A vehicle flow path planning method based on double constraints of logistics park and surrounding road network

Abstract

The application discloses a kind of vehicle flow path planning method based on logistics park and surrounding road network double constraint, by constructing mathematical model, simultaneously consider the processing capacity limit of internal service node of logistics park and the traffic capacity limit of surrounding road network section and node, accurately present the interaction mechanism of both, solve the problem that prior art cannot accurately depict its complex relationship;Second, based on the solution algorithm, the model is solved, in the double-constrained environment of comprehensive and accurate, fully excavate path combination, quickly find the vehicle flow path scheme that meets the conditions and makes the optimal solution of transportation efficiency, cost and other targets, overcome the problem that optimal solution is difficult to obtain;In addition, the influence of different constraint conditions is analyzed in depth, the action mechanism of vehicle flow path is mastered, and the model and algorithm are optimized accordingly, and the planning scheme can be adjusted in real time according to the actual situation, so that the planning can follow the changes of actual demand, effectively make up for the defects that the prior art is difficult to dynamically optimize and adjust.

Description

Technical Field

[0001] This invention relates to the field of traffic flow path planning technology, and in particular to a traffic flow path planning method based on the dual constraints of logistics parks and surrounding road networks. Background Technology

[0002] With the rapid development of e-commerce and the acceleration of urbanization, the logistics industry is playing an increasingly important role in the modern economy. As a key node in the logistics network, logistics parks undertake important functions such as the collection, distribution, storage, and processing of goods. Their efficient operation is crucial to ensuring the stability and smooth flow of the supply chain. Traffic flow planning, as one of the core aspects of logistics park operation and management, directly affects the efficiency and cost of goods transportation, as well as the surrounding traffic environment.

[0003] Currently, existing traffic flow planning methods have many shortcomings when dealing with such dual constraints. Some methods lack systematic modeling of dual constraints and cannot accurately depict the complex relationship between logistics parks and surrounding road networks. Some solution algorithms suffer from low solution efficiency and difficulty in obtaining optimal solutions when faced with complex mathematical models under dual constraints. Furthermore, there is a lack of in-depth analysis of the impact of different constraints on traffic flow paths, making it difficult to dynamically optimize and adjust the planning scheme according to the actual situation. Summary of the Invention

[0004] In view of this, the present invention proposes a traffic flow path planning method based on the dual constraints of logistics parks and surrounding road networks, which can effectively solve the defects of existing technologies, such as the inability to accurately depict the complex relationship between logistics parks and surrounding road networks, the difficulty in obtaining optimal solutions, and the difficulty in dynamically optimizing and adjusting the planning scheme according to actual conditions.

[0005] The technical solution of this invention is implemented as follows:

[0006] A traffic flow path planning method based on dual constraints of logistics park and surrounding road network, specifically including:

[0007] Construct a mathematical model that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of surrounding road network segments and nodes;

[0008] The mathematical model is solved based on the solution algorithm to obtain a traffic flow path scheme that satisfies the dual constraints.

[0009] The impact of different constraints on traffic flow routes is analyzed, and the mathematical model and solution algorithm are optimized based on the analysis results.

[0010] As a further optional solution to the traffic flow path planning method based on the dual constraints of logistics park and surrounding road network, the construction of a mathematical model that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of road segments and nodes in the surrounding road network specifically includes:

[0011] Define the scope of service nodes within the logistics park and clarify the processing capacity limit parameters for each node. The processing capacity limit parameters include the service rate of loading and unloading platforms, yard capacity limit, gate passage speed, reservation system processing capacity, and internal road capacity.

[0012] Define the scope of the surrounding road network and quantify the traffic capacity limitation parameters of each road segment and node in the road network. The traffic capacity limitation parameters include road traffic capacity, intersection delay function, and real-time and predicted traffic flow status.

[0013] Based on queuing theory, a queuing network model of service nodes within a logistics park is constructed using parameters such as the range of service nodes within the logistics park and the processing capacity of each node. This model is used to simulate the queuing and loading / unloading process of vehicles within the park.

[0014] Based on traffic flow theory, a traffic flow allocation model for the surrounding road network is constructed using parameters such as the scope of the surrounding road network and the capacity constraints of each road segment and node. This model is used to simulate the driving process of vehicles on the road network and the dynamic allocation of traffic flow.

[0015] By integrating queuing network models and traffic flow allocation models, a two-layer coupled mathematical model is constructed that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of surrounding road network segments and nodes. This two-layer coupled mathematical model is used to reflect the comprehensive dynamic behavior of vehicles within the park and on the road network.

[0016] As a further alternative to the traffic flow path planning method based on the dual constraints of the logistics park and the surrounding road network, the method based on queuing theory constructs a queuing network model for the service nodes within the logistics park, using parameters such as the range of service nodes within the logistics park and the processing capacity limitations of each node. Specifically, this includes:

[0017] Based on queuing theory, each service node is abstracted into a queuing subsystem by the service node range within the logistics park and the processing capacity limit parameters of each node, and the vehicle arrival process, service time distribution and queuing rules are defined.

[0018] Based on the input-output relationship of traffic flow between nodes, a multi-level queuing network is established to simulate the dynamic behavior of vehicles throughout the entire process from park entry reservation, gate passage, platform loading and unloading, temporary storage in the yard to leaving the park.

[0019] A capacity constraint is introduced, and a congestion feedback mechanism is triggered when the queue length of any node exceeds the threshold.

[0020] As a further alternative to the traffic flow path planning method based on the dual constraints of the logistics park and the surrounding road network, the traffic flow allocation model of the surrounding road network, based on traffic flow theory and using the scope of the surrounding road network and the capacity constraints of each road segment and node, is constructed, specifically including:

[0021] Based on traffic flow theory, vehicle route selection behavior is defined in the traffic flow assignment model using parameters such as the surrounding road network and the capacity constraints of each road segment and node.

[0022] Based on vehicle routing behavior, the flow rate of each road segment is calculated using the road segment impedance function.

[0023] Based on the traffic flow of each road segment, the capacity parameters of each road segment in the traffic flow assignment model are iteratively updated.

[0024] As a further optional scheme of the traffic flow path planning method based on the dual constraints of logistics park and surrounding road network, the step of solving the mathematical model based on the solution algorithm to obtain a traffic flow path scheme that satisfies the dual constraints specifically includes:

[0025] Based on the mathematical model, the set of service nodes within the logistics park, the set of surrounding road network segments, the origin-end point demand matrix, and the dual constraints are obtained.

[0026] A metaheuristic algorithm is adopted, with the objective function of minimizing the total travel time of the road network. The algorithm solves the set of service nodes inside the logistics park, the set of road segments in the surrounding road network, the origin-destination demand matrix, and the dual constraints, and iteratively optimizes the path allocation.

[0027] Generate a set of paths that satisfy the two constraints.

[0028] As a further alternative to the traffic flow path planning method based on the dual constraints of logistics park and surrounding road network, the dual constraints include any two of the following constraints: capacity constraint, time constraint, cost constraint, and environmental constraint.

[0029] As a further optional solution to the traffic flow path planning method based on the dual constraints of logistics park and surrounding road network, the analysis of the impact of different constraints on traffic flow paths specifically includes:

[0030] Adjust the constraints of the traffic flow route plan, solve the adjusted route plan, and analyze the conflicts or synergistic effects between constraints.

[0031] The sensitivity of the calculated path scheme to each constraint is sorted from high to low based on the absolute value of the sensitivity, and a constraint priority ranking table is generated.

[0032] A traffic flow path planning system based on dual constraints of a logistics park and surrounding road network includes:

[0033] The construction module is used to build a mathematical model that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of surrounding road network segments and nodes;

[0034] The solution module is used to solve the mathematical model based on the solution algorithm to obtain the traffic flow path scheme that satisfies the dual constraints.

[0035] The analysis and optimization module is used to analyze the impact of different constraints on traffic flow routes in traffic flow schemes, and optimize the mathematical model and solution algorithm based on the analysis results.

[0036] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described traffic flow path planning methods based on dual constraints of a logistics park and surrounding road network.

[0037] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described traffic flow path planning methods based on dual constraints of a logistics park and its surrounding road network.

[0038] The beneficial effects of this invention are as follows: By constructing a mathematical model that simultaneously characterizes the processing capacity limitations of service nodes within a logistics park and the traffic capacity limitations of surrounding road network segments and nodes, it can comprehensively and meticulously consider various correlation factors between the logistics park and the surrounding road network. The processing capacity of service nodes within the logistics park directly affects the operating time and queuing situation of vehicles within the park, while the traffic capacity of surrounding road network segments and nodes determines the smoothness of vehicle entry and exit from the park and its travel in the surrounding area. This mathematical model organically combines these two factors, accurately presenting their interaction and influence mechanisms in the form of mathematical expression, thereby effectively solving the problem that existing technologies cannot accurately characterize the complex relationship between the two. Secondly, by solving the above mathematical model based on the solution algorithm, it is possible to quickly and effectively find traffic flow path schemes that meet the conditions for complex planning problems under dual constraints. Taking into account the dual constraints of the logistics park and the surrounding road network, the solution process actually seeks the optimal solution in a more comprehensive and accurate constraint environment. Unlike existing technologies that solve under a single constraint or a simple model, this method can fully explore various possible path combinations. Under the premise of satisfying both constraints, it finds the optimal traffic flow route scheme that maximizes transportation efficiency and minimizes costs, effectively overcoming the difficulty of obtaining the optimal solution in existing technologies. In addition, by deeply analyzing the impact of different constraints, it can promptly grasp the mechanism by which these changes affect traffic flow routes. Based on these analysis results, the mathematical model and solution algorithm can be optimized, allowing for real-time adjustment of the traffic flow route planning scheme according to actual conditions. This ensures that the planning scheme always meets actual needs, effectively solving the deficiency of existing technologies in dynamically optimizing and adjusting the planning scheme according to actual conditions, and improving the flexibility and practicality of traffic flow route planning. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating a traffic flow path planning method based on dual constraints of logistics parks and surrounding road networks according to the present invention.

[0041] Figure 2 This is a schematic diagram of the composition of a traffic flow path planning system based on dual constraints of logistics park and surrounding road network according to the present invention.

[0042] Figure 3 This is a schematic diagram of the composition of a computing device according to the present invention. Detailed Implementation

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] refer to Figures 1 to 3 A traffic flow path planning method based on dual constraints of logistics parks and surrounding road networks, specifically including:

[0045] A mathematical model is constructed that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of surrounding road network segments and nodes, specifically including:

[0046] Define the scope of service nodes within the logistics park and clarify the processing capacity limit parameters for each node. The processing capacity limit parameters include the service rate of loading and unloading platforms, yard capacity limit, gate passage speed, reservation system processing capacity, and internal road capacity.

[0047] Define the scope of the surrounding road network and quantify the traffic capacity limitation parameters of each road segment and node in the road network. The traffic capacity limitation parameters include road traffic capacity, intersection delay function, and real-time and predicted traffic flow status.

[0048] Based on queuing theory, a queuing network model of service nodes within a logistics park is constructed using parameters such as the range of service nodes within the logistics park and the processing capacity of each node. This model is used to simulate the queuing and loading / unloading process of vehicles within the park.

[0049] Based on traffic flow theory, a traffic flow allocation model for the surrounding road network is constructed using parameters such as the scope of the surrounding road network and the capacity constraints of each road segment and node. This model is used to simulate the driving process of vehicles on the road network and the dynamic allocation of traffic flow.

[0050] By integrating queuing network models and traffic flow allocation models, a two-layer coupled mathematical model is constructed that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of surrounding road network segments and nodes. This two-layer coupled mathematical model is used to reflect the comprehensive dynamic behavior of vehicles within the park and on the road network.

[0051] Specifically, the scope of service nodes within the logistics park is defined and the processing capacity limit parameters of each node are clarified, covering key factors such as loading and unloading platform service rate and yard capacity limit. This enables the model to accurately simulate the actual operation of each service node in the park. For example, the service efficiency of the loading and unloading platform directly affects the loading and unloading time of vehicles, and the yard capacity limit determines the space range for vehicles to queue.

[0052] The surrounding road network is defined, and the capacity limit parameters of each road segment and node in the road network are quantified, including road capacity, intersection delay function, etc. These parameters can truly reflect the driving conditions of vehicles on the surrounding road network. For example, road capacity determines the traffic flow that a road segment can accommodate, while intersection delay function reflects the time loss when vehicles pass through intersections, making the model more in line with the actual traffic environment.

[0053] The surrounding road network is defined, and the capacity limit parameters of each road segment and node in the road network are quantified, including road capacity, intersection delay function, etc. These parameters can truly reflect the driving conditions of vehicles on the surrounding road network. For example, road capacity determines the traffic flow that a road segment can accommodate, while intersection delay function reflects the time loss when vehicles pass through intersections, making the model more in line with the actual traffic environment.

[0054] By integrating queuing network models and traffic flow assignment models, a two-layer coupled mathematical model is constructed, which can simultaneously reflect the comprehensive dynamic behavior of vehicles within the park and on the road network. This model breaks through the limitations of traditional single-level modeling, closely integrates the operational process within the park with the traffic flow of the road network, and comprehensively and systematically depicts the operational status of vehicle flow in the entire logistics transportation system.

[0055] By using a two-layer coupled mathematical model and corresponding solution methods, a traffic flow route plan that comprehensively considers the dual constraints of the logistics park and the surrounding road network can be formulated. This plan can make full use of the processing capacity of service nodes within the park and the traffic capacity of the surrounding road network, reduce vehicle queuing time within the park and travel time on the road network, and improve overall transportation efficiency.

[0056] In some embodiments, the construction of a queuing network model for service nodes within the logistics park, based on queuing theory and using parameters such as the range of service nodes within the logistics park and the processing capacity limitations of each node, specifically includes:

[0057] Based on queuing theory, each service node is abstracted into a queuing subsystem by the service node range within the logistics park and the processing capacity limit parameters of each node, and the vehicle arrival process, service time distribution and queuing rules are defined.

[0058] Based on the input-output relationship of traffic flow between nodes, a multi-level queuing network is established to simulate the dynamic behavior of vehicles throughout the entire process from park entry reservation, gate passage, platform loading and unloading, temporary storage in the yard to leaving the park.

[0059] A capacity constraint is introduced, and a congestion feedback mechanism is triggered when the queue length of any node exceeds the threshold.

[0060] Specifically, by abstracting each service node into a queuing subsystem and defining the vehicle arrival process, service time distribution, and queuing rules, the actual operation of each service node can be accurately simulated. For example, for the loading and unloading platform node, by reasonably setting the vehicle arrival pattern (such as Poisson arrival) and service time distribution (such as exponential distribution), the queuing and loading / unloading process of vehicles at the loading and unloading platform can be accurately reflected, providing strong support for a deeper understanding of the micro-operational status of traffic flow within the park. Secondly, a multi-level queuing network is established based on the input-output relationship of traffic flow between nodes, which fully simulates the dynamic behavior of vehicles from park entry reservation, gate passage, platform loading and unloading, temporary storage in the yard to leaving the park. This helps to grasp the flow trajectory and time consumption of traffic flow within the park from a macro perspective, and to discover the connection relationship and potential problems between each link. For example, it can identify the coordination bottleneck between gate passage and platform loading and unloading, providing a basis for optimizing the park's operation process.

[0061] By introducing capacity constraints and a congestion feedback mechanism, feedback is triggered when the queue length at any node exceeds a threshold. This allows the model to monitor the operational status of each service node within the park in real time and provide early warnings of potential congestion. For example, when the number of vehicles queuing at a gate approaches or exceeds its processing capacity, a timely warning signal is issued, allowing management personnel to take preventative measures, such as increasing the number of gates open or guiding vehicles to queue in an orderly manner, to prevent further congestion. Secondly, by analyzing the queuing network model, the resource demands of service nodes at different times and in different operational stages can be understood. Based on this information, human and material resources within the park can be rationally allocated, such as dynamically adjusting the number of loading and unloading personnel according to the busyness of platform loading and unloading operations. Simultaneously, vehicle scheduling strategies can be optimized to guide vehicles to flow rationally within the park, reducing unnecessary queuing time and improving the overall operational efficiency of the park.

[0062] In some embodiments, the construction of a traffic flow allocation model for the surrounding road network based on traffic flow theory, using the surrounding road network range and the capacity limitation parameters of each road segment and node, specifically includes:

[0063] Based on traffic flow theory, and using the surrounding road network and the capacity constraints of each road segment and node, vehicle route selection behavior is defined in the traffic flow assignment model. The vehicle route selection behavior is defined using the User Equilibrium (UE) principle, that is, all drivers independently choose routes to minimize their own travel time, and finally reach a stable state where the travel time of each route is equal; or the System Optimality (SO) principle, which optimizes route assignment with the goal of minimizing the total travel time of the global road network.

[0064] Based on vehicle routing behavior and combined with the segment impedance function, the flow rate of each segment is calculated. The segment impedance is calculated using the BPR function, with the following formula:

[0065] ;

[0066] in, This represents the actual travel time for road segment a. For free-flow travel time, Traffic flow on the road segment For the traffic capacity of the road section, , For model parameters (usually taken as follows) =0.15、 =4);

[0067] Based on the traffic flow of each road segment, the capacity parameters of each road segment in the traffic flow assignment model are iteratively updated. Specifically, when the traffic flow of a road segment... Exceeding traffic capacity When the capacity reaches 90%, the congestion feedback mechanism is triggered, and the actual traffic capacity is adjusted accordingly. The iteration termination condition is that the traffic flow change rate of the road segment in two adjacent iterations is less than 5%, or the maximum number of iterations of 100 is reached.

[0068] Specifically, the model defines vehicle route selection behavior using the user equilibrium principle, meaning that all drivers independently choose the route that minimizes their own travel time, ultimately reaching a stable state where the travel time for each route is equal. This principle fully considers the rational decision-making behavior of individual drivers. In real traffic, drivers usually choose the optimal route based on their understanding and judgment of road conditions. This model can effectively simulate this actual route selection behavior, making the traffic flow allocation results more consistent with reality. Secondly, the model adopts the system optimality principle, optimizing route allocation with the goal of minimizing the total travel time of the global road network. This considers the operational efficiency of the entire road network from a macro perspective, which helps to achieve overall optimization in traffic planning and management. For example, when dealing with sudden traffic events or conducting long-term traffic planning, it can rationally allocate traffic flow, reduce the total delay of the road network, and improve the overall capacity of the road network.

[0069] The BPR function is used to calculate road segment impedance. This function comprehensively considers factors such as road segment flow, free-flow travel time, and road segment capacity, and is expressed by the formula. It can accurately reflect the impact of changes in road traffic flow on actual travel time. When the road traffic flow is low, the actual travel time is close to the free-flow travel time. As the road traffic flow increases, the actual travel time will increase significantly. This is consistent with the phenomenon that vehicle speed decreases and travel time increases when the road is congested in actual traffic, thus finely depicting the traffic characteristics of the road segment.

[0070] Based on the traffic flow of each road segment, the capacity parameters of each road segment in the traffic flow assignment model are iteratively updated. When the traffic flow of a road segment exceeds 90% of its capacity, a congestion feedback mechanism is triggered to adjust the actual capacity. This mechanism can reflect the impact of road segment congestion on capacity in real time. In reality, road segment congestion leads to a decrease in vehicle speed and an increase in headway, thus reducing actual capacity. The model simulates the real traffic conditions of road segments under congestion more accurately in this way. Setting the rate of change of road segment traffic flow between two adjacent iterations to be less than 5% or reaching the maximum number of iterations of 100 as the iteration termination condition ensures the convergence and stability of the model calculation. This avoids the waste of computational resources caused by too many iterations and ensures that a relatively accurate traffic flow assignment result is obtained within a reasonable time, enabling the model to be applied efficiently and reliably in actual traffic analysis and planning.

[0071] The mathematical model is solved using a solution algorithm to obtain traffic flow path schemes that satisfy the dual constraints, specifically including:

[0072] Based on the mathematical model, the set of service nodes within the logistics park, the set of surrounding road network segments, the origin-end point demand matrix, and the dual constraints are obtained.

[0073] A metaheuristic algorithm (such as genetic algorithm or particle swarm optimization) is employed, with the objective function of minimizing the total travel time of the road network. This algorithm solves for the set of service nodes within the logistics park, the set of road segments in the surrounding road network, the origin-destination demand matrix, and the dual constraints. The path allocation is iteratively optimized, and in each iteration, the dual constraints are checked: if a road segment exists... > or > If the path flow is not met, the path flow will be adjusted or the path will be reallocated until all constraints are met and the objective function converges (e.g., the rate of change of total travel time between two adjacent iterations is less than a preset threshold).

[0074] Generate a set of paths that satisfy the dual constraints, including the optimal path between each origin and destination and the corresponding traffic flow on each road segment. Trip duration .

[0075] Specifically, metaheuristic algorithms (such as genetic algorithms and particle swarm optimization) are used to solve the problem by minimizing the total travel time of the road network. These algorithms have global search capabilities and can quickly find optimal solutions in complex solution spaces, greatly improving the efficiency of route planning. Compared with traditional exact algorithms, they can obtain a satisfactory solution within a reasonable time when dealing with large-scale logistics traffic route planning problems. Secondly, the dual constraints are checked in each iteration. If there are situations such as road segment flow exceeding capacity or travel time exceeding the maximum limit, the route flow is adjusted or the route is reallocated in a timely manner. This dynamic iterative approach can continuously optimize the route allocation scheme, ensuring that the final route scheme not only satisfies the dual constraints but also minimizes the total travel time of the road network, thus improving the quality of route planning. In addition, setting the rate of change of the total travel time between two adjacent iterations to be less than a preset threshold is one of the iteration termination conditions, which ensures the convergence of the algorithm, avoids the algorithm from getting stuck in an infinite loop, and ensures that a stable solution is obtained within a finite time, improving the reliability and stability of the solution process.

[0076] The system generates a set of paths that satisfy the dual constraints, including the optimal path between each origin and destination, as well as information such as the corresponding road segment traffic and travel time. This provides logistics companies with a wealth of path selection options, enabling them to flexibly choose the appropriate path based on different transportation needs and actual conditions.

[0077] In some embodiments, the dual constraint condition includes a combination of any two constraints selected from capacity constraint, time constraint, cost constraint, and environmental constraint; wherein:

[0078] Capacity constraints: Processing capacity of service nodes in logistics parks Traffic flow on road sections ;

[0079] Time constraints: travel time for each route Service node processing time ;

[0080] Cost constraint: Total path cost ( Cost per unit of traffic (total cost ceiling);

[0081] Environmental constraints: Total carbon emissions along the route ( Carbon emissions per unit flow rate (This is the upper limit for total carbon emissions).

[0082] Specifically, the processing capacity constraints of service nodes in logistics parks Traffic flow constraints on road sections This ensures the feasibility of the planning scheme in reality. Service nodes have limited processing capacity; if traffic flow exceeds their capacity, it will lead to long queues and affect logistics efficiency. Road segment capacity also has limitations; exceeding capacity will cause traffic congestion. These two constraints prevent the planning scheme from exceeding actual resource capacity, ensuring the smooth operation of logistics and transportation activities. Secondly, there are constraints on road segment travel time. Service node processing time constraints It meets the strict time requirements of logistics and transportation. In modern logistics, many goods have specific time requirements for transportation, such as fresh food and emergency supplies. By constraining time, it can ensure that the planning of vehicle flow routes meets the timeliness requirements of goods and improve customer satisfaction.

[0083] Total Path Cost Constraint The planning scheme should aim to minimize transportation costs and unit flow costs while meeting other constraints. This encompasses various costs, including fuel consumption, vehicle wear and tear, and toll fees. Cost constraints can guide traffic flow to choose lower-cost routes, improving the economic efficiency of logistics companies; total carbon emissions from the route are also constrained. This reflects the importance attached to environmental protection. With the increasing awareness of environmental protection, the logistics industry is also actively exploring green development models. This constraint prompts traffic route planning to take carbon emission factors into account, guide vehicles to choose routes with lower carbon emissions, reduce the impact on the environment, and promote the logistics industry to develop in a green and sustainable direction.

[0084] Dual constraints can be any combination of two constraints from capacity constraints, time constraints, cost constraints, and environmental constraints. This flexibility allows the planning scheme to be adjusted according to different logistics scenarios and needs. For example, in general freight transportation where cost and time are important, a combination of cost constraints and time constraints can be chosen; in urban distribution with high environmental protection requirements, a combination of environmental constraints and time constraints can be used to meet diverse logistics transportation needs.

[0085] The impact of different constraints on traffic flow routes in traffic flow path schemes is analyzed, and the mathematical model and solution algorithm are optimized based on the analysis results. In some embodiments, the impact of different constraints on traffic flow routes in traffic flow path schemes is analyzed, specifically including:

[0086] Select at least two types of constraints (such as capacity constraints). Time constraints Combined adjustments are performed, including single-constraint gradient adjustment and multi-constraint combined adjustment:

[0087] Single-constraint gradient adjustment: Keep other constraints fixed, gradually relax / tighten a certain type of constraint (e.g., by step size). Adjusting the traffic flow limit for road sections);

[0088] Multi-constraint combination adjustment: Simultaneously adjust at least two types of constraints (such as synchronous tightening time constraints). and cost constraints );

[0089] Metaheuristic algorithms (such as genetic algorithms) are used to solve the adjusted path scheme, and the total path cost, total time, constraint violation rate and other indicators are recorded.

[0090] Based on the solution results of the path scheme, analyze the situation under multiple constraint combinations:

[0091] Feasibility: Does the proposed path satisfy all constraints (e.g., are there regions with no feasible solutions)?

[0092] Changes in objective function values: such as the changing trends of total travel time and total cost of the road network as constraints are adjusted;

[0093] Relationships between constraints: Identify conflicting constraints (such as increased costs due to tightening time constraints) or cooperating constraints (such as simultaneous optimization of capacity and cost constraints to reduce total costs).

[0094] The sensitivity of the computational path to various constraints is quantified by the following metrics:

[0095] Single constraint sensitivity: such as the rate of change of total path cost as the upper limit of traffic flow on a road segment is relaxed. ;

[0096] Multi-constraint interaction sensitivity: such as the partial derivative of the total path time with respect to the joint adjustment of time and cost constraints. ;

[0097] Generate a constraint priority sorting table by sorting the constraints from highest to lowest based on their absolute sensitivity values.

[0098] Specifically, by adjusting the constraints of traffic flow routing schemes, solving for the adjusted routing schemes, and analyzing the conflicts or synergistic effects between constraints, we can gain a deeper understanding of the interactions between various constraints. For example, we can discover that capacity constraints and time constraints may conflict in some cases, i.e., meeting capacity limits may lead to increased travel time; while in other cases, reasonable route planning may make the two synergistic, ensuring that capacity is not exceeded while shortening time. This insight helps to better coordinate various constraints and optimize traffic flow routing schemes in subsequent planning. Secondly, analyzing the impact of different constraints on traffic flow routes can clarify the mechanism of each constraint in traffic flow routing planning. For example, we can understand how cost constraints guide traffic flow to choose low-cost routes, and how environmental constraints prompt vehicles to avoid high-carbon emission areas. This provides a theoretical basis for developing more scientific and reasonable route planning strategies, making the planning schemes more in line with actual needs.

[0099] The system calculates the sensitivity of route options to various constraints and generates a constraint priority ranking table based on the absolute value of sensitivity from high to low. This clearly determines the importance of each constraint in traffic flow route planning. Constraints with higher sensitivity have a greater impact on the route options and should be given more attention during decision-making. For example, if the time constraint has high sensitivity, it means that the travel time has a significant impact on the choice of traffic flow route, and the planning should prioritize meeting the time requirements. Secondly, the constraint priority ranking table provides decision-makers with an intuitive reference. When facing complex logistics and transportation scenarios and changing demands, it can assist decision-makers in making reasonable decisions quickly. When encountering emergencies or needing to adjust the planning scheme, decision-makers can quickly determine the focus and direction of adjustments based on the constraint priority, improving decision-making efficiency and ensuring that traffic flow route planning can adapt to changes in a timely manner.

[0100] Since different logistics scenarios may place varying degrees of emphasis on various constraints, this technical solution, by analyzing the impact of constraints and determining priorities, enables traffic flow planning schemes to better adapt to the needs of various scenarios. For example, in emergency material transportation scenarios, time constraints may have a higher priority; while in areas with strict environmental protection requirements, environmental constraints will have a higher priority. By adjusting the constraint weights and priorities according to different scenarios, more realistic traffic flow planning schemes can be generated.

[0101] A traffic flow path planning system based on dual constraints of a logistics park and surrounding road network includes:

[0102] The construction module is used to build a mathematical model that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of surrounding road network segments and nodes;

[0103] The solution module is used to solve the mathematical model based on the solution algorithm to obtain the traffic flow path scheme that satisfies the dual constraints.

[0104] The analysis and optimization module is used to analyze the impact of different constraints on traffic flow routes in traffic flow schemes, and optimize the mathematical model and solution algorithm based on the analysis results.

[0105] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described traffic flow path planning methods based on dual constraints of a logistics park and surrounding road network.

[0106] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described traffic flow path planning methods based on dual constraints of a logistics park and its surrounding road network.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for vehicle flow path planning based on double constraints of logistics park and surrounding road network, characterized in that, Specifically, it includes: Construct a mathematical model that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of surrounding road network segments and nodes; The mathematical model is solved based on the solution algorithm to obtain a traffic flow path scheme that satisfies the dual constraints. The impact of different constraints on traffic flow routes is analyzed, and the mathematical model and solution algorithm are optimized based on the analysis results.

2. The logistics park and surrounding road network dual constraint-based vehicle flow path planning method according to claim 1, characterized in that, The construction of the mathematical model that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of surrounding road network segments and nodes specifically includes: Define the scope of service nodes within the logistics park and clarify the processing capacity limit parameters for each node. The processing capacity limit parameters include the service rate of loading and unloading platforms, yard capacity limit, gate passage speed, reservation system processing capacity, and internal road capacity. Define the scope of the surrounding road network and quantify the traffic capacity limitation parameters of each road segment and node in the road network. The traffic capacity limitation parameters include road traffic capacity, intersection delay function, and real-time and predicted traffic flow status. Based on queuing theory, a queuing network model of service nodes within a logistics park is constructed using parameters such as the range of service nodes within the logistics park and the processing capacity of each node. This model is used to simulate the queuing and loading / unloading process of vehicles within the park. Based on traffic flow theory, a traffic flow allocation model for the surrounding road network is constructed using parameters such as the scope of the surrounding road network and the capacity constraints of each road segment and node. This model is used to simulate the driving process of vehicles on the road network and the dynamic allocation of traffic flow. By integrating queuing network models and traffic flow allocation models, a two-layer coupled mathematical model is constructed that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of surrounding road network segments and nodes. This two-layer coupled mathematical model is used to reflect the comprehensive dynamic behavior of vehicles within the park and on the road network.

3. The traffic flow path planning method based on dual constraints of logistics park and surrounding road network as described in claim 2, characterized in that, Based on queuing theory, a queuing network model for service nodes within a logistics park is constructed using parameters such as the range of service nodes within the logistics park and the processing capacity limitations of each node. Specifically, this includes: Based on queuing theory, each service node is abstracted into a queuing subsystem by the service node range within the logistics park and the processing capacity limit parameters of each node, and the vehicle arrival process, service time distribution and queuing rules are defined. Based on the input-output relationship of traffic flow between nodes, a multi-level queuing network is established to simulate the dynamic behavior of vehicles throughout the entire process from park entry reservation, gate passage, platform loading and unloading, temporary storage in the yard to leaving the park. A capacity constraint is introduced, and a congestion feedback mechanism is triggered when the queue length of any node exceeds the threshold.

4. The traffic flow path planning method based on dual constraints of logistics park and surrounding road network as described in claim 3, characterized in that, Based on traffic flow theory, a traffic flow allocation model for the surrounding road network is constructed using the surrounding road network range and the capacity constraints of each road segment and node. Specifically, this includes: Based on traffic flow theory, vehicle route selection behavior is defined in the traffic flow assignment model using parameters such as the surrounding road network and the capacity constraints of each road segment and node. Based on vehicle routing behavior, the flow rate of each road segment is calculated using the road segment impedance function. Based on the traffic flow of each road segment, the capacity parameters of each road segment in the traffic flow assignment model are iteratively updated.

5. The traffic flow path planning method based on dual constraints of logistics park and surrounding road network as described in claim 4, characterized in that, The method of solving the mathematical model based on the solution algorithm to obtain a traffic flow path scheme that satisfies the dual constraints includes: Based on the mathematical model, the set of service nodes within the logistics park, the set of surrounding road network segments, the origin-end point demand matrix, and the dual constraints are obtained. A metaheuristic algorithm is adopted, with the objective function of minimizing the total travel time of the road network. The algorithm solves the set of service nodes inside the logistics park, the set of road segments in the surrounding road network, the origin-destination demand matrix, and the dual constraints, and iteratively optimizes the path allocation. Generate a set of paths that satisfy the two constraints.

6. The traffic flow path planning method based on dual constraints of logistics park and surrounding road network as described in claim 5, characterized in that, The dual constraint condition includes any two of the following constraints: capacity constraint, time constraint, cost constraint, and environmental constraint.

7. The traffic flow path planning method based on dual constraints of logistics park and surrounding road network as described in claim 6, characterized in that, The analysis of the impact of different constraints on traffic flow routes in the traffic flow route scheme specifically includes: Adjust the constraints of the traffic flow route plan, solve the adjusted route plan, and analyze the conflicts or synergistic effects between constraints. The sensitivity of the calculated path scheme to each constraint is sorted from high to low based on the absolute value of the sensitivity, and a constraint priority ranking table is generated.

8. A traffic flow path planning system based on dual constraints of logistics parks and surrounding road networks, characterized in that, include: The construction module is used to build a mathematical model that simultaneously characterizes the processing capacity limitations of service nodes within the logistics park and the traffic capacity limitations of surrounding road network segments and nodes; The solution module is used to solve the mathematical model based on the solution algorithm to obtain the traffic flow path scheme that satisfies the dual constraints. The analysis and optimization module is used to analyze the impact of different constraints on traffic flow routes in traffic flow schemes, and optimize the mathematical model and solution algorithm based on the analysis results.

9. A computing device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the traffic flow path planning method based on dual constraints of logistics park and surrounding road network as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the traffic flow path planning method based on the dual constraints of logistics park and surrounding road network as described in any one of claims 1-7.

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