A multi-factor-based adaptive wide-area traffic guidance method and device

Abstract

This invention relates to the field of traffic control technology, specifically to a multi-factor-based adaptive wide-area traffic guidance method and apparatus, comprising: monitoring road traffic data within the road network space; predicting congested road segments within the road network space based on road traffic conditions; determining the associated nodes of the congested road segments and selecting guidance paths based on the associated nodes; dynamically allocating traffic flow to the guidance paths based on driver preferences; and providing traffic guidance according to the dynamic traffic flow allocation results. By selecting guidance paths through associated nodes related to congested roads and dynamically allocating traffic flow based on the guidance paths and driver preferences, the method fully considers driver preferences when using guidance paths to disperse traffic from congested road segments, which helps improve the reliability and compliance of the guidance scheme, thereby increasing the success rate and effectiveness of traffic guidance, and also helps reduce road traffic congestion.

Description

Technical Field

[0001] This invention relates to the field of traffic control technology, specifically to a multi-factor-based adaptive wide-area guidance method and device for road traffic. Background Technology

[0002] Road congestion at intersections is one of the major problems plaguing urban traffic, manifesting as vehicles queuing up at downstream intersections and entrances / exits, causing blockages. When downstream intersections become congested, traffic flows back up the road segment as more vehicles arrive, leading to slow traffic or even complete blockages. At this point, the traffic capacity has been reached, necessitating timely traffic guidance. Road traffic pre-control and guidance refers to guiding and controlling traffic flow before it becomes completely congested, preventing further deterioration of the traffic environment, paralysis of that segment, and even impact on the entire road network.

[0003] Numerous studies and designs have been conducted on road traffic guidance schemes, such as the patent "Traffic Guidance and Signal Control Co-optimization Method, Electronic Device and Storage Medium" (Publication No. CN115188199A), which aims to determine detour routes with the goal of balancing traffic distribution, and then formulate guidance schemes. However, such guidance schemes rely on relatively singular guidance factors and do not consider the human factors of vehicle drivers. As a result, in actual use, drivers may choose alternative routes based on human factors, reducing the success rate of the guidance scheme's adoption and thus affecting its implementation. Consequently, the traffic guidance effect is poor and the success rate is not high. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a multi-factor-based adaptive wide-area traffic guidance method and device, which improves the success rate of traffic guidance.

[0005] In a first aspect, the present invention provides a road traffic adaptive wide-area guidance method based on multiple factors.

[0006] In the first feasible approach, a multi-factor-based adaptive wide-area traffic guidance method includes:

[0007] Monitor road traffic data within the spatial scope of the road network;

[0008] Predict congested road sections within the road network space based on road traffic conditions;

[0009] Identify the associated nodes of congested road sections and select guidance routes based on these nodes;

[0010] Dynamically allocate traffic flow to the guidance route based on driver preference.

[0011] Traffic guidance is provided based on the dynamic distribution of traffic flow.

[0012] In the second feasible method, combining the first feasible method, the associated nodes of the congested road segment are identified, and a guidance path is selected based on the associated nodes, including:

[0013] Downstream nodes of congested road sections are identified as congested nodes, upstream nodes of congested road sections are identified as critical nodes, and adjacent upstream nodes of critical nodes are identified as pre-control nodes.

[0014] Determine the pre-control path based on key nodes and pre-control nodes;

[0015] Obtain the priority of the pre-control nodes and determine the induction starting point according to the priority of the pre-control nodes;

[0016] The multiple pre-control paths corresponding to the induction starting point are determined as induction paths.

[0017] In the third possible implementation method, in conjunction with the second implementation method, the priority of obtaining the pre-control node includes:

[0018] Obtain the functional importance and detour degree of the pre-controlled nodes;

[0019] The priority of the pre-control nodes is determined based on their functional importance and detour requirements.

[0020] In conjunction with the first feasible approach, the fourth feasible approach, before dynamically allocating traffic flow to the guidance path based on driver preference, includes:

[0021] Obtain the ratio of detour time to detour distance;

[0022] Construct a relative cost-utility function based on the ratio of detour time to detour distance;

[0023] Driver preference for induced routes is obtained using relative cost-utility functions and path scales.

[0024] Combining the fourth feasible approach, the fifth feasible approach constructs a relative cost-utility function based on the ratio of detour time to detour distance, including:

[0025] ;

[0026] in, It is a relative cost-utility function. and For utility weight, For constants in the utility equation, This is the ratio of detour distances. This represents the ratio of detour time.

[0027] Combining the fourth feasible approach, the sixth feasible approach utilizes a relative cost-utility function and path metric to obtain driver preference for induced paths, including:

[0028] The relative cost-utility function is used to sort the induced paths in descending order, and the driver's preference for each induced path is calculated using the path selection formula.

[0029] The path selection formula is:

[0030] ;

[0031] in, For the induction path Driver selection preference, induced path For nodes To the node The path, It is a relative cost-utility function. These are model constants. It indicates the traveler's familiarity with the road network; For the induction path Path scale.

[0032] In conjunction with the first feasible approach, the seventh feasible approach involves dynamically allocating traffic flow to the guidance path based on driver preference, including:

[0033] Determine the target induced traffic flow for congested road sections;

[0034] The traffic volume allocated on each guidance path is obtained based on the target induced flow and the driver selection preference of the guidance path;

[0035] The guidance routes are selected in descending order of driver preference, and the capacity of each guidance route is obtained.

[0036] The allocated traffic volume and the capacity flow of the current guidance path are compared. If the allocated traffic volume is greater than the capacity flow, the current guidance path is removed from the guidance path set, and the overflow of the current guidance path is redistributed to the guidance paths in the guidance path set until all target guidance flow is allocated.

[0037] In conjunction with the seventh feasible method, the eighth feasible method involves redistributing the traffic overflowing from the current induced path to induced paths within the induced path set, including:

[0038] After updating the allocation of traffic volume on each guidance path according to the allocated traffic volume on each guidance path, the new traffic volume carried by each guidance path is obtained.

[0039] The relative cost-utility function is updated based on the new traffic volume of each induced path;

[0040] Update the driver preference for each induced path in the induced path set according to the updated relative cost-utility function;

[0041] The overflow traffic from the current guidance path is redistributed based on the driver's preference after each guidance path is updated.

[0042] In conjunction with the first feasible method, the ninth feasible method also includes:

[0043] Within the road network space, the tolerable travel time at the tolerable speed of each road is obtained based on the length and tolerable speed of each road.

[0044] The induction time boundary conditions are determined based on the tolerance passage time and the optimal induction cycle length for each road.

[0045] The boundary conditions for the induction time are as follows: when the optimal induction period length is less than the maximum tolerable passage time, the induction period length is the maximum tolerable passage time; when the optimal induction period length is greater than or equal to the maximum tolerable passage time, the induction period length is the optimal induction period length.

[0046] Determine the boundary conditions of the induced space based on the length of the induced period;

[0047] The boundary condition for the induced space is the maximum induced space distance that can be reached within the induced period length.

[0048] Secondly, the present invention provides a road traffic adaptive wide-area guidance device based on multiple factors.

[0049] In the tenth feasible method, a multi-factor-based adaptive wide-area traffic guidance device includes:

[0050] The monitoring module is configured to monitor road traffic data within the road network spatial area;

[0051] The prediction module is configured to predict congested road segments within the road network space based on road traffic conditions.

[0052] The associated node determination module is configured to determine the associated nodes of congested road segments;

[0053] The induced path selection module is configured to select an induced path based on associated nodes;

[0054] The traffic flow dynamic allocation module is configured to dynamically allocate traffic flow to the guidance path based on driver selection preferences;

[0055] The road traffic guidance module is configured to provide road traffic guidance based on the dynamic allocation results of traffic flow.

[0056] As can be seen from the above technical solution, the beneficial technical effects of the present invention are as follows:

[0057] 1. This scheme selects guidance routes by associating them with nodes related to congested roads, and dynamically allocates traffic flow to congested road segments based on these guidance routes and driver preferences. This approach fully considers driver preferences when using guidance routes to disperse traffic from congested areas, improving the reliability and compliance of the guidance scheme, thereby increasing the success rate and effectiveness of traffic guidance and ultimately reducing road congestion.

[0058] 2. Traffic capacity is the maximum flow rate at a certain cross-section of a road. Traffic capacity is not related to the length of the road segment or the traffic characteristics of upstream and downstream nodes; it is only an attribute of that cross-section. Therefore, when roads are congested, road capacity cannot accurately express the traffic state. This scheme uses the queue length of road segments as the unit of measurement and as a constraint condition for guidance and control. It fully considers the mutual influence between road segments and intersection nodes, and establishes a node (intersection) queue length calculation model based on traffic flow and traffic wave theory. It identifies the road segment where congestion occurs first and performs pre-control and guidance. Based on the directionality of traffic flow, it locates road nodes associated with the traffic volume of congested road segments and manages them, achieving optimal pre-control of road congestion without increasing road capacity.

[0059] 3. In actual traffic guidance systems, the timing and location of guidance are determined by management personnel. This method is heavily influenced by subjective human factors, with empiricism playing a significant role and lacking scientific rigor and rationality. The adaptive wide-area traffic guidance method and device provided in this solution achieves automated and information-based traffic guidance through a strategy that rationally and effectively determines the guidance range and time period. This improves scientific rigor and rationality, and reduces the adverse effects of subjective human factors on traffic guidance in existing technologies.

[0060] 4. This scheme constructs an adaptive wide-area traffic guidance method based on the coupled characteristics of multiple factors such as traffic volume, speed, road length, and traveler tolerance, providing technical support for urban road traffic optimization and traffic management. It is of great significance for improving urban road service levels, reducing urban road traffic congestion, and enhancing the resilience of the road network. Attached Figure Description

[0061] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0062] Figure 1 This is a schematic diagram of a multi-factor-based adaptive wide-area traffic guidance method provided in this embodiment;

[0063] Figure 2 This is a schematic diagram of road segment nodes provided in this embodiment;

[0064] Figure 3 This is a spatiotemporal trajectory diagram of the vehicle between two nodes provided in this embodiment;

[0065] Figure 4 This is a schematic diagram of the road network within the guidance range provided in this embodiment;

[0066] Figure 5 This is a schematic diagram of the pre-control path provided in this embodiment;

[0067] Figure 6 The induction cycle length curve provided in this embodiment;

[0068] Figure 7 This is a schematic diagram of traffic congestion queuing provided in this embodiment;

[0069] Figure 8 This is a schematic diagram of the induction grading provided in this embodiment;

[0070] Figure 9 This is a schematic diagram of the structure of a multi-factor-based adaptive wide-area guidance device for road traffic provided in this embodiment. Detailed Implementation

[0071] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0072] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for implementation of the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. Unless otherwise stated, the term "a plurality of" means two or more. In this disclosure, the character " / " indicates an "or" relationship between the preceding and following objects. For example, A / B means: A or B. The term "and / or" describes an association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B. The term "corresponding" can refer to an association or binding relationship; A corresponding to B means that there is an association or binding relationship between A and B.

[0073] Combination Figure 1 As shown, this embodiment provides a multi-factor-based adaptive wide-area traffic guidance method, including:

[0074] Step S01: Monitor road traffic data within the spatial range of the road network;

[0075] Step S02: Predict congested road sections within the road network space based on road traffic conditions;

[0076] Step S03: Determine the associated nodes of the congested road section;

[0077] Step S04: Select the guiding path based on the associated nodes;

[0078] Step S05: Dynamically allocate traffic flow to the guidance route based on driver preference.

[0079] Step S06: Conduct road traffic guidance based on the dynamic distribution results of traffic flow.

[0080] In some embodiments, the adaptive wide-area guidance method for road traffic provided by this solution is based on traffic flow prediction and real-time dynamic traffic allocation. It applies modern communication technology, electronic technology, computer technology and other technologies to provide necessary traffic information to travelers on the road network, pointing out the best current travel route for them, thereby avoiding traffic congestion caused by blind travel, and achieving the goal of smooth and efficient operation of the road network.

[0081] Optionally, predicting congested road segments within the road network space based on road traffic conditions includes: continuously searching for queue lengths of all road segments within the road network space within a preset time range, and using road traffic data to predict the road segment where the queue length is most likely to exceed the road segment length, which is then identified as a congested road segment.

[0082] Optionally, the upstream of each road segment is designated as the first node, and the downstream as the second node. Vehicles travel from upstream to downstream, and the queue length of the road segment is calculated based on the traffic capacity of the first and second nodes, the passing speed of the second node, and the passing time.

[0083] In some embodiments, combined with Figure 2 As shown, let two adjacent nodes... and The length of the road segment between them is ( ), within the node range ( ), at speed ( ) and traffic capacity ( ) represent nodes respectively Traffic flow parameters, ( )and ( ) represent nodes respectively Traffic flow parameters, vehicles passing through nodes The time is ( The traffic capacity of a road segment is the maximum number of vehicles that can pass through a road segment under a specific traffic flow condition; it is a quantity that changes dynamically in real time.

[0084] Combination Figure 3 As shown, when the vehicle passes through the node Then, at speed On the road section Driving, subject to nodes Due to the impact of traffic capacity, the speed decreases to ,Right now The maximum queue length is .

[0085] Queue length calculation model Calculate the queue length of the road segment K1 and K2 are nodes and Lane density.

[0086] Optionally, It is an increasing function, which increases with... Increases with the increase. If Less than or equal to the maximum length Then the maximum traffic capacity of the road segment is When a road's traffic capacity approaches its maximum capacity, restricting the amount of traffic entering that section of road can effectively avoid and reduce traffic congestion.

[0087] Optionally, the associated nodes of the congested road segment are determined, and the guidance path is selected based on the associated nodes, including: determining the downstream node of the congested road segment as the congested node, the upstream node of the congested road segment as the critical node, and the adjacent upstream node of the critical node as the pre-control node; determining the pre-control path based on the critical node and the pre-control node; obtaining the priority of the pre-control node, determining the guidance starting point according to the priority of the pre-control node; and determining the multiple pre-control paths corresponding to the guidance starting point as the guidance path.

[0088] In some embodiments, the set of regulated road network spaces is represented as ;in, , ; , ; , S (Scope) represents the set of the monitored road network space, EP (Endpoint) represents the endpoint of the monitored road network, that is, the intersection of the boundary line of the monitored road network space and the road, ND (Node) represents the node of the monitored road network, that is, the intersection of the road, RS (Road Section) represents the road section of the monitored road network, and M, N, and U represent the number of EP, ND, and RS, respectively.

[0089] In some embodiments, the path most likely to have a queue length greater than or equal to the road segment length is searched, and congestion nodes and critical nodes on the path are identified. Pre-control nodes are then determined based on the critical nodes. For example... Figure 4 As shown, if node To the node Queue length ≥ node To the node Road segment length ( And the event that occurs first, i.e., the node. To the node The sections of road that will be blocked first will then be determined. For congested nodes, nodes This is a critical node. The traffic volume at the congested node is related to the traffic volume upstream of the critical node. , , It is related to three nodes. , , For nodes To the node Pre-control nodes for road sections.

[0090] In some embodiments, a road segment is known to consist of two nodes, and the traffic flow is directional, determined by the nodes. →Node The road segment can be represented as , ;in, Represents a node →Node The section of road, Represents a node , Represents a node If the search finds that the first road segment to become blocked is... Then the congested node is a node. ,Right now Key Nodes ,Right now All containing ,and The set of road segments that are downstream nodes is , This solution is achieved through Guidance and control at three nodes or their upstream nodes can prevent excessive vehicle traffic. This caused the node To the node The road section was completely blocked.

[0091] Optionally, for the endpoints of the monitored road network, that is, the intersection of the boundary line of the monitored road network space and the road, when the traffic flow at the endpoint is controllable, the endpoint can be used as a pre-control node; when the endpoint is uncontrollable, the endpoint has no impact on the pre-control and therefore does not need to be considered.

[0092] Optionally, key nodes are marked as primary control points, primary control points as secondary control points, and upstream nodes of primary control points as tertiary control points, according to their distance from congested nodes. The primary, secondary, and tertiary control points are stored, and they can be retrieved directly from the stored files when inducing or controlling the congestion. This avoids the drawbacks of using mathematical models such as search algorithms when performing pre-control, saves search time, and has a significant effect.

[0093] Optionally, the pre-control path is determined based on the key node and the pre-control node, including: determining the path that starts from the pre-control node and ends at the key node as the pre-control path.

[0094] In some embodiments, such as Figure 5 As shown, It is a key node Pre-control nodes, For congested road sections, from Induction is performed at three nodes to avoid Traffic congestion on this section of the road has worsened, and a system will be established starting from the pre-control node to... The set of driving paths is called the "pre-controlled path set". express.

[0095] Optionally, congested road sections The corresponding set of pre-controlled paths for:

[0096]

[0097] in, Indicates from arrive The nodes visited are as follows: , , The pre-control path, Indicates from arrive The nodes visited are as follows: , , The pre-control path, Indicates from arrive The nodes passed through are The pre-control path, Indicates from arrive The nodes passed through are The pre-control path.

[0098] Optionally, , , This is the set of all pre-controlled paths in the road network within this range.

[0099] Optionally, obtaining the priority of the pre-control node includes: obtaining the functional importance and detour degree of the pre-control node, and obtaining the priority of the pre-control node based on the functional importance and detour degree of the pre-control node.

[0100] Alternatively, functional importance can be described by the traffic flow load of a node in the road network. The more traffic a node carries, the more important it is in the road network. Functional importance can be obtained using the following formula. :

[0101] ;in, To pre-control the functional importance of node i, For the set of pre-controlled nodes The node with the highest traffic flow under medium load; Pre-control node Traffic volume.

[0102] Optionally, detour degree refers to the ratio of the shortest path travel time from this node to the destination to the actual shortest travel time, and is obtained using the following formula. :

[0103] ;in, For nodes To the node Traffic volume of the k-th shortest path; For nodes To the node The average travel time of the k-th shortest path; For nodes To the node Passing through congested sections of road The average travel time.

[0104] Optionally, the priority of the pre-controlled node can be obtained using the following formula:

[0105] ;in, To determine the priority of pre-control node i, and For indicator weights, .

[0106] Optionally, determining the induction starting point according to priority includes: determining the pre-control node with the highest priority as the induction starting point.

[0107] Optionally, obtaining the priority of pre-controlled nodes also includes: setting evaluation indicators for pre-controlled nodes, and evaluating the priority of pre-controlled nodes based on the evaluation indicators; the evaluation indicators include benefit-type indicators and cost-type indicators, the benefit-type indicators include node degree, node effectiveness betweenness and functional importance, and the cost-type indicators include detour degree and travel cost.

[0108] Optionally, before dynamically allocating traffic flow to the induced route based on driver preference, the process includes: obtaining the detour time ratio and the detour distance ratio; constructing a relative cost-utility function based on the detour time ratio and the detour distance ratio; and using the relative cost-utility function and the route scale to obtain the driver preference for the induced route.

[0109] In some embodiments, a relative cost-utility function is constructed using the ratio of detour time to detour distance as selection targets. This function, along with path metrics, is used to obtain the driver's preference for the induced route. The induced route is then selected from highest to lowest driver preference. Using the ratio of detour time to detour distance as selection targets fully considers the driver's psychological expectations and value orientation when choosing a route. The selected induced route targets are more in line with the driver's wishes, thereby improving the reliability of the induced route and the obedience of the induced information.

[0110] Alternatively, the detour time ratio can be obtained using the following formula:

[0111] ;in, This is the ratio of detour time. The distance of the original route is in meters. The distance of the guided driving route is expressed in meters (m).

[0112] Alternatively, the detour distance ratio can be obtained using the following formula:

[0113] ;in, This is the ratio of the detour distance; Original travel time, in minutes; The time for guiding the travel route is in minutes.

[0114] Optionally, the relative cost-utility function takes the form of a linear function, i.e.

[0115] ;

[0116] In the above formula, It is a relative cost-utility function. For nodes To the node The driving route between them; and Utility weight; It is a constant in the utility equation.

[0117] Optionally, the driver's preference for the induced route is obtained using a relative cost-utility function and a path scale, including:

[0118] The relative cost-utility function is used to sort the induced paths in descending order, and the driver's preference for each induced path is calculated using the path selection formula.

[0119] The path selection formula is:

[0120] ;

[0121] in, For the induction path Driver selection preference, induced path For nodes To the node The path, It is a relative cost-utility function. These are model constants. It indicates the traveler's familiarity with the road network; For the induction path Path scale.

[0122] Optionally, path scale Calculated using the following formula:

[0123] ;

[0124] In the formula: For the induction path A collection of the road sections passed through; For road segment collection One from node To the node The section of road; For road section length; For the induction path length; Indicates road segment The number of times is shared across all induced paths.

[0125] Optional, road section The more times the data is shared, the greater the path scale of that road segment. The smaller the value, the more likely it is to induce a path. The lower the probability of being selected.

[0126] Optionally, dynamic traffic flow allocation to the guidance paths based on driver preference includes: determining the target guidance flow for congested road segments; obtaining the allocated traffic volume for each guidance path based on the target guidance flow and the driver preference of the guidance path; selecting guidance paths as current guidance paths in descending order of driver preference, and obtaining the capacity flow of the current guidance path; comparing the allocated traffic volume and the capacity flow of the current guidance path, and if the allocated traffic volume is greater than the capacity flow, removing the current guidance path from the guidance path set, and redistributing the overflow traffic of the current guidance path to the guidance paths in the guidance path set, until all target guidance flows have been allocated.

[0127] Optionally, determining the target induced flow for congested road segments includes: obtaining the communication capacity of the congested road segment at the tolerance speed based on the congestion density, free-driving speed, and tolerance speed of the congested road segment; and obtaining the target induced flow based on the communication capacity of the congested road segment at the tolerance speed.

[0128] Optionally, the communication capability of congested road sections at the tolerable speed is calculated using the following formula:

[0129] ;in, Tolerable speed for this section of road Traffic capacity below; Indicates road segment Blocking density, Indicates road segment Free speed.

[0130] Optionally, the targeted induced traffic is calculated using the following formula:

[0131] ;in, for Congested road sections during the time period The required induced traffic volume, i.e. the target induced flow, for Congested road sections during the time period Predicted traffic volume Tolerable speed for this section of road Traffic capacity.

[0132] Optionally, the allocated traffic volume on each guidance path can be obtained using the following formula:

[0133] ;

[0134] In the above formula, To guide the allocation of traffic volume on the path. For the number of iterations of the flow, for Congested road sections during the time period The corresponding target induced traffic, For the induction path Driver preference.

[0135] Optionally, obtaining the capacity of the current guidance path includes: the current guidance path includes several sub-segments, obtaining the maximum additional capacity of each sub-segment; and obtaining the capacity of the current guidance path based on the maximum additional capacity of each sub-segment.

[0136] Optionally, the maximum additional capacity of a sub-segment can be calculated using the following formula:

[0137] ;in, for Guidance Sections during the Time Period Maximum additional capacity; For guidance section Tolerance speed; In the guidance section tolerance speed Traffic capacity below; Indicates road segment Blocking density; Indicates road segment Free speed; In order to be in Guidance Sections during the Time Period Predicted traffic.

[0138] Optionally, the capacity of the current induced path can be determined using the following formula:

[0139] ;in, for Induction path within the time period Maximum additional capacity.

[0140] Optionally, the overflow traffic from the current induced path is reallocated to induced paths in the induced path set, including: updating the allocation of each induced path according to the allocated traffic volume on each induced path to obtain the new traffic volume carried by each induced path; updating the relative cost-utility function according to the new traffic volume of each induced path; updating the driver selection preference degree of each induced path in the induced path set according to the updated relative cost-utility function; and reallocating the overflow traffic from the current induced path based on the updated driver selection preference degree of each induced path.

[0141] Optionally, it further includes: within the road network space, obtaining the tolerable travel time at the tolerable speed of each road based on the length and tolerable speed of each road; determining the guidance time boundary conditions based on the tolerable travel time of each road and the optimal guidance cycle length; the guidance time boundary conditions are: when the optimal guidance cycle length is less than the maximum tolerable travel time, the guidance cycle length is the maximum tolerable travel time; when the optimal guidance cycle length is greater than or equal to the maximum tolerable travel time, the guidance cycle length is the optimal guidance cycle length; determining the guidance space boundary conditions based on the guidance cycle length; the guidance space boundary conditions are: the maximum guidance space distance that can be reached within the guidance cycle length.

[0142] Optionally, the induction period length H: ,in, , The length of the road segment; Tolerance speed The order-part function; For road section Tolerable passage time at the tolerance speed.

[0143] In some embodiments, Figure 6 For the induced period length curve, combined with Figure 6 As shown, with the increase of the induction period length, the total travel time of travelers first decreases and then increases. The optimal induction period length is when the total travel time of travelers reaches its minimum value. min, and when The overall travel time curve for travelers at that time was better than that of travelers in general. The total travel time curve of travelers at that time is used to determine the induced time boundary conditions. Therefore, the induced time boundary conditions are: when the optimal induced period length is less than the maximum tolerable travel time, the induced period length is the maximum tolerable travel time; when the optimal induced period length is greater than or equal to the maximum tolerable travel time, the induced period length is the optimal induced period length. The induced time boundary conditions are expressed by the formula: .

[0144] In some embodiments, traffic congestion and queuing scenarios such as Figure 7 As shown, in During the time period, the road section The queue length did not overflow; During the time period, the road section The queue overflowed to the upstream section of the road. Vehicles on this section of road are unable to travel along their original planned routes and are forced to queue. Assuming all vehicles receive immediate guidance information at the guidance starting point, a time boundary condition is set, with the guidance cycle length as the guidance cycle interval. Vehicles about to enter the congested section are guided once every guidance cycle length to ensure they enter the congested section. After receiving the guidance information, vehicles have enough time to leave the congested section of road before it becomes completely blocked, and at the same time, they do not receive guidance information too frequently.

[0145] Optionally, the induced space boundary conditions are determined based on the induced period length; the induced space boundary conditions are: the maximum induced space distance that can be reached within the induced period length.

[0146] In some embodiments, the induction period length H determines an optimal induction time period, and the road segment length corresponding to the optimal induction time period is the length L of the pre-control space. space The induction period length H is expressed as: ; Indicates road segment Tolerance travel time at the tolerance speed; then induced spatial distance Represented as ; Indicates road segment The length of the road segment.

[0147] In some embodiments, the pre-controlled space length Once determined, the nodes are classified sequentially upstream of the critical node, using the critical node as the starting point for classification. The classification method is as follows: Figure 8 As shown. The point set formed by all pre-controlled nodes is This will serve as the point of application for traffic control and the starting point for guiding routes. Short-term traffic volume forecasting is typically used. , The length is generally no greater than the length of the road segment of the first three pre-control nodes. The first three pre-control nodes of each road segment are stored in advance, and can be directly called when guidance and control are needed, which helps to save search time.

[0148] In some embodiments, determining the spatial range and time period of traffic guidance is the most basic requirement for guidance strategies. However, in actual traffic guidance systems, the time and space of guidance are determined by management personnel. This approach is greatly influenced by subjective human factors, with empiricism playing a significant role and lacking scientific rigor and rationality. The adaptive wide-area traffic guidance method and device provided in this solution achieves automated and information-based traffic guidance by rationally and effectively determining the guidance range and time period, thus improving scientific rigor and rationality and reducing the adverse effects of subjective human factors on traffic guidance in existing technologies.

[0149] Combination Figure 9 As shown, a multi-factor-based adaptive wide-area road traffic guidance device includes: a monitoring module 101 configured to monitor road traffic data within the road network space; a prediction module 102 configured to predict congested road sections within the road network space based on road traffic conditions; an association node determination module 103 configured to determine the association nodes of the congested road sections; a guidance path selection module 104 configured to select a guidance path based on the association nodes; a traffic flow dynamic allocation module 105 configured to dynamically allocate traffic flow to the guidance path based on driver preference; and a road traffic guidance module 106 configured to provide road traffic guidance according to the traffic flow dynamic allocation results.

[0150] In some embodiments, the factors influencing road segment traffic capacity, in descending order of influence, are: traffic volume entering the road segment from downstream intersections, road segment speed, road segment length, time to cross the downstream intersection, and speed at the downstream intersection. It has been found that the traffic volume entering the road segment from downstream intersections and the road segment speed have an even greater impact on road segment traffic capacity than road segment length. Therefore, by scientifically controlling the traffic flow and speed entering the road segment from downstream intersections using this scheme, road segment capacity and speed can be increased without increasing road length.

[0151] In some embodiments, this solution has been applied in mountainous cities. First, the collaborative mechanism between road networks and traffic flow in mountainous cities was analyzed, and the spatiotemporal scope of traffic guidance was determined. Then, the adaptive wide-area traffic guidance method and device based on multiple factors provided in this solution were used for traffic guidance. The final conclusion is that, considering the unique geographical environment and traffic characteristics of mountainous cities, this solution can provide technical support for traffic optimization and management in mountainous cities, and is of great significance for improving the service level of roads in mountainous cities, reducing urban traffic congestion, and enhancing the resilience of road networks.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A multi-factor-based adaptive wide-area traffic guidance method, characterized in that, include: Monitor road traffic data within the spatial scope of the road network; Predict congested road sections within the road network space based on road traffic conditions; Identify the associated nodes of congested road segments and select guidance paths based on these nodes. This includes: identifying downstream nodes of the congested road segment as congested nodes, upstream nodes as critical nodes, and adjacent upstream nodes of critical nodes as pre-control nodes; determining pre-control paths based on critical nodes and pre-control nodes; obtaining the priority of pre-control nodes and determining guidance starting points according to their priorities; and determining multiple pre-control paths corresponding to the guidance starting points as guidance paths. Obtaining the priority of pre-control nodes includes: obtaining the functional importance and detour degree of each pre-control node; and obtaining the priority of pre-control nodes based on their functional importance and detour degree. Obtain the ratio of detour time to detour distance; Construct a relative cost-utility function based on the ratio of detour time to detour distance; The driver preference for guided routes is obtained using relative cost-utility functions and path scales, including: sorting each guided route in descending order using the magnitude of the relative cost-utility function, and calculating the driver preference for each guided route using the path selection formula. Dynamically allocate traffic flow to the guidance route based on driver preference. Traffic guidance is provided based on the dynamic distribution of traffic flow. The path selection formula is: ； in, For the induction path Driver selection preference, induced path Pre-control node To the congestion point The path, It is a relative cost-utility function. These are model constants. It indicates the traveler's familiarity with the road network; For the induction path Path scale: ； In the formula: For the induction path A collection of the sections of road passed through; For road segment collection One from node To the node The section of road; For road section length; For the induction path length; Indicates road segment The number of times is shared across all induced paths.

2. The method according to claim 1, characterized in that, A relative cost-utility function is constructed based on the ratio of detour time to detour distance, including: ； in, It is a relative cost-utility function. and For utility weight, For constants in the utility equation, This is the ratio of detour distances. This represents the ratio of detour time.

3. The method according to claim 1, characterized in that, Dynamic traffic flow allocation based on driver preference for guided routes includes: Determine the target induced traffic flow for congested road sections; The traffic volume allocated on each guidance path is obtained based on the target induced flow and the driver selection preference of the guidance path; The guidance routes are selected in descending order of driver preference, and the capacity of each guidance route is obtained. The allocated traffic volume and the capacity flow of the current guidance path are compared. If the allocated traffic volume is greater than the capacity flow, the current guidance path is removed from the guidance path set, and the overflow traffic of the current guidance path is redistributed to the guidance paths in the guidance path set until all target guidance traffic is allocated.

4. The method according to claim 3, characterized in that, Redistribute the traffic overflowing from the current induced path to induced paths in the induced path set, including: After updating the allocation of traffic volume on each guidance path according to the allocated traffic volume on each guidance path, the new traffic volume carried by each guidance path is obtained. The relative cost-utility function is updated based on the new traffic volume of each induced path; Update the driver preference for each induced path in the induced path set according to the updated relative cost-utility function; The overflow traffic from the current guidance path is redistributed based on the driver's preference after each guidance path is updated.

5. The method according to claim 1, characterized in that, Also includes: Within the road network space, the tolerable travel time at the tolerable speed of each road is obtained based on the length and tolerable speed of each road. The induction time boundary conditions are determined based on the tolerance passage time and the optimal induction cycle length for each road. The boundary conditions for the induction time are as follows: when the optimal induction period length is less than the maximum tolerable passage time, the induction period length is the maximum tolerable passage time; when the optimal induction period length is greater than or equal to the maximum tolerable passage time, the induction period length is the optimal induction period length. Determine the boundary conditions of the induced space based on the length of the induced period; The boundary condition for the induced space is the maximum induced space distance that can be reached within the induced period length.

6. A multi-factor-based adaptive wide-area road traffic guidance device, characterized in that, include: The monitoring module is configured to monitor road traffic data within the road network spatial area; The prediction module is configured to predict congested road segments within the road network space based on road traffic conditions. The associated node determination module is configured to determine the associated nodes of congested road segments; The guidance path selection module is configured to select guidance paths based on associated nodes, including: identifying downstream nodes of congested road segments as congested nodes, upstream nodes of congested road segments as critical nodes, and adjacent upstream nodes of critical nodes as pre-control nodes; determining pre-control paths based on critical nodes and pre-control nodes; obtaining the priority of pre-control nodes and determining guidance starting points according to the priority of pre-control nodes; and determining multiple pre-control paths corresponding to the guidance starting points as guidance paths; wherein obtaining the priority of pre-control nodes includes: obtaining the functional importance and detour degree of pre-control nodes; and obtaining the priority of pre-control nodes based on the functional importance and detour degree of pre-control nodes. The traffic flow dynamic allocation module obtains the detour time ratio and detour distance ratio; constructs a relative cost-utility function based on the detour time ratio and detour distance ratio; and obtains the driver selection preference degree of the induced routes using the relative cost-utility function and path scale, including: sorting each induced route in descending order using the magnitude of the relative cost-utility function, calculating the driver selection preference degree of each induced route using the path selection formula; and is configured to dynamically allocate traffic flow to the induced routes based on the driver selection preference degree. The road traffic guidance module is configured to provide road traffic guidance based on the dynamic allocation results of traffic flow. The path selection formula is: ； in, For the induction path Driver selection preference, induced path Pre-control node To the congestion point The path, It is a relative cost-utility function. These are model constants. It indicates the traveler's familiarity with the road network; For the induction path Path scale: ； In the formula: For the induction path A collection of the sections of road passed through; For road segment collection One from node To the node The section of road; For road section length; For the induction path length; Indicates road segment The number of times is shared across all induced paths.

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