CN116311914B - Estimation method of isochronal eigenvalues of discrete points on road lines for traffic isochrones - Google Patents

Estimation method of isochronal eigenvalues of discrete points on road lines for traffic isochrones Download PDF

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CN116311914B
CN116311914B CN202310141807.6A CN202310141807A CN116311914B CN 116311914 B CN116311914 B CN 116311914B CN 202310141807 A CN202310141807 A CN 202310141807A CN 116311914 B CN116311914 B CN 116311914B
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saturation
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travel time
isochronous
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CN116311914A (en
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马莹莹
陈曦
谢文艺
许明朗
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South China University of Technology SCUT
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Abstract

本发明公开了一种面向交通等时线的道路线离散点等时特征值估计方法，包括：1)选定目标路网，获取路网基础资料；2)基于选定路网，输入表征交通状态的路网平均饱和度，根据道路条件和路网平均饱和度情况，分别对路段饱和度和节点饱和度进行修正；3)分别计算所有路段行程时间和交叉口转向延误，输出行程时间基本元；4)通过行程时间基本元，计算各离散点的等时特征值：将行程时间基本元设为Dijkstra最短路径算法的权重，结合路网拓扑结构计算等时线中心点到道路线离散点集合中所有点的最短路径及其对应的最短行程时间，该最短行程时间即为离散点的等时特征值。本发明可解决不同道路条件和交通状态下的道路线离散点等时特征值估计问题。

The present invention discloses a method for estimating isochronous characteristic values of discrete points of road lines oriented to traffic isochronous lines, comprising: 1) selecting a target road network and obtaining basic road network data; 2) based on the selected road network, inputting the average saturation of the road network representing the traffic state, and respectively correcting the road section saturation and the node saturation according to the road conditions and the average saturation of the road network; 3) respectively calculating the travel time of all road sections and the turning delay at the intersection, and outputting the travel time basic element; 4) calculating the isochronous characteristic value of each discrete point through the travel time basic element: setting the travel time basic element as the weight of the Dijkstra shortest path algorithm, and calculating the shortest path from the center point of the isochronous line to all points in the road line discrete point set and the corresponding shortest travel time in combination with the road network topological structure, and the shortest travel time is the isochronous characteristic value of the discrete point. The present invention can solve the problem of estimating isochronous characteristic values of discrete points of road lines under different road conditions and traffic states.

Description

Road line discrete point isochronous characteristic value estimation method for traffic isochronous line
Technical Field
The invention relates to the technical field of urban road network aging performance evaluation and analysis, in particular to a traffic isochrone-oriented road line discrete point isochrone characteristic value estimation method considering traffic state change and different road conditions.
Background
The aging performance of the road network is the embodiment of the capacity of the road network to bear traffic flow in the time dimension, and is an important evaluation parameter for road network planning, design and management. The traffic isochrone is a closed curve formed by connecting all points with equal travel time from a certain point in the road network, is a reflection of the travel time on the traffic network in a space distribution state, and is an important means for researching the space timeliness performance of the road network. The isochronous characteristic value estimation is an important link for ensuring precision in the process of traffic isochronous line generation, and the calculation method mainly comprises two types at present: 1) Acquiring corresponding travel time data based on a path planning service in an online map service; 2) And calculating by using a Dijskra algorithm and other shortest path algorithms in the topology road network.
The method based on the online map service is suitable for an example road network, needs stronger data foundation as basic support of an algorithm, cannot generate isochrones of different traffic states, different traffic organizations and different management control schemes under the condition without the data foundation, and is not beneficial to simulation evaluation of the traffic planning management scheme. While the existing method using the shortest algorithm in the topological road network has the limitations that the path is difficult to load constraint conditions, the inherent connection between the isochrone and the road network structure is ignored, and the like, and the characteristic values of discrete points and the like of the road line are difficult to estimate under the conditions of dynamically loading traffic states and changing road conditions.
The isochronal characteristic value is a traffic information attribute of discrete points of the road network, and is essentially the shortest travel time value from the determined traffic occurrence point (i.e. isochrone central point) to the discrete point under the determined traffic condition and the determined road condition of the road network. Isochronous characteristic values have the following characteristics: 1) The isochronous characteristic value changes along with the change of traffic running conditions, and has time-varying characteristics and spatial imbalance characteristics; 2) The dynamic change modes of characteristic values such as road network discrete points and the like at different positions of the road network are different due to the change of traffic flow distribution results or path selection changes caused by the dynamic change of traffic states. The method for estimating the isochronous characteristic value provided by the invention comprehensively considers the characteristics, and is mainly decomposed into two parts, wherein one part is to solve the travel time of all road sections and the steering delay of all nodes, namely the traffic impedance, under the determined traffic state; and secondly, solving the shortest travel time from the central point to any discrete point based on the traffic impedance under the determined road condition.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, and provides a road line discrete point and other characteristic value estimation method for a traffic and other lines, which takes the influence of traffic states and road conditions into consideration, breaks through the limitations of high data requirements, difficult load constraint conditions and the like in the existing method, and adds the traffic impedance of road sections and intersections in a topological road network so as to establish an urban road traffic impedance model and an other characteristic value estimation method which can dynamically load traffic states, change road conditions, do not need traffic requirement data and are dynamically variable.
In order to achieve the above purpose, the technical scheme provided by the invention is as follows: the road line discrete point isochronous characteristic value estimation method facing traffic isochronous lines comprises the following steps:
1) Selecting a target road network, and acquiring road network basic data including road network topological structures and traffic state related data;
2) Based on the selected road network, inputting the road network average saturation representing the traffic state, and respectively correcting road sections and nodes according to road conditions and road network average saturation conditions to obtain road section saturation and node saturation, wherein the nodes are intersections in the road network;
3) Calculating the travel time and the node steering delay of all road sections based on the road section saturation and the node saturation respectively, and outputting the travel time basic element of the road network;
4) Calculating the isochronous characteristic value of each discrete point through the travel time basic element: and (3) setting the travel time basic element calculated in the step (3) as the weight of the Dijkstra shortest path algorithm based on the Dijkstra shortest path algorithm, and calculating the shortest paths from the isochrone central point to all points in the road line discrete point set and the shortest travel time corresponding to the shortest paths by combining the road network topology structure, wherein the shortest travel time is the isochrone characteristic value of the discrete points.
Further, in step 1), the road network basic data includes road network topology structure, road class of each road section, node connection relationship, road section travel time, node inlet flow and node steering flow ratio.
Further, in step 2), according to the congestion state of the road network, the saturation of the road and the node is respectively corrected, and the method comprises the following steps:
2.1 Calculating the road saturation: providing a saturation threshold gamma, if the average road network saturation is greater than the threshold, fitting a functional relation between the road saturation of different levels and the average road network saturation by combining with an actual road network to obtain a correction function f (highway, N), so as to correct the road saturation, if not greater than the threshold, then all the road saturation is equal to the average road network saturation, and calculating and outputting the road saturation as shown in the following formula:
Wherein: saturation for road segment e j; /(I) The average saturation of the road network is; epsilon is the fluctuation error generated by the traffic jam state and is the random error with the expected 0 and the variance as the specific value a;
2.2 Calculating node saturation): firstly judging whether the node is a key node or not, and if not, not calculating the saturation; if the node is the key node, determining a road section connected with an inlet of the key node and saturation of the road section pointing to the direction of the inlet of the node according to the road network connection attribute Then, the road section and node attributes related to steering saturation calculation are read; and finally, calculating and outputting the saturation of different steering of each inlet, wherein a calculation model is as follows:
Left turn:
And (3) straight running:
Turning right:
Wherein: n ui Respectively representing the number of lanes of the connecting road section and the road section saturation; /(I)Respectively representing the flow ratios of left turn, straight run and right turn; saturation of node v i IncludesSaturation of left turn, straight run and right turn of inlet are respectively represented; /(I)The number of lanes for left-turn, straight-turn and right-turn, respectively, of the entrance of node v i.
Further, in step 3), a road segment travel time and a node steering delay are calculated, comprising the steps of:
3.1 Calculating the road section travel time: based on road section saturation, referring to a BPR model, according to the free flow speeds of roads of different grades of an actual urban road network, the road section travel time of a road section j corresponding to the edge e j is proposed The calculation model is shown below, here a modified BPR model:
Wherein: The free flow running speeds of the expressway, the main road, the secondary main road and the branch road are respectively marked by road sections with different road grades; l j is the road section length; /(I) The saturation of road segment j; alpha 1、β1、α2、β2、α3、β3、α4、β4 is a parameter to be calibrated, the corresponding road grade is a expressway, a trunk road, a secondary trunk road and a branch road in sequence, the model fitting can be carried out based on the speed and saturation data of all states of other existing road sections in the city, and the parameter value to be calibrated with the minimum overall fitting error is the parameter calibration result; by summarizing the existing BPR model calibration results of different cities, the BPR model calibration parameters of the same road grade in different cities are not greatly different, so that the existing parameter calibration values of different road grades can be used;
3.2 Computing node turn delay): the nodes are intersections in the road network, only delays of different steering at each inlet of the key node are calculated, and the steering delays of other nodes are assumed to be fixed values of average delay time Depending on the actual road network situation; based on the node saturation, considering the difference of road travel time expressions under different saturation states, dividing the saturation into two states of low saturation and high saturation, and respectively using a Webster model and a Akcelik model to establish a node travel time estimation model as follows:
Wherein: t i d is the intersection steering delay of different steering of the node v i inlet; v key is a key node set; c i is the signal period length of node v i; c 0 represents the single lane actual saturation flow rate; lambda i, R i is the green-to-signal ratio, saturation and flow ratio corresponding to different inlet steering traffic flows respectively; the saturation threshold is gamma 2, which has the meaning of road network traffic running state classification, and is only the basis for judging the use model, namely the demarcation value of low saturation and high saturation;
3.3 The travel time of the output road section and the node steering delay are tidied, namely travel time basic elements, and a foundation is established for the subsequent calculation of discrete point equation characteristic values.
Further, in step 4), calculating the shortest path from the isochrone center point to all points in the set of discrete points of the road line and the isochrone feature value, including the steps of:
4.1 Point weight to edge weight of node): setting a travel time basic element as the weight of Dijkstra shortest path algorithm, converting all node point weights into road section side weights, simplifying the shortest path solving problem, enabling key nodes to have different inlets and different steering delay times, converting the key nodes into corresponding side weights when outputting steering delay, and storing the corresponding side weights, wherein only a fixed value of the average delay time of non-key nodes is needed Assigning a travel time attribute to the virtual edge, wherein the virtual edge is an edge between inlet nodes, and each inlet of the intersection generates a node respectively;
4.2 Estimating isochronous feature values of discrete points of a node According to Dijkstra shortest path algorithm, setting weight as road section travel time and node steering delay, calculating the shortest path from each node discrete point to an isochrone central point, and adding the road section travel time and the node steering delay on the shortest path to obtain an isochrone characteristic value of the node discrete point, but excluding the travel time of the node;
4.3 Estimating isochronous feature values of discrete points of a road segment The isochronous characteristic value of the discrete points of the road segment is equal to the isochronous characteristic value/>, of the discrete points of the nodes of the road segment where the isochronous characteristic value is locatedReading all the road section discrete points by the sum of the time required from the node to the road section discrete pointsIncluding dotV s、ve, v s、ve is the pointTwo nodes connected on the road section; point(s)Road segment direction dir se, road segment length l se and road segment travel time/>, of the edge e se The isochronous characteristic value estimation model is selected according to the road segment direction as follows:
When dir se =0, the road section bidirectional traffic is represented, and the road section discrete points are at the moment Through both the node v s and the node v e, the road section discrete pointsShortest travel timeIs the minimum of the two paths;
When dir se =1, the road section unidirectional traffic is shown, the direction is from node v s to node v e, and the road section discrete points are shown Can only be reached through node v s;
Wherein d sp represents a road section discrete point The distance to the node v s is calculated according to the point coordinates; l se denotes the road segment length of the edge e se; /(I)Representing the road travel time of edge e se in the direction from v s to v e,Representing the road travel time of edge e se in the direction from v e to v s,AndIsochronous feature values of discrete points of the node v e and the node v s respectively;
4.4 Outputting the isochronous characteristic values of all the node discrete points and the road section discrete points on the road line.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. The model has less traffic operation parameters required to be input, and is also applicable to planning and designing stage road networks lacking traffic data basis.
2. The correction model based on the road network average saturation can reflect the unbalance of the road network demand distribution.
3. Road conditions such as a road topology, road class, lane data, intersection characteristics and the like can be loaded.
4. The isochronous characteristic values of all discrete points in any traffic state can be determined, so that static traffic isochrones with any time gradient can be generated conveniently.
5. Traffic conditions of the whole traffic running state of the urban road network at different moments can be dynamically loaded, and the road network dynamic traffic isochrones can be conveniently generated.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a road segment saturation calculation flowchart.
Fig. 3 is a node saturation calculation flowchart.
Fig. 4 is a flow chart of road network traffic impedance estimation.
Fig. 5 is a flowchart of calculating the characteristic value of the road line discrete time.
Fig. 6 is a case road network diagram.
Fig. 7 is a graph showing the result of calculation of isochronous characteristic values at discrete points on a road line.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
As shown in fig. 1, the embodiment discloses a road line discrete point isochronous characteristic value estimation method for a traffic isochronous line, which comprises the following steps:
1) Selecting a target road network, and acquiring road network basic data, wherein the road network basic data comprises a road network topological structure, road grades of all road sections, a node connection relationship, road section travel time, node inlet flow and node steering flow proportion.
2) Based on the selected road network, inputting the road network average saturation representing the traffic state, and respectively correcting road sections and nodes according to road conditions and road network average saturation conditions to obtain road section saturation and node saturation, wherein the nodes are intersections in the road network; the saturation of the road and the node is respectively corrected according to the road network congestion state, and the method comprises the following steps:
2.1 Calculating the road saturation: providing a saturation threshold gamma, if the average road network saturation is greater than the threshold, fitting a functional relation between the road saturation of different levels and the average road network saturation by combining with an actual road network to obtain a correction function f (highway, N), so as to correct the road saturation, if not greater than the threshold, then all the road saturation is equal to the average road network saturation, and calculating and outputting the road saturation as shown in the following formula:
Wherein: saturation for road segment e j; /(I) The average saturation of the road network is; epsilon is the fluctuation error generated by the traffic jam state and is the random error with the expected 0 and the variance as the specific value a;
2.2 Calculating node saturation): firstly judging whether the node is a key node or not, and if not, not calculating the saturation; if the node is the key node, determining a road section connected with an inlet of the key node and saturation of the road section pointing to the direction of the inlet of the node according to the road network connection attribute Then, the road section and node attributes related to steering saturation calculation are read; and finally, calculating and outputting the saturation of different steering of each inlet, wherein a calculation model is as follows:
Left turn:
And (3) straight running:
Turning right:
Wherein: n ui Respectively representing the number of lanes of the connecting road section and the road section saturation; /(I)Respectively representing the flow ratios of left turn, straight run and right turn; saturation of node v i IncludesSaturation of left turn, straight run and right turn of inlet are respectively represented; /(I)The number of lanes for left-turn, straight-turn and right-turn, respectively, of the entrance of node v i.
3) Based on the road section saturation and the node saturation, respectively calculating the travel time and the node steering delay of all road sections, and outputting the travel time basic element of the road network, wherein the method comprises the following steps:
3.1 Calculating the road section travel time: based on road section saturation, referring to a BPR model, according to the free flow speeds of roads of different grades of an actual urban road network, the road section travel time of a road section j corresponding to the edge e j is proposed The calculation model is shown below, here a modified BPR model:
Wherein: The free flow running speeds of the expressway, the main road, the secondary main road and the branch road are respectively marked by road sections with different road grades; l j is the road section length; /(I) The saturation of road segment j; alpha 1、β1、α2、β2、α3、β3、α4、β4 is a parameter to be calibrated, the corresponding road grade is a expressway, a trunk road, a secondary trunk road and a branch road in sequence, the model fitting can be carried out based on the speed and saturation data of all states of other existing road sections in the city, and the parameter value to be calibrated with the minimum overall fitting error is the parameter calibration result; by summarizing the existing BPR model calibration results of different cities, the BPR model calibration parameters of the same road class in different cities are not greatly different, so that the existing parameter calibration values of different road classes can be used, as shown in the following table.
Expressway Main road Secondary trunk road Branch circuit
α 0.459 0.673 0.861 1.028
β 3.73 2.537 2.436 1.476
3.2 Computing node turn delay): the nodes are intersections in the road network, in this embodiment, only delays of different steering at each entrance of the key node are calculated, and the steering delays of other nodes are assumed to be fixed values of average delay timeDepending on the actual road network situation. Considering that road travel time expressions under different saturation states are different, dividing the saturation into two states of low saturation and high saturation, and respectively using a Webster model and a Akcelik model to establish a node travel time estimation model as follows:
Wherein T i d is the intersection steering delay of different steering of the node v i inlet; v key is a key node set; c i is the signal period length of node v i; c 0 represents the single lane actual saturation flow rate; lambda i, R i is the green-to-signal ratio, saturation and flow ratio corresponding to different inlet steering traffic flows respectively; the saturation threshold is gamma 2, which has the meaning of road network traffic running state classification, and is only the basis for judging the use model, namely the demarcation value of low saturation and high saturation, and the recommended value is gamma 2 =0.8.
3.3 The travel time of the output road section and the node steering delay are tidied, namely travel time basic elements, and a foundation is established for the subsequent calculation of discrete point equation characteristic values.
4) Calculating the isochronous characteristic value of each discrete point through the travel time basic element: setting the travel time basic element calculated in the step 3) as the weight of the Dijkstra shortest path algorithm based on the Dijkstra shortest path algorithm, and calculating the shortest paths from the isochrone central point to all points in a road line discrete point set and the shortest travel time corresponding to the shortest paths by combining a road network topological structure, wherein the shortest travel time is the isochrone characteristic value of the discrete points; the shortest path and the isochronous characteristic value from the isochrone central point to all points in the road line discrete point set are calculated, and the method comprises the following steps:
4.1 Point weight to edge weight of node): and setting the travel time basic element as the weight of the Dijkstra shortest path algorithm, converting all node point weights into road section side weights, and simplifying the shortest path solving problem. The key nodes have different inlets and different delay time of different steering, and the key nodes are converted into corresponding side weights for storage when the steering delay is output, and only the reference delay time of the common node is needed Assigning a travel time attribute to the virtual edge, wherein the virtual edge is an edge between inlet nodes, and each inlet of the intersection generates a node respectively;
4.2 Estimating isochronous feature values of discrete points of a node According to Dijkstra shortest path algorithm, setting weight as road section travel time and node steering delay, calculating the shortest path from each node discrete point to an isochrone central point, and adding the road section travel time and the node steering delay on the shortest path to obtain an isochrone characteristic value of the node discrete point, but excluding the travel time of the node;
4.3 Estimating isochronous feature values of discrete points of a road segment The isochronous characteristic value of the discrete points of the road segment is equal to the isochronous characteristic value/>, of the discrete points of the nodes of the road segment where the isochronous characteristic value is locatedAnd the sum of the time required from the node to the road section discrete point, and reading all road section discrete pointsIncluding dotV s、ve, v s、ve is the pointTwo nodes connected on the road section; pointRoad segment direction dir se, road segment length l se and road segment travel time/>, of the edge e se The isochronous characteristic value estimation model is selected according to the road segment direction as follows:
When dir se =0, the road section bidirectional traffic is represented, and the road section discrete points are at the moment Through both the node v s and the node v e, the road section discrete pointsShortest travel timeIs the minimum of the two paths;
When dir se =1, the road section unidirectional traffic is shown, the direction is from node v s to node v e, and the road section discrete points are shown Can only be reached through node v s;
Wherein d sp represents a road section discrete point The distance to the node v s is calculated according to the point coordinates; l se denotes the road segment length of the edge e se; /(I)Representing the road travel time of edge e se in the direction from v s to v e,Representing the road travel time of edge e se in the direction from v e to v s,AndIsochronous feature values of discrete points of the node v e and the node v s respectively;
4.4 Outputting the isochronous characteristic values of all the node discrete points and the road section discrete points on the road line.
In the following we choose the central business area road network of the Tianhe area as the case, the road network area is 8.56. The road network is particularly shown in fig. 6 in the major research areas of north to Tianhe road, south to Lingjiang road, west to Guangzhou road and east to Hunde road. There are 140 road segments in the road network, wherein the ratio of the expressway, the main road, the secondary main road and the branch road is 27.2%, 5.7%, 35.7% and 31.4%, respectively. And researching the current road condition, intersection current situation and signal timing current situation of the research area to acquire and arrange basic road network data and signal timing data. The invention provides an isochronal characteristic value from a central point to other discrete points.
As shown in the flowchart of fig. 2, the average saturation of the road network is input, and the saturation of different road sections is output according to the model calibration results corresponding to the roads of different grades of the example road network. And as shown in the flow chart of fig. 3, based on the road section saturation, judging whether the node is a key node, and outputting different steering saturation of the key node. According to the flow chart shown in fig. 4, the road section and node saturation obtained in the previous step are processed, the corresponding road section and node impedance model is input, and then the impedance of different road sections and key nodes for steering is output to the topological road network. According to the flow chart shown in fig. 5, the isochronous characteristic value of the discrete points of the road line can be calculated by the topological road network assigned with the traffic impedance, and the calculation result is shown in fig. 7.
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.

Claims (4)

1.面向交通等时线的道路线离散点等时特征值估计方法，其特征在于，包括以下步骤：1. A method for estimating the isochronous eigenvalues of discrete points on road lines for traffic isochronous lines, characterized by comprising the following steps: 1)选定目标路网，获取路网基础资料，包括路网拓扑结构和交通状态相关数据；1) Select the target road network and obtain basic road network data, including road network topology and traffic status data; 2)基于选定的路网，输入表征交通状态的路网平均饱和度，根据道路条件和路网平均饱和度情况，分别对路段、节点进行修正得到路段饱和度和节点饱和度，节点即为路网中的交叉口；2) Based on the selected road network, input the average saturation of the road network representing the traffic state. According to the road conditions and the average saturation of the road network, the road segments and nodes are corrected to obtain the road segment saturation and node saturation. The nodes are the intersections in the road network. 3)基于路段饱和度和节点饱和度，分别计算所有路段行程时间和节点转向延误，输出路网的行程时间基本元；3) Based on the road segment saturation and node saturation, calculate the travel time and node turning delay for all road segments respectively, and output the basic elements of travel time for the road network; 4)通过行程时间基本元，计算各离散点的等时特征值：基于Dijkstra最短路径算法，将步骤3)中计算的行程时间基本元设为Dijkstra最短路径算法的权重，结合路网拓扑结构计算等时线中心点到道路线离散点集合中所有点的最短路径及其对应的最短行程时间，该最短行程时间即为离散点的等时特征值；4) Calculate the isochronous characteristic value of each discrete point using the travel time basic element: Based on Dijkstra's shortest path algorithm, set the travel time basic element calculated in step 3) as the weight of Dijkstra's shortest path algorithm, and calculate the shortest path from the center point of the isochronous line to all points in the set of discrete points of the road line and its corresponding shortest travel time in combination with the road network topology. The shortest travel time is the isochronous characteristic value of the discrete point. 计算等时线中心点到道路线离散点集合中所有点的最短路径与等时特征值，包括以下步骤：Calculate the shortest path and isochronous characteristic values from the center point of the isochronous line to all points in the discrete point set of the road line, including the following steps: 4.1)节点的点权转边权：将行程时间基本元设为Dijkstra最短路径算法的权重，将所有的节点点权转换为路段边权，简化最短路径求解问题，关键节点不同进口和不同转向的延误时间不同，在输出转向延误时已经转化为对应边权储存，只需将非关键节点平均延误时间固定值赋值给虚拟边的行程时间属性即可，其中，虚拟边即为在交叉口各进口分别生成一个节点，进口节点之间的边；4.1) Node Weight to Edge Weight Conversion: The travel time primitives are set as the weights of Dijkstra's shortest path algorithm. All node weights are converted into road segment edge weights, simplifying the shortest path problem. The delay times of critical nodes differ depending on the entrance and turn. These delay times are already converted into corresponding edge weights when outputting turn delays. Only the average delay time of non-critical nodes needs to be fixed. The travel time attribute of the virtual edge can be assigned. The virtual edge is the edge between the nodes at each entrance of the intersection, where a node is generated at each entrance. 4.2)估计节点离散点的等时特征值根据Dijkstra最短路径算法，权重设置为路段行程时间和节点转向延误，计算各节点离散点到等时线中心点的最短路径，并将最短路径上的各路段行程时间和节点转向延误相加得到节点离散点的等时特征值，但不包括该节点本身的行程时间；4.2) Estimate the isochronous eigenvalues of discrete nodes. According to Dijkstra's shortest path algorithm, the weights are set as segment travel time and node turning delay. The shortest path from each node's discrete point to the center point of the isochronous line is calculated, and the segment travel time and node turning delay of each node on the shortest path are added together to obtain the isochronous characteristic value of the node's discrete point, but the travel time of the node itself is not included. 4.3)估计路段离散点的等时特征值路段离散点的等时特征值等于所在路段的节点离散点的等时特征值与节点到路段离散点所需时间之和，读取所有路段离散点的相关属性，包括点vs、ve的坐标，vs、ve为点所在路段连接的两个节点；点所在边ese的路段方向dirse、路段长度lse和路段行程时间根据路段方向选择等时特征值估计模型如下：4.3) Estimate the isochronous characteristic values of discrete points on the road segment The isochronous characteristic value of a discrete point in a road segment is equal to the isochronous characteristic value of the discrete points of the nodes in that road segment. The sum of the time required to travel from a node to a discrete point on a road segment is used to read all discrete points on the road segments. Related attributes, including points The coordinates of vs and ve , where vs and ve are points. The two nodes connected by the road segment; point The direction of the road segment on edge e se , the length of the road segment l se , and the travel time of the road segment. The isochronous eigenvalue estimation model selected based on the road segment direction is as follows: 当dirse＝0时，表示路段双向通行，此时路段离散点经过节点vs和节点ve均能够到达，故路段离散点的最短行程时间为两条路径中的最小值；When dir se = 0, it indicates that the road segment allows two-way traffic, and the discrete points of the road segment are... The road segment can be reached via both nodes vs and ve , therefore the discrete point of the road segment Shortest travel time The minimum value between the two paths; 当dirse＝1时，表示路段单向通行，方向从节点vs到节点ve，此时路段离散点只能经过节点vs达到；When dir se = 1, it indicates that the road segment is one-way, with the direction from node vs to node ve . At this time, the discrete points of the road segment are... It can only be reached via node vs ; 其中，dsp表示路段离散点到节点vs的距离，根据点坐标计算；lse表示边ese的路段长度；表示边ese从vs到ve方向的路段行程时间，表示边ese从ve到vs方向的路段行程时间，和分别是节点ve和节点vs离散点的等时特征值；Where dsp represents the discrete points of the road segment The distance to node vs is calculated based on the node's coordinates; lse represents the length of the path segment along edge ese ; Let e represent the travel time of the edge e se from vs to e in the direction of v e . This represents the travel time of edge e se from ve to s in the direction of the path. and These are the isochronous eigenvalues of the discrete points of nodes ve and vs, respectively; 4.4)输出道路线上所有节点离散点和路段离散点的等时特征值。4.4) Output the isochronous characteristic values of all discrete points of nodes and road segments along the road. 2.根据权利要求1所述的面向交通等时线的道路线离散点等时特征值估计方法，其特征在于：在步骤1)中，所述路网基础资料包括路网拓扑结构、各路段道路等级、节点连接关系、路段行程时间、节点进口流量和节点转向流量比例。2. The method for estimating the isochronous eigenvalues of discrete points of road lines oriented towards traffic isochronous lines according to claim 1, characterized in that: in step 1), the basic road network data includes the road network topology, road grade of each road segment, node connection relationship, road segment travel time, node inlet flow and node turning flow ratio. 3.根据权利要求2所述的面向交通等时线的道路线离散点等时特征值估计方法，其特征在于：在步骤2)中，根据路网拥堵状态，分别修正得到道路和节点的饱和度，包括以下步骤：3. The method for estimating the isochronous eigenvalues of discrete points on road lines based on traffic isochronous lines according to claim 2, characterized in that: in step 2), the saturation of roads and nodes is corrected according to the road network congestion status, including the following steps: 2.1)计算路段饱和度：规定饱和度阈值γ，若路网平均饱和度大于该阈值，需要结合实际路网，拟合出不同等级道路饱和度与路网平均饱和度之间的函数关系，得到修正函数f(highway,N)，从而修正路段饱和度，若不大于该阈值，则所有路段饱和度等于路网平均饱和度，计算并输出各路段饱和度，如下式所示：2.1) Calculate road segment saturation: Define a saturation threshold γ. If the average saturation of the road network is greater than this threshold, it is necessary to combine the actual road network to fit the functional relationship between the saturation of different road levels and the average saturation of the road network, and obtain the correction function f(highway,N) to correct the road segment saturation. If it is not greater than this threshold, then the saturation of all road segments is equal to the average saturation of the road network. Calculate and output the saturation of each road segment, as shown in the following formula: 式中：为路段ej的饱和度；为路网平均饱和度；ε是交通拥堵状态产生的波动误差，是期望为0、方差为特定值a的随机误差；In the formula: Let e be the saturation level of road segment ej ; ε represents the average saturation of the road network; ε is the fluctuation error caused by traffic congestion, which is a random error with an expected value of 0 and a variance of a specific value a. 2.2)计算节点饱和度：首先判断节点是否为关键节点，若非关键节点，则无需计算饱和度；若为关键节点，则根据路网连接属性确定与关键节点进口相连的路段及路段指向节点进口方向的饱和度然后读取转向饱和度计算相关的路段和节点属性；最后计算并输出各进口不同转向的饱和度，计算模型如下：2.2) Calculate node saturation: First, determine whether the node is a critical node. If it is not a critical node, there is no need to calculate saturation. If it is a critical node, determine the saturation of the road segments connected to the critical node's entrance and the direction of the road segments pointing towards the node's entrance based on the road network connectivity attributes. Then, the relevant road segment and node attributes are read to calculate the steering saturation; finally, the saturation of different steering angles at each entrance is calculated and output. The calculation model is as follows: 左转： Turn left: 直行： straight: 右转： Turn right: 式中：Nui和分别表示连接路段的车道数和路段饱和度；分别表示左转、直行和右转的流量比例；节点vi的饱和度包括分别表示进口左转、直行和右转的饱和度；分别表示节点vi进口左转、直行和右转的车道数。In the formula: N <sub>ui </sub> and These represent the number of lanes connecting road segments and the road segment saturation, respectively. These represent the traffic flow ratios for left turns, straight ahead, and right turns, respectively; the saturation of node vi. include These represent the saturation levels for left turns, straight ahead, and right turns at the entrance, respectively. These represent the number of lanes for left turns, straight ahead, and right turns at node VI , respectively. 4.根据权利要求3所述的面向交通等时线的道路线离散点等时特征值估计方法，其特征在于：在步骤3)中，计算路段行程时间和节点转向延误，包括以下步骤：4. The method for estimating the isochronous eigenvalues of discrete points on road lines based on traffic isochronous lines according to claim 3, characterized in that: in step 3), calculating the road segment travel time and node turning delay includes the following steps: 3.1)计算路段行程时间：基于路段饱和度，参考BPR模型，根据实际城市道路路网不同等级道路的自由流速度，提出边ej对应的路段j的路段行程时间计算模型如下所示，在此为修正BPR模型：3.1) Calculate the segment travel time: Based on the segment saturation and referring to the BPR model, the segment travel time of segment j corresponding to edge ej is proposed according to the free flow velocity of different levels of roads in the actual urban road network. The computational model is shown below; this is the modified BPR model: 式中：分别为快速路、主干路、次干路、支路的自由流行驶速度，需要对不同道路等级的路段分别标定；lj为路段长度；为路段j的饱和度；α1、β1、α2、β2、α3、β3、α4、β4为待标定的参数，对应的道路等级依次为快速路、主干路、次干路和支路，能够基于城市中其它现有路段全状态的速度和饱和度数据，带入模型拟合，整体拟合误差最小时的待标定参数值即为参数标定的结果；通过总结已有的不同城市的BPR模型标定结果发现，不同城市相同道路等级的BPR模型标定参数区别不大，因此能够使用已有的不同道路等级的参数标定值；In the formula: These represent the free-flow driving speeds for expressways, arterial roads, secondary arterial roads, and local roads, respectively, and need to be marked separately for road segments of different road grades; lj represents the road segment length. Let be the saturation of road segment j; α1 , β1 , α2 , β2 , α3 , β3 , α4 , and β4 are parameters to be calibrated, corresponding to road grades of expressway, arterial road, secondary arterial road, and local road, respectively. Based on the speed and saturation data of other existing road segments in the city under full-state conditions, these parameters can be substituted into the model for fitting. The parameter values with the smallest overall fitting error are the calibration results. By summarizing the existing BPR model calibration results of different cities, it was found that the calibration parameters of BPR models for the same road grade in different cities are not significantly different. Therefore, existing parameter calibration values for different road grades can be used. 3.2)计算节点转向延误：节点即为路网中的交叉口，在此仅计算关键节点各进口不同转向的延误，其它节点的转向延误假设为平均延误时间固定值视实际路网情况而定；基于节点饱和度，考虑到不同饱和度状态下的道路行程时间表达不同，将饱和度分为低饱和度和高饱和度两个状态，分别使用Webster模型和Akcelik模型，建立节点行程时间估计模型如下所示：3.2) Calculate node turning delays: Nodes are intersections in the road network. Here, only the turning delays of different turns at each entrance of key nodes are calculated. The turning delays of other nodes are assumed to be a fixed average delay time. Depending on the actual road network conditions; based on node saturation, and considering the different expressions of road travel time under different saturation states, saturation is divided into two states: low saturation and high saturation. The Webster model and Akcelik model are used respectively to establish the node travel time estimation model as shown below: 式中：Ti d为节点vi进口不同转向的交叉口转向延误；Vkey为关键节点集合；Ci为节点vi的信号周期长度；c0表示单车道实际饱和流率；λi、Ri分别为进口不同转向车流对应的绿信比、饱和度和流量比例；饱和度阈值为γ2，它具有路网交通运行状态分类的含义，仅为判断使用模型的依据，即低饱和度与高饱和度的分界值；Where: T <sub> id</sub> represents the intersection turning delay at different turns of node v<sub> i </sub>;V<sub> key </sub> represents the set of key nodes; C <sub>i </sub> represents the signal cycle length of node v <sub>i</sub>;c<sub>0</sub> represents the actual saturation flow rate of a single lane; λ <sub>i </sub>, R<sub> i </sub> represents the green ratio, saturation, and flow rate ratio corresponding to different turning traffic flows at the entrance; the saturation threshold is γ<sub> 2 </sub>, which has the meaning of classifying the traffic operation status of the road network and is only used as the basis for judging the model to be used, that is, the boundary value between low saturation and high saturation. 3.3)整理输出路段行程时间和节点转向延误，即行程时间基本元，为后续计算离散点等式特征值建立基础。3.3) Organize the output road segment travel time and node turning delay, i.e. travel time basic elements, to lay the foundation for subsequent calculation of discrete point equation eigenvalues.
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