Resident travel behavior dynamic simulation system based on social network modeling

Abstract

The invention relates to the technical field of traffic travel, and particularly discloses a resident travel behavior dynamic simulation system based on social network modeling, which comprises a network communication module, a simulation model module, a thread management module and a traffic network module, the simulation model module is responsible for completing a simulation process based on a small-world network model to enable the small-world network model to independently run in a thread form; according to the method, the small-world network model is constructed, social resident interaction is simulated through resident travel behavior selection rules, traffic policy reality serves as a constraint condition, complex interaction between individuals can be captured and substituted into real data to reflect social relations and network structures in the real world, meanwhile, the effects of different traffic policies can be simulated, and the method is suitable for popularization and application. And decision makers are helped to evaluate potential influences of policy or behavior changes.

Description

Technical Field

[0001] The present invention belongs to the technical field of transportation, and in particular relates to a dynamic simulation system for residents' travel behavior based on social network modeling. Background Art

[0002] Resident travel usually refers to the activities of residents to travel from one place to another in order to meet their own needs, and to choose one or more modes of transportation to complete the journey. The frequent travel mode of residents refers to the pattern with relatively high frequency in the regular set of spatial distribution formed by residents' travel. The formation of frequent patterns is closely related to the distribution of functional areas in the city. Residents usually choose travel destinations based on the distribution of functional areas, such as science and education areas, shopping service areas, industrial areas, etc. In addition, frequent patterns are also related to dates. On weekdays or weekends, residents' travel purposes are different. By studying the frequent travel patterns of residents, we can discover the activity habits of residents, which is of great significance for traffic management, road planning, and commercial layout.

[0003] In recent years, with the acceleration of urbanization and the increasing prominence of traffic problems, residents' travel behavior simulation has received widespread attention and development. Many research teams and institutions at home and abroad have developed similar systems for analyzing traffic congestion, optimizing traffic planning, and evaluating policy effects. These systems usually use big data analysis and machine learning techniques, combined with geographic information systems (GIS) and traffic flow data, to achieve high-precision travel behavior predictions. At the same time, with the improvement of computing power and the optimization of algorithms, the operating efficiency and simulation accuracy of simulation systems are also constantly improving. However, existing systems often focus on a single aspect of simulation, such as only focusing on traffic flow changes or individual travel choices, but rarely consider the complex relationship between social interaction, policy impact, and individual decision-making mechanisms.

[0004] Therefore, it is necessary to propose a dynamic simulation system of residents' travel behavior based on social network modeling to at least partially solve the problems existing in the prior art.

[0005] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to provide a resident travel behavior dynamic simulation system based on social network modeling to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The system for dynamic simulation of residents' travel behavior based on social network modeling includes: a network communication module, a simulation model module, a thread management module, a traffic network module, a system management module, and a data management module. The network communication module is responsible for connecting with a control node and communicating data to realize decoding, encoding, and processing of data packets. The simulation model module is responsible for completing the simulation process based on a small-world network model so that it can run independently in the form of threads. The thread management module is responsible for controlling the execution state of the simulation thread through a thread Wait / Notify mechanism. The traffic network module is responsible for simulating an urban traffic network, real-time traffic condition monitoring, and traffic policy management. The system management module is responsible for overall management and monitoring of the system to ensure stable operation of the system. The data management module is responsible for storing and managing user data, traffic data, and historical travel data to ensure data security and privacy protection.

[0009] Preferably, the network communication module performs signaling and connection management through WebSocket, uses UDP to transmit large-capacity or real-time data streams, and implements a data retransmission mechanism to ensure that key data is not lost, while using the confirmation mechanism of WebSocket to improve the reliability of data transmission.

[0010] Preferably, the simulation model module adopts a discrete event simulation method, updates the status according to traffic policy events to reduce unnecessary calculations, uses a timing synchronization control mechanism based on simulation node relationships to manage the interaction between different simulation nodes and control nodes, and uses a genetic algorithm in combination with simulation results to find the optimal traffic policy and network configuration.

[0011] The simulation model module performs the simulation process of residents' travel behavior based on the small-world network model, and manages it as the main thread by the thread management module. After that, the traffic policy event is used as a new constraint condition, and a new thread is created by the thread pool based on the main thread to execute the simulation process again. In this process, the introduced traffic policy event can be a simulation process in which multiple policies work together. At the same time, in the simulation process where some constraints exist before, a new traffic policy event can be added to generate a new thread to refer to the situation where new traffic policy events need to be implemented over time.

[0012] Timing synchronization control mechanism processing flow based on simulation node relationship:

[0013] First, the control node parses the simulation result data received each time, obtains the data source node object according to the parsing result, and updates the current simulation time of the data source node object. Secondly, the successor nodes of the data source node are traversed to determine whether the difference between the current simulation time of the data source node and the successor node meets the simulation time interaction window between the two nodes. If not, it means that the source data is not forwarded to the successor node, and the next successor node is detected. Otherwise, the data is temporarily stored in the successor node.

[0014] After confirming the forwarding of the successor node, traverse all the predecessor nodes of the successor node to determine whether the difference between the simulation time of the successor node and the simulation time of its predecessor node meets the simulation time window between the two nodes. If so, it means that the simulation result data set to be sent this time should include the data of the predecessor node. If not, it means that the predecessor node is still in simulation, and it is necessary to continue waiting without sending data. On the contrary, when the simulation result data to be sent of the successor node contains all the predecessor node data that meet the time window, the data is sent.

[0015] Preferably, the thread management module uses thread pool technology to manage the creation and destruction of threads to avoid frequent thread overhead, adopts Wait operation timeout control design to avoid indefinite thread waiting, combines semaphores to control access to shared resources to prevent competition conditions and data inconsistencies, and dynamically adjusts the execution order of threads according to task priority and system load through an adaptive scheduling algorithm.

[0016] Preferably, the traffic network module is visually edited through the Openlayers framework based on the MATSim road network model, and the intersection node types and allowed vehicle types of the SUMO road network model are introduced. A multi-person collaborative editing solution is adopted to distribute the source road network file to different workers, and the editing results are integrated through the multi-road network file merging service. Policy evaluation is carried out according to the implementation effects of different simulations to provide a basis for decision-making by the traffic management department.

[0017] Preferably, the system management module uses an automated script to generate a timestamp and version number for each system file in order to track the source of the version, support integration with popular monitoring tools, enhance monitoring and visualization capabilities, develop response and alarm mechanisms, and quickly respond to the impact of system failures.

[0018] Preferably, the data management module removes or replaces sensitive information through data desensitization technology to protect user privacy, and uses incremental backup technology to regularly back up changed data to ensure that data can be restored after loss or damage.

[0019] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing any one of the above-mentioned resident travel behavior dynamic simulation systems based on social network modeling.

[0020] A terminal device includes a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executed by any one of the above-mentioned dynamic simulation systems for residents' travel behavior based on social network modeling.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The present invention constructs a small-world network model and simulates social resident interactions through resident travel behavior selection rules. It takes traffic policies as actual constraints, can capture complex interactions between individuals, substitute real data to reflect social relations and network structures in the real world, and can simulate the effects of different traffic strategies to help decision makers evaluate the potential impact of policy or behavior changes. It uses thread management to simulate basic resident travel behavior in a variety of scenarios, helps identify key factors and potential risks, supports different types of individual behaviors and network topologies, and adapts to the research needs of various social phenomena.

[0023] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a framework diagram of the dynamic simulation system of residents' travel behavior based on social network modeling of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Embodiment 1:

[0027] See also Figure 1As shown, the dynamic simulation system of residents' travel behavior based on social network modeling includes: a simulation model module and a thread management module. The simulation model module is responsible for completing the simulation process based on the small-world network model, so that it can run independently in the form of threads. The thread management module is responsible for controlling the execution status of the simulation thread through the thread Wait / Notify mechanism.

[0028] The simulation model module adopts discrete event simulation method, updates the status according to traffic policy events to reduce unnecessary calculations, uses a timing synchronization control mechanism based on the relationship between simulation nodes to manage the interaction between different simulation nodes and control nodes, and uses genetic algorithms based on simulation results to find the optimal traffic policy and network configuration.

[0029] A small-world network model L of residents’ travel behavior choice is constructed, where L = (N, S, R). In the network model, N = (1, 2, …, n), where n represents the number of nodes in the network; S = {S(i)∈N} is the set of all edges in the small-world network; R = {r ij i,j∈N} represents the set of all relationship concentrations between residents in the small-world network, r ij represents the relationship concentration between residents i and j. Here, it is assumed that the relationship concentration between resident nodes is symmetric, that is, r ij =r ji .

[0030] In the network model, the parameters are set as follows: the number of nodes N in the network model is set to 500; the number of nodes k is set to 8; the probability of reconnection of broken edges p is set to 0.09; the simulation step size T is 200,000 times, and the current step size is set to 1. At the same time, it is set that each node can choose multiple behaviors and can correctly mark its own behavior. Therefore, it is assumed that each node in the constructed resident travel behavior selection small-world network model can choose at most X behaviors. i represents the resident node, i = (1,2,…,n); c represents the type of behavior, c∈[1,X], v∈[i,c] is the value selected by resident node i on h behavior, and the behavior selected by resident node i in the small-world network at time t is the average of X behaviors.

[0031] Network simulation process: Residents' travel behavior nodes are divided into persuasion, acceptance and freedom. These three nodes follow certain selection evolution strategies, and different selection strategies affect the overall balance of the small-world network. For the convenience of comparison, the selection methods of residents' travel behavior are set to three types: random selection, agglomeration priority and distance priority mode.

[0032] (1) Random selection mode setting

[0033] The random mode resident travel behavior selection is set as follows: when selecting, the condition for any receiving node a in the network to diffuse with a certain adjacent node b is that there is a "different resident travel behavior" class h with a hierarchy. At this moment, the probability of resident travel behavior selection of the persuasion node q is greater than that of the receiving node a, while for the free party, the probability of its resident travel behavior selection remains unchanged.

[0034] (2) The principle of setting cluster priority

[0035] The travel behavior selection of residents in the agglomeration priority mode is set as follows: when residents choose their travel behavior, among all adjacent nodes that meet the "travel behavior of different residents", based on the "agglomeration priority" principle, residents choose the node with the largest agglomeration degree as the receiver. At this time, the premise for each resident to drive other residents is v[i,c]>v[j,c], and r ij =Max{r ij}.

[0036] (3) Setting principle of distance priority

[0037] The travel behavior selection of residents in the distance priority mode is set as follows: when residents make travel behavior choices, among all adjacent nodes that meet the "inconsistent travel behavior selection of residents" condition, based on the "distance priority" principle, residents choose the node with the largest difference from their own choice as the receiver. At this time, the premise for each resident to drive other residents is v[i,c]>v[j,c], and v[i,c]=Max{v[i,c]}.

[0038] In the network model, the persuasion party has three behavior selection modes, while the recipient and free party are randomly selected. For the occurrence of resident travel behavior, any resident node is randomly selected as the recipient from all resident nodes that meet the "different resident travel behavior" condition. Therefore, the recipient must satisfy the condition that its resident travel behavior is different from that of the influencing party.

[0039] Example: To explore the evolution trend of residents' travel behavior choices under the influence of relationship agglomeration, the travel behavior choice formulas of residents i and j are as follows:

[0040]

[0041] v[i,c](d+1)=v[j,c](d)

[0042] In the formula, is the travel behavior selection coefficient of resident j, and its value interval is (0,0.2], and the relationship aggregation degree between the travel behavior selection nodes of residents is r ij , whose value interval is (0,1], and d is the step length of residents’ travel behavior selection.

[0043] In the network model, the travel behavior choice value of residents at time t is calculated as follows:

[0044]

[0045] The thread management module uses thread pool technology to manage the creation and destruction of threads to avoid frequent thread overhead, adopts Wait operation timeout control design to prevent threads from waiting indefinitely, combines semaphores to control access to shared resources to prevent competition conditions and data inconsistencies, and dynamically adjusts the execution order of threads according to task priority and system load through an adaptive scheduling algorithm.

[0046] Embodiment 2:

[0047] The dynamic simulation system of residents' travel behavior based on social network modeling also includes: network communication module, system management module, data management module, and traffic network module. The network communication module is responsible for connecting with the control node and data communication, and realizes decoding, encoding and processing of data packets. The system management module is responsible for the overall management and monitoring of the system to ensure stable operation of the system. The data management module is responsible for storing and managing user data, traffic data and historical travel data to ensure data security and privacy protection. The traffic network module is responsible for simulating the urban traffic network, real-time traffic condition monitoring and traffic policy management.

[0048] The network communication module performs signaling and connection management through WebSocket, uses UDP to transmit large-capacity or real-time data streams, and implements a data retransmission mechanism to ensure that key data is not lost. At the same time, it uses the WebSocket confirmation mechanism to improve the reliability of data transmission.

[0049] The traffic network module is based on the MATSim road network model and is visually edited through the Openlayers framework. It introduces the intersection node types and vehicle types allowed by the SUMO road network model, adopts a multi-person collaborative editing solution to distribute the source road network files to different workers, and integrates the editing results through the multi-road network file merging service. It conducts policy evaluation based on the implementation effects of different simulations, providing a basis for decision-making by traffic management departments.

[0050] The system management module uses automated scripts to generate timestamps and version numbers for each system file to track the source of the version. It supports integration with popular monitoring tools, enhances monitoring and visualization capabilities, develops response and alarm mechanisms, and quickly responds to the impact of system failures.

[0051] The data management module uses data desensitization technology to remove or replace sensitive information to protect user privacy, and uses incremental backup technology to regularly back up changed data to ensure that data can be restored after loss or damage.

[0052] From the above, it can be seen that the present invention constructs a small-world network model and simulates social resident interactions through resident travel behavior selection rules. It takes traffic policies as actual constraints, can capture complex interactions between individuals, and substitute real data to reflect social relations and network structures in the real world. At the same time, it can simulate the effects of different traffic strategies and help decision makers evaluate the potential impact of policy or behavior changes. It uses thread management to simulate basic resident travel behavior in a variety of scenarios, help identify key factors and potential risks, support different types of individual behaviors and network topologies, and adapt to the research needs of various social phenomena.

[0053] Embodiment 3:

[0054] An embodiment of the present invention also provides a terminal device, including a processor and a computer-readable storage medium, the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing any of the above-mentioned residents' travel behavior dynamic simulation systems.

[0055] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned resident travel behavior dynamic simulation system embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium, such as read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random ACGessMemory, referred to as RAM), a disk or an optical disk, etc.

[0056] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0057] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a program, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A way to specify functions in one or more boxes.

[0058] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0060] In the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0061] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0062] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Dynamic simulation system of residents' travel behavior based on social network modeling, including: The network communication module, simulation model module, thread management module and traffic network module are characterized in that the network communication module is responsible for connecting with the control node and data communication to realize decoding, encoding and processing of data packets; the simulation model module is responsible for completing the simulation process based on the small-world network model so that it can run independently in the form of threads; the thread management module is responsible for controlling the execution status of the simulation thread through the thread Wait / Notify mechanism; and the traffic network module is responsible for simulating the urban traffic network, real-time traffic condition monitoring and traffic policy management.

2. The resident travel behavior dynamic simulation system based on social network modeling according to claim 1 is characterized by: The network communication module performs signaling and connection management through WebSocket, uses UDP to transmit large-capacity or real-time data streams, and implements a data retransmission mechanism to ensure that key data is not lost, while using the WebSocket confirmation mechanism to improve the reliability of data transmission.

3. The resident travel behavior dynamic simulation system based on social network modeling according to claim 2 is characterized by: The simulation model module adopts discrete event simulation, updates the status according to traffic policy events to reduce unnecessary calculations, uses a timing synchronization control mechanism based on simulation node relationships to manage the interaction between different simulation nodes and control nodes, and uses a genetic algorithm combined with simulation results to find the optimal traffic policy and network configuration.

4. The resident travel behavior dynamic simulation system based on social network modeling according to claim 3 is characterized by: The thread management module uses thread pool technology to manage the creation and destruction of threads to avoid frequent thread overhead, adopts Wait operation timeout control design to avoid indefinite thread waiting, combines semaphores to control access to shared resources to prevent competition conditions and data inconsistency, and dynamically adjusts the execution order of threads according to task priority and system load through an adaptive scheduling algorithm.

5. The resident travel behavior dynamic simulation system based on social network modeling according to claim 4 is characterized by: The traffic network module is visually edited through the Openlayers framework based on the MATSim road network model, and the intersection node types and allowed vehicle types of the SUMO road network model are introduced. A multi-person collaborative editing solution is adopted to distribute the source road network files to different workers, and the editing results are integrated through the multi-road network file merging service. Policy evaluation is carried out according to the implementation effects of different simulations, providing a basis for decision-making by traffic management departments.

6. The resident travel behavior dynamic simulation system based on social network modeling according to claim 5 is characterized by: The system also includes a system management module and a data management module. The system management module is responsible for the overall management and monitoring of the system to ensure stable operation of the system. The data management module is responsible for storing and managing user data, traffic data and historical travel data to ensure data security and privacy protection.

7. The resident travel behavior dynamic simulation system based on social network modeling according to claim 6 is characterized by: The system management module uses automated scripts to generate timestamps and version numbers for each system file in order to track the source of the version, support integration with popular monitoring tools, enhance monitoring and visualization capabilities, develop response and alarm mechanisms, and quickly respond to the impact of system failures.

8. The resident travel behavior dynamic simulation system based on social network modeling according to claim 7 is characterized by: The data management module removes or replaces sensitive information through data desensitization technology to protect user privacy, and uses incremental backup technology to regularly back up changed data to ensure that data can be restored after loss or damage.

9. A computer-readable storage medium, characterized in that: There are a plurality of instructions, which are suitable for being loaded by a processor of a terminal device and executing the resident travel behavior dynamic simulation system based on social network modeling according to any one of claims 1-8.

10. A terminal device, characterized in that: It includes a processor and a computer-readable storage medium, the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded and executed by the processor to implement the dynamic simulation system of residents' travel behavior based on social network modeling as described in any one of claims 1-8.

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