Travel path planning method and system based on city perception

Abstract

The invention relates to the technical field of travel path planning, in particular to a travel path planning method and system based on city perception, and the method comprises the steps: obtaining the required time for passing through each road section in each time period based on the traffic flow of each road section in each historical time period in a city by using a BPR function, and obtaining the required time for passing through each road section in each time period in combination with the weather state of each time period; determining a road resistance factor of each road section in each time period; obtaining predicted traffic flow of each road section in each time period and a preset number of time periods after the time periods by using the traffic flow, and obtaining the traffic fluctuation of each road section in each time period according to the difference between the traffic flow of each road section in each time period and the predicted traffic flow and the dispersion degree of the predicted traffic flow of the preset number of time periods; determining a road selection coefficient of each road section based on the number of the branch intersections of each intersection in each road section; and constructing a target function, and obtaining an optimal travel path by using an optimization algorithm. Therefore, the rationality of travel path planning is improved.

Description

Technical Field

[0001] The present application relates to the technical field of travel route planning, and specifically to a travel route planning method and system based on urban perception. Background Art

[0002] Urban perception refers to the collection, monitoring, and analysis of data on the urban environment, traffic, and population flows through various sensors, smart devices, and information technology, enabling real-time understanding and awareness of the city's operational status. In terms of traffic management, by real-time monitoring of traffic flow, road conditions, parking availability, and other aspects, urban perception technology can help managers promptly adjust traffic signals, redirect drivers, or implement other measures to reduce congestion.

[0003] Travel route planning involves using algorithms and models, combined with factors such as traffic data and route information, to help users select the optimal travel route based on varying travel needs and constraints, avoiding traffic congestion and unnecessary detours, and significantly improving travel efficiency. However, existing travel route planning methods often rely on static road network data and preset rules, failing to dynamically respond to sudden changes in traffic flow in real time. This means they fail to account for the time-varying nature of traffic flow during travel, potentially causing the recommended route to become ineffective during travel, reducing the accuracy and reliability of route planning. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a travel route planning method and system based on urban perception. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a travel path planning method based on city perception, the method comprising the following steps: Based on the traffic volume of each road section in the city in each historical time period, the BPR function is used to obtain the time required to pass each road section in each time period. Combined with the weather conditions in each time period, the road resistance factor of each road section in each time period is determined; The traffic flow is used to obtain the predicted traffic flow of each road section in each time period and a preset number of time periods thereafter, and the traffic fluctuation degree of each road section in each time period is obtained based on the difference between the traffic flow of each road section in each time period and the predicted traffic flow, as well as the degree of dispersion of the predicted traffic flow in the preset number of time periods; Based on the number of forked intersections at each intersection in each road section, the road selection coefficient of each road section is determined; combining the road resistance factor, the traffic fluctuation, and the road selection coefficient of all road sections in the travel path, an objective function is constructed, and an optimization algorithm is used to obtain the optimal travel path.

[0005] In one embodiment, determining the road resistance factor includes: For each time period of each road section on the current day, calculate the average of the traffic flow of multiple historical same time periods, substitute the average into the BPR function, and obtain the time required to pass each road section in each time period; Determine the weather influencing factors of each road section based on the weather conditions in each time period; The road resistance factor is positively correlated with the required time and the weather influence factor.

[0006] In one embodiment, the value range of the weather impact factor is [1, 1.5]. The value of the weather impact factor of each road section is divided into three situations according to the weather conditions: icy road surface, water accumulation on the road surface, and normal road surface. Among them, the weather impact factor of icy road surface is greater than the weather impact factor of water accumulation on the road surface, which is greater than the weather impact factor of normal road surface.

[0007] In one embodiment, determining the predicted traffic flow includes: For the e-th road section in the t-th time period on the current day, obtain the traffic volume of the e-th road section in the same time period on multiple historical days, and compose the traffic volume sequence of the e-th road section in the t-th time period in chronological order. Use the time series prediction algorithm to obtain the predicted traffic volume of the e-th road section in the t-th time period on the current day; Accordingly, the predicted traffic volume of the e-th road section on the current day for a preset number of time periods after the t-th time period is obtained.

[0008] In one embodiment, determining the traffic fluctuation includes: The absolute value of the difference between the predicted traffic flow and the actual traffic flow of the e-th road section in the t-th time period of the current day is calculated, and the traffic fluctuation degree is determined in combination with the discrete degree.

[0009] In one embodiment, further determining the traffic fluctuation degree includes: Preset a first weight and a second weight, calculate the product of the first weight and the absolute value of the difference, record it as the first product, calculate the product of the second weight and the degree of dispersion, record it as the second product, merge the first product and the second product to obtain the traffic fluctuation degree of the e-th road section in the t-th time period on the current day.

[0010] In one embodiment, the sum of the first weight and the second weight is 1, and the first weight is greater than the second weight.

[0011] In one embodiment, determining the road selectivity coefficient includes: The ratio of the number of forked intersections at each intersection in each road section to the maximum number of forked intersections at all intersections in the road section is calculated, and the road selection coefficient is the average of the ratios of all intersections in each road section.

[0012] In one embodiment, the objective function is expressed as: Where, is the objective function, M is the number of road segments included in the travel path, To preset the third weight, is the road resistance factor of the e-th road section in the travel path at the t-th time period, is the traffic fluctuation of the e-th road section in the travel path in the t-th time period, To preset the fourth weight, is the road selection coefficient of the e-th road segment in the travel path; is the preset fifth weight, where ;The optimization goal is to minimize the objective function.

[0013] In a second aspect, an embodiment of the present application also provides a travel route planning system based on urban perception, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0014] This application has at least the following beneficial effects: This application uses the BPR function based on the traffic volume of each road section in each historical time period in the city to obtain the time required to pass each road section in each time period, and determines the road resistance factor of each road section in each time period in combination with the weather conditions of each time period; the determination of the road resistance factor improves the modeling accuracy of road section planning for complex spatiotemporal scenarios, and avoids the lack of adaptability of a single static parameter to special weather or periodic congestion scenarios; the traffic volume is used to obtain the predicted traffic volume of each road section in each time period and a preset number of time periods thereafter, and the traffic fluctuation of each road section in each time period is obtained according to the difference between the traffic volume of each road section in each time period and the predicted traffic volume, as well as the discrete degree of the predicted traffic volume in the preset number of time periods; the determination of the traffic fluctuation enhances the path planning for The ability to resist risks of emergencies quantifies the uncertainty of the future traffic status of road sections, which helps route planning to proactively avoid high-risk sections; based on the number of fork intersections at each intersection in each section, the road selection coefficient of each section is determined; it realizes the scientific quantification of the topological complexity of the road network. Since the number of fork intersections directly affects the difficulty of driving decision-making and the risk of accidents, when planning the route, it avoids selecting sections with high road selection coefficients to reduce the driving cognitive load and the probability of operational errors; combining the road resistance factor, the traffic fluctuation, and the road selection coefficient of all sections in the travel path, constructs an objective function, and uses an optimization algorithm to obtain the optimal travel path. Through multi-objective collaborative optimization, the global rationality of path planning is improved, and the accuracy and reliability of path planning are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0016] Figure 1 A flowchart of the steps of a travel route planning method based on city perception provided in one embodiment of the present application; Figure 2 Construct a flowchart for the objective function. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by this application to achieve the predetermined invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation methods, structures, features and effects of the travel path planning method and system based on urban perception proposed in this application. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics of one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The specific solutions of the travel route planning method and system based on urban perception provided by this application are described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flowchart of a method for city-aware travel route planning provided by an embodiment of the present application, the method comprising the following steps: S1, based on the traffic volume of each road section in the city in each historical time period, uses the BPR function to obtain the time required to pass each road section in each time period. Combined with the weather conditions in each time period, the road resistance factor of each road section in each time period is determined.

[0021] In city-aware travel route planning, mapping software is used to obtain the basic road network, including the desired urban area, and real-time weather conditions. This data is then preprocessed using the Douglas-Peucker algorithm to cleanse redundant information from the road network data. By setting a distance tolerance threshold of 0.5 meters, redundant nodes in the route are automatically identified and deleted, while nodes representing intersections and landmarks are retained. This ensures road shape accuracy while further reducing data volume and improving computational efficiency for subsequent route planning. The Douglas-Peucker algorithm is a well-known technique.

[0022] When traveling in cities, the specific conditions of the corresponding route areas vary at different times. For example, morning and evening rush hours often experience congestion at specific times, which dynamically changes the road network's capacity. Alternatively, sections of the travel route may experience heavy rain or snow at different times, potentially causing waterlogging and ice formation on the roads, causing the actual travel time to far exceed the theoretical distance. Furthermore, considering only a single influencing factor during route planning can often lead to significant errors due to ignoring multiple factors. This can lead users into congested or high-risk areas, extending their journeys and even posing safety risks. Therefore, after determining the user's departure point and destination, optimal travel route planning is necessary.

[0023] First, to analyze the traffic flow at different times on each section of the travel route, this embodiment divides each day into 24 time periods, with each time period consisting of one hour. 0:00-1:00 is the first time period, 1:00-2:00 is the second time period, and so on, 23:00-24:00 is the 24th time period. Implementers can freely divide time periods based on actual circumstances, and this embodiment does not impose any restrictions.

[0024] Secondly, in daily travel, under normal circumstances, users' travel times and travel routes are relatively regular, especially during weekdays. Therefore, the traffic flow distribution is likely to be similar in the same time period on the same road section on different days. Therefore, this embodiment assists in planning the current travel route by referring to the traffic flow data of each road section in each historical time period. Specifically, a historical period is set, and the N days before the current day are taken as a historical period. In this embodiment, N=30, and the implementer can set it according to actual conditions. The traffic flow data of each road section in each historical time period can be obtained through the city perception system, which is an existing public technology and is not limited in this embodiment.

[0025] This embodiment uses the BPR function (Federal Highway Administration function) to obtain the time required to pass each road section in each time period. The expression of the BPR function is: Where, is the time required to pass the e-th road section in the t-th time period, is the mean of the traffic volume of the e-th road section in all t-th time periods in the historical cycle of the current day, is the road capacity of the e-th road section, which can be obtained through the city perception system. is a constant, is a constant. In this embodiment , , is the free travel time of the e-th road section. In this embodiment, ;in, is the length of the e-th road segment, is the vehicle free flow speed of the e-th road section, which can be obtained through the city perception system. The BPR function is a well-known technology and will not be described in detail in this embodiment.

[0026] Furthermore, weather conditions can affect the state of the road surface, which in turn affects the vehicle's driving time. For example, rainfall in some areas can cause water accumulation on the roads, making the ground relatively slippery. The vehicle will be affected to a certain extent during driving, slowing down the speed and prolonging the driving time. If snowfall occurs in some areas, the road may accumulate snow and ice, which will seriously affect the driving speed.

[0027] Based on the above analysis, the weather impact factor of each road section is determined, and the value range of the weather impact factor is set to [1, 1.5]. According to city perception, the different situations in which the road surface is affected by the weather are evaluated in real time through the Internet of Things. The values ​​of the weather impact factors of each road section are divided into three situations according to the weather conditions: icy road surface, water accumulation on the road surface, and normal road surface. Among them, the weather impact factor when the road surface is icy is greater than the weather impact factor when the road surface is water accumulation, which is greater than the weather impact factor when the road surface is normal. In this embodiment, the weather impact factor when the road surface is icy is set to 1.5, the weather impact factor when the road surface is water accumulation is set to 1.2, and the weather impact factor when the road surface is normal is set to 1. The implementer can set the value of the weather impact factor within the value range of the weather impact factor according to the actual situation, and this embodiment does not impose any restrictions on this.

[0028] Furthermore, by combining the time required to travel through each road section in each time period with the weather impact factor of each road section, the road resistance factor of each road section in each time period is determined, and the road congestion status of each road section in each time period is evaluated. The larger the road resistance factor of any road section in any time period, the more congested the road section is in that time period, and the less suitable the road section is for travel route planning in that time period. Based on the above analysis, the road resistance factor is positively correlated with the required time and the weather impact factor. In this embodiment, the expression for the road resistance factor of each road section in each time period is: Where, is the road resistance factor of the e-th road section in the t-th time period, is the time required to pass the e-th road section in the t-th time period, is the weather impact factor of the e-th road section in the t-th time period.

[0029] The longer the time required to pass through the e-th road section in the t-th time period, and the greater the weather impact factor of the e-th road section in the t-th time period, it indicates that the e-th road section is more likely to be congested in the t-th time period. When planning travel routes, avoid choosing routes affected by bad weather that affect vehicle driving safety, and avoid choosing congested routes during peak hours that result in excessive driving time.

[0030] In another embodiment, the time required to pass the e-th road segment in the t-th time period and the weather impact factor of the e-th road segment in the t-th time period are used as the road resistance factor of the e-th road segment in the t-th time period.

[0031] S2, using the traffic volume of each road section in each historical time period to obtain the predicted traffic volume of each road section in each time period and a preset number of time periods thereafter, according to the difference between the traffic volume of each road section in each time period and the predicted traffic volume, as well as the degree of discreteness of the predicted traffic volume in the preset number of time periods, the traffic fluctuation of each road section in each time period is obtained.

[0032] For each road section, considering that its traffic volume in historical time periods is of reference significance to the traffic volume in the current time period, this embodiment takes the e-th road section in the current t-th time period as an example, obtains the traffic volume of the e-th road section in all t-th time periods in the historical period, and composes the traffic volume sequence of the e-th road section in the t-th time period in chronological order. Using the time series prediction algorithm, obtain the predicted traffic volume of the e-th road section in the t-th time period on the current day and the predicted traffic volume for a preset number of time periods after the t-th time period. The time series prediction algorithm of this embodiment adopts the ARIMA time series model, which is an existing well-known technology. The preset number is set to 5, which can be set by the implementer according to actual conditions. This embodiment does not impose any restrictions on this.

[0033] Based on the above analysis, the difference between the real-time traffic flow data and the predicted traffic flow data for each time period of the day, as well as the traffic flow data prediction results for subsequent adjacent time periods, is used to evaluate the stability of the traffic efficiency of each road section in each time period of the day, and the traffic fluctuation of each road section in each time period is calculated. The specific expression is: Where, is the traffic fluctuation of the e-th road section in the t-th time period, is the first weight, is the second weight, is the actual traffic flow of the e-th road section in the t-th time period, is the predicted traffic flow of the e-th road section in the t-th time period, is the discrete degree of the predicted traffic flow of the e-th road section in the k time periods after the t-th time period. In this embodiment, k=5. , this embodiment sets , , the implementer can set it according to the actual situation. Denote it as the first product, Recorded as the second product. It is the fusion result of the first product and the second product. Fusion means combining multiple variables. Specifically, the fusion can be performed by addition, multiplication, addition and multiplication mixture, etc. This embodiment does not limit this.

[0034] It should be noted that It reflects the difference between the traffic volume of each road section in each time period and the predicted traffic volume. The difference indicates the degree of difference between the two variables. Specifically, it can be calculated by using the absolute value of the difference, the square of the difference, the ratio, etc. This embodiment does not limit this. The degree of dispersion can be calculated by using the variance, standard deviation, coefficient of variation, etc. This embodiment uses variance as the calculation method for the degree of dispersion.

[0035] It should be understood that the greater the difference between the traffic flow on the e-th road segment in the t-th time period and the traffic flow prediction result, the more unstable the traffic flow on the e-th road segment in the t-th time period. The more unstable the traffic flow prediction results over multiple consecutive time periods, that is, the greater the degree of dispersion, the more significant the changes in traffic flow on the e-th road segment in a relatively short period of time, and the greater its instability as a travel segment in the planned route. The more unstable the traffic flow on the e-th road segment in the t-th time period, the greater the uncontrollability of planning this road segment in the travel route, the more likely it is to experience unexpected situations, and therefore, the less suitable this road segment is for planning into the travel route.

[0036] S3, based on the number of fork intersections at each intersection in each road section, determine the road selection coefficient of each road section; combine the road resistance factor, the traffic fluctuation, and the road selection coefficient of all road sections in the travel path, construct an objective function, and use an optimization algorithm to obtain the optimal travel path.

[0037] Furthermore, when planning a travel route, it is also necessary to avoid simply pursuing the shortest route, which may result in a tortuous route and frequent vehicle turns, thus affecting the driving experience. Therefore, this embodiment determines the road selection coefficient for each road section based on the number of forks at each intersection in each road section. The specific calculation method is: Where, is the road selection coefficient of the e-th road segment, is the number of forked intersections at the mth intersection in the eth section, is the maximum number of fork intersections among all intersections in the e-th road segment, and n is the number of intersections in the e-th road segment.

[0038] At any intersection, the more possible subsequent driving directions a vehicle can choose, the more dynamic the intersection's options are. During actual route travel, a high number of turn options at an intersection indicates greater vehicle instability. This means the route is prone to frequent vehicle inflows and outflows, resulting in lower efficiency and stability, making it less suitable for route planning.

[0039] Finally, the objective function is constructed by combining the road resistance factor, the traffic fluctuation, and the road selection coefficient of all road sections in the travel path, and the optimal travel path is obtained using the optimization algorithm, where the objective function is: Where, is the objective function, M is the number of road segments included in the travel path, To preset the third weight, is the road resistance factor of the e-th road section in the travel path at the t-th time period, is the traffic fluctuation of the e-th road section in the travel path in the t-th time period, To preset the fourth weight, is the road selection coefficient of the e-th road segment in the travel path; is the preset fifth weight, where In this embodiment, the road resistance factor is taken as the main influencing factor, and the traffic fluctuation and road selection coefficient are taken as secondary influencing factors. Therefore, , , The objective function construction flow chart is as follows: Figure 2 shown.

[0040] This embodiment uses the butterfly optimization algorithm to solve the optimal solution of the objective function, that is, to obtain the optimal travel path. The optimal travel path can consider passing through each road section in the optimal time period. The race size of the butterfly optimization algorithm is set according to the number of routes between the starting point and the target point, the maximum number of iterations is set to 500 times, the sensory modality and power index are set to 0.01 and 0.2 respectively, and the switch function is set to 0.8. After setting the parameters, the obtained objective function, starting point, target point and road network data are calculated using the butterfly optimization algorithm and the optimal path is output. The butterfly optimization algorithm is an existing well-known technology, and the specific calculation steps will not be described in detail. Among them, the optimization goal of the butterfly optimization algorithm for the objective function is to minimize the objective function. The implementer can set other existing feasible optimization algorithms according to actual conditions, such as particle swarm optimization algorithm, genetic algorithm, etc., and this embodiment does not limit it here.

[0041] Based on the same inventive concept as the above method, an embodiment of the present application also provides a travel path planning system based on city perception, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned travel path planning methods based on city perception.

[0042] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0043] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0044] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A travel path planning method based on city perception, characterized in that: The method comprises the following steps: Based on the traffic volume of each road section in the city in each historical time period, the BPR function is used to obtain the time required to pass each road section in each time period. Combined with the weather conditions in each time period, the road resistance factor of each road section in each time period is determined; The traffic flow is used to obtain the predicted traffic flow of each road section in each time period and a preset number of time periods thereafter, and the traffic fluctuation degree of each road section in each time period is obtained based on the difference between the traffic flow of each road section in each time period and the predicted traffic flow, as well as the degree of dispersion of the predicted traffic flow in the preset number of time periods; Based on the number of forked intersections at each intersection in each road section, the road selection coefficient of each road section is determined; combining the road resistance factor, the traffic fluctuation, and the road selection coefficient of all road sections in the travel path, an objective function is constructed, and an optimization algorithm is used to obtain the optimal travel path.

2. The travel route planning method based on city perception according to claim 1, characterized in that: The determination of the road resistance factor includes: For each time period of each road section on the current day, calculate the average of the traffic flow of multiple historical same time periods, substitute the average into the BPR function, and obtain the time required to pass each road section in each time period; Determine the weather influencing factors of each road section based on the weather conditions in each time period; The road resistance factor is positively correlated with the required time and the weather influence factor.

3. The travel route planning method based on city perception according to claim 2, characterized in that: The value range of the weather impact factor is [1, 1.5]. The value of the weather impact factor of each road section is divided into three situations according to the weather conditions: icy road surface, water accumulation on the road surface, and normal road surface. Among them, the weather impact factor of icy road surface is greater than the weather impact factor of water accumulation on the road surface, which is greater than the weather impact factor of normal road surface.

4. The travel route planning method based on city perception according to claim 1, characterized in that: The determination of the predicted traffic flow includes: For the e-th road section in the t-th time period on the current day, obtain the traffic volume of the e-th road section in the same time period on multiple historical days, and compose the traffic volume sequence of the e-th road section in the t-th time period in chronological order. Use the time series prediction algorithm to obtain the predicted traffic volume of the e-th road section in the t-th time period on the current day; Accordingly, the predicted traffic volume of the e-th road section on the current day for a preset number of time periods after the t-th time period is obtained.

5. The travel route planning method based on city perception according to claim 4, characterized in that: The determination of the traffic fluctuation degree includes: The absolute value of the difference between the predicted traffic flow and the actual traffic flow of the e-th road section in the t-th time period of the current day is calculated, and the traffic fluctuation degree is determined in combination with the discrete degree.

6. The travel route planning method based on city perception according to claim 5, characterized in that: The further determination of the traffic fluctuation degree includes: Preset a first weight and a second weight, calculate the product of the first weight and the absolute value of the difference, record it as the first product, calculate the product of the second weight and the degree of dispersion, record it as the second product, merge the first product and the second product to obtain the traffic fluctuation degree of the e-th road section in the t-th time period on the current day.

7. The travel route planning method based on city perception according to claim 6, characterized in that: The sum of the first weight and the second weight is 1, and the first weight is greater than the second weight.

8. The travel route planning method based on city perception according to claim 1, characterized in that: Determination of the road selection coefficient includes: The ratio of the number of forked intersections at each intersection in each road section to the maximum number of forked intersections at all intersections in the road section is calculated, and the road selection coefficient is the average of the ratios of all intersections in each road section.

9. The travel route planning method based on city perception according to claim 1, characterized in that: The expression of the objective function is: Where, is the objective function, M is the number of road segments included in the travel path, To preset the third weight, is the road resistance factor of the e-th road section in the travel path at the t-th time period, is the traffic fluctuation of the e-th road section in the travel path in the t-th time period, To preset the fourth weight, is the road selection coefficient of the e-th road segment in the travel path; is the preset fifth weight, where ;The optimization goal is to minimize the objective function.

10. A travel path planning system based on city perception, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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