Traffic Bottlenecks Research Articles

A method for estimating vehicle delays at parking lot exits in urban networks

Parking lot entrances and exits are places where traffic conflicts are serious and traffic delays often occur. The accurate calculation of vehicle delays is crucial for parking lot entrance and exit management and is beneficial for reducing the interaction between static and dynamic traffic. Field data can be used to determine the vehicle delay, but the process of collecting field data is costly and time-consuming. Therefore, it is necessary to determine the vehicle delay for vehicles at the entrance and exit of the parking lot through mathematical models, as this method is efficient and cost-saving. This paper provides an estimation model based on renewal theory, the Poisson distribution, and the negative exponential distribution to study the delay time of a vehicle preparing to make a right turn at the curb of a parking lot exit (VCPLE). The study focuses on the yielding behaviors of the closest approaching vehicle (CAV) that is travelling in the lane into which the VCPLE is about to merge, and that will be positioned immediately behind the VCPLE once the merge is completed. Furthermore, the headway distance between the VCPLE and the CAV is also considered in addressing the problem. Additionally, a comparative analysis of vehicle delays was conducted via field data alongside predictions from the proposed model and the Cowan M3 model. The results demonstrate the success of the model developed in this study in estimating vehicle delays for the VCPLE. Furthermore, upon comparing the calculation results with those of the M3 model, it becomes evident that the model constructed in this study offers a more precise estimation of vehicle delays at parking lot exits. By calculating the delays experienced by vehicles within the parking lot entrance and exit zones, a comprehensive understanding of the traffic operational status in these areas can be obtained. This, in turn, aids in assessing the efficiency of the transportation system. This analysis enables traffic management authorities to promptly identify and address traffic bottlenecks, ultimately contributing to an improvement in road capacity.

Addressing the urban congestion challenge based on traffic bottlenecks.

Historically, urban congestion and street life quality depended on city network hierarchies, shaped by land use and street layout. Yet navigation apps have shifted focus to travel time as the key route selection factor, challenging traditional urban structures. We review the development of an innovative approach to urban traffic management that leverages real-time data for the identification and analysis of traffic bottlenecks. This approach, combined with urban planning, aims to improve traffic flow and tackle modern urban challenges. It includes real-time bottleneck detection and cost, congestion analysis and designing a decentralized traffic management system that can serve planners. Based on complex system principles, it promises dynamic traffic optimization, merging urban planning with digital advancements. The research demonstrates the potential of different applications of the proposed methodologies to predict significant congestion from early bottleneck formation, offering urban planners a powerful toolset for reasserting their role in shaping the urban experience. This article posits that a nuanced understanding of traffic dynamics, coupled with advanced traffic management technologies, can restore the influence of urban planning in the digital era, fostering more liveable, equitable and efficient urban environments. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Real-time Vehicle Monitoring and Route Prediction using Geolocation through LSTM

Vehicle travel, such as by bus, automobile, etc., is a common way to use public transportation since it is convenient, economical, easily accessible, and eco-friendly. However, there are moments when its challenging to keep an eye on everyone while traveling, avoid traffic bottlenecks, and provide enough service, which makes the passengers bored. In this paper, we offer a real-time geolocation Android application that gathers real-time geolocation data (longitude and latitude), date, time, and speed, and integrates it with Google Firebase to assist predictive analytics for the operational efficiency of a vehicle firm. After being exported from the database as a JSON file, the data must be transformed into a CSV file. The company will be able to anticipate a vehicles future location on a predetermined route based on date, time, and speed by using this database to train an LSTM (Long short-term memory) model, a sort of sequential neural network. Determining the vehicles expected arrival time, allocating passengers, and predicting traffic conditions will therefore be useful in preventing congestion. Utilizing this technology allows a car company to improve scheduling, boost monitoring, and provide better overall service.

From Virtual Trees to Real Forests: Public Participation in Virtual Forest Realization Projects in China

With the rapid development of Internet technology, the channels for the public to participate in forest protection and ecological construction have been expanded. In China, virtual forest realization projects have successfully heightened environmental awareness among the public. However, these projects have also faced a “green gap”, characterized by a substantial disconnect between environmental intentions and actual participation behaviors. Based on 69 articles about virtual forest realization projects in China, this paper aims to reveal the roots of the intention–behavior gap, influencing factors, and potential mitigation strategies. Our findings indicate the following: (1) While virtual forest realization initiatives have made progress, they still face multiple challenges such as governmental inefficiencies and platform traffic bottlenecks. (2) The factors influencing public participation in virtual forest realization projects can generally be categorized into three types: external situational factors, internal psychological factors, and demographic variables. Initially, external influences like online word-of-mouth and gamified platforms played a crucial role in attracting the public. Over time, internal factors such as public trust in the platform and perceived enjoyment have become more prominent. Inconsistent findings regarding demographic variables suggest a need for further research. (3) Current solutions primarily focus on macro policies and have not fully addressed existing challenges. This study reveals the reasons and influencing factors of the intention–behavior gap, with the goal of offering valuable insights for future public engagement and project optimization.

Data-driven bottleneck detection on Tehran highways

In metropolitan areas, traffic congestion has become a prevalent challenge due to rapid urbanization and increased vehicle usage, adversely impacting mobility, productivity, and quality of life. Identifying and mitigating persistent traffic bottlenecks is crucial for developing efficient transportation systems and guiding infrastructure planning decisions. This research proposes an innovative data-driven methodology to pinpoint recurrent traffic bottlenecks in Tehran's extensive highway network, addressing the limitations of traditional traffic monitoring methods. Through data mining and image processing techniques applied to 16 months of traffic flow maps from Google Maps, diverse information is extracted, including traffic nodes, congestion hotspots, and locations with the longest queue lengths. The image processing approach involves color-based segmentation, pixel-level analysis, and machine learning algorithms to determine congestion levels across the highway network. The identified bottlenecks are validated against ground truth data from CCTV cameras, demonstrating a remarkable 92 % correlation for key identified points. The proposed approach leverages the power of advanced analytics to comprehensively analyze all major highways, including areas lacking CCTV infrastructure. The robust validation process reinforces the reliability of this data-driven solution in capturing real-world traffic dynamics. As urban mobility challenges escalate globally, the integration of diverse data sources and cutting-edge techniques will be instrumental in guiding intelligent transportation planning and policy decisions.

Unveiling the impact of heterogeneous driving behaviors on traffic flow: A mesoscale multi-agent modeling approach

There are fewer simulation studies that comprehensively consider the impact of collision events due to heterogeneous driving behaviour on multi-lane traffic flow. This comprehensive study utilized multi-agent modeling to simulate the driver-vehicle-road system at the mesoscale level, integrating the characteristics of heterogeneous driving behavior. We developed a traffic flow simulation model for a three-lane urban arterial road segment and used rule-based algorithms to simulate vehicle following, lane changing, and collision behavior. A visualization simulation program was developed using NetLogo software to observe and analyze vehicle behavior. The findings revealed that collision events were the leading cause of traffic bottlenecks, impacting the continuity and coherence of traffic flow. Even after the collision event subsided and congestion eased, the average vehicle speed could not fully recover to its original level, reaching only about 70% of the maximum vehicle speed. Aggressive drivers exhibited distinct speed control strategies compared to conservative and ordinary drivers. This study demonstrates the effectiveness of multi-agent modeling in capturing the relationship between traffic bottlenecks and collision events, highlighting the influence of dynamic traffic events on driving behavior.

DCoMA: A dynamic coordinative merging assistant strategy for on-ramp vehicles with mixed traffic conditions

Merging sections on highways are identified as traffic bottlenecks, leading to congestion and accidents. The emergence of Connected and Autonomous Vehicles (CAVs) technology promises an optimized solution to on-ramp merging issues. Existing cooperative merging strategies typically focus on determining the merging sequence of vehicles in specific merging areas, overlooking the influence of the macroscopic traffic flow conditions on the merging process. In this paper, we innovatively introduce the Dynamic Cooperative Merging Assistance (DCoMA) strategy, a traffic management approach designed to enhance merging operations under variable traffic demands. At the macroscopic level, DCoMA employs the fundamental diagram of traffic flow to develop a platoon formation algorithm for mainline vehicles, tailored to the dynamic macroscopic states of traffic. The algorithm adaptively adjusts the size of platoons based on the volumes of both the mainline and the on-ramp. Subsequently, the spatio-temporal dynamics of these platoons function as the ’red phase’ of traffic signals, with the intervals between platoons analogous to the ’green phase’. This information is then converted into a time-series format and transmitted to all vehicles on the on-ramp. At the microscopic level, vehicles on the on-ramp alter their driving strategies in response to this time-series data, ensuring a seamless merging into the mainline flow without causing halting. Through simulations and comparative analysis with three existing strategies (i.e., CoopMA, ALINEA, and X-ALINEAQ), the proposed DCoMA strategy exhibits considerable potential for application across diverse traffic volumes and CAV penetration rates. The results indicate that the proposed approach can significantly improve the efficiency of the mainline and on-ramp, achieving a maximum improvement of 18.32%. More importantly, the DCoMA strategy effectively enlarges merging gaps and, coupled with the speed control capabilities of CAVs, significantly mitigates the risk of accidents and reduces exhaust emissions.

Traffic state estimation from vehicle trajectories with anisotropic Gaussian processes

Accurately monitoring road traffic state is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making. This paper proposes a novel method for imputing traffic state data using Gaussian processes (GP) to address this issue. We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel, which can better model the congestion propagation in traffic flow data. The model parameters can be estimated by statistical inference using data from sparse probe vehicles or loop detectors. Moreover, the rotated GP method provides statistical uncertainty quantification for the imputed traffic state, making it more reliable. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. We evaluate our method using real-world traffic data from the Next Generation simulation (NGSIM) and HighD programs, along with simulated data representing a traffic bottleneck scenario. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation (TSE) scheme from 5% to 50% available trajectories, mimicking different CV penetration rates in a mixed traffic environment. We also test the traffic state estimation when traffic flow information is obtained from loop detectors. The results demonstrate the adaptability of our TSE method across different CV penetration rates and types of detectors, achieving state-of-the-art accuracy in scenarios with sparse observations.

TRAFFIC FLOW PREDICTION USING MACHINE LEARNING

As a result of the ongoing increase in the number of vehicles, there are increasingly severe traffic bottlenecks. The amount of time and money that users of transportation spend traveling will immediately change as a result. To some extent, this issue can be mitigated by projecting future traffic patterns. Three machine learning algorithms—Linear Regression, Decision Tree, and Support Vector Machine—are used in this work to predict the traffic flow after the data has been preprocessed using Selenium, OSS, and Message Queue. Next, we examine real-time Beijing traffic data as it shows up on the Baidu map. According to this study, Random Forest is the most accurate of all three techniques, with an accuracy of 0.719. Second are logistic regression and SVM. Keywords - Traffic FlowPrediction; Decision Tree; Random Forest; SVM; Baysian Ridge; CNN; LSTM

Detection and mitigation of vehicle platooning disruption attacks

In response to the escalating challenges posed by traffic bottlenecks, accidents, and intersection delays, the deployment of platooning mechanisms emerges as an imperative remedy. This investigation revolves around a pivotal scenario wherein a closely-knit platoon of cooperative vehicles approaches an intersection. Orchestrated by a lead vehicle, this convoy navigates the traffic light to expedite the journey of its followers towards their respective destinations. However, given that the size of the platoon can be increased, it will become difficult for the leader to manage it smoothly. Therefore, dividing the platoon into small groups led by co-leaders who deal directly with the leader is considered an ideal solution to speed up and facilitate the process of maintaining platoon stability. In this paper, we propose a Cooperative Adaptive Platooning Control Algorithm (CAPCA) for the hybrid communication topology in platooning. In fact, CAPCA aims to achieve stability in the platoon by distributing tasks in a parallel manner to mini-platoons. Moreover, inherent complexities arise as select members within the platoon strive to undermine its stability. Within this context, the research addresses the intricacies of Vehicle Platooning Disruption (VPD) attacks, a menacing phenomenon characterized by deliberate efforts to destabilize or seize control of a platoon. These attacks manifest through tactics such as false data injection (FDI) and replaying of control messages. In response, a robust and multifaceted countermeasure is conceptualized. The Vehicle Platooning Disruption Attacks Detection Protocol (VPD-ADP) takes center stage as a foundational component. By identifying instability within the platoon's dynamics model, VPD-ADP lays the essential groundwork for mitigating the repercussions of FDI and replay attacks. Employing advanced stochastic time series analysis, the impact of VPD attacks is scrutinized via anomalies in the Cartesian coordinates of vehicles deviations from the trajectory anticipated by the platoon. Moreover, the study introduces the Reputation-based Reliable Mitigation Protocol (RRMP), a pioneering approach that leverages collaborative data gleaned from neighboring vehicles. RRMP is devised to assess the veracity of received messages and gauge their reliability in real-time. Through extensive simulations and experiments, the proposed approach's effectiveness in fortifying the resilience of vehicle platooning systems against an array of disruption attacks is convincingly demonstrated.

Design and Application of Electronic Toll Collection Special Situation Processing System

<div>In 2018, the state explicitly proposed to “promote the cancellation of expressway toll stations at provincial boundaries.” Electronic toll collection has become the main toll collection method on expressways. With the construction of ETC toll lanes, the proportion of ETC vehicles in the expressway traffic flow is increasing, and the rapid processing of vehicle special situations is facing challenges. At present, various provinces have adopted various methods to improve the traffic efficiency and transaction success rate of ETC from the issuance link, customer service link, and lane transaction link. According to statistical data, the average transaction success rate of ETC lane is not higher than 99% at present. As of October 2021, the number of ETC users nationwide has reached 256 million, and there are an average of 40 million ETC transactions per day across the network, that is, about 400,000 special cases need to be processed. How to efficiently deal with special vehicles in the new mode and avoid ramp toll stations becoming traffic bottlenecks is a new problem facing at this stage. In order to fit the application scenarios of ETC lanes and solve the problems of time-consuming, labor-intensive, and high safety risks of traditional special situation processing methods, this article sorts out the classification and application scenarios of expressway ETC special situations under the new model. The development status and existing problems of ETC special situation processing are studied and analyzed. This article also establishes a more rapid and convenient special situation handling mechanism, which simplifies the processing process of ETC lane special situations to the greatest extent, and avoids traffic congestion and potential safety problems caused by ETC special situations. Finally, the self-service processing and unattended lanes are realized, which improves the efficiency of special situation processing and ensures fast passage.</div>

Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment

With the gradual development of autonomous driving and connected communication technology, urban road networks will be shared by a combination of Human-driven vehicles (HDVs) and Connected vehicles (CVs) for a long era. Besides, as the actual vehicle operator, the driver regulates the vehicle state with an accelerator, gear lever, or brake, whereas the stepwise acceleration process is overlooked in the literature. Motivated by this, comprehensive kinetic difference between the two types of vehicles, we propose a novel heterogeneous multi-phase traffic flow accounting for the HDVs and CVs to close this hole. In the section on linear stability analysis, the reductive perturbation approach was applied to figure out the stability norm of the new model. The findings show that, on the one hand, inhibiting traffic bottlenecks is positively impacted by the penetration rate of CVs and the number of preceding vehicles taken into account; on the other hand, by varying the headway between following vehicles, the multiple-phase transition occurs; consequently, the number of turning points in the optimal velocity function is the sole factor that affects the number of stages in multi-phase transitions. Subsequently, the modified Korteweg-de Vries (mKdV) equation corresponding to the new model is deduced to investigate the nonlinear phenomenon of traffic flow at the vicinity of the critical point, and the kink-antikink soliton wave solution by solving the above characteristic equation can describe the “stop-and-go” phenomenon in real traffic flow. Finally, the conclusion of the numerical experiments is consistent with the above theoretical analysis. This research can provide insight into the dynamic evolution of road traffic flow during the transition from HDVs to semi-CVs and eventually to CVs.

Analysis and Improvement of Geographic Information Systems for Problem Solving and Decision Making

The complexity of our way of life has increased due to the numerous parts of life developing quickly, which results in ongoing issues. To address these issues and respond to these rapid changes in the environment, solutions that were practical, quick, and easy to implement were needed. The use of geographic information tools, a contemporary innovation, allows for the implementation of difficult issues by enhancing users' abilities to comprehend problems thoroughly through the analysis of spatial data and the creation of digital maps. This allows decision-makers to save time, effort, and money by making informed choices that will result in the best possible solution to the issue at hand. In addition to a significant urban expansion, an increase in the number of people using vehicles, and a significant emigration of people from rural and small towns, the traffic jams that have recently engulfed much of the world, particularly in the major capitals and cities, are a result of these factors for transportation, have contributed to a complex problem in modern times. The goal of this study is to examine proposed elements and gauge their impact on the issue of traffic bottlenecks. It then suggests both long- and short-term remedies for this issue based on the study's findings, which are generally not found in the pertinent departments. In order to create a unique surface for these components and provide a comprehensive picture of the research region, it also makes satellite photos of the area available. geographical database for each of them, evaluate each layer's influence on the traffic jam independently by analyzing it, and then merge these levels to create full map that shows their location and the extent to which they affect the study problems. This process was done in order to arrive at the aforementioned solution.

Travel Plan Sharing and Regulation for Managing Traffic Bottleneck Based on Blockchain Technology

To alleviate traffic congestion, it is necessary to effectively manage traffic bottlenecks. In existing research, travel demand prediction for traffic bottlenecks is based on travel behavior assumptions, and prediction accuracy is low in practice. Thus, the effect of traffic bottleneck management strategies cannot be guaranteed. Management strategies are often mandatory, leading to problems such as unfairness and low social acceptance. To address such issues, this paper proposes managing traffic bottlenecks based on shared travel plans. To solve the information security and privacy problems caused by travel plan sharing and achieve information transparency, travel plans are shared and regulated by blockchain technology. To optimize the operation level of traffic bottlenecks, travel plan regulation models under scenarios where all/some travelers share travel plans are proposed and formulated as linear programming models, and these models are integrated into the blockchain with smart contract technology. Furthermore, travel plan regulation models are tested and verified using traffic flow data from the Su-Tong Yangtze River Highway Bridge, China. The results indicate that the proposed travel plan regulation models are effective for alleviating traffic congestion. The vehicle transfer rate and total delay rate increase as the degree of total demand increases; the vehicle transfer rate increases as the length of the time interval decreases; and the vehicle transfer rate and total delay rate increase as the number of vehicles not sharing their travel plans increases. By using the model and method proposed in this paper, the sustainability of urban economy, society, and environment can be promoted. However, there are many practical situations that have not been considered in this paper, such as multiple entry and exit bottlenecks, multiple travel modes, and other control strategies. In addition, this paper considers only one bottleneck rather than road networks because of the throughput limitations of blockchain technology.

YOLOV4 Deepsort ANN for Traffic Collision Detection

Every collision must be handled right away to prevent further harm, damage, and traffic bottlenecks. Hence, the implementation of a systematic approach for accident detection becomes imperative to expedite response mechanisms. Our proposed accident detection system operates in three stages, encompassing vehicle object detection, multiple object tracking, and vehicle interaction analysis. YOLOv4 is employed for object detection, while DeepSort is utilized to the tracking of multiple vehicle objects. Subsequently, the positional and interactional data of each object within the video frame undergo thorough analysis to identify collisions, utilizing an Artificial Neural Network (ANN). Notably, collisions involving a single vehicle and not affecting other road users are excluded from the scope of this study. The evaluation of our approach reveals that the ANN model achieves a commendable F-Measure of 0.97 for detecting objects without collisions and 0.88 for objects involved in collisions, based on the conducted tests.

Edge-Enabled Smart Traffic Management System: An IoT Implementation for Urban Mobility

Effective traffic management has emerged as a critical issue in today's rapidly urbanising areas with a rise in the number of cars. By developing an Edge-Enabled Smart Traffic Management System (EESTMS) run on the Internet of Things (IoT), this paper puts forth a novel approach. EESTMS makes use of edge computing and IoT technology's promise to improve urban transportation. An large network of thoughtfully placed sensors and cameras dispersed around the city forms the system's central structure. These gadgets continuously gather data on the volume, speed, and congestion of moving vehicles. This information offers insightful information about traffic trends. We can lessen latency and lighten the load on centralised systems by processing this data at the edge. In order to analyse the data and identify traffic bottlenecks and congestion hotspots, machine learning techniques are used. Real-time analysis allows for dynamic traffic signal adjustments, which optimise traffic flow and shorten commuter travel times. EESTMS also offers a user-friendly interface with real-time traffic information, alternate routes, and tailored navigation advice that is accessible via mobile applications and web platforms. By making wise decisions, commuters can lessen their stress and carbon footprint. EESTMS plays a critical role in advancing sustainability by reducing fuel consumption and greenhouse gas emissions through effective traffic management, in addition to enhancing urban mobility. By giving emergency vehicles priority routing, this system also improves emergency response times. The application of EESTMS has shown promising outcomes in terms of lessened traffic congestion, improved commuter experiences, and lower environmental impact. Innovative solutions like EESTMS can open the door for smarter, more sustainable urban mobility as cities continue to grow, eventually enhancing citizens' quality of life.

Reducing traffic congestion in makkah during Hajj through the use of AI technology

This research addresses the pervasive issue of traffic congestion during the Hajj, where approximately 250,000 vehicles substantially exacerbate travel times and road accidents, while also escalating pollution levels, thereby adversely affecting public health. Aimed at bolstering the Kingdom's Vision 2030, the study focuses on the incorporation of artificial intelligence (AI) and advanced communication technologies to optimize traffic management in Mecca. Through the innovative deployment of smart cameras and real-time data analytics, the proposed system seeks to predict, manage, and alleviate traffic congestion by providing alternative routes and facilitating smoother vehicular movement. An exploration into the myriad benefits of this AI-integrated system reveals potentials such as enhanced road safety, improved emergency response efficiencies, and elevated air quality, thereby contributing to the overall wellbeing of the community and environment. In addition, the research anticipates that reducing traffic bottlenecks will indirectly invigorate local businesses and augment tourism revenues, aligning with the objectives of enhancing economic prosperity. By advocating a multi-faceted approach to crowd monitoring and management, this study underscores the indispensable role of AI in revolutionizing traffic management strategies, despite the challenges posed by the complexity of real-time data simulation and the unique intricacies of the Hajj.

Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions

Heavy traffic jams are difficult to predict due to the complexity of traffic dynamics. Understanding the network dynamics of traffic bottlenecks can help avoid critical large traffic jams and improve overall traffic conditions. Here, we develop a method to forecast heavy congestions based on their early propagation stage. Our framework follows the network propagation and dissipation of the traffic jams originated from a bottleneck emergence, growth, and its recovery and disappearance. Based on large-scale urban traffic-speed data, we find that dissipation duration of jams follows approximately power-law distributions, and typically, traffic jams dissolve nearly twice slower than their growth. Importantly, we find that the growth speed, even at the first 15 minutes of a jam, is highly correlated with the maximal size of the jam. Our methodology can be applied in urban traffic control systems to forecast heavy traffic bottlenecks and prevent them before they propagate to large network congestions.

SMS Spam Detection Using Multiple Linear Regression and Extreme Learning Machines

With the growth of the use mobile phones, people have become increasingly interested in using Short Message Services (SMS) as the most suitable communications service. The popularity of SMS has also given rise to SMS spam, which refers to any unwanted message sent to a mobile phone as a text. Spam may cause many problems, such as traffic bottlenecks or stealing important users' information. This paper, presents a new model that extracts seven features from each message before applying a Multiple Linear Regression (MLR) to assign a weight to each of the extracted features. The message features are fed into the Extreme Learning Machine (ELM) to determine whether they are spam or ham. To evaluate the proposed model, the UCI benchmark dataset was used. The proposed model produced recall, precision, F-measure, and accuracy values of 98.7%, 93.3%, 95.9%, and 98.2%, respectively.

Research on Optimization Algorithm for Urban Traffic Flow Based on Computer Simulation

With the acceleration of urbanization, the problem of urban traffic congestion is becoming increasingly prominent. In order to address this issue, this paper proposes an optimization algorithm for urban traffic flow based on computer simulation. The algorithm simulates urban traffic flow, analyzes traffic bottlenecks and congestion points, and proposes corresponding optimization strategies. Through experiments and data analysis, the effectiveness and feasibility of the algorithm are verified. The achievements of this research have important implications for urban traffic management and optimization.