Traffic Flow Control Research Articles

The optimization algorithm of intelligent transportation systems based on reinforcement learning

With the acceleration of urbanization and the increase in traffic load, traditional traffic management methods struggle to cope with the complexity of modern traffic conditions. Intelligent Transportation Systems (ITS), utilizing modern information technology and artificial intelligence, have improved the efficiency and safety of transportation systems. Reinforcement Learning (RL), as an adaptive optimization method, can dynamically adjust traffic signals and flow control strategies through trial and error and reward mechanisms, optimizing traffic flow management and signal control. This paper investigates the optimization algorithms of intelligent transportation systems based on reinforcement learning, including their design, application, and performance evaluation. Research indicates that RL optimization algorithms can significantly enhance the organizational performance of transportation systems, increasing the efficiency and flexibility of traffic management. Empirical analysis verifies the effectiveness of the algorithm, and future improvement directions are proposed.

TrafficPro: A road speed prediction framework for urban signal-controlled road networks

In view of the problem that traditional deep learning models do not consider the active time-varying characteristics of traffic flow (signal control information) when predicting the speed of urban road networks, and have low prediction accuracy, this paper proposes a speed prediction framework based on generative adversarial networks and graph neural networks. In this framework, the generator network simultaneously encodes the road network traffic flow and signal control information through active and passive prediction modules to generate prediction results, and then uses the discriminator network to improve the generalization of the prediction results. This framework can obtain higher prediction accuracy than traditional time series models and deep learning models. In the real road network speed prediction scenario, this framework can reduce the prediction error compared to the best benchmark model 3%-4%.

Assessing movement-specific resilience of a signalized road network under lane-level cascading failure

Accurately assessing the resilience of the road network is crucial for responding to emergencies and enhancing public safety. Signal control plays a significant role in managing traffic flow. However, its impact is often overlooked in resilience assessments, where traffic flow and signal control are usually considered separately. A Movement-Specific Resilience (MSR) assessment model is proposed to integrate signal timing into resilience analysis. To accurately represent traffic flow paths under phase control, a dual graph is used to depict the topological network, allowing the assessment of relationships among all movements at an intersection. Based on this, a cascading failure model is developed to analyze the impact of signal control on traffic flow reassignment, reflecting how signal timing influences traffic flow propagation after failures. The method is validated using data collected from a sub-road network in Xi’an city. Results reveal the cumulative resilience of single lanes is not equivalent to the resilience of road segments. The MSR is higher when the network’s failure degree is low and decreases as the failure level increases. Furthermore, road saturation is inversely related to MSR, while MSR is proportional to capacity. MSR remains unaffected by failures and oversaturation when capacity exceeds a certain threshold. These insights could be a theoretical foundation for bolstering resilience via signal control adjustments.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance: A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows, comprising human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), face challenges from uncertain disturbances, such as sensor faults, inaccurate driver operations, and mismatched model errors. Furthermore, misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’ perception and platoon safety. In this paper, we develop a two-dimensional robust control method for a mixed platoon, including a single leading CAV and multiple following HDVs that incorporate robust information sensing and platoon control. To effectively detect and locate unknown obstacles ahead of the leading CAV, we propose a cooperative vehicle–infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV. This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner. Additionally, we propose a distributed car-following control scheme with robustness to guarantee the following HDVs, considering uncertain disturbances. We also provide theoretical proof of the string stability under this control framework. Finally, extensive simulations are conducted to validate our approach. The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers, significantly reduce the mean-square deviation in obstacle sensing, and approach the theoretical error lower bound. Moreover, the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Hopf bifurcation control for the traffic flow model considering the tail light effect

The research on traffic congestion control has seen rapid development in recent years. Investigating the bifurcation characteristics of traffic flow and designing control schemes for unstable bifurcation points can offer new methods for alleviating traffic congestion. This paper focuses on studying the bifurcation characteristics and nonlinear control of traffic flow based on the continuous model and the taillight effect. Firstly, the traffic flow model is transformed into a stability model suitable for branching analysis through the use of the traveling wave transform. This transformation facilitates the analysis of stability that reflects unstable traffic characteristics such as congestion. Based on this stability model, the existence condition of Hopf bifurcation is proved and some bifurcation points of the traffic system are identified. Secondly, the congestion and stability mutation behaviors near equilibrium and branching points are studied to understand the formation mechanism of traffic congestion. Finally, control schemes are designed using Chebyshev polynomial approximation and stochastic feedback control to delay or eliminate unstable bifurcation points and relieve traffic congestion. This improved traffic flow model helps explain changes in system stability through bifurcation analysis and identify unstable bifurcation points. It can also effectively manage these points by designing a feedback controller. It is beneficial for controlling sudden changes in traffic system stability behavior and mitigating traffic congestion, with important theoretical significance and practical application value.

An internal stochastic car-following model: Stochasticity analysis of mixed traffic environment

This paper investigates the impact of adaptive cruise control(ACC) vehicles on the stochasticity of human driving behavior by constructing a stochastic car-following model of human-driven vehicles (HDVs). Utilizing NGSIM dataset, the relationship between acceleration variance and space headway is analyzed, and a novel stochastic car-following model with headway is proposed to capture the internal stochasticity of drivers. Furthermore, the interaction between HDVs and AVs is explored by discussing stochasticity and stability in mixed traffic flow, using the proposed HDV model. The model parameters are calibrated based on NGSIM dataset and the simulation results indicate that the proposed stochastic car-following model can effectively reproduce the generation and propagation of traffic shocks without lane changes. Additionally, the simulations reveal that as the penetration rate of AVs increases in a lower range (0%–50%), the stochasticity of HDVs and stability in mixed traffic flow is substantially reduced. However, at higher penetration rates, increases in the AV penetration rate have a limited effect on the stochasticity of human driving behavior and the stability of mixed traffic flow. Concurrently, under conditions of low penetration rates, a smaller AV platoon size contributes more effectively to enhancing the stability of traffic flow and suppressing the stochastic behavior of HDVs. This research provides new insights for optimizing traffic flow control with automated vehicles.

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

A Mixed Traffic Flow Capacity Vehicle Flow Control Strategy Combining Vehicle Networking Technology and Autonomous Driving Technology

A Mixed Traffic Flow Capacity Vehicle Flow Control Strategy Combining Vehicle Networking Technology and Autonomous Driving Technology

Secure Routing Protocol Based on Junction Selection in VANETs (SERPROV)

Road safety has become a major issue over the last twenty years. The existing transportation system has become inefficient as the number of vehicles has increased remarkably, causing more accidents and other problems. To meet these challenges, the field of Intelligent Transport Systems (ITS) has been proposed. ITS, also known as intelligent transport, is based on vehicle ad hoc networks (VANETs), a subset of mobile ad hoc networks (MANETs) that use moving cars as nodes to create mobile networks. ITS includes various applications such as cooperative traffic monitoring, blind crossings, collision avoidance and traffic flow control. However, dynamic topology and secure communication remain major challenges due to the high mobility of nodes and the random speed of vehicles. This paper proposes a secure protocol called SERPROV (Secure Routing Protocol based on Junction Selection in VANETs) that combines routing and security using a junction selection mechanism and applies an asymmetric cryptography protocol. Simulation results show that SERPROV improves response time and packet delivery ratio compared to existing protocols for a number of vehicles less than or equal to 300. We plan to implement blockchain technology in the future to replace the public key register, thus making the protocol fully decentralized and further exploring the confidentiality of messages exchanged in the VANET environment.

A Novel Method for Reducing Vehicle Emissions Utilizing IoT-Based IS-APCPSO Algorithm

ABSTRACT Urban road traffic network optimization based on the Internet of Things (IoT) is one of the important methods to reduce vehicle exhaust and ease traffic congestion. A two-level planning model on road traffic network optimization in IoT and the IS-APCPSO algorithm is deployed, which can effectively solve the above problems. The experimental results show that the total vehicle emissions of the proposed scheme are lower than those of traditional schemes. Moreover, the real-time perception, interactive coupling, and coordinated control of “people-vehicle-road-environment” can be achieved through the use of the IoT. We discuss the sensitivity of the model to establish a scene selection that takes into account different regional and road traffic conditions, avoiding the subjective randomness in model parameter selection. A novel multi-objective method based on IoT proposed in this paper helps to alleviate “urban diseases,” such as traffic congestion, vehicle emissions, and energy waste, and to emphasize the overall benefits of energy-saving and emission reduction in urban road networks. By setting up traffic lights reasonably and guiding the driver behavior, the integrated control and management of traffic flow and exhaust emission under multiple driving cycles are realized.

Joint Energy-Efficiency Communication Optimization and Perimeter Traffic Flow Control for Multi-Region LTE-V2V Networks

Joint Energy-Efficiency Communication Optimization and Perimeter Traffic Flow Control for Multi-Region LTE-V2V Networks

Multi-gated perimeter flow control for monocentric cities: Efficiency and equity

A control scheme for the multi-gated perimeter traffic flow control problem of monocentric cities is presented. The proposed scheme determines feasible and optimally distributed input flows for various gates located at the periphery of a protected network area. A parsimonious model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, the basic state-space model is augmented with additional state variables to account for vehicle queues at store-and-forward origin links at the periphery. The multi-gated perimeter flow control problem is formulated as a convex optimisation problem with finite horizon, and constrained control and state variables. This scheme aims to equalise the relative queues at origin links and to maintain the vehicle accumulation in the protected network around a desired set point, while the system’s throughput is maximised. For real-time control, the optimal control problem is embedded in a rolling-horizon scheme using the current state of the whole system as the initial state as well as predicted demand flows at origin/entrance links. Furthermore, practical flow allocation policies for single-region perimeter control strategies without explicitly considering entrance link dynamics are presented. These policies allocate a global perimeter-ordered flow to a number of candidate gates at the periphery of a protected network area by taking into account the different geometric characteristics of origin links. The proposed flow allocation policies are then benchmarked against the multi-gated perimeter flow control. A meticulous study is carried out for a 2.5 square mile protected network area of San Francisco, CA, including fifteen gates of different geometric characteristics. The results showed that the proposed approach is able to manage excessive queues outside of the protected network area and to optimally distribute the input flows, which confirms its efficiency and equity properties. Similar policies are expected to be utilised for dynamic routing and road pricing.

A large-scale traffic signal control algorithm based on multi-layer graph deep reinforcement learning

Due to its capability in handling complex urban intersection environments, deep reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control (ATSC). However, most existing algorithms are designed for specific road networks or traffic conditions, making it difficult to transfer them to new environments. Moreover, current graph-based algorithms do not fully capture the geometric and spatial features of intersections, leading to incomplete embedding of the agent's environment depiction. Additionally, the actions adopted by these algorithms are inherently based on fixed-cycle phases, limiting the flexibility of traffic signal control. To address the aforementioned issues, this paper proposes a Multi-layer Graph Mask Q-Learning (MGMQ) algorithm for multi-intersection ATSC to optimize traffic and reduce delay. Unlike previous graph-based algorithms, this paper introduces a method for computing multi-layer graphs, dividing the traffic environment into upper-level traffic network-layer graphs and lower-level intersection-layer graphs, and employs the graph attention algorithm and an improved GraphSAGE algorithm for computation. This method not only enables the generation of embedded state for intersections that include geometric and spatial features, but also allows the algorithm to adapt to different traffic conditions and road networks. Additionally, we introduce an action masking mechanism, allowing this algorithm can be adapted to different action spaces. As a result, the algorithm uses arbitrary signal phases as actions to achieve flexible traffic flow control, and can be directly applied to intersections with arbitrary geometry. The final test results demonstrate that a model trained solely on synthetic road networks can be directly transferred to other synthetic network configurations or real-world urban road networks, outperforming current state-of-the-art algorithms.

Bicycle as a traffic mode: From microscopic cycling behavior to macroscopic bicycle flow

Cities allocate dedicated road space to bicycles in favor of active-mode road users. For urban environments with a mass bicycle volume, bicycle traffic congestion is likely to occur. Hence, a thorough understanding of bicycle traffic flow is necessary for the assessment of cycling infrastructure and the development of traffic management strategies considering cycling efficiency. This study aims to investigate bicycle flow characteristics using microscopic traffic simulation. As bicycle flow performance is subject to the non-lane-based movement strategy and the behavioral heterogeneity among cyclists, various scenarios with different microsimulation settings are evaluated. Ultimately, we derive the functional form fundamental diagrams and macroscopic fundamental diagrams using a curve-fitting approach and an analytical method, respectively. Important macroscopic traffic flow parameters, such as capacity, critical speed, critical density, backward wave speed, etc., are estimated. The results show that lane width, overtaking incentive, and desired speed distribution are factors that affect bicycle flow performance. The distinct features of bicycle flow under different traffic states are identified by discussing the simulation outcome and comparing the estimated flow parameters. The findings can be utilized by future research regarding large-scale bicycle traffic flow modeling and control.

High magnitude of road traffic accident among traumatized patients in Addis Ababa, Ethiopia: A facility-based cross-sectional study

Background: Globally, road traffic accidents cause around 50 million injuries and disabilities and close to 1.35 million deaths every year. In Ethiopia, approximately five thousand deaths are estimated to occur annually due to road traffic accidents. However, there is limited data on the precise burden of the problem in Ethiopia. This study aimed to assess the magnitude of road traffic accidents and associated factors among traumatized patients who visited the emergency units of selected public hospitals in Addis Ababa, Ethiopia. Methods: A facility-based cross-sectional study design was conducted on 381 trauma patients who visited the emergency department of three public hospitals in Addis Ababa between June 1 and July 12, 2022. The data were collected using a structured questionnaire, entered into Epi-data, and exported to SPSS for statistical analysis. A logistic regression model was used to identify factors contributing to road traffic accidents at a significant level of P < 0.05. Results: The majority of participants were males (73.5%), between 20 and 39 years old (65.1%), and married (45.7%). The prevalence of road traffic accidents among traumatized patients was 37.3% (95% CI: 32.5, 42.3) in public hospitals in Addis Ababa. After adjusting for potential confounders, being male [AOR (95% CI) = 2.77 (1.6, 4.8)], traveling in the afternoon [AOR (95% CI) = 0.6 (0.3, 0.9)], and alcohol-drinking before travel [AOR (95% CI) = 2.50 (1.6, 4.0)] were factors contributing to road traffic accidents. Conclusion: The prevalence of road traffic accidents was very high in Addis Ababa public hospitals. Being male, traveling in the afternoon, and drinking alcohol before travel were significantly associated with an increased risk of road traffic accidents. Community awareness creation, strict traffic flow control in the morning, and alcohol breath tests will ultimately decrease road traffic accidents. In the meantime, expansion and strengthening of pre-hospital care and post-crash trauma care services.

Democratizing traffic control in smart cities

To improve the performance of systems, optimization has been the prevailing approach in the past. However, the approach faces challenges when multiple goals shall be simultaneously achieved. For illustration, we study a multi-agent system, where agents have a plurality of different, and mutually inconsistent goals. We then allow agents in the system to vote on which traffic signal controllers, which were trained on different goals using deep reinforcement learning, would control the intersection. Taking decisions based on suitable voting procedures turns out to lead to favorable solutions, which perform highly for several goals rather than optimally for one goal and poorly for others. This opens up new opportunities for the management or even self-governance of complex systems that require the consideration and achievement of multiple goals, such as many systems involving humans. Here, we present results for traffic flows in urban street networks, which suggest that “democratizing traffic” would be a promising alternative to centralized control of traffic flows.

Algorithm for Control of Traffic Flow in an Intelligent Transport System

Traffic management in modern Intelligent Transport Systems (ITS) includes monitoring the actual traffic situation in real time and management transport traffic using this information. At the same time, cars as ITS components must be equipped with communication capabilities for exchanging information with other vehicles (V2V) and road infrastructure (V2I). Such approach is connected with usage of special equipment connected to on-board network for local data collection, which can be exchanged between cars and with a central communication station using wireless Internet. At the same time, the issue of developing the traffic organization algorithms themselves is still open. This problem can be solved with the help of game theory, a fairly new but rapidly developing part of modern mathematics. Unlike optimization theory, which studies the possibilities of constructing an optimal solution for the entire system as a whole, game theory studies ways to optimize individual benefits in competition with other persons (events) that rationally seek to satisfy their own benefits. The problem of "smart" regulation of intersections is quite difficult to solve a problem, nevertheless it is possible due to rapid development of ICT technologies. Keywords: ITS, transport crossroads, game theory

The Influence of the Stationarity Property on Traffic Flow Control

This study provides an overview of traffic control methods using automated traffic control systems (ATCS). The main approach highlighted during the analysis is the application of rigid multi-program management, including a library of coordination programs. The study also found a dependence of the period of operation of each coordination program on the state and intensity of traffic flow (TF), noting an increase in the period of stationarity with an increase in the intensity of TF. Based on the system analysis, a mathematical dependence of the control parameter on the intensity affecting the efficiency of TF management is proposed. The results obtained can be applied to optimize the operation of the automated control system and develop effective traffic management strategies.

Distributed Multi-Agent Reinforcement Learning for Cooperative Low-Carbon Control of Traffic Network Flow Using Cloud-Based Parallel Optimization

Distributed Multi-Agent Reinforcement Learning for Cooperative Low-Carbon Control of Traffic Network Flow Using Cloud-Based Parallel Optimization

Analysis of Bilibili’s Social Media Marketing Strategy

Nowadays, social media has become one of the main ways of entertainment for people after dinner, which is accompanied by the huge industrial structure and commercial chain behind it. How to attract more customers and reap the maximum benefits in the fierce platform competition has become the basic goal of every platform. In essence, there is a three-way connection between the platform, the company and the user and the communication between them to get the content and traffic they want. Therefore, the cooperation between companies, the innovative use of popular IP, and the accurate grasp of user preferences have become the key to the control of traffic flow of social media. By studying the collected literature on social media and Bilibili’s marketing strategies, this paper explores the marketing strategies of social media platforms in the current era and draws out the development prospects of the social media platform Bilibili in the post-epidemic era. Bilibili innovates marketing strategies in such an industry background, fights for a better marketing environment and timely adjusts marketing methods, so as to quickly seize a certain position in the increasingly fierce competitive market and improve its competitive advantage. This article takes Bilibili as an example to analyze the social media operation strategy and the reasons why it can get a head start in the post-pandemic era.