provides a responsive bus intelligent scheduling and path planning method, system and electronic device, which can respond to the to-be-traveled orders of passengers based on the running vehicles (already dispatched vehicles) and idle vehicles (not yet dispatched vehicles) respectively, not only effectively reducing the waiting time of passengers, but also being more in line with the actual needs in terms of the rationality of path planning, greatly improving the flexibility and operating efficiency of the bus system.

[0006] The first aspect of the present invention discloses a responsive bus intelligent scheduling and path planning method, which includes the following steps:

[0007] S1, obtaining an alternative path set based on historical travel data;

[0008] S2. Obtain the current to-be-traveled orders that have been formed before the current moment in real time;

[0009] S3. Judge in real time whether the current to-be-traveled orders are on the way of the currently dispatched vehicles. If they are on the way, allocate the corresponding orders to the corresponding dispatched vehicles and define the corresponding orders as responded orders;

[0010] S4. Define the orders judged as not on the way in the current to-be-traveled orders as unresponded orders, and generate an unresponded order set after aggregating them;

[0011] S5. Construct a set covering problem model between the alternative path set and the unresponded order set, which is set to minimize the number of paths in the alternative path set to cover the maximum number of unresponded orders in the unresponded order set as the goal;

[0012] S6. Construct a mixed integer programming calculation model, and solve the set covering problem model based on the constructed mixed integer programming calculation model to obtain an approximate optimal solution close to the goal of the set covering problem model;

[0013] S7. According to the solution result of the mixed integer programming calculation model, determine the departure paths for meeting the unresponded orders within the limit of the current available number of buses for departure.

[0014] In the above implementation process, the historical travel data includes vehicle historical operation data and passenger historical order data. Among them, the vehicle historical operation data at least includes the GPS data of the vehicle, and the passenger historical order data at least includes the passenger card swiping data; the vehicle GPS data includes time, vehicle number and GPS data; the passenger card swiping data includes the swiped vehicle number and time. Based on these historical travel data, historical station statistical data can be generated, and then an alternative path set can be obtained based on the historical station statistical data and combined with the road map generated from the original geographic information. It should be particularly noted that in the present invention, the alternative path set is a complete set of the shortest paths between stations, that is, the alternative path set can be an optimal path set jointly constituted by the complete set of the shortest paths between stations, or is called an optimal line network.

[0015] In the implementation process of the intelligent scheduling and path planning method of the present invention, the current to-be-traveled orders can be orders generated online or orders generated offline. First, judge whether the current to-be-traveled orders are in line with the on-the-way situation. When it is judged to be on the way, it is judged that the corresponding orders can be carpooled. Based on this, the corresponding orders can be allocated to the on-the-way dispatched vehicles.

[0016] In the specific implementation process, considering that the path planning problem of responsive buses is actually a problem of finding the shortest path passing through specified stops, and the problem of finding the shortest path passing through specified stops is an NP problem, and there is no generally recognized optimal solution currently. However, considering the time sequence of orders in the scenario of this method, therefore, for the determination of whether orders are on the same route, the NP-hard problem of finding the shortest path passing through specified stops can be decomposed and simplified into a problem that can be solved within polynomial time. Its specific implementation steps, that is, in step S3 for determining whether orders are on the same route, include:

[0017] S301, Set each order to be traveled as a newly added order, and define its starting point and ending point as the starting point of the new order and the ending point of the new order;

[0018] S302, In all current paths of all currently dispatched vehicles, first insert the starting point of the new order of the newly added order after all stops included in the current path of the currently dispatched vehicle, and then insert the ending point of the new order at any position after the insertion position of the starting point of the new order to form a set of potential shortest paths;

[0019] S303, Set the path directly from the starting point of the order to the ending point of the order as the ideal shortest path of the order, set the completion time of the ideal shortest path of the order as t0, and set the completion time of the order from the starting point to the ending point in the current path of the currently dispatched vehicle as t1, and define the value of t1 / t0 as the satisfaction value, and set the satisfaction value to be less than or equal to the satisfaction upper limit value;

[0020] S304, Traverse the set of potential shortest paths. If traveling along the current path can make the satisfaction value of each passenger on the currently dispatched vehicle and the passenger corresponding to the newly added order less than or equal to the satisfaction upper limit value, then determine that the newly added order is on the same route, and add the current vehicle, the current path, the newly added order, and the change in the in-vehicle time of the passengers caused by the current path to the set of alternative paths together; among them, after the current vehicle, the current path, the newly added order, and the change in the in-vehicle time of the passengers caused by the current path are added to the set of alternative paths together, the set of alternative paths forms the set of alternative paths for the new order;

[0021] S305, Sort the set of alternative paths in ascending order according to the change in the in-vehicle time of the passengers caused by the current path, and use the path at the forefront as the path of the new order and assign it to the corresponding dispatched vehicle.

[0022] That is, in step S3 of the present invention, first insert the start point and end point of the new order into the current path of the dispatched vehicle in sequence, and then traverse the set of potential shortest paths to determine whether each current path with the start point and end point of the new order inserted can make the satisfaction values of all passengers on the currently dispatched vehicle (including passengers who have accepted the order / boarded the vehicle and passengers of the new order) meet the threshold, thereby determining whether the newly added order is a convenient route. Finally, all current paths with satisfaction values meeting the requirements are sorted in ascending order according to the change in the in-vehicle time of the passengers caused, and the path at the forefront is selected as the new order path, that is, the path with the least impact on the in-vehicle time of the passengers among all current paths with satisfaction values meeting the requirements is used as the new order path. Based on the process disclosed in step S3 of the present invention, since the orders in the scenario of this method have a sequential order in time, the determination of whether the new order is a convenient route is effectively completed, that is, the NP-hard problem of the shortest path passing through the specified stations is effectively decomposed and simplified into a problem that can be solved within polynomial time.

[0023] According to the method disclosed in the first aspect of the present invention, after step S305, it further includes: S306, remove the newly added order corresponding to the new order path from the set of alternative paths (i.e., the set of alternative paths after adding the new order), recalculate the remaining current paths corresponding to the dispatched vehicle corresponding to the determined new order path, and re-execute step S305 after recalculation. That is to say, after adopting the path at the forefront as the new order path, the order corresponding to this path at the forefront should be removed from the set of alternative paths to recalculate the other paths corresponding to the dispatched vehicle corresponding to this path at the forefront, that is, re-perform the calculation in step S305, and after recalculation, sort the set of alternative paths in ascending order according to the change in the in-vehicle time of the passengers caused by the current path, and select the path currently at the forefront as the final new order path. By executing this step, it can be ensured that the new order path does not conflict with the path of the dispatched vehicle, minimize duplicate paths to the greatest extent, improve the service scope, respond to all orders with the fastest efficiency, and also keep the system in an optimal state.

[0024] After processing the on-the-way orders, for the set of unresponsive orders composed of unresponsive orders, it forms a set covering problem together with the alternative path set. That is, for this scenario, there are two sets, one is the set of unresponsive orders and the other is the alternative path set. In the present invention, a shortest path planning problem is constructed between the set of unresponsive orders and the alternative path set, that is, it aims to solve the problem of selecting the shortest path planning for unresponsive orders. To solve this problem, the present invention innovatively designs to convert the shortest path planning problem into a set covering problem, that is, to construct a set covering problem with the goal of covering the maximum number of unresponsive orders in the set of unresponsive orders with the minimum number of paths in the alternative path set; that is to say, in the present invention, first a shortest path planning problem is constructed between the set of unresponsive orders and the alternative path set, and then the shortest path planning problem is converted into a set covering problem, that is, select as few (minimize) paths as possible from the alternative path set, so that the set of vertices covered by these paths includes the starting and ending points of as many (that is, maximize) unresponsive orders as possible.

[0025] However, for the set covering problem, it is also an NP-complete problem, which means that this problem cannot be solved by an algorithm in polynomial time. But in the present invention, by means of a mixed integer programming calculation model, an operation of finding an approximate optimal solution is carried out, so as to obtain a relatively good solution. Finally, based on this solution and limited by the current number of buses that can depart, the paths for satisfying unresponsive orders can be determined, that is, the departure paths in this article.

[0026] Specifically, the mixed integer programming calculation model is pre-constructed based on mixed integer programming variables and mixed integer programming constraint conditions. The mixed integer programming variables include:

[0027] A first variable family formed by a number of first variables, where the first variable represents whether a certain order is responded to, and the number of first variables is the number of orders;

[0028] A second variable family formed by a number of second variables, where the second variable represents whether a certain order is responded to by a certain path, and the number of second variables is the number of orders multiplied by the number of paths;

[0029] A third variable family formed by a number of third variables, where the third variable represents whether a certain path has taken an order, and the number of third variables is the number of routes;

[0030] The mixed integer programming calculation model is constructed to solve with the goal of minimizing the sum of the third variables.

[0031] In addition, the mixed integer programming constraint conditions include:

[0032] The first condition, including that the orders at the core stations should be responded to;

[0033] The second condition is that if an order is responded to, at least one path needs to be allocated for it;

[0034] The third condition is that the allocated path needs to pass through the starting point and the ending point of the corresponding order, and the starting point should be before the ending point;

[0035] The fourth condition is that each order can only be responded to by one path;

[0036] The fifth condition is that the sum of the second variables corresponding to the paths where the first variable is 1 is not zero.

[0037] Among them, the first variable being 1 represents that the order is responded to, and the sum of the second variables corresponding to the paths where the first variable is 1 not being zero means that the responded order must have a route.

[0038] Based on the above variable settings and condition settings, it can effectively ensure that the order can be responded to, that is, it can effectively complete the optimization goal of selecting as few (minimizing) paths as possible from the alternative path set and making the set of vertices covered by these paths include as many (that is, maximizing) starting and ending points of unresponded orders as possible. Furthermore, based on the next solution, the paths used to satisfy the unresponded orders can be determined.

[0039] Furthermore, in step S7, after obtaining the solution in step S6, all paths with the third variable being 1 are taken from the alternative path set, and the taken paths are sorted from largest to smallest according to the sum of the second variables corresponding to each taken path. The first N taken paths are taken as the departure paths, where N needs to satisfy both being less than or equal to the current number of buses that can depart and being less than or equal to the solution result. That is, N is limited by the smaller of the current number of buses that can depart and the solution result.

[0040] In addition, in step S1 of obtaining the alternative path set based on historical travel data disclosed by the method of the present invention, it includes:

[0041] S101, generating historical station statistical data based on historical passenger card - swiping data and the historical GPS data of vehicles;

[0042] S102, selecting historical stations with the number of card - swipes exceeding a specific value from the historical station statistical data as core stations, and aggregating all core stations into a core station set;

[0043] S103, calculating the intersection information of the roads within the vehicle operation area based on the original geographic information, and calculating the road map of the vehicle operation area based on the road information and the intersection information;

[0044] S104. Based on the road map, establish the correspondence between the locations of the core stations and the original geographical information, thereby calculate the edges of the core stations from the road map. Then, take the core station as the break point to divide the edge where the core station is located into two new edges, and the two new edges take the two endpoints of the original edge and the core station as endpoints respectively;

[0045] S105. Enumerate all non-repeated pairs of core stations, calculate the shortest paths for all pairs of core stations in the road map to obtain the shortest paths between each group of core station pairs, and define the set of the shortest paths of all core station pairs as the shortest path set between core stations;

[0046] S106. Take each core station as the starting point of a path set respectively, find the station closest to the last station of the corresponding path set from the stations outside each path set, and iterate in this way until all stations are added to the full-station path set that covers all stations;

[0047] S107. Combine the full-station path set and the core-station path set as the alternative path set.

[0048] In the implementation process of the above steps S101 - S107, since the alternative path set is generated based on the road map information (original geographical information) and the shortest path algorithm, this makes the finally formed alternative path set fully close to the geographical information and historical operation data of the operation area, thereby greatly improving the rationality of the alternative path set in actual operation. That is, since the alternative path set has been generated based on the road map information and the shortest path algorithm, the responsiveness and real-time performance of the system for processing user orders are greatly improved during system operation, and the overall stability of the system can also be improved.

[0049] The second aspect of the present invention discloses a system for implementing the responsive bus intelligent scheduling and path planning method disclosed in the first aspect of the present invention. The system includes:

[0050] The first acquisition unit, which is configured to obtain the alternative path set based on historical travel data;

[0051] The second acquisition unit, which is configured to obtain the current to-be-traveled orders formed before the current moment in real time;

[0052] The carpooling judgment unit, which is configured to judge in real time whether the current to-be-traveled order is on the way in the currently dispatched vehicles. If it is on the way, allocate the corresponding order to the corresponding dispatched vehicle and define the corresponding order as a responded order;

[0053] The unresponded order set generation unit, which is configured to define the orders judged as not on the way in the current to-be-traveled orders as unresponded orders and generate an unresponded order set after aggregating them;

[0054] The set covering problem model establishment unit is configured to establish a set covering problem model between the alternative path set and the unresponded order set, with the goal of minimizing the number of paths in the alternative path set to cover the maximum number of unresponded orders in the unresponded order set;

[0055] The mixed integer programming construction and calculation unit is configured to construct a mixed integer programming calculation model and solve the set covering problem model based on the constructed mixed integer programming calculation model to obtain an approximate optimal solution close to the goal of the set covering problem model;

[0056] The departure path determination unit is configured to determine the departure paths for meeting the unresponded orders according to the solution result of the mixed integer programming calculation model, limited by the current available number of departing buses.

[0057] The third aspect of the present invention discloses an electronic device, which includes a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the responsive bus intelligent scheduling and path planning method disclosed in the first aspect of the present invention.

[0058] The fourth aspect of the present invention discloses a computer-readable storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored. The at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the responsive bus intelligent scheduling and path planning method disclosed in the first aspect of the present invention.

[0059] Beneficial effects: In the responsive bus intelligent scheduling and path planning method of the present invention, it is possible to respond to the passenger's pending travel orders based on the running vehicles (departed vehicles) and idle vehicles (undeparted vehicles) respectively, which not only effectively reduces the waiting time of passengers, but also can better meet the actual needs in terms of the rationality of path planning, greatly improving the flexibility and operation efficiency of the bus system. Moreover, in the path planning for unresponded orders, first, a set covering problem is constructed between the alternative path set and the unresponded order set, thus converting the route planning problem into a set covering problem. Then, by means of the solution of the mixed integer programming calculation model, it is possible to quickly and conveniently determine the path planning for responding to unresponded orders based on the obtained better approximate solution, thereby solving the problem in the prior art that the route planning cannot be solved by an algorithm with polynomial time because it belongs to an NP-complete problem.

[0060] The responsive bus intelligent scheduling and path planning method and system of the present invention will be detailedly disclosed below in combination with the embodiments shown in the drawings and the reference numerals. Brief Description of the Drawings

[0061] Figure 1Shows the step flowchart of the responsive bus intelligent scheduling and path planning method of the present invention.

[0062] Figure 2 Shows the implementation step flowchart of step S1 in the responsive bus intelligent scheduling and path planning method of the present invention.

[0063] Figure 3 Shows the implementation step flowchart of step S3 in the responsive bus intelligent scheduling and path planning method of the present invention. Detailed implementation manners

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0065] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0066] Figure 1 Shows the responsive bus intelligent scheduling and path planning method and system of the present invention. In combination with Figure 1 As shown, the first aspect of the present invention discloses a responsive bus intelligent scheduling and path planning method, which includes the following steps:

[0067] S1, obtaining an alternative path set based on historical travel data;

[0068] S2, obtaining the current to-be-traveled orders formed before the current moment in real time;

[0069] S3, judging in real time whether the current to-be-traveled orders are on the way in the currently dispatched vehicles. If so, assign the corresponding orders to the corresponding dispatched vehicles and define the corresponding orders as the responded orders;

[0070] S4, defining the orders judged as not on the way in the current to-be-traveled orders as unresponded orders, and generating an unresponded order set after aggregating them;

[0071] S5, constructing a set covering problem model between the alternative path set and the unresponded order set, which is set to minimize the number of paths in the alternative path set to cover the maximum number of unresponded orders in the unresponded order set as the goal;

[0072] S6, constructing a mixed integer programming calculation model, and solving the set covering problem model based on the constructed mixed integer programming calculation model to obtain an approximate optimal solution close to the goal of the set covering problem model;

[0073] S7. Based on the solution result of the mixed integer programming calculation model, determine the departure routes for fulfilling the unresponsive orders, limited by the current number of buses that can depart.

[0074] Figure 2 The flowchart of the implementation steps of step S1 in the responsive bus intelligent scheduling and route planning method of the present invention is shown. In combination with Figure 2 as shown, step S1 is implemented based on the following specific steps:

[0075] S101. Generate historical station statistical data based on historical passenger card - swiping data and historical GPS data of vehicles.

[0076] S102. Select historical stations with the number of card - swiping exceeding a specific value from the historical station statistical data as core stations, and gather all core stations into a core station set.

[0077] S103. Calculate the intersection information of the roads within the vehicle operation area based on the original geographic information, and calculate the road map of the vehicle operation area based on the road information and intersection information.

[0078] S104. Based on the road map, map the positions of the core stations to the original geographic information, thereby calculate the edges of the core stations from the road map, and then divide the edge where the core station is located into two new edges with the core station as the break point. The two new edges take the two endpoints of the original edge and the core station as endpoints respectively.

[0079] S105. Enumerate all non - repeating core station pairs, perform the shortest path calculation for all core station pairs in the road map to obtain the shortest path between each group of core station pairs, and define the set of the shortest paths of all core station pairs as the shortest path set between core stations.

[0080] S106. Take each core station as the starting point of a path set respectively, find the station closest to the last station of the corresponding path set from the stations outside each path set, and iterate in this way until all stations are added to the full - station path set that covers all stations.

[0081] S107. Merge the full - station path set and the core - station path set as the alternative path set.

[0082] Among them, the main function of the process of step S101 is to process the historical passenger card - swiping data and vehicle GPS data to analyze the boarding stations and boarding times of passengers. Specifically, it includes the following sub - steps:

[0083] Sub - step a. Generate card - swiping position information from vehicle GPS data and passenger card - swiping data.

[0084] Among them, the vehicle GPS data mainly includes time, vehicle number, and GPS data; the clock-in data mainly includes vehicle number and time. Classify the vehicle GPS data and clock-in data according to their vehicle numbers, and place the clock-in data and GPS data of each vehicle in a list and sort them according to time. Subsequently, for each piece of clock-in data, calculate the GPS range of this piece of clock-in data based on the two nearest GPS data to it.

[0085] Sub-step b: Calculate the station information of the clock-in data according to the clock-in data, GPS data, and station data information.

[0086] There is generally a certain error in GPS data. Assuming that this error conforms to a two-dimensional normal distribution, the probability that this clock-in data occurs at a certain station can be calculated based on the GPS data of each clock-in. For a certain piece of clock-in data, if the occurrence probability of a certain station among all stations is greater than a specified threshold (tentatively set at 95%), set this station as the station of this piece of clock-in data.

[0087] Sub-step c: Generate virtual orders based on the clock-in station information and its statistical information. By generating virtual orders, some line sets can be generated in advance, so that the line sets are not generated in real time when the user places an order, which can improve the efficiency during actual operation.

[0088] First, count the total number of clock-ins at each station from the station information of all clock-in data for all dates, and then calculate the occurrence probability of each station according to the number of clock-ins at each station and the total number of clock-ins at all stations. Then, in order to be as close as possible to the distribution of real orders, select all clock-in data for a certain date, use the clock-in stations of these clock-in data as the boarding stations of the orders, use the clock-in times of these clock-in data as the initiation times of the orders, and then calculate the end point of an order using random numbers according to the occurrence frequencies of each station.

[0089] For step S102, its function is to form a core station set so that an alternative path set can be constructed based on the core stations, which can make the finally formed alternative path set closer to the historical actual travel demand. Among them, the core stations refer to the stations where the number of clock-ins exceeds a certain value. In the present invention, the threshold is set to a total of 10,000 clock-ins. Add the stations whose statistical data exceeds this threshold to the core station set.

[0090] For step S103, its specific implementation process is as follows: In the original geographic information data, each straight road is represented by its starting point, ending point, and other road attributes. However, the intersection points of the roads intersecting with this road, that is, the intersections, are not represented in the dataset. Therefore, each road needs to be represented as a line segment group, and the intersection points between every two roads are calculated to obtain the geographic information coordinates of the intersections. On this basis, considering the intersections as vertices in the graph structure and the partial line segments of the road between two intersections as edges in the graph structure, a graph structure can be calculated from the road information and intersection information to represent all the road information in this area, and this graph structure is called a road graph. The road graph can be an undirected graph or a directed graph. For convenience, the algorithm used in the design of the solution of the present invention is an undirected graph, but in actual applications, to reduce errors, a directed graph is recommended.

[0091] For steps S104 and S105, their specific implementation process is as follows: First, the location of the core site is corresponded to the original geographic information to calculate the edge where the core site is located. The edge where the core site is located is divided into two new edges with the core site as the break point, and these two edges take the two end points of the original edge and the core site as end points respectively. Then, all non-repeated core site pairs are enumerated, and the Dijkstra shortest path algorithm is applied to all core site pairs in the road graph, and all these calculation results are saved. The set of the shortest paths between all core site pairs is called the shortest path set between core sites.

[0092] Based on the above steps S101 - S105, steps S106 - S107 are then carried out to finally form an alternative path set for use in responding to the to-be-traveled order.

[0093] The steps for path planning in response to the to-be-traveled order are described as follows.

[0094] First, step S3 is carried out, that is, it is judged in real time whether the current to-be-traveled order is on the way of the currently dispatched vehicle. If it is on the way, the corresponding order is assigned to the corresponding dispatched vehicle, and the corresponding order is defined as a responded order.

[0095] In the specific implementation process of step S3, the present invention designs a problem that can be solved based on polynomial time, thereby solving the technical problem that the existing path planning is an NP problem and has no recognized optimal solution. Specifically, for bus routes, it must cover the destinations of all passengers, and it must also cover the starting points and destinations of all accepted orders. Therefore, the path planning problem that responds to the bus is actually to solve the shortest path problem passing through designated sites, and the shortest path problem passing through designated sites is an NP problem, which has no recognized optimal solution. The inventors of the present application noticed that the orders to be traveled have a time sequence, and because the algorithm steps for judging the en route are aimed at the situation of vehicles that have already been dispatched, the shortest path algorithm for multiple orders can be restricted to simplify the problem, that is, the NP-difficult shortest path problem passing through designated sites can be decomposed and simplified into a problem that can be solvable in polynomial time.

[0096] Figure 3 The flowchart of the implementation steps of step S3 in the responsive bus intelligent scheduling and path planning method of the present invention is shown. Specifically, step S3 can be designed to be implemented based on the following steps:

[0097] S301, setting each pending travel order as a newly added order, and defining its starting point and end point as a new order starting point and a new order end point;

[0098] S302, among all current paths of all currently dispatched vehicles, the starting point of the newly added order is first inserted into all the stations included in the current path of the currently dispatched vehicles, and then the end point of the new order is inserted into any position after the insertion position of the new order starting point, so as to form a potential shortest path set;

[0099] S303, the path directly from the order start point to the order end point is set as the ideal shortest path of the order, the completion time of the ideal shortest path of the order is set as t0, and the completion time of the order from the order start point to the order end point in the current path of the currently dispatched vehicle is set as t1, and the value of t1 / t0 is defined as the satisfaction value, and the satisfaction value is set to be less than or equal to the satisfaction upper limit value;

[0100] S304, traversing the potential shortest path set, if traveling along the current path can make the satisfaction value of each passenger on the currently dispatched vehicle and the passenger corresponding to the newly added order less than or equal to the satisfaction upper limit value, then the newly added order is determined to be on the way, and the current vehicle, the current path, the newly added order, and the change in the passenger's time in the vehicle caused by the current path are added to the alternative path set;

[0101] S305, sorting the candidate path set from small to large according to the change in passenger on-board time caused by the current path, taking the path at the front as the new order path, and allocating it to the corresponding dispatched vehicle.

[0102] Among them, for step S301-step S302, assuming that there is a vehicle route R, passing through points B, s1...sn, where B refers to the current position of the vehicle, then for a newly added order D and its starting and ending points S and T, its potential shortest path can be generated by this method: insert S into all nodes included in R first, and then insert T into any position after the insertion position of S. The size of this potential shortest path set is n^2, which decomposes and simplifies the NP-hard shortest path problem passing through specified nodes into a problem that can be solved in polynomial time.

[0103] As for steps S303-304, the specific implementation process can be as follows: For passengers, there is a shortest path ST that goes directly from their starting point S to the end point T. Let the time taken along this path from S to T be t0; the time taken by passengers on the bus from S to T is t1. In order to satisfy passengers, it is now required that t1 / t0 must be within a certain range, and this limit is called the upper limit of passenger satisfaction. The upper limit of satisfaction set by this algorithm is less than or equal to 1.2. Traverse the aforementioned potential shortest path set. If traveling along this path can satisfy every passenger on the bus and the passenger corresponding to the latest order, then [the vehicle, the change in the passenger's time on the bus caused by the path, the order, and the path] are added to the set of alternative paths.

[0104] Then proceed to step S305 to determine the new order path and assign it to the corresponding dispatched vehicle.

[0105] In another embodiment of the present invention, after step S305, it also includes: S306, removing the newly added order corresponding to the new order path from the new order alternative path set, and recalculating the remaining current paths corresponding to the dispatched vehicles corresponding to the new order path, and re-executing step S305 after recalculation.

[0106] Based on the above steps S301 to S306, it is completed to determine whether each order in the pending travel orders is on the way in the current route of the currently dispatched vehicle.

[0107] After the above steps are completed, if there are still unresponded orders, they are defined as unresponded orders, and all unresponded orders before the current moment can be aggregated into an unresponded order set.

[0108] In the present invention, a passenger loading algorithm based on solving the set covering problem is designed to solve the demand response problem of unresponded orders. The following is an explanation of the dispatch response steps for unresponded orders.

[0109] As mentioned above, route planning is a class of NP-complete problems. On this basis, for the demand response scenario of unresponded orders, there are two sets, namely the unresponded order set and the alternative path set. The unresponded order set is the part of the orders that occurred before the current moment and have not been responded to yet, and the alternative path set is the optimal path set jointly composed of the shortest path set between core stations and the path set covering all stations.

[0110] In the scenario of responding to unresponded orders, since there are no existing travel orders, there is no chronological order between the orders, and thus the method of decomposing the NP-hard shortest path problem passing through a specified station used in the above-mentioned ride-along judgment cannot be used.

[0111] To solve the path planning problem in the departure scenario of responding to unresponded orders, the present invention adopts such a method, that is: Step S5, a set covering problem model is constructed between the alternative path set and the unresponded order set, which is set to minimize the number of paths in the alternative path set to cover the maximum number of unresponded orders in the unresponded order set. That is, the set covering problem is described as covering the maximum number of passenger orders with the minimum number of paths; S6, a mixed integer programming calculation model is constructed, and based on the constructed mixed integer programming calculation model, the set covering problem model is solved to obtain an approximate optimal solution close to the objective of the set covering problem model; S7, according to the solution result of the mixed integer programming calculation model, within the limit of the current available departure buses, the departure paths for meeting the unresponded orders are determined.

[0112] That is to say, the path planning problem for unresponded orders is transformed into a set covering problem. Even for the set covering problem, it is also an NP-complete problem and cannot be solved by an algorithm in polynomial time. However, by using the mixed integer programming calculation model to perform the operation of finding an approximate optimal solution, a relatively good solution can be obtained. Based on this solution and within the limit of the current available departure buses, the paths for meeting the unresponded orders can be determined, that is, the departure paths in this article.

[0113] The implementation process of path planning in the departure scenario for unresponded orders is as follows:

[0114] According to the description of the set covering problem, a mixed integer programming calculation model including mixed integer programming variables and mixed integer programming constraint conditions is constructed. Among them, the mixed integer programming variables include:

[0115] A first variable family formed by several first variables, where the first variable represents whether a certain order is responded to, and the number of first variables is the number of orders;

[0116] A second variable family formed by a number of second variables, where the second variable represents whether a certain order is responded to by a certain path, and the number of second variables is the number of orders multiplied by the number of paths;

[0117] A third variable family formed by a number of third variables, where the third variable represents whether a certain path has taken an order, and the number of third variables is the number of routes;

[0118] The mixed-integer programming calculation model is constructed to solve the problem with the optimization goal of minimizing the sum of the third variables.

[0119] The mixed-integer programming constraint conditions include:

[0120] The first condition, including that the orders at the core site need to be responded to;

[0121] The second condition, if an order is to be responded to, at least one path needs to be allocated for it;

[0122] The third condition, the allocated path needs to pass through the starting point and the ending point of the corresponding order, and the starting point should be before the ending point;

[0123] The fourth condition, each order can only be responded to by one path;

[0124] The fifth condition, the total amount of the second variables corresponding to the orders with the first variable being 1 is not zero.

[0125] After the model is established, call the mixed-integer programming code package (in actual Python implementation, the mip package is used), and solve for an approximate optimal solution with the minimum sum of the third variables as the optimization condition. After obtaining this solution, take all the paths with the third variable being 1, and sort them in descending order according to the sum of the corresponding second variables, and take the first N paths as the departure paths, where N needs to satisfy both being less than or equal to the current available departure bus quantity and less than or equal to the solution result. That is, N is limited by the smaller of the current available departure bus quantity and the solution result. Suppose there are M buses. If M is less than N, then vehicles can be allocated to at most the first M routes that meet the departure conditions.

[0126] Based on all the above steps, it is possible to complete the convenient response and departure response to the to-be-traveled orders.

[0127] The present invention also discloses a system for implementing the responsive bus intelligent scheduling and path planning method of the present invention. The system includes:

[0128] The first acquisition unit, which is constructed to obtain an alternative path set based on historical travel data;

[0129] The second acquisition unit, which is constructed to obtain the current to-be-traveled orders formed before the current moment in real time;

[0130] A carpooling judgment unit, configured to judge in real time whether the current to-be-traveled order is on the way in the currently dispatched vehicles. If it is on the way, allocate the corresponding order to the corresponding dispatched vehicle and define the corresponding order as a responded order;

[0131] A non-responded order set generation unit, configured to define the orders judged as not on the way in the current to-be-traveled orders as non-responded orders, and generate a non-responded order set after aggregating them;

[0132] A set covering problem model establishment unit, configured to construct a set covering problem model between the alternative path set and the non-responded order set, which is set to minimize the number of paths in the alternative path set to cover the maximum number of non-responded orders in the non-responded order set as the goal;

[0133] A mixed integer programming construction and calculation unit, configured to construct a mixed integer programming calculation model, and solve the set covering problem model based on the constructed mixed integer programming calculation model to obtain an approximate optimal solution close to the goal of the set covering problem model;

[0134] A departure path determination unit, configured to determine the departure paths for meeting the non-responded orders according to the solution result of the mixed integer programming calculation model, limited by the current number of buses that can be dispatched.

[0135] The present invention further discloses an electronic device, which includes a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the responsive bus intelligent scheduling and path planning method disclosed by the present invention.

[0136] The present invention further discloses a computer-readable storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored. The at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the responsive bus intelligent scheduling and path planning method disclosed by the present invention.

[0137] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0138] In addition, each functional module in various embodiments of this application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0139] If the above functions are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0140] It should be further noted that the terms "first", "second", etc. in the description, claims and the above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0141] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A responsive public transportation intelligent scheduling and path planning method, characterized in that: The following steps are involved: S1, obtain a set of alternative routes based on historical travel data; S2, real-time acquisition of the current pending travel orders that have been formed before the current moment; S3, determine in real time whether the current order to be traveled is on the way among the currently dispatched vehicles. If so, assign the corresponding order to the corresponding dispatched vehicle and define the corresponding order as a responded order; S4, defining the orders that are judged as non-on-the-way in the current waiting travel orders as unresponded orders, and collecting them to generate an unresponded order set; S5, constructing a set covering problem model between the alternative path set and the unresponded order set, which is set to cover the maximum number of unresponded orders in the unresponded order set with the minimum number of paths in the alternative path set as the goal; S6, constructing a mixed integer programming calculation model, and solving the set covering problem model based on the constructed mixed integer programming calculation model to obtain an approximate optimal solution that is close to the target of the set covering problem model; S7, according to the solution result of the mixed integer programming calculation model, the departure route for satisfying the unresponded orders is determined with the current number of dispatchable buses as the limit.

2. The method according to claim 1, characterized in that The mixed integer programming calculation model is pre-constructed based on mixed integer programming variables and mixed integer programming constraints, and the mixed integer programming variables include: A first variable family formed by a plurality of first variables, wherein the first variable indicates whether a certain order is responded to, and the number of the first variables is the number of orders; A second variable family formed by a plurality of second variables, wherein the second variable indicates whether a certain order is responded to by a certain path, and the number of the second variables is the number of orders multiplied by the number of paths; A third variable family formed by several third variables, where the third variable indicates whether a certain route has accepted an order, and the number of third variables is the number of routes; The mixed integer programming calculation model is constructed to solve the problem by minimizing the sum of the third variables as the optimization goal.

3. The method according to claim 2, characterized in that The mixed integer programming constraints include: The first condition is that orders from core sites must be responded to; The second condition is that if an order is responded to, at least one path must be assigned to it; The third condition is that the assigned path must pass through the starting point and end point of the corresponding order, and the starting point must be before the end point; The fourth condition is that each order can only be responded to by one path; The fifth condition is that the sum of the second variables corresponding to the corresponding paths where the first variable is 1 is not zero.

4. The method according to claim 3, characterized in that In step S7, after obtaining the solution of step S6, all paths whose third variable is 1 are selected from the alternative path set, and the selected paths are sorted from large to small according to the sum of the second variables corresponding to each of the selected paths, and the first N selected paths are selected as the departure paths, where N must simultaneously satisfy the requirement of being less than or equal to the current number of buses that can be dispatched and less than or equal to the solution result.

5. The method according to claim 1, characterized in that In step S3, it includes: S301, setting each pending travel order as a newly added order, and defining its starting point and end point as a new order starting point and a new order end point; S302, among all current paths of all currently dispatched vehicles, the starting point of the new order added to the order is first inserted after all the stations included in the current path of the currently dispatched vehicles, and then the end point of the new order is inserted into any position after the insertion position of the new order starting point, so as to form a potential shortest path set; S303, the path directly from the order start point to the order end point is set as the ideal shortest path of the order, the completion time of the ideal shortest path of the order is set as t0, and the completion time of the order from the order start point to the order end point in the current path of the currently dispatched vehicle is set as t1, and the value of t1 / t0 is defined as the satisfaction value, and the satisfaction value is set to be less than or equal to the satisfaction upper limit value; S304, traversing the potential shortest path set, if traveling along the current path can make the satisfaction value of each passenger on the currently dispatched vehicle and the passenger corresponding to the newly added order less than or equal to the satisfaction upper limit value, then the newly added order is determined to be on the way, and the current vehicle, the current path, the newly added order, and the change in the passenger's time in the vehicle caused by the current path are added to the alternative path set; S305, sorting the candidate path set from small to large according to the change in passenger on-board time caused by the current path, taking the path at the front as the new order path, and allocating it to the corresponding dispatched vehicle.

6. The method according to claim 5, characterized in that After step S305, the method also includes: S306, removing the newly added order corresponding to the new order path from the new order alternative path set, recalculating the remaining current paths corresponding to the dispatched vehicles corresponding to the new order path, and re-executing step S305 after recalculation.

7. The method according to any one of claims 1 to 6, characterized in that The step S1 comprises: S101, generating historical station statistics based on historical passenger card swiping data and historical GPS data of vehicles; S102, selecting historical sites with a card swipe count exceeding a specific value from historical site statistics as core sites, and aggregating all core sites into a core site set; S103, calculating the intersection information of the roads in the vehicle operation area based on the original geographic information, and calculating the road map of the vehicle operation area based on the road information and the intersection information; S104, based on the road map, the location of the core site is matched with the original geographic information, so as to calculate the edge of the core site from the road map, and then the edge where the core site is located is divided into two new edges with the core site as a breakpoint, and the two new edges respectively use the two endpoints of the original edge and the core site as endpoints; S105, enumerate all non-repeated core site pairs, perform shortest path calculation on all core site pairs in the road graph to obtain the shortest path between each group of core site pairs, and define the set of shortest paths of all core site pairs as the shortest path set between core sites; S106, taking each core site as the starting point of a path set, searching for the site closest to the last site of the corresponding path set from sites outside each path set, and iterating in this way until all sites are added to the full site path set covering all sites; S107: Combine the full site path set and the core site path set as the candidate path set.

8. A system for implementing the responsive public transportation intelligent scheduling and path planning method according to any one of claims 1 to 7, characterized in that: include: A first acquisition unit, configured to obtain a set of candidate routes based on historical travel data; A second acquisition unit is configured to acquire in real time the current pending travel orders that have been formed before the current moment; The carpooling judgment unit is configured to judge in real time whether the current to-be-traveled order is on the way among the currently dispatched vehicles. If so, the corresponding order is assigned to the corresponding dispatched vehicle and the corresponding order is defined as a responded order. The unresponded order set generating unit is constructed to define the orders that are judged as non-on-the-way in the current waiting travel orders as unresponded orders, and to generate an unresponded order set after collecting them; A set covering problem model building unit, which is constructed to build a set covering problem model between the alternative path set and the unresponded order set, which is set to cover the maximum number of unresponded orders in the unresponded order set with the minimum number of paths in the alternative path set as the goal; A mixed integer programming construction and calculation unit, which is configured to construct a mixed integer programming calculation model, and solve the set covering problem model based on the constructed mixed integer programming calculation model to obtain an approximate optimal solution that is close to the target of the set covering problem model; The departure route determination unit is constructed to determine the departure route for satisfying the unresponded orders based on the solution result of the mixed integer programming calculation model and subject to the current number of dispatchable buses.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the responsive public transportation intelligent scheduling and path planning method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the responsive public transportation intelligent scheduling and path planning method as described in any one of claims 1-7.

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