Traffic cost simulation method and device and related equipment

Abstract

The invention provides a traffic travel cost simulation method and device and related equipment. The method comprises the following steps: acquiring geographic information data containing a plurality of traffic road sections and travel demands of a plurality of travel starting and ending point pairs; determining corresponding traffic modes and cost parameters according to road section attributes of the traffic road sections, and calculating initial road section travel cost of each traffic mode; searching and calculating a plurality of candidate travel paths and corresponding initial path travel costs in the traffic network for each travel start-end point pair based on the initial road section cost; according to the initial path cost, distributing a travel demand to a plurality of candidate travel paths to obtain initial traffic flow of each traffic mode of each road section; performing congestion punishment on the initial road section cost according to the traffic flow threshold value and the initial traffic flow to obtain the travel cost of the target road section; and searching and calculating a plurality of target travel paths and target path travel costs in the traffic network according to the target road section cost. According to the invention, urban all-path and all-traffic-mode travel cost calculation can be realized.

Description

Technical Field

[0001] The embodiments of the present application relate to, but are not limited to, the field of urban planning, and particularly relate to a method, device and related equipment for simulating traffic travel costs. Background Art

[0002] The traffic travel of urban residents aims to achieve spatial displacement. Travelers expect to obtain the best travel service within an affordable cost range during the traffic travel process. And the travel cost is the core basis for travelers to formulate travel plans and select transportation modes. Its calculation and simulation are the key indicators for the construction, structural optimization and operation evaluation of urban traffic infrastructure, and also the core link of urban traffic modeling and simulation.

[0003] In related technologies, due to technical bottlenecks such as the complexity of the cost parameter system, the difficulty in quantifying the connection and transfer costs between transportation modes, and the limited coverage of transportation mode types, the current technologies usually only design cost calculation methods with a limited area or a specific itinerary of a path as the research object, and cannot generalize them to the travel of all residents on the city-wide scale. Summary of the Invention

[0004] The present application aims to at least solve one of the technical problems existing in the prior art. For this purpose, the present application provides a method, device and related equipment for simulating traffic travel costs, which can calculate the travel costs of all paths and all transportation modes in the city.

[0005] To achieve the above object, a first aspect of the embodiments of the present application proposes a method for simulating traffic travel costs, the method comprising: Obtaining geographical information data of a target city and travel demands of a plurality of travel origin-destination pairs; wherein, the geographical information data includes a plurality of traffic segments; Determining, according to the preset segment attributes of each traffic segment, the corresponding transportation mode of each traffic segment and the traffic cost parameters corresponding to the transportation mode; For each traffic segment, calculating the initial segment travel costs of each transportation mode on the traffic segment according to the traffic cost parameters of each transportation mode; For each travel origin-destination pair, searching for a plurality of candidate travel paths in a traffic network composed of a plurality of traffic segments according to the initial segment travel costs of each transportation mode on each traffic segment, and calculating the initial path travel costs of each candidate travel path; For each travel origin-destination pair, distributing the travel demand to each candidate travel path according to the initial path travel costs of each candidate travel path to obtain the initial traffic flow of each transportation mode on each traffic segment in the traffic network; For each traffic mode on each of the traffic sections, perform congestion penalty processing on the initial section travel cost according to a preset traffic flow threshold and the initial traffic flow to obtain the target section travel cost; For each pair of travel origin and destination, based on the target section travel costs of each traffic mode on each of the traffic sections, search for multiple target travel paths in the traffic network and calculate the target path travel cost of each target travel path.

[0006] In some embodiments, the section attributes include the section length, the traffic cost parameters include the free flow speed and the unit distance energy consumption cost. For each traffic section, calculating the initial section travel cost of each traffic mode on the traffic section according to the traffic cost parameters of each traffic mode includes: For each traffic mode, calculate the travel time cost on the traffic section according to the corresponding free flow speed and the section length; For each traffic mode, calculate the energy currency cost on the traffic section according to the corresponding unit distance energy consumption cost and the section length; For each traffic mode, calculate the initial section travel cost on the traffic section according to the travel time cost and the energy currency cost.

[0007] In some embodiments, transfer nodes allowing traffic mode transfers are preset in the traffic network, and transfer time costs and transfer currency costs preset for different transfer combinations between traffic modes are set at each transfer node. For each pair of travel origin and destination, based on the initial section travel costs of each traffic mode on each of the traffic sections, search for multiple candidate travel paths in the traffic network composed of multiple traffic sections and calculate the initial path travel cost of each candidate travel path, including: Based on a preset path search algorithm, using the initial section travel cost of each traffic mode on each traffic section as the cost basis for path search, search for at least one candidate travel path connecting the pair of travel origin and destination in the traffic network; where each candidate travel path includes multiple successively connected traffic sections and corresponding traffic modes, and includes traffic mode transfers at one or more transfer nodes; For each of the candidate travel paths, accumulate the initial section travel costs of all the traffic sections included in the candidate travel path, and based on the travel mode transfers occurring in the candidate travel path, at the corresponding transfer nodes, accumulate and include the corresponding transfer time cost and the transfer currency cost to obtain the initial path travel cost of the candidate travel path.

[0008] In some embodiments, for each travel origin-destination pair, based on the initial path travel costs of the candidate travel paths, distribute the travel demand to each of the candidate travel paths to obtain the initial traffic flows of each traffic mode on each traffic section in the traffic network, including: Calculate the initial selection probability of each candidate travel path according to the initial path travel cost of each candidate travel path; Distribute the travel demand to multiple candidate travel paths according to the initial selection probability of each candidate travel path to obtain the initial path flow on each candidate travel path; For each traffic section, accumulate the initial path flows of all the candidate travel paths of all the travel origin-destination pairs to obtain the initial traffic flow of each traffic mode on each traffic section in the traffic network.

[0009] In some embodiments, for each traffic mode on each traffic section, perform congestion penalty processing on the initial section travel cost according to a preset traffic flow threshold and the initial traffic flow to obtain the target section travel cost, including: For each traffic mode on each traffic section, calculate a congestion penalty coefficient according to the initial traffic flow and the traffic flow threshold; wherein, the congestion penalty coefficient is not less than 1; Multiply the congestion penalty coefficient by the initial section travel cost to obtain the target section travel cost.

[0010] In some embodiments, for each travel origin-destination pair, based on the target section travel costs of each traffic mode on each traffic section, search for multiple target travel paths in the traffic network and calculate the target path travel cost of each target travel path, including: Using the target section travel cost of each traffic mode on each traffic section as the cost basis for path search, search for at least one target travel path connecting the travel origin-destination pair in the traffic network; For each of the target travel paths, accumulate the target section travel costs of the corresponding transportation modes on all the transportation sections included in the target travel path, and based on the transportation mode transfers that occur in the target travel path, add the corresponding transfer time cost and transfer currency cost for each transportation mode transfer that occurs at the corresponding transfer node to obtain the target path travel cost.

[0011] In some embodiments, the method further includes iteratively performing the following steps until a preset convergence condition is met: For each pair of travel origin and destination, based on the target path travel cost obtained from the most recent calculation, re - allocate the travel demand to the corresponding multiple target travel paths to obtain the traffic flow of each transportation mode on each transportation section in the updated traffic network. For each transportation mode on each transportation section, based on the updated traffic flow and the traffic flow threshold, re - perform congestion penalty processing on the section travel cost to obtain the updated target section travel cost. For each pair of travel origin and destination, based on the updated target section travel cost, re - search in the traffic network to obtain multiple target travel paths, and calculate the updated target path travel cost of each target travel path; where the convergence condition is that the change in traffic flow on each transportation section obtained from two consecutive iterative calculations is less than the first preset change amount threshold, or the change in total travel cost obtained from two consecutive iterative calculations is less than the second preset change amount threshold.

[0012] In a second aspect, an embodiment of the present application provides a traffic travel cost calculation device, including: An acquisition module, which acquires the geographical information data of the target city and the travel demands of multiple pairs of travel origin and destination; where the geographical information data includes multiple transportation sections. A determination module, which determines the corresponding transportation mode and the traffic cost parameters corresponding to the transportation mode for each transportation section according to the preset section attributes of each transportation section. A calculation module, which calculates the initial section travel costs of each transportation mode on each transportation section according to the traffic cost parameters of each transportation mode for each transportation section. A search module, which, for each pair of travel origin and destination, searches in the traffic network composed of multiple transportation sections to obtain multiple candidate travel paths according to the initial section travel costs of each transportation mode on each transportation section, and calculates the initial path travel costs of each candidate travel path. An allocation module, for each of the origin-destination pairs of trips, allocates the travel demand to each of the candidate travel paths according to the initial path travel costs of the candidate travel paths, to obtain the initial traffic flow of each transportation mode on each traffic section in the traffic network; A congestion penalty processing module, for each transportation mode on each traffic section, performs congestion penalty processing on the initial section travel cost according to a preset traffic flow threshold and the initial traffic flow, to obtain the target section travel cost; A cost calculation module, for each origin-destination pair of trips, searches in the traffic network to obtain multiple target travel paths according to the target section travel costs of each transportation mode on each traffic section, and calculates the target path travel cost of each target travel path.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, it implements the traffic travel cost simulation method according to any one of the embodiments in the first aspect of the present application.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the storage medium stores a program, and when the program is executed by a processor, it implements the traffic travel cost simulation method according to any one of the embodiments in the first aspect of the present application.

[0015] The traffic travel cost simulation method proposed in the embodiments of this application includes: obtaining the geographical information data of the target city and the travel demand of multiple origin-destination pairs; wherein, the geographical information data includes multiple traffic segments; determining the corresponding transportation mode and the traffic cost parameters corresponding to the transportation mode for each traffic segment according to the preset segment attributes of each traffic segment; for each traffic segment, calculating the initial segment travel cost of each transportation mode on the traffic segment according to the traffic cost parameters of each transportation mode; for each origin-destination pair, searching for multiple candidate travel paths in the traffic network composed of multiple traffic segments according to the initial segment travel cost of each transportation mode on each traffic segment, and calculating the initial path travel cost of each candidate travel path; for each origin-destination pair, distributing the travel demand to each candidate travel path according to the initial path travel cost of each candidate travel path to obtain the initial traffic flow of each transportation mode on each traffic segment in the traffic network; for each transportation mode on each traffic segment, performing congestion penalty processing on the initial segment travel cost according to the preset traffic flow threshold and the initial traffic flow to obtain the target segment travel cost; for each origin-destination pair, searching for multiple target travel paths in the traffic network according to the target segment travel cost of each transportation mode on each traffic segment, and calculating the target path travel cost of each target travel path.

[0016] The traffic travel cost simulation method proposed in this application first obtains the geographical information data of the target city, which covers multiple traffic sections of the urban traffic network and the travel demands of multiple origin-destination pairs, laying a data foundation for the traffic cost simulation of the whole city. Next, for each traffic section that makes up the urban traffic network, based on its preset attributes, multiple supported traffic modes and corresponding traffic cost parameters on this section are determined, and the initial section travel costs of each traffic mode on this section are calculated. On this basis, for each origin-destination pair, using the initial section costs of each traffic mode on all sections, a search is carried out in the entire traffic network to obtain multiple candidate travel paths, and the initial travel costs of these paths are calculated. Compared with the background technology, it avoids the sharp increase in congestion costs caused by centrally selecting the optimal path. Then, the travel demands of all origin-destination pairs are allocated to each candidate path according to the initial path costs, so as to obtain the initial traffic flow of each traffic mode on each traffic section in the network. Based on this flow information, a congestion penalty mechanism is further adopted. According to the preset flow threshold and the calculated initial flow, the initial section travel costs are adjusted to obtain the target section travel costs that can better reflect the actual road conditions. Finally, using the target section travel costs adjusted by the congestion penalty, multiple target travel paths are searched again in the traffic network for each origin-destination pair, and the final target path travel costs that are more in line with the actual traffic conditions are calculated. To sum up, the method proposed in this application obtains and processes the geographical information and travel demands of the whole city, thereby defining multi-mode traffic costs for each section, performing network-level multi-path search and demand allocation, and introducing a flow-based congestion penalty mechanism to dynamically adjust the section costs, thus overcoming the technical bottlenecks of limited scope, single mode, and difficulty in quantification in the background technology, and being able to calculate the travel costs of all paths and all traffic modes in the city.

[0017] Other features and advantages of this application will be described in the subsequent specification, and part of them will become obvious from the specification, or be understood by implementing this application. The objectives and other advantages of this application can be achieved and obtained through the structures specifically pointed out in the specification, claims, and drawings. Brief Description of the Drawings

[0018] Figure 1 is a flowchart of the traffic travel cost simulation method provided by an embodiment of this application; Figure 2 is a flowchart of the traffic travel cost simulation method provided by another embodiment of this application; Figure 3 is a flowchart of the traffic travel cost simulation method provided by another embodiment of this application; Figure 4It is a schematic flowchart of a traffic travel cost simulation method provided by another embodiment of the present application; Figure 5 It is a schematic flowchart of a traffic travel cost simulation method provided by another embodiment of the present application; Figure 6 It is a schematic flowchart of a traffic travel cost simulation method provided by another embodiment of the present application; Figure 7 It is a schematic flowchart of a traffic travel cost simulation method provided by another embodiment of the present application; Figure 8 It is a schematic diagram of a traffic travel cost calculation device provided by an embodiment of the present application; Figure 9 It is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0019] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] It should be noted that although functional module division is performed in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from the module division in the device or the flowchart in the flowchart. Terms such as "first" and "second" in the specification, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0022] The traffic travel of urban residents is essentially to meet their activity needs in the urban space. In the decision-making process, individual travelers generally expect to obtain the most optimized or satisfactory travel service experience within the range of affordable generalized travel costs (including multiple dimensions such as time, cost, comfort, and convenience). Therefore, travel cost has become the core basis for influencing a series of behaviors such as the travel path planning, travel time decision-making, and traffic mode selection of individual residents. Correspondingly, accurately and comprehensively accounting and simulating the overall travel cost of urban residents is not only a key technical indicator for scientific planning of urban traffic infrastructure, reasonable optimization of road network structure, assessment of traffic system operation status, and improvement of service level, but also a core link for constructing a high-precision urban traffic model and conducting effective traffic simulation prediction analysis.

[0023] However, in the existing related technologies, there are many challenges in accurately simulating the travel costs of residents across the city. On the one hand, the parameter system of travel costs itself is extremely complex and dynamically changing, involving different charging rules for various transportation modes, time value differences, congestion delay costs, etc.; on the other hand, the implicit costs generated during the connection and transfer processes between different transportation modes are difficult to standardize and accurately quantify; moreover, existing research or models often fail to comprehensively cover all feasible transportation modes in the city. These factors together result in that the current technical solutions usually can only limit the research object to a specific and limited geographical area (such as a certain area, a corridor), or conduct cost accounting and analysis for specific trips of a single or a few transportation modes, and it is still impossible to construct a general framework that can be effectively promoted and applied to simulate and evaluate the comprehensive costs of all residents' travel activities at the entire city scale, which limits its application value in macro traffic management.

[0024] Based on this, the embodiments of this application provide a method, device and related equipment for simulating travel costs, which can calculate the travel costs of the entire city's full paths and all transportation modes.

[0025] The method, device and related equipment for simulating travel costs provided by the embodiments of this application are specifically described through the following embodiments. First, the method for simulating travel costs in the embodiments of this application is described.

[0026] This application can be used in many general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0027] It should be noted that in each specific embodiment of the present application, when it comes to relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first. Moreover, the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or separate consent will be obtained through methods such as pop-up windows or redirecting to a confirmation page. After clearly obtaining the user's separate permission or separate consent, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0028] Figure 1 is an optional flowchart of the traffic travel cost simulation method provided by the embodiments of the present application. Figure 1 The method in may include but is not limited to steps 101 to 107.

[0029] Step 101, obtain the geographical information data of the target city and the travel demands of multiple travel origin-destination pairs.

[0030] Step 102, according to the preset road section attributes of each traffic road section, determine the corresponding traffic mode of each traffic road section and the traffic cost parameters corresponding to the traffic mode.

[0031] Step 103, for each traffic road section, calculate the initial road section travel cost of each traffic mode on the traffic road section according to the traffic cost parameters of each traffic mode.

[0032] Step 104, for each travel origin-destination pair, search for multiple candidate travel paths in the traffic network composed of multiple traffic road sections according to the initial road section travel cost of each traffic mode on each traffic road section, and calculate the initial path travel cost of each candidate travel path.

[0033] Step 105, for each travel origin-destination pair, distribute the travel demand to each candidate travel path according to the initial path travel cost of each candidate travel path to obtain the initial traffic flow of each traffic mode on each traffic road section in the traffic network.

[0034] Step 106, for each traffic mode on each traffic road section, perform congestion penalty processing on the initial road section travel cost according to the preset traffic flow threshold and the initial traffic flow to obtain the target road section travel cost.

[0035] Step 107, for each travel origin-destination pair, search for multiple target travel paths in the traffic network according to the target road section travel cost of each traffic mode on each traffic road section, and calculate the target path travel cost of each target travel path.

[0036] Steps 101 to 107 illustrated in the embodiments of the present application first obtain the geographical information data of the target city, which covers multiple traffic sections of the urban traffic network and the travel demand of multiple origin-destination pairs, laying a data foundation for the traffic cost simulation of the whole city. Then, for each traffic section constituting the urban traffic network, based on its preset attributes, various traffic modes supported on the section and the corresponding traffic cost parameters are determined, and the initial section travel cost of each traffic mode on the section is calculated. On this basis, for each origin-destination pair, using the initial section costs of each traffic mode on all sections, a search is performed in the entire traffic network to obtain multiple candidate travel paths, and the initial travel costs of these paths are calculated. Compared with the background technology, not only the single optimal path is considered, but also various possible travel options are considered. Then, the travel demand of all origin-destination pairs is allocated to each candidate path according to the initial path cost, so as to obtain the initial traffic flow of each traffic mode on each traffic section in the network. Based on this flow information, a congestion penalty mechanism is further adopted, and according to the preset flow threshold and the calculated initial flow, the initial section travel cost is adjusted to obtain the target section travel cost that can better reflect the actual road conditions. Finally, using the target section travel cost adjusted by the congestion penalty, multiple target travel paths are searched again in the traffic network for each origin-destination pair, and the final target path travel cost that is more in line with the actual traffic conditions is calculated. In summary, the method proposed in the present application overcomes the technical bottlenecks of limited scope, single mode, and difficulty in quantification in the background technology by obtaining and processing the geographical information and travel demand of the whole city, so as to define the multi-mode traffic cost for each section, perform network-level multi-path search and demand allocation, and introduce a flow-based congestion penalty mechanism to dynamically adjust the section cost, and can realize the calculation of the travel cost of all paths and all traffic modes in the city.

[0037] In step 101 of some embodiments, the "basic geographic information data" constitutes the simulated geospatial basis, which is a digital description of the physical environment and transportation infrastructure of the target city. Specifically, such data may include, but is not limited to: traffic analysis zones used to divide urban areas and serve as the sources of traffic generation and attraction, such as regional units and their coordinates divided by specific grids (such as one-kilometer grids); road network data including road connection relationships, geometric attributes (length), physical attributes (number of lanes, grade), and operating attributes (speed limit, traffic capacity); rail transit network data depicting the routes, station locations, transfer relationships, operating frequencies, and fare structures of subways, light rails, etc.; signal distribution data affecting intersection delays and traffic efficiency; and parking lot distribution data related to the convenience and cost of the travel and parking links, such as location, capacity, and charging standards. These geographic information data jointly build the urban environment required for the simulation. At the same time, it is also necessary to obtain travel demand data describing the scale and spatial distribution of residents' travel activities, which is usually organized in the form of origin-destination pairs, specifying the number of trips from one origin to another destination within a specific time period. These travel demand data can be obtained through traditional traffic surveys, analysis of residents' travel logs, or estimated by analyzing big data such as mobile phone signaling.

[0038] In some embodiments, considering that there are significant differences in travel behaviors (such as mode choice, route preference, time value, etc.) among different characteristic resident groups, in order to achieve more refined cost simulation, preferably, when obtaining travel demand data, it is collected or marked according to a preset data classification dimension. For example, travel demand can be segmented according to travel population attributes (such as gender, age group) and car ownership attributes (such as whether the family owns a private car). In this way, the overall OD travel demand can be decomposed into different subcategories, providing data support for subsequent personalized traffic mode selection modeling and cost calculation for specific populations. For example, the population attributes are cross-classified into 6 categories according to gender (male, female) and age (<18 years old, 18 - 60 years old, >60 years old), and the car ownership attributes are divided into 2 categories according to the local private car ownership as having / no private car.

[0039] In step 102 of some embodiments, based on the preset section attributes of each traffic section (such as road grade, length, and whether a certain traffic mode is allowed to pass), it is determined which traffic modes the section can carry. For example, a certain road allows cars, buses, and bicycles to pass, but does not allow pedestrians; a certain subway section only allows subway trains to pass. At the same time, for each allowed traffic mode, its basic cost calculation factor on this section, that is, the "traffic cost parameter", will be determined. These parameters may include free flow speed, maximum road speed limit, currency cost per unit distance (such as fuel cost), fixed cost (such as toll fee for passing a certain section, single - fare base for taking a bus or subway), time - value coefficient, etc. For example, for a main road, its attributes may define the free flow speed of cars as 60 km / h and the free flow speed of buses as 40 km / h.

[0040] In step 103 of some embodiments, for each traffic section in the network, using the traffic cost parameters of each traffic mode determined in step 102, calculate the "initial section travel cost" of each feasible traffic mode on this traffic section. This cost is the cost in the theoretical or free - flow state without considering the impact of traffic congestion. The calculation method depends on the type of cost parameters. For example, the time cost can be obtained by dividing the section length by the free flow speed, and the currency cost may be obtained by multiplying the section length by the unit - distance rate and then adding the section fixed cost.

[0041] Please refer to Figure 2 , in some embodiments, the section attributes include section length, the traffic cost parameters include free flow speed and energy consumption cost per unit distance, and step 103 may include, but is not limited to, steps 201 to 203.

[0042] Step 201, for each traffic mode, calculate the travel - time cost on the traffic section according to the corresponding free flow speed and section length.

[0043] Step 202, for each traffic mode, calculate the energy - currency cost on the traffic section according to the corresponding energy consumption cost per unit distance and section length.

[0044] Step 203, for each traffic mode, calculate the initial section travel cost on the traffic section according to the travel - time cost and energy - currency cost.

[0045] In step 201 of some embodiments, for each traffic segment with assigned attributes, and distinguishing different traffic modes, a preliminary calculation of travel time cost is performed. Specifically, for a specific traffic mode (such as car, bus, walking, cycling, etc.), the corresponding free-flow speed of this mode on this segment (i.e., the theoretical maximum driving speed without being affected by traffic congestion, and this parameter has been assigned in step 102) and the length of this segment are called. By dividing the segment length by the free-flow speed of this traffic mode and then considering the average signal waiting time, the theoretical shortest travel time of this traffic mode on this segment can be obtained. This calculated time value constitutes the travel time cost of this traffic mode on this segment, which reflects the time consumption required to pass through this segment under ideal conditions. This calculation will be repeated for all segments and all applicable traffic modes in the network.

[0046] In step 202 of some embodiments, for each traffic mode and each segment, using the unit distance energy consumption cost defined in step 102, which quantifies the monetary value corresponding to the energy (such as fuel, electricity) consumed by a specific traffic mode for traveling a unit distance (such as per kilometer). Multiplying this unit cost by the segment length, the energy monetary cost directly generated by this traffic mode passing through this specific segment can be obtained. This cost directly reflects part of the explicit economic expenditure of the trip, such as fuel costs or electricity bills. Similarly, this calculation also needs to cover all segments and all traffic modes.

[0047] In step 203 of some embodiments, the calculation results of the first two sub-steps are integrated to determine a comprehensive initial travel cost for each traffic mode on each traffic segment. Specifically, the travel time cost calculated in step 201 is combined with the energy monetary cost calculated in step 202. This combination can take various forms. For example, the time cost can be converted into a monetary unit through the Value of Time (VOT) parameter and then directly added to the energy monetary cost to obtain an initial segment travel cost representing the basic impedance of this traffic mode on this segment. It should be emphasized that the "initial" here means that this cost is the benchmark cost before considering the influence of network traffic flow interaction (such as congestion).

[0048] Steps 201 to 203 establish a basic cost metric for each transportation mode on each transportation section by separately quantifying and combining travel time consumption and direct monetary expenditures related to energy. This initial cost is a key input for subsequent route selection and traffic assignment simulations. It enables the model to distinguish the inherent attractiveness or deterrence levels of different sections and transportation modes at the beginning, laying a foundation for more accurately simulating the travel decision-making behavior of travelers in a congestion-free or low-traffic state.

[0049] In step 104 of some embodiments, for each "origin-destination pair" (OD pair) obtained in step 101, based on the "initial section travel cost" of each transportation mode on each transportation section calculated in step 103, a route search is performed in the entire "transportation network" composed of multiple transportation sections to find multiple "candidate travel routes" connecting the OD pair, and calculate the "initial route travel cost" of each of these routes. The "transportation network" is a graph structure composed of all transportation sections and their connection relationships (such as intersections, transfer stations). A "candidate travel route" refers to a combination of a series of consecutive transportation sections and transportation modes that may be selected from the origin to the destination, and may include single-mode routes (such as driving the whole journey) or multi-mode combination routes (such as bus + walking). Route search usually adopts classical graph algorithms, such as Dijkstra algorithm, A* algorithm or K-shortest path algorithm (for finding multiple routes), using the initial section travel cost as the weight of the network edge. Among them, the "initial route travel cost" is the sum of the initial travel costs of all sections constituting the route plus the transfer cost (time or cost) at the transfer node. Multiple candidate routes are for simulating the diversity of travelers in route selection and preparing for the iterative adjustment of routes after introducing congestion penalties.

[0050] Please refer to Figure 3 , in some embodiments, transfer nodes allowing transportation mode transfers are preset in the transportation network, and transfer time costs and transfer monetary costs preset for different transfer combinations between transportation modes are set at each of the transfer nodes. Step 104 may include, but is not limited to, steps 301 to 302.

[0051] Step 301, based on the preset route search algorithm, using the initial section travel cost of each transportation mode on each transportation section as the cost basis for route search, search for at least one candidate travel route connecting the origin-destination pair in the transportation network.

[0052] In step 302, for each candidate travel path, accumulate the initial section travel costs of all traffic sections included in the candidate travel path, and at the corresponding transfer nodes according to the traffic mode transfers occurring in the candidate travel path, accumulate and include the corresponding transfer time cost and transfer currency cost to obtain the initial path travel cost of the candidate travel path.

[0053] In step 301 of some embodiments, using a preset path search algorithm (Path Search Algorithm), the initial section travel cost calculated in step 103 is used as the cost basis or impedance for moving in the network. This search process not only considers paths of a single traffic mode but also needs to be able to identify and construct multi-modal paths involving traffic mode transfers, which requires clearly defining transfer nodes in the traffic network model where traffic mode transfers are allowed, such as bus stops, subway stations, parking lots, etc. The search result is to generate at least one candidate travel path for each OD pair, and each such path details the sequence of traffic sections connected in sequence it contains, the traffic mode used on each section, and the specific traffic mode transfer information at one or more transfer nodes that the path may contain.

[0054] In some embodiments, the path search process must comply with preset transfer rules and traffic mode operation range restrictions. For example, it is not allowed to transfer between preset private traffic modes (such as private cars, bicycles, motorcycles, electric motorcycles); while different public traffic modes (such as buses, rail transit) are allowed to transfer at designated transfer nodes (such as bus stops, subway stations). At the same time, the operation restrictions of various traffic modes also need to be considered. For example, buses and rail transit can only run along their fixed routes; bicycles, pedestrians, motorcycles, and electric motorcycles may be prohibited from passing on express sections; in some urban central areas, motorcycles and electric motorcycles may be prohibited from traveling. When the path search algorithm explores the network, it must check whether each step of movement and each transfer comply with these rules, and only sections and transfers that meet the conditions will be taken into account.

[0055] In step 302 of some embodiments, after obtaining a series of candidate travel paths, it is necessary to calculate the comprehensive initial travel cost for each path. This calculation process first accumulates the initial section travel costs of all traffic sections included in the candidate travel path, adding up the cost values (a combination of travel time cost and energy currency cost) calculated for each section according to its corresponding traffic mode. In addition, since the path may involve traffic mode switches at transfer nodes, it is also necessary to consider the additional costs brought by transfers. For each traffic mode transfer that occurs in the path, look up the preset transfer time cost and transfer currency cost (which may include entry fees, etc.) for this specific transfer combination (e.g., from walking to bus, or from car to subway) at the corresponding transfer node. And accumulate these preset transfer costs into the total cost of the path. By adding the initial travel costs of all sections and the transfer costs of all transfer points, the initial path travel cost of the candidate travel path is finally obtained.

[0056] In some embodiments, the travel cost functions of each traffic mode considering transfer costs are formulated as follows: For section l, the traffic travel cost is the sum of the currency cost, time cost, and congestion additional cost of a certain traffic mode k on it. The expression is:

[0057] Where, is the travel impedance function (generalized travel cost) for an individual to select traffic mode k and use section l, VOT is the value of unit time of urban residents (yuan / hour), which can be calculated by dividing the per capita disposable income by the working hours; , , are respectively the time cost, currency cost, and congestion additional cost of using the kth traffic mode on section l during time period t. On a specific section , the cost calculation details of various traffic modes are as follows: Private car: In terms of travel expenses, private car travel mainly considers two types of expenses, fuel cost and road tolls. Combining the congestion additional cost and the resident's unit time value VOT (yuan / hour), the generalized travel cost of private car travel can be obtained as: (the unit is hour)

[0058] Where, is the section length, is the average fuel consumption cost per kilometer of car driving (yuan / km), is the road toll (including parking fees, tolls, and congestion fees, etc.), is the additional time cost brought by section congestion, is the car pick-up start time of the private car, is the travel factor for new private cars. If a passenger chooses to travel by private car when starting from the origin or transfers to a private car from other transportation modes, the variable value is 1; otherwise, it is 0.

[0059] In some embodiments, considering the traffic congestion phenomenon locally generated within the road system, an additional time cost for traffic congestion is formed. The expression of

[0060] is as follows: is the additional time cost brought by urban road traffic congestion. represents different grades of roads. represents the transportation modes (private cars, buses, taxis) affected by road congestion. represents the free flow speed of transportation mode k on class C roads, which is the critical value for whether the vehicles on section l are congested. is the additional cost brought by factors such as passing through intersections and waiting for traffic lights. represents the actual speed of transportation mode k on section l, which is obtained through the following steps: First, calculate the vehicle density considering different vehicle types equivalent distributed on unit road area of section l is as follows:

[0061] where D is the road area occupied by a unit standard small car. is the area of section l on the C-type road, and the speed considering congestion is obtained as:

[0062] where is the maximum speed limit for judging transportation mode k on class C roads. The calculation methods for the additional costs of buses and taxis below are the same and will not be elaborated.

[0063] The travel time cost of taxis is similar to that of private cars, but the average starting price needs to be calculated through the taxi starting price and the average driving mileage to calculate the average starting price, and the currency cost is jointly calculated with the marginal cost (i.e., the taxi charge per unit mileage) The generalized travel cost of taxis is expressed as: ×

[0064] where is the taxi starting time, which can be understood as the taxi waiting time. It is a new taxi travel factor. If a passenger chooses taxi travel when starting from the origin or transfers to a taxi from other transportation modes, the variable value is 1; otherwise, it is 0.

[0065] The travel time of the bus includes the passing time on this section, transfer time and stop time. The travel cost is charged by the bus fare per trip. The generalized cost of bus travel is obtained as follows: +

[0066] Whether the last two terms of the formula are involved in the calculation depends on the traffic mode change situation of the individual on the section, which is divided into two types: : The passenger boards a new bus line, which may be transferred from other transportation modes or transfers to a new bus line. The variable value is 1; otherwise, it is 0. : The passenger did not transfer on the bus originally, and the starting point of this section is a stop of this bus line. If this condition is met, the variable value is 1; otherwise, it is 0.

[0067] Rail transit has independent rights of way, and its operation time is basically not affected by the outside world. Therefore, the generation of additional time costs other than the initial waiting (starting) time is not considered for the time being (the additional time costs can be calculated by adding subway traffic volume data later). The operation time can be obtained by dividing the travel distance by the subway operation speed. The travel cost is calculated by adding the starting price and the product of the average subway unit mileage cost and the mileage. and the mileage.

[0068]

[0069] Whether the last term of the formula is involved in the calculation depends on the traffic mode change situation of the individual on the section: : The passenger boards a new subway line, which may be transferred from other transportation modes or transfers to a new subway line. The variable value is 1; otherwise, it is 0.

[0070] Bicycle travel is not greatly affected by the traffic volume on the section. The travel time can be expressed as the quotient of the section length and the average speed of this mode. Considering the differences in the usage preferences of bicycles and walking at different distances and the actual situation that the pick-up time of some shared bicycles is relatively long, the starting time of bicycle pick-up is included in the generalized travel cost formula: ×

[0071] is a new bicycle travel factor. If a passenger chooses to travel by bicycle when starting from the origin or transfers to a bicycle from other transportation modes, the variable value is 1; otherwise, it is 0.

[0072] The generalized travel cost of walking is similar to that of cycling:

[0073]

[0074] The running time of a motorcycle can be expressed as the quotient of the road segment length and the average speed of this mode. The cost generated can be calculated by multiplying the unit mileage fuel consumption or power consumption (measured in unit mileage) of the motorcycle by the road segment mileage as shown. In summary, the generalized travel cost of a motorcycle is: ×

[0075] where is the motorcycle pick-up start time. is a motorcycle travel factor. If a passenger chooses to travel by motorcycle when starting from the origin or transfers to a motorcycle from other transportation modes, the variable value is 1; otherwise, it is 0.

[0076] Through steps 301 and 302, this embodiment can search for possible travel paths (including transfers) and calculate an initial total cost including road segment travel costs and transfer costs for them, providing a set of travel options for subsequent traffic assignment and selection models. It can not only evaluate the distances or times of different routes, but also quantify the inconveniences and costs brought by mode conversion, thus more comprehensively reflecting the comprehensive cost considerations faced by travelers when choosing travel modes and paths, and being able to simulate the theoretical optimal or feasible path selection under non-congested conditions.

[0077] In step 105 of some embodiments, for each travel origin-destination pair, based on the "initial path travel costs" of each "candidate travel path" calculated in step 104, the "travel demand" corresponding to this OD pair is assigned to these candidate paths. This assignment process simulates the path selection behavior of travelers. Generally, it is assumed that travelers tend to choose paths with lower costs. The assignment model can use the Logit model to assign demands according to the relative magnitudes of path costs by probability. By assigning the demands of all OD pairs, the "initial traffic flow" carried by each traffic mode on each traffic segment in the traffic network can be accumulated. "Traffic flow" refers to the number of travelers or vehicles passing through a certain segment or using a certain traffic mode within a unit time. This initial flow is a preliminary traffic distribution result calculated based on the idealized situation without congestion costs.

[0078] Please refer to Figure 4, in some embodiments, step 105 may include, but is not limited to, steps 401 to 403.

[0079] Step 401: Calculate the initial selection probability of each candidate travel path according to the initial path travel cost of each candidate travel path.

[0080] Step 402: Allocate the travel demand to multiple candidate travel paths according to the initial selection probability of each candidate travel path, and obtain the initial path flow on each candidate travel path.

[0081] Step 403: For each traffic section, accumulate the initial path flows of all candidate travel paths of all travel origin-destination pairs to obtain the initial traffic flow of each traffic mode on each traffic section in the traffic network.

[0082] In step 401 of some embodiments, a preset travel selection model, such as the Logit model, is applied. It is assumed that travelers tend to choose paths with lower costs, but their choice behavior has a certain degree of randomness or unobserved preferences. The model takes the initial path travel costs of each candidate path as input and outputs the initial selection probability of each candidate path being selected. This probability reflects the likelihood that a typical traveler will choose a specific path among all feasible path options for a given OD pair without considering the impact of traffic congestion.

[0083] In step 402 of some embodiments, based on the initial selection probability of each candidate travel path calculated in step 401 and the travel demand of the corresponding travel origin-destination pair obtained in step 101, an initial traffic demand allocation is performed. The specific operation is to allocate the total travel demand of the travel origin-destination pair to the corresponding candidate travel paths according to the proportion of the initial selection probabilities of each candidate path. By performing this allocation process for each OD pair, the initial path flow carried on each candidate travel path is finally obtained. This flow represents the number of travelers expected to use this specific path to complete their trips when ignoring the congestion effect.

[0084] In step 403 of some embodiments, for each traffic segment in the traffic network, all candidate travel paths using this segment and the transportation modes used are counted. Then, the initial path flows of all candidate travel paths passing through this segment are accumulated according to the transportation modes used on this segment. For example, a segment may be used by multiple private car paths and multiple bus paths simultaneously. Then, the path flows of all private cars passing through this segment are accumulated separately to obtain the initial private car flow on this segment; at the same time, the path flows of all buses passing through this segment are accumulated to obtain the initial bus flow on this segment. This process is repeated for all segments and all transportation modes in the network, and finally, the initial traffic flow of each transportation mode on each traffic segment in the traffic network is obtained.

[0085] Through steps 401 to 403, this embodiment combines the individual travel path selection probability based on the initial cost with the travel demand, and finally converts it into the specific traffic flow of different transportation modes on each traffic segment in the network. This process simulates the first traffic load distribution without considering the congestion interaction. Based on the traveler's preference for cost selection, it maps the overall travel demand to specific network facilities. The resulting initial traffic flow distribution reflects the relative attractiveness of different paths and modes, providing the original input for subsequent consideration of traffic congestion effects, iterative correction of travel costs and flows.

[0086] In step 106 of some embodiments, in order to make the simulation closer to reality, the impact of traffic congestion on travel costs needs to be considered. Therefore, in this step, for each transportation mode on each traffic segment in the network, according to the preset "traffic flow threshold" and the "initial traffic flow" obtained in step 105, "congestion penalty processing" is performed on the "initial segment travel cost" of this segment and this mode, so as to obtain the updated "target segment travel cost". The "traffic flow threshold" usually refers to the designed traffic capacity of the segment or a traffic flow level that triggers the congestion effect. When the initial traffic flow exceeds or approaches this threshold, the "congestion penalty processing" is triggered, and the processed "target segment travel cost" reflects the additional delay and cost increase caused by traffic congestion, making the segment cost associated with the actual traffic volume it bears.

[0087] Please refer to Figure 5 , in some embodiments, step 106 may include, but is not limited to, steps 501 to 502.

[0088] Step 501, for each transportation mode on each traffic segment, calculate the congestion penalty coefficient according to the initial traffic flow and the traffic flow threshold.

[0089] Step 502: Multiply the congestion penalty coefficient by the initial travel cost of the section to obtain the target travel cost of the section.

[0090] In step 501 of some embodiments, the initial traffic flow can be understood as an indicator measuring the degree of use or demand of the section and the corresponding transportation mode. For example, it can be the cumulative number of times the section and the corresponding transportation mode are included in the finally selected travel route or the theoretically allocated demand in the previous route selection. Compare this initial traffic flow representing the usage frequency or demand with the preset traffic flow threshold for the section (characterizing the traffic capacity of the section or the critical usage level at which significant congestion begins to occur) to calculate a congestion penalty coefficient. This congestion penalty coefficient reflects the negative impact of the section usage degree on travel efficiency. When the number of times the section is selected or the allocated flow is much lower than its traffic flow threshold, it indicates a low section load and the congestion penalty coefficient is close to 1 (representing free flow or no significant congestion); while when the usage times or flow approaches or even exceeds the traffic flow threshold, the congestion penalty coefficient will increase accordingly (set to be not less than 1) to reflect the additional time or cost loss caused by high-frequency use or increased congestion. At the same time, factors such as road grade can also be considered in the setting of this coefficient. For example, for roads with a higher grade, even if the usage frequency is relatively high, the growth of its penalty coefficient may be relatively gentle.

[0091] In step 502 of some embodiments, use the congestion penalty coefficient calculated in step 501 for a specific traffic section and a specific transportation mode to perform a multiplication operation with the previously calculated corresponding initial travel cost of the section, dynamically adjust the basic cost according to the actual usage situation of the section, and the obtained result is the target travel cost of the section. This target travel cost of the section is a more realistic unit travel cost considering the congestion or repeated use effect caused by the path selection frequency or traffic flow distribution.

[0092] Through steps 501 to 502, this embodiment effectively quantifies the congestion effect or the cost increase effect caused by high-frequency use in the traffic system and integrates it into the calculation of the travel cost of the section. By counting the number of times the section is used in the path selection as the "initial traffic flow", it realizes the transformation from the initial travel cost of the section in an idealized or low-load state to the target travel cost of the section reflecting the actual network load and considering the influence of the usage intensity. This method of dynamically adjusting the cost enables the subsequent calculation of the total travel cost of the travel route to more accurately simulate the comprehensive travel cost borne due to network congestion or path overlap in the real traffic environment, thereby improving the accuracy and authenticity of the entire traffic travel cost simulation.

[0093] In step 107 of some embodiments, finally, using the "target road section travel cost" calculated in step 106 and considering the impact of congestion, for each "travel origin-destination pair" again, path search is performed in the traffic network. The goal of this search is to find "multiple target travel paths" based on the cost after congestion and calculate the final "target path travel cost" corresponding to each "target travel path". Similar to step 104, the path search algorithm is also used, but this time the updated target road section cost is used as the weight of the network edge. The "target travel paths" obtained by the search represent the paths that travelers may actually choose and have relatively optimized costs after considering congestion. The calculated "target path travel cost" is the final result of this simulation, which reflects the more realistic comprehensive cost of traveling from the origin to the destination under the current traffic demand and network conditions.

[0094] Please refer to Figure 6 , in some embodiments, step 107 may include but is not limited to steps 601 to 602.

[0095] Step 601, using the target road section travel cost of each transportation mode on each traffic road section as the cost basis for path search, at least one target travel path connecting the travel origin-destination pair is searched for in the traffic network.

[0096] Step 602, for each target travel path, accumulate the target road section travel costs of the corresponding transportation modes on all traffic road sections included in the target travel path, and based on the transportation mode transfers that occur in the target travel path, for each transportation mode transfer that occurs at the corresponding transfer node, the transfer time cost and transfer currency cost, to obtain the target path travel cost.

[0097] In step 601 of some embodiments, using the target road section travel cost of each transportation mode on each traffic road section as the core path search basis, based on these dynamically adjusted cost data, in the traffic network, for a given travel origin-destination pair, use the path search algorithm to find and determine at least one target travel path connecting the origin and the destination. These target travel paths represent the travel route plans that travelers will theoretically choose and have relatively low or optimal costs after considering network congestion and the cost characteristics of each transportation mode.

[0098] In step 602 of some embodiments, all traffic segments included in each target travel path are counted, and the target segment travel costs of the transportation modes corresponding to these segments are accumulated. Meanwhile, it is checked whether there is a conversion of transportation modes in the target travel path. If the path contains a transfer of transportation modes at a preset transfer node, then according to the preset rules, the transfer time cost and transfer currency cost corresponding to each eligible transfer are included in the total cost. By adding the travel costs of all segments and all necessary transfer costs, the comprehensive travel cost of the target travel path, that is, the target path travel cost, is finally obtained.

[0099] Through steps 601 to 602, this embodiment realizes path selection based on the target segment travel cost reflecting the actual network load, and calculates the complete target path travel cost including travel cost and transfer cost. This process simulates the decision-making behavior of travelers when facing real traffic conditions (congestion) and multimodal transportation systems (transfer), and obtains a comprehensive and accurate simulation result of the total cost for residents to complete a specific origin-destination travel.

[0100] Please refer to Figure 7 , in some embodiments, the method provided by the embodiments of the present application further includes iteratively performing the following steps until a preset convergence condition is met: Step 701, for each origin-destination pair of trips, according to the target path travel cost calculated most recently, reallocate the travel demand to the corresponding multiple target travel paths, and obtain the traffic flow of each transportation mode on each traffic segment in the updated traffic network.

[0101] Step 702, for each transportation mode on each traffic segment, according to the updated traffic flow and traffic flow threshold, re-perform congestion penalty processing on the segment travel cost to obtain the updated target segment travel cost.

[0102] Step 703, for each origin-destination pair of trips, according to the updated target segment travel cost, re-search for multiple target travel paths in the traffic network, and calculate the updated target path travel cost of each target travel path.

[0103] In step 701, based on the target path travel costs of each target travel path calculated in the previous iteration, for each origin-destination pair of trips, reallocate the total travel demand of this origin-destination pair to its corresponding multiple target travel paths. By summarizing the demands allocated to each path for all origin-destination pairs, the traffic flow of each transportation mode carried on each traffic segment in the updated traffic network can be counted. This step simulates the behavior of travelers adjusting their path selection according to the currently perceived path cost, resulting in a redistribution of network traffic.

[0104] In step 702, the traffic flow of each transportation mode on each traffic section obtained in step 701 is utilized, and in combination with a preset traffic flow threshold (representing the traffic flow level at which significant congestion begins to occur on a section), the section travel cost of each section is recalculated. This means that when the flow of a certain transportation mode on a certain section exceeds its threshold, the corresponding section travel cost (mainly time cost, but may also affect energy cost and expense cost) will increase accordingly. The greater the flow, the usually greater the increase. Through this processing, a set of updated target section travel costs reflecting the latest network congestion status can be obtained.

[0105] In step 703, the updated target section travel cost calculated in step 702 is used as the new network base cost. Then, for each origin-destination pair, in the traffic network with updated costs, the path search algorithm is applied again to re-find and determine multiple target travel paths connecting the origin and the destination. Since the section cost has changed due to congestion, the newly found target travel paths may be different from the paths found in the previous round of iteration. Subsequently, the updated target path travel costs of these newly found target travel paths are calculated.

[0106] This iterative process (701 - 703) will continue until a preset convergence condition is met. For example, when the change in traffic flow on each traffic section calculated in two consecutive iterations is small enough (less than the first preset change threshold), or when the change in the total system travel cost (the sum of the costs of all travelers) calculated in two consecutive iterations is small enough (less than the second preset change threshold), it can be considered that the network state has reached a relatively stable equilibrium point, and the iteration stops. The formula for this iterative process is shown as follows: The formula used for calculating the path general cost in the first-round path selection is as follows, where is the transfer cost between sections:

[0107] Starting from the second round, the path general travel cost considering the additional congestion time cost, that is, applying the congestion penalty coefficient will be calculated using this formula:

[0108] Through the iterative execution of steps 701 to 703, this embodiment can simulate the dynamic process in which traffic flow and cost influence and feedback on each other in a traffic system until a stable state is reached. In this stable state, the path cost selected by travelers tends to be consistent with the actual cost caused by the network congestion generated by this selection. This iterative method significantly improves the authenticity and accuracy of traffic flow assignment and travel cost simulation because it no longer simply performs a one-time calculation based on the initial, congestion-free cost, but takes into account the congestion effect caused by the distribution of travel demand in the network and the reaction of travelers to congestion (adjusting path selection), and finally obtains a simulation result of residents' travel cost that more conforms to the actual traffic operation law.

[0109] The traffic travel cost simulation method proposed in this application first obtains the geographical information data of the target city, which covers multiple traffic sections of the urban traffic network and the travel demand volumes of multiple origin-destination pairs, laying a data foundation for the traffic cost simulation of the whole city. Then, for each traffic section constituting the urban traffic network, based on its preset attributes, it determines various traffic modes supported on this section and the corresponding traffic cost parameters, and calculates the initial section travel cost of each traffic mode on this section. On this basis, for each origin-destination pair, using the initial section costs of each traffic mode on all sections, it searches in the entire traffic network to obtain multiple candidate travel paths and calculates the initial travel costs of these paths. Compared with the background technology, it not only considers the single optimal path but also considers multiple possible travel options. Then, it distributes the travel demand volumes of all origin-destination pairs to each candidate path according to the initial path costs, thereby obtaining the initial traffic flow of each traffic mode on each traffic section in the network. Based on this flow information, it further adopts a congestion penalty mechanism to adjust the initial section travel cost according to the preset flow threshold and the calculated initial flow, obtaining the target section travel cost that can better reflect the actual road conditions. Finally, using the target section travel cost adjusted by the congestion penalty, it searches in the traffic network again for each origin-destination pair to obtain multiple target travel paths and calculates the final target path travel cost that more conforms to the actual traffic conditions. In summary, the method proposed in this application overcomes the technical bottlenecks of limited scope, single mode, and difficulty in quantification in the background technology by obtaining and processing the geographical information and travel demand of the whole city, thereby enabling the calculation of travel costs for all paths and all traffic modes in the city.

[0110] Please refer to Figure 9 , this embodiment of the application also provides a traffic travel cost simulation device that can implement the above traffic travel cost simulation method, including: An acquisition module that acquires geographical information data of a target city and travel demand volumes of multiple pairs of travel origin and destination points; wherein the geographical information data includes multiple traffic segments; A determination module that determines, according to the preset segment attributes of each traffic segment, the corresponding traffic mode and the traffic cost parameter corresponding to the traffic mode for each traffic segment; A calculation module that, for each traffic segment, calculates the initial segment travel cost of each traffic mode on the traffic segment according to the traffic cost parameters of each traffic mode; A search module that, for each pair of travel origin and destination points, searches for multiple candidate travel paths in the traffic network composed of multiple traffic segments according to the initial segment travel cost of each traffic mode on each traffic segment, and calculates the initial path travel cost of each candidate travel path; An allocation module that, for each pair of travel origin and destination points, allocates the travel demand volume to each candidate travel path according to the initial path travel cost of each candidate travel path, to obtain the initial traffic flow of each traffic mode on each traffic segment in the traffic network; A congestion penalty processing module that, for each traffic mode on each traffic segment, performs congestion penalty processing on the initial segment travel cost according to a preset traffic flow threshold and the initial traffic flow, to obtain the target segment travel cost; A cost calculation module that, for each pair of travel origin and destination points, searches for multiple target travel paths in the traffic network according to the target segment travel cost of each traffic mode on each traffic segment, and calculates the target path travel cost of each target travel path.

[0111] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, it implements the traffic travel cost simulation method according to any one of the embodiments in the first aspect of the present application.

[0112] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the storage medium stores a program, and when the program is executed by a processor, it implements the traffic travel cost simulation method according to any one of the embodiments in the first aspect of the present application.

[0113] Please refer to Figure 9 , Figure 9 which schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes: The processor 901 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 902 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 902 and are called by the processor 901 to execute the traffic travel cost simulation method of the embodiments of the present application; The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to implement communication interaction between this device and other devices, and can achieve communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); The bus 905 transmits information between the various components of the device (such as the processor 901, the memory 902, the input / output interface 903, and the communication interface 904); Among them, the processor 901, the memory 902, the input / output interface 903, and the communication interface 904 achieve communication connections with each other inside the device through the bus 905.

[0114] The embodiments of the present application also provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned traffic travel cost simulation method is implemented.

[0115] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0116] The embodiments described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0117] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown, or combine certain steps, or different steps.

[0118] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations.

[0120] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not 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 "including" 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 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.

[0121] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (item) of the following" or its similar expression refers to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0122] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0123] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0125] When an integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or 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 multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of this application. The aforementioned storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0126] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, and thus do not limit the scope of the rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall be within the scope of the rights of the embodiments of this application.

Claims

1. A traffic travel cost simulation method, characterized in that, The method includes: Obtaining geographical information data of a target city and travel demand amounts for a plurality of travel origin-destination pairs; wherein, the geographical information data includes a plurality of traffic segments; Determining, according to the preset segment attributes of each of the traffic segments, the corresponding transportation mode for each of the traffic segments and the traffic cost parameters corresponding to the transportation mode; For each of the traffic segments, calculating the initial segment travel costs of each of the transportation modes on the traffic segment according to the traffic cost parameters of each of the transportation modes; For each of the travel origin-destination pairs, searching for a plurality of candidate travel paths in a traffic network composed of a plurality of the traffic segments according to the initial segment travel costs of each of the transportation modes on each of the traffic segments, and calculating the initial path travel costs of each of the candidate travel paths; For each of the travel origin-destination pairs, distributing the travel demand amounts to each of the candidate travel paths according to the initial path travel costs of each of the candidate travel paths, to obtain the initial traffic flows of each of the transportation modes on each of the traffic segments in the traffic network; For each of the transportation modes on each of the traffic segments, performing congestion penalty processing on the initial segment travel costs according to a preset traffic flow threshold and the initial traffic flow, to obtain the target segment travel costs; For each of the travel origin-destination pairs, searching for a plurality of target travel paths in the traffic network according to the target segment travel costs of each of the transportation modes on each of the traffic segments, and calculating the target path travel costs of each of the target travel paths.

2. The traffic travel cost simulation method according to claim 1, wherein The segment attributes include segment length, and the traffic cost parameters include free flow speed and unit distance energy consumption cost. The step of calculating, for each of the traffic segments, the initial segment travel costs of each of the transportation modes on the traffic segment according to the traffic cost parameters of each of the transportation modes includes: For each of the transportation modes, calculating the travel time cost on the traffic segment according to the corresponding free flow speed and the segment length; For each of the transportation modes, calculating the energy currency cost on the traffic segment according to the corresponding unit distance energy consumption cost and the segment length; For each of the transportation modes, calculating the initial segment travel costs on the traffic segment according to the travel time cost and the energy currency cost.

3. The traffic travel cost simulation method according to claim 1, characterized in that In the traffic network, transfer nodes allowing transportation mode transfers are preset in advance, and transfer time costs and transfer currency costs preset for different transfer combinations between different transportation modes at each of the transfer nodes. The step of searching for a plurality of candidate travel paths in a traffic network composed of a plurality of the traffic segments according to the initial segment travel costs of each of the transportation modes on each of the traffic segments for each of the travel origin-destination pairs, and calculating the initial path travel costs of each of the candidate travel paths includes: Based on a preset path search algorithm, using the initial section travel cost of each transportation mode on each transportation section as the cost basis for path search, in the transportation network, at least one candidate travel path connecting the origin-destination pair is searched; wherein, each candidate travel path includes a plurality of successively connected transportation sections and corresponding transportation modes, and includes transportation mode transfers at one or more of the transfer nodes. For each candidate travel path, accumulate the initial section travel costs of all the transportation sections included in the candidate travel path, and according to the transportation mode transfers occurring in the candidate travel path, at the corresponding transfer nodes, accumulate and include the corresponding transfer time cost and transfer currency cost to obtain the initial path travel cost of the candidate travel path.

4. The traffic travel cost simulation method according to claim 1, wherein For each origin-destination pair, distribute the travel demand to each candidate travel path according to the initial path travel costs of each candidate travel path to obtain the initial traffic flow of each transportation mode on each transportation section in the transportation network, including: Calculate the initial selection probability of each candidate travel path according to the initial path travel cost of each candidate travel path. Distribute the travel demand to multiple candidate travel paths according to the initial selection probability of each candidate travel path to obtain the initial path flow on each candidate travel path. For each transportation section, accumulate the initial path flows of all candidate travel paths of all origin-destination pairs to obtain the initial traffic flow of each transportation mode on each transportation section in the transportation network.

5. The traffic travel cost simulation method according to claim 1, wherein For each transportation mode on each transportation section, perform congestion penalty processing on the initial section travel cost according to a preset traffic flow threshold and the initial traffic flow to obtain the target section travel cost, including: For each transportation mode on each transportation section, calculate a congestion penalty coefficient according to the initial traffic flow and the traffic flow threshold; wherein, the congestion penalty coefficient is not less than 1. Multiply the congestion penalty coefficient by the initial section travel cost to obtain the target section travel cost.

6. The method according to claim 3, wherein For each origin-destination pair, search for multiple target travel paths in the transportation network according to the target section travel costs of each transportation mode on each transportation section, and calculate the target path travel cost of each target travel path, including: Using the target section travel cost of each transportation mode on each transportation section as the cost basis for path search, search for at least one target travel path connecting the origin-destination pair in the transportation network. For each of the target travel paths, accumulate the target section travel costs of the corresponding transportation modes on all the transportation sections included in the target travel path, and based on the transportation mode transfers that occur in the target travel path, add the transfer time costs and transfer currency costs corresponding to each transportation mode transfer that occurs at the corresponding transfer node, to obtain the target path travel cost.

7. The traffic travel cost simulation method according to claim 1, wherein The method further includes iteratively performing the following steps until a preset convergence condition is satisfied: For each pair of travel origin and destination, according to the target path travel cost obtained from the most recent calculation, redistribute the travel demand to the corresponding multiple target travel paths again, to obtain the traffic flow of each transportation mode on each transportation section in the updated traffic network. For each transportation mode on each transportation section, based on the updated traffic flow and the traffic flow threshold, perform congestion penalty processing on the section travel cost again, to obtain the updated target section travel cost. For each pair of travel origin and destination, based on the updated target section travel cost, search for multiple target travel paths again in the traffic network, and calculate the updated target path travel cost of each target travel path; where the convergence condition is that the change amount of the traffic flow on each transportation section obtained from two consecutive iterative calculations is less than the first preset change amount threshold, or the change amount of the total travel cost obtained from two consecutive iterative calculations is less than the second preset change amount threshold.

8. A traffic travel cost calculation device, characterized in that, It includes: An acquisition module, which acquires the geographical information data of the target city and the travel demands of multiple pairs of travel origin and destination; where the geographical information data includes multiple transportation sections. A determination module, which determines the corresponding transportation mode and the traffic cost parameters corresponding to the transportation mode for each transportation section according to the preset section attributes of each transportation section. A calculation module, which calculates the initial section travel costs of each transportation mode on each transportation section according to the traffic cost parameters of each transportation mode for each transportation section. A search module, which, for each pair of travel origin and destination, searches for multiple candidate travel paths in the traffic network composed of multiple transportation sections according to the initial section travel costs of each transportation mode on each transportation section, and calculates the initial path travel costs of each candidate travel path. A distribution module, which, for each pair of travel origin and destination, distributes the travel demand to each candidate travel path according to the initial path travel costs of each candidate travel path, to obtain the initial traffic flow of each transportation mode on each transportation section in the traffic network. A congestion penalty processing module, which performs congestion penalty processing on the initial section travel cost according to the preset traffic flow threshold and the initial traffic flow for each transportation mode on each transportation section, to obtain the target section travel cost. A cost calculation module, for each of the travel origin-destination pairs, searches for multiple target travel paths in the transportation network according to the target section travel costs of each transportation mode on each transportation section, and calculates the target path travel cost of each target travel path.

9. An electronic device, characterized in that, It includes: A memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the transportation travel cost simulation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a program, and when the program is executed by the processor, it implements the transportation travel cost simulation method according to any one of claims 1 to 7.

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