Method and system for constructing network space-time prism

Abstract

The invention relates to the technical field of space-time prisms, in particular to a network space-time prism construction method and system. The method comprises the following steps: acquiring a starting point, an ending point, departure time and arrival time; according to an A * algorithm and a branch and bound algorithm, determining the earliest arrival time of a plurality of reachable edges starting from the starting point according to the starting time and reaching the end point at the arrival time; according to an A * algorithm and a branch and bound algorithm, determining the latest departure time when the edge arrives at the end point at the arrival time; and generating space-time polygons of the path according to the earliest arrival time and the latest departure time, and generating a network space-time prism based on the plurality of space-time polygons. According to the method, the efficiency of constructing the space-time prism can be improved.

Description

Technical Field

[0001] The present invention generally relates to the field of space-time prism technology. More specifically, the present invention relates to a method and system for constructing a network space-time prism. Background Art

[0002] Space Time Prism (STP) is a three-dimensional geometric structure that combines the two dimensions of time and space to describe the possible activity space of an individual or object within a specific time range. On a two-dimensional plane, it is usually centered on the starting point and expands outward over time to form a prism-like shape. The projection of the space-time prism on the two-dimensional plane is called the Potential Path Area (PPA), such as Figure 1 As shown. Space-time prisms are widely used in geographic information science, transportation and urban planning, location services and other fields. They are often used for individual reachability analysis, inference of possible encounter locations between multiple individuals, inference of moving vehicle trajectories, and outdoor search and rescue.

[0003] In the prior art, the space-time prism is generally constructed by using two one-to-many Dijkstra algorithms. The method first performs a forward shortest path search from the starting point to determine the earliest arrival time to each node in the road network; then, a reverse shortest path search is performed from the destination to determine the latest departure time required for each node in the road network to reach the destination on time; finally, whether each road section is accessible is determined based on whether the earliest arrival time of each node is greater than the latest departure time. Although this method is easy to implement, the accessible nodes in the network-time prism (NTP) are usually only a small part of the entire network. However, the algorithm needs to traverse the entire road network twice during the search, which is time-consuming. In order to reduce the number of visited nodes, the prior art constructs NTP in a sub-road network rather than the entire road network, where the sub-road network is generated by selecting nodes and links in the STP in the plane space. Although this method can reduce the number of visited nodes, the range of the plane space PPA is usually too large, which still causes a large number of invalid node visits. Summary of the invention

[0004] In order to solve the above-mentioned technical problem of low efficiency in constructing space-time prisms, the present invention provides solutions in the following aspects.

[0005] In a first aspect, the present invention provides a method for constructing a network space-time prism, comprising: obtaining a starting point, an end point, a departure time, and an arrival time; determining, according to an A* algorithm and a branch-and-bound algorithm, the earliest arrival time of a plurality of reachable edges starting from the starting point at the departure time and arriving at the end point at the arrival time, wherein the evaluation function F1 of the forward path in the A* algorithm is i for:

[0006]

[0007] In the formula, represents the shortest travel time from the starting point o to the current node i, represents the travel time from the current node i to the destination d, w i represents the dynamic weight coefficient, which is determined by the ratio of the distance from the current node i to the end point d to the distance from the starting point o to the end point d;

[0008] According to the A* algorithm and the branch and bound algorithm, the latest departure time of the edge to reach the end point at the arrival time is determined; according to the earliest arrival time and the latest departure time, the space-time polygon of the edge is generated, and based on multiple space-time polygons, a network space-time prism is generated.

[0009] Furthermore, in the process of determining the earliest arrival time, it includes: generating a forward path with the starting point as the tail node, and inserting the forward path into the first queue; selecting and removing the target path from the first queue; judging whether the head node of the target path is the end point, and if so, saving the shortest travel time of the target path; if not, extending the target path in the direction from the starting point to the end point.

[0010] Further, the target path is extended from the starting point to the end point, including: integrating the target edge into the target path to obtain a new forward path; determining a path reachability value of the new forward path, the path reachability value T j The calculation expression is:

[0011]

[0012] In the formula, represents the new forward path from the starting point o to the current node j The shortest travel time, represents the travel time from the current node j to the destination d;

[0013] In response to the path reachability value being less than or equal to a preset value and the shortest travel time of the new forward path being less than the shortest travel time of the original forward path corresponding to the new forward path, the new forward path replaces the original forward path.

[0014] Furthermore, it also includes: determining whether the original forward path is in the first queue, and if so, removing the original forward path from the first queue and inserting the new forward path into the first queue.

[0015] Furthermore, the dynamic weight coefficient w i for:

[0016]

[0017] Where D id represents the Euclidean distance from the current node i to the end point d, D od It represents the Euclidean distance from the starting point o to the end point d, and a is the preset threshold.

[0018] Furthermore, in the process of determining the latest departure time, it includes: generating a backward path with the end point as the head node, and inserting the backward path into the second queue; selecting and removing the target path from the second queue; judging whether the tail node of the target path is the starting point, and if so, saving the shortest travel time of the target path; if not, extending the target path from the end point to the starting point.

[0019] Further, the target path is extended from the end point to the starting point, including:

[0020] The target edge is merged into the backward path to obtain a new backward path; an evaluation function value of the new backward path is determined according to the shortest travel time of the new backward path, the shortest travel time of the forward path of the target edge and the shortest travel time of the target edge; in response to the evaluation function value being less than or equal to a preset value and the shortest travel time of the new backward path being less than the shortest travel time of the original backward path corresponding to the new backward path, the new backward path replaces the original backward path.

[0021] Furthermore, the evaluation function F2 of the new backward path i for:

[0022]

[0023] Where, t ij represents the travel time of the target edge consisting of node i and node j, represents the shortest travel time of the forward path of the target edge, Represents the new backward path from node i to end point d The shortest travel time.

[0024] Furthermore, it also includes: determining the network space-time prism of the user according to the starting point, end point, departure time, and arrival time input by the user to determine the travel plan of the user.

[0025] In a second aspect, the present invention provides a system for constructing a network space-time prism, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for constructing a network space-time prism according to any one of the first aspects is implemented.

[0026] The beneficial effect of the present invention is that the method of the present invention sets the heuristic function in the A* algorithm so that the search direction in the two searches is intelligently inclined to the target point, thereby narrowing the search range and further improving the efficiency of obtaining the shortest path. Among them, by increasing the dynamic weight coefficient, the A* algorithm can speed up the search speed in the early stage of the search, further improving the efficiency of obtaining the shortest path. In addition, by applying the branch and bound algorithm, invalid nodes and sections can be dynamically filtered in the process of searching for the shortest path, reducing the number of traversals, improving the efficiency of obtaining the shortest path, and thus improving the efficiency of constructing the network space-time prism. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0028] Figure 1 is a diagram schematically illustrating a conventional space-time prism;

[0029] Figure 2 is a flow chart schematically illustrating a method for constructing a network space-time prism according to an embodiment of the present invention;

[0030] Figure 3 is a schematic diagram schematically illustrating a network space-time prism according to an embodiment of the present invention;

[0031] Figure 4 Schematically shows a structural block diagram of a system for constructing a network space-time prism according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0033] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0034] Figure 2 is a flow chart schematically illustrating a method for constructing a network space-time prism according to an embodiment of the present invention.

[0035] In a first aspect, the present invention provides a method for constructing a network space-time prism, such as Figure 2 As shown, the method of the present invention comprises:

[0036] S1. Get the starting point, end point, departure time and arrival time.

[0037] In a road network, there are nodes, links (edges), and allowed turns. For any directed link a ij , each consists of a tail node i and a head node j and the link travel time t ij Turns are allowed at any position on an undivided link (e.g., a secondary road), while turns on a divided link (e.g., a main road) are restricted, such as only being able to turn left or right.

[0038] S2. According to the A* algorithm and the branch and bound algorithm, determine the earliest arrival time of a plurality of reachable edges starting from the starting point at the departure time and arriving at the end point at the arrival time.

[0039] The A* algorithm is a heuristic search algorithm that combines the assurance of the Dijkstra algorithm with the efficiency of the greedy algorithm. When searching for the shortest path, the A* algorithm selects the next node for path extension by evaluating the function f(n) = g(n) + h(n) to obtain the shortest path from the start point to the end point. Among them, f(n) represents the total cost estimate of node n, g(n) represents the actual path cost from the start point to node n, and h(n) represents the estimated cost from node n to the destination (end point). How to set the appropriate h(n) is the key to whether the shortest path can be obtained efficiently.

[0040] In one embodiment, step S2 includes:

[0041] S201: Generate a forward path with the starting point as a tail node, and insert the forward path into a first queue.

[0042] Specifically, traverse each downstream edge of the starting point o (the edge with the starting point o as the tail node), for each downstream edge a oi , generating the downstream edge a oi The forward path P corresponding to the edge / link consisting of the starting point o and the node i oi (i.e., the forward path P oi contains downstream edge a oi ), let the forward path P oi Shortest travel time Equal to the downstream edge a oi The travel time t oi , and set the forward path P oi The evaluation function F1 i , and then the forward path P oi Insert into the first queue (priority queue).

[0043] In this embodiment, the forward path P oi The evaluation function F1 i for:

[0044]

[0045] In the formula, represents the path from the starting point o to the current node i (i.e. the forward path P oi ), represents the travel time from the current node i to the destination d, w i Represents the dynamic weight coefficient, which is determined by the ratio of the distance from the current node i to the end point d to the distance from the starting point o to the end point d.

[0046] It should be noted that That is the actual cost from the starting point o to the current node i, That is, the estimated cost from the current node i to the end point d, which is also the heuristic function h(n=i) set by the present invention. It can be determined based on the Euclidean distance from the current node i to the end point d and the maximum travel speed in the road network.

[0047] In one embodiment, the dynamic weight coefficient w i The calculation expression is:

[0048]

[0049] Where D id represents the Euclidean distance from the current node i to the end point d, D od It represents the Euclidean distance from the starting point o to the end point d, and a is the preset threshold.

[0050] In one embodiment, the preset threshold a is 0.4. It is understandable that If it is greater than or equal to 0.4, it means that the current node i is far away from the end point d. In this case, a higher weight can be selected (the dynamic weight coefficient is set to be greater than 1) so that the A* algorithm pays more attention to speed in the early stage of path search (i.e., speeds up the initial search speed), thereby improving the efficiency of obtaining the shortest path and thus improving the construction of the network space-time prism. If it is less than 0.4, it indicates that the current node i is close to the end point d. By selecting the original weight (i.e., the weight is 1), the reliability of the A* algorithm in obtaining the shortest path is high. In other optional embodiments, the preset threshold a can also be 0.3. In addition, those skilled in the art can also change the values ​​of 1.8 and 1 in the calculation expression of the dynamic weight coefficient according to actual needs.

[0051] Therefore, by dynamically adjusting the dynamic weight coefficient during the search process, the efficiency of the search path can be accelerated, thereby improving the construction of the network space-time prism.

[0052] S202: Select and remove a target path from the first queue.

[0053] Specifically, determine whether there is a forward path in the first queue. If not, it indicates that there is no path that can be searched (accessed), and the process is stopped; if so, select and remove the forward path at the top (ie, the target path) from the first queue.

[0054] S203: Perform path expansion on the target path.

[0055] Specifically, determine whether the head node of the target path is the end point. If so, it indicates that the target path has been extended to the end point, that is, the shortest path has been searched, then save the target path (composed of multiple edges) and its shortest travel time (the departure time plus the shortest travel time is the earliest arrival time); if not, then expand the target path from the starting point to the end point.

[0056] Specifically, for any target path P oi , you can achieve expansion through the following steps:

[0057] S2001, determine the target path P oi The last edge a ki , is there a selected edge a ki To the untraversed target edge a ij If not, it indicates that the target path cannot be expanded (impassable), then return to S202; if so, then the target edge a ij Merge to the target path P oiIn the new forward path At this time, the new forward path Shortest travel time Equal to the target path P oi Shortest travel time With target edge a ij The travel time t ij The sum (i.e. ), and the new forward path The evaluation function F1 j for:

[0058]

[0059] In the formula, represents the travel time from the current node j to the destination d, w j Represents the dynamic weight coefficient, which is determined by the ratio of the distance from the current node j to the end point d to the distance from the starting point o to the end point d.

[0060] S2002: Determine the new forward path The path reachability value is used to determine the new forward path. Is the path reachability value less than or equal to the preset value? If not, it indicates that the new forward path If it is unreachable (i.e., it is impossible to reach the destination from the starting point within the specified time), it should be abandoned and return to S2001; if so, it indicates that the new forward path If the new forward path is reachable, Shortest travel time Is it less than the new forward path The corresponding original forward path P oj Shortest travel time If not, it indicates that the forward path is not the shortest path, and then return to S2001; if so, determine whether there is an original forward path P in the first queue. oj If so, then the original forward path P oj Remove it from the first queue and add the new forward path Insert into the first queue; if not, directly add the new forward path Insert into the first queue. When inserting into the first queue, the new forward path Replace the original forward path P oj , and the new forward path Shortest travel time Replace the original forward path P oj Shortest travel time

[0061] In one embodiment, the path reachability value T j The calculation expression is as follows:

[0062]

[0063] In the formula, Represents the new forward path The shortest travel time, represents the travel time from the current node j to the end point d.

[0064] It can be understood that the branch and bound technology is applied in the above step S2002. As long as the path reachability value of the forward path is less than or equal to the preset value, the forward path will be dynamically abandoned during the search process. Compared with deciding whether to abandon the path after obtaining the earliest arrival time and the latest departure time, the search efficiency is higher, thereby constructing the network space-time prism more efficiently.

[0065] In one embodiment, the preset value is the difference between the arrival time and the departure time.

[0066] S3. According to the A* algorithm and the branch and bound algorithm, determine the latest departure time for the edge to arrive at the end point at the arrival time.

[0067] In one embodiment, step S3 includes:

[0068] S301: Generate a backward path with the end point as the head node, and insert the backward path into a second queue.

[0069] Specifically, traverse each upstream edge of the end point d (that is, the edge with the end point d as the head node), for each upstream edge a id (edge / link consisting of node i and end point d), determine the upstream edge a id The forward path P od Is it empty? If not, generate the upstream edge a id The corresponding backward path P id (i.e., the backward path P id contains the upstream edge a id ), let the backward path P id Shortest travel time Equal to the upstream edge a id The travel time t id , and set the backward path P id The corresponding evaluation function F2 i , and then insert the backward path into the second queue (priority queue).

[0070] In this embodiment, the backward path P id The evaluation function F2 i as follows:

[0071]

[0072] In the formula, Represents the backward path P id The shortest travel time, represents the shortest travel time from the starting point o to the end point d, t id Represents edge a id travel time.

[0073] S302: Select and remove a target path from the second queue.

[0074] Specifically, it is determined whether there is a backward path in the second queue. If not, it indicates that there is no path that can be searched, and the process is stopped; if so, the backward path at the top (ie, the target path) is selected and removed from the second queue.

[0075] S303: Perform path expansion on the target path.

[0076] Specifically, determine whether the tail node of the target path is the starting point. If so, it indicates that the target path has been extended to the starting point, that is, the shortest path has been searched, and the target path and its shortest travel time are saved; if not, it indicates that the target path has not been extended to the starting point, and the target path is extended from the end point to the starting point.

[0077] Specifically, for any target path P jd , you can achieve expansion through the following steps:

[0078] S3001, determine the target path P jd The last edge a jk , is there an untraversed target edge a ij To selected edge a jk The feasible turn (i.e., whether it is possible to turn), if not, it indicates that the target path P jd If it cannot be extended, return to S302; if so, determine the target edge a ij The forward path P oj Is it empty (is there no edge at all), if so, it indicates the target edge a ij If it is an invalid edge, it is discarded and the process returns to S302; if not, the target edge a ij Merge to the target path P jd In the new backward path At this time, the new backward path The shortest travel time is Equal to the target path P jd Shortest travel time With target edge aij The travel time t ij The sum (i.e. ), and the new backward path The evaluation function F2 j for:

[0079]

[0080] Where, t ij Represents the target edge a consisting of nodes i and j ij The travel time, Represents the target edge a ij The forward path P oj The shortest travel time, represents the new backward path from node i to end point d The shortest travel time.

[0081] It should be noted that is the shortest travel time of the forward path obtained in step S2. By using the shortest travel time obtained in the forward search (i.e., the search process of S2) as the heuristic function in the evaluation function of the backward path, and the paths that are not visited in the forward search will not be visited in the backward search (i.e., the search process of S3), the access of a large number of invalid nodes and edges is avoided, thereby greatly improving the computational efficiency of the backward search, and then improving the efficiency of building the network spatiotemporal model.

[0082] S3002: Determine the new backward path The evaluation function value of the new backward path is determined to determine whether the evaluation function value of the new backward path is less than or equal to the preset value. If not, it indicates that the new backward path If it is unreachable, it should be abandoned and return to S3001; if so, determine the new backward path Shortest travel time Is it less than the new backward path The corresponding original backward path P id Shortest travel time If not, it indicates that the new backward path is not the shortest path, and then return to S3001; if so, determine whether there is an original backward path P in the second queue. id If so, then the original backward path P id Remove it from the second queue and add the new backward path Insert into the second queue; if not, directly insert the new backward path Insert it into the second queue. While inserting into the second queue, the new backward path Replace the original backward path P id, and the new backward path Shortest travel time Replace the original backward path P id Shortest travel time

[0083] By applying the branch and bound algorithm, unreachable edges can be dynamically filtered out during the backward search process, thereby improving the efficiency of obtaining the shortest path and further improving the efficiency of constructing the network space-time prism.

[0084] S4. Generate the space-time polygon of the edge according to the earliest arrival time and the latest departure time, and generate a network space-time prism based on multiple space-time polygons.

[0085] Specifically, according to the calculation results of the forward search (the search process of S2) and the backward search (the search process of S3), a set of reachable segment chains (i.e., reachable edges) is generated. For each reachable edge a in the set, ij , calculate the edge a ij The earliest arrival time t of the tail node i and the head node j - and the latest departure time t + ,Right now:

[0086]

[0087] In the formula, represents the earliest arrival time of node i, represents the latest departure time of node i, represents the earliest arrival time of node j, represents the latest departure time of node j, t o represents the departure time of starting point o, t d represents the arrival time of the destination d, represents the forward path P oj The shortest travel time, t ij Represents edge a ij The travel time, Represents the backward path P id The shortest travel time.

[0088] According to the earliest arrival time t of each edge - and the latest departure time t + , construct the edge a ij The network space-time polygon Q ij , based on the network space-time polygons of all reachable edges, establish the network space-time prism NTP between the starting point o and the end point d (NTP = {Q om ,...,Q ij ,...,Q nd}).

[0089] Among them, the network space-time polygon Q ij for:

[0090]

[0091] In the formula, x i Represents the horizontal coordinate of node i, y i represents the ordinate of node i, x j represents the horizontal coordinate of node j, y j Represents the vertical coordinate of node j.

[0092] In one embodiment, when a user needs to plan a travel route and wants to choose a suitable travel destination and itinerary within a limited time, the user's network time-space prism can be determined based on the user's time budget, transportation mode, etc. Through the network time-space prism, the range of scenic spots that the user can visit within a specific time can be determined, thereby formulating a travel plan suitable for the user. For example, if a user is in place A and wants to travel to place B in 6 days, the user's network time-space prism is determined based on the starting point, end point, departure time, and arrival time input by the user. The user can choose a travel plan based on the set of places that can be reached within the specified time displayed by the network time-space prism (for example, the set includes place A, place B, place C, and place D). For example, the user can choose to go from place A to place C and then to place B, or from place A to place D and then to place B.

[0093] In one embodiment, the network space-time prism can also be applied in the field of logistics. For example, the network space-time prism corresponding to the user can be determined based on the merchant's address (i.e., starting point), the user's address (i.e., end point) and the time of the user's demand (departure time, arrival time), thereby determining the spatial distribution of the delivery locations, and thus determining the optimal delivery plan (delivery time, delivery route) based on the spatial distribution of the delivery locations.

[0094] Figure 4 It is a structural block diagram schematically showing a system for constructing a network space-time prism according to this embodiment.

[0095] In a second aspect, the present invention also provides a system for constructing a network space-time prism. Figure 4 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for constructing a network space-time prism according to the first aspect of the present invention is implemented.

[0096] The system also includes other components well known to those skilled in the art, such as a communication interface, whose configuration and functions are known in the art and thus will not be described in detail here.

[0097] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that may be used to store the required information and may be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0098] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0099] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for constructing a network space-time prism, characterized in that: include: Get the starting point, end point, departure time, and arrival time; According to the A* algorithm and the branch and bound algorithm, determine the earliest arrival time of multiple reachable edges starting from the starting point at the departure time and arriving at the end point at the arrival time, wherein the evaluation function F1 of the forward path in the A* algorithm is i for: In the formula, represents the shortest travel time from the starting point o to the current node i, represents the travel time from the current node i to the destination d, w i represents the dynamic weight coefficient, which is determined by the ratio of the distance from the current node i to the end point d to the distance from the starting point o to the end point d; Determine the latest departure time for the edge to arrive at the end point at the arrival time according to the A* algorithm and the branch and bound algorithm; According to the earliest arrival time and the latest departure time, the space-time polygon of the edge is generated, and based on a plurality of the space-time polygons, a network space-time prism is generated.

2. The method for constructing a network space-time prism according to claim 1, characterized in that: The process of determining the earliest arrival time includes: Generate a forward path with the starting point as a tail node, and insert the forward path into the first queue; Selecting and removing a target path from the first queue; Determine whether the head node of the target path is the end point. If so, save the shortest travel time of the target path; if not, extend the target path from the starting point to the end point.

3. The method for constructing a network space-time prism according to claim 2, characterized in that: Extending the target path from the starting point to the end point includes: Merge the target edge into the target path to obtain a new forward path; Determine the path reachability value of the new forward path, the path reachability value T j The calculation expression is: In the formula, represents the new forward path from the starting point o to the current node j The shortest travel time, represents the travel time from the current node j to the destination d; In response to the path reachability value being less than or equal to a preset value and the shortest travel time of the new forward path being less than the shortest travel time of the original forward path corresponding to the new forward path, the new forward path replaces the original forward path.

4. The method for constructing a network space-time prism according to claim 3, characterized in that: Also includes: It is determined whether the original forward path is in the first queue. If so, the original forward path is removed from the first queue and the new forward path is inserted into the first queue.

5. The method for constructing a network space-time prism according to claim 1, characterized in that: The dynamic weight coefficient w i for: Where D id represents the Euclidean distance from the current node i to the end point d, D od It represents the Euclidean distance from the starting point o to the end point d, and a is the preset threshold.

6. The method for constructing a network space-time prism according to claim 1, characterized in that: In determining the latest departure time, the following shall be included: generating a backward path with the end point as a head node, and inserting the backward path into the second queue; Selecting and removing a target path from the second queue; Determine whether the tail node of the target path is the starting point. If so, save the shortest travel time of the target path; if not, extend the target path from the end point to the starting point.

7. The method for constructing a network space-time prism according to claim 1, characterized in that: Extending the target path from the end point to the starting point includes: Merging the target edge into the backward path to obtain a new backward path; Determining an evaluation function value of the new backward path according to the shortest travel time of the new backward path, the shortest travel time of the forward path of the target edge, and the shortest travel time of the target edge; In response to the evaluation function value being less than or equal to a preset value and the shortest travel time of the new backward path being less than the shortest travel time of the original backward path corresponding to the new backward path, the new backward path replaces the original backward path.

8. The method for constructing a network space-time prism according to claim 1, characterized in that: The evaluation function F2 of the new backward path i for: In the formula, t ij represents the travel time of the target edge consisting of node i and node j, represents the shortest travel time of the forward path of the target edge, Represents the new backward path from node i to end point d The shortest travel time.

9. The method for constructing a network space-time prism according to claim 1, characterized in that: Also includes: According to the starting point, end point, departure time, and arrival time input by the user, the network space-time prism of the user is determined to determine the travel plan of the user.

10. A network space-time prism construction system, characterized in that: It comprises a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for constructing a network space-time prism according to any one of claims 1-9 is implemented.

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