Traffic Control Strategies Research Articles

A Feedback Control Method with Connected Vehicles in a Lattice Hydrodynamic Model at Highway On-Ramps

This paper presents a traffic flow control scheme of connected vehicles to stabilize a traffic system with on-ramps from a macromodel point of view. Firstly, this paper establishes a lattice hydrodynamic model with on-ramps, and an output feedback controller is designed based on the characteristics of connected vehicles. Then, by using the Lyapunov–Krasovskill functional analysis method, this paper studies the delay-dependent convergence conditions of the control method. String stability is defined as the nonamplification of the downstream disturbance of a traffic flow when it propagates upstream. The influence of the on-ramps is regarded as a stochastic disturbance in this paper. Through a theoretical analysis, the control conditions that can ensure the string stability of the traffic system under the influence of on-ramp are obtained. Finally, numerical experiments are carried out to compare the traffic states of the traffic system with and without control. The results show that the proposed control can effectively suppress the instability of the traffic system.

Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks

Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion.

A Novel Reinforcement Learning-Based Cooperative Traffic Signal System Through Max-Pressure Control

Improving the efficiency of traffic signal control is an effective way to alleviate traffic congestion at signalized intersections. To achieve effective management of the system-wide traffic flows, current research tends to focus on applying reinforcement learning (RL) techniques for collaborative traffic signal control in a traffic road network. However, the existing collaboration-based methods often ignore the impact of transmission delay for exchanging traffic flow information on the system. Most of the studies assume that the signal controllers can collect all instantaneous vehicular features without delay. To fill the gap, we propose an RL-based cooperative traffic signal control scheme considering the data transmission delay issue in a traffic road network. In this paper, we (1) design our new RL agents to cooperatively control the traffic signals by improving the reward and state representation based on the state-of-the-art max-pressure control theory; (2) propose a traffic state prediction method to address the data transmission delay issue by decreasing the discrepancy between the real-time and delayed traffic conditions; (3) evaluated the performance of our proposed work on both synthetic and real-world scenarios with a different range of data transmission delays. The results demonstrate that our method surpassed the performance of the previous max-pressure-based traffic signal control methods and addressed the data transmission delay issue.

IPDALight: Intensity- and phase duration-aware traffic signal control based on Reinforcement Learning

Reinforcement Learning (RL) has been recognized as one of the most effective methods to optimize traffic signal control. However, due to the inappropriate design of RL elements (i.e., reward and state) for complex traffic dynamics, existing RL-based approaches suffer from slow convergence to optimal traffic signal plans. Meanwhile, to simplify the traffic modeling, most optimization methods assume that the phase duration of traffic signals is constant, which strongly limits the RL capability to search for traffic signal control policies with shorter average vehicle travel time and better GreenWave control. To address these issues, this paper proposes a novel intensity- and phase duration-aware RL-based method named IPDALight for the optimization of traffic signal control. Inspired by the Max Pressure (MP)-based traffic control strategy used in the transportation field, we introduce a new concept named intensity, which ensures that our reward design and state representation can accurately reflect the status of vehicles. By taking the coordination of neighboring intersections into account, our approach enables the fine-tuning of phase duration of traffic signals to adapt to dynamic traffic situations. Comprehensive experimental results on both synthetic and real-world traffic scenarios show that, compared with the state-of-the-art RL methods, IPDALight can not only achieve better average vehicle travel time and greenwave control for various multi-intersection scenarios, but also converge to optimal solutions much faster.

Structural Safety Assessment and Traffic Control Strategies of Widened Highway Bridges under Maintenance Works Requiring Partial Lane Closure

An increasing number of existing bridges need to be widened due to the rapid growth of road transport. Owing to the discontinuities in design specifications for the new and old structures in the widened bridges, it is expected that the old bridge might fail to meet the new design standard, leading to a potential need for vastly retrofit or even totally reconstruction. However, the new and old bridges carry slow and fast traffic lanes respectively, resulting in an uneven distribution of traffic loads between them. A previous study indicated that most components of the old bridge possessed adequate security reserves under normal traffic conditions of all lanes open. However, there are occasional scenarios that the new bridge requires closing traffic lanes to perform maintenance works. Under such circumstances, the traffic is completely diverted from the new bridge to the old bridge, and the safety of the old bridge becomes a critical issue. This study proposes a numerical approach to investigate the structural safety of widened bridges under maintenance works requiring partial lane closure on the new bridge and recommends traffic control measures to ensure bridge safety. The results show that several old slabs are unsafe under the maintenance works requiring lane closure. The weight restriction with a limit value not exceeding 35 t can ensure the safety of the widened bridge, whereas speed control is not effective. The hybrid uses of weight restriction and speed control further enhance the safety of the bridge. When the speed limit is 60 km/h, the weight limits should be not greater than 50 t and 45 t for the one-lane and two-lane closure scenarios, respectively. The research findings are of benefit to bridge engineers when maintenance works requiring lane closures are planned on widened bridges.

State-of-the-Art Review on Traffic Control Strategies for Emergency Vehicles

Emergency vehicles (EVs) play an essential role in emergency services. One of the most intuitive indicators of the emergency service process is the response time of EVs. This survey focuses on reviewing the latest traffic control strategies to reduce response time during EV traveling. Firstly, it classifies traffic control strategies into route optimization, signal preemption, lane reservation, and mixed traffic control strategies. Then, a systematic literature review of traffic control strategies in terms of different algorithms is presented. In addition, this survey classifies the articles by objective metrics. In addition to response time, several other objective metrics are summarized. Finally, this survey reviews the limitations of existing emergency traffic control strategies and critically analyzes them. Based on this, it indicates the core problems and proposes potential research areas to be explored.

A Novel Real-Time Traffic Signal Control Strategy for Emergency Vehicles

Intelligent traffic signal control strategies for emergency vehicles (EV) is a research hotspot in intelligent transportation systems. Its research results will promote the shortening of the travel time of EVs, which is vital to saving lives and reducing property losses. This paper proposes a novel real-time traffic signal control strategy. The first step is called on-demand signal timing for reducing road saturation, which makes it possible that ordinary vehicles(OV) give way to EVs and make EVs travel faster, according to three indicators: the emergency response level(ERL), the congestion level of the road section(CLRS), and the time urgency level(TUL). The second step is called novel signal preemption, which combines non-intrusive preemption and intrusive preemption. This step provides green indicates and makes EVs pass through intersections quickly without stopping. The final step is called the recovery cycle strategy, which restores the road network to the normal situation as soon as possible by using linear programming(LP) to find the shortest green time in each phase after an EV passes the intersection. This paper conducts simulation experiments on an urban traffic simulator SUMO. The experimental results show that our novel strategy can shorten the travel time and reduce the traffic impact on the road network caused by prioritizing the EVs significantly. Compared with the fixed-time control method (FTCM), Min’s flexible signal preemption method(FSPM), and Qin’s intrusive signal preemption method (ISPM), our method can optimize travel time by up to 62.85%, 50.83%, and 11.62%, respectively.

CAPTON—Centralized All-Path Transmission for Over-Subscribed Datacenter Networks

This work addresses an often-overlooked challenge in designing datacenter traffic control schemes. Specifically, most existing schemes were designed for network topologies with non-blocking network core. While this enabled the development of elegant solutions, core networks in practice are more likely to be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">over-subscribed</i> by the access layer network due to cost considerations. Consequently, existing traffic control schemes may perform sub-optimally in real-world over-subscribed datacenter networks. This work proposes a new centralized flow-based scheduling scheme called CAPTON which schedules flow according to remaining flow size and link capacities to avoid congestion in over-subscribed networks. CAPTON employs all-path transmission to exploit all available paths and redundant links between source and destination hosts to maximize resource utilization and eliminate the need for routing. Unlike switch-based approaches, CAPTON does not require additional support from network switches and thus can be readily deployed in current datacenters equipped with commodity switches.

The Effect of Speed Limits and Traffic Signal Control on Emissions

Traffic signals strongly influence traffic in cities – in terms of delay but also in terms of the emissions emitted by traffic. This work analyzes the effect of different traffic control strategies on emissions like NOx and PMx. Simulations of an inner-city arterial show that reducing the speed limit leads to a lower production of NOx and PMx, but to an increase in delay. The impact of the traffic signal control on the emissions is less distinct: A carefully designed co-ordination reduces emissions, while a simple actuated control cannot improve traffic flow and therefore increases pollutant production.

Research on Data Sharing Access Control Based on Blockchain Technology

Blockchain is now widely used in various industries due to its openness, transparency, autonomy, imtamability, decentralization, and traceability. With the wide application of blockchain technology, its security problems have also brought significant challenges, seriously affecting the implementation of blockchain applications. Data on the traditional blockchain is open to the entire network node. The update operation of data records is also transparent, accompanied by the leakage of user information. Although blockchain has the characteristics of anonymity and privacy, with its development, it has not been easy to meet users' needs. How to effectively protect user data privacy, realize data sharing on the blockchain, and ensure the security of data integrity, transmission efficiency, storage efficiency, and application is very important to break through the bottleneck of the development of blockchain technology and promote its application. This paper proposes a traffic data resource access control scheme based on blockchain technology by combining the attribute encryption access control system based on ciphertext strategy based on blockchain's distributed ledger technology and the openness and imtamability it brings. In this scheme, the task of attribute encryption is to ensure the security involved in the process of plaintext and ciphertext conversion and the efficiency of data sharing, and to implement a one-to-many access sharing policy, to enhance the safety of the access sharing system and improve the efficiency of the access control module. This paper introduces the access control mechanism from two aspects of system architecture and the access sharing process. Then the attribute encryption mechanism is designed. The design process includes symbol description, primary structure, algorithm design idea and algorithm construction. Finally, the functionality and security of the access control mechanism are analysed.

Examining Traffic Operations at Multi-Legged Intersection Operating under Heterogeneous Traffic: A Case Study in India

An intersection is a critical component of any road network, where traffic from different approaches merges and diverges. These merging and diverging traffic movements affect traffic operations and safety significantly. Different traffic management measures and control strategies and necessary geometric improvements are suggested to optimize traffic operations at a given intersection. It is also more prudent to assess the effectiveness of potential strategies towards improvement using simulation at first before certain appropriate strategies/alternatives are finally selected for on-field implementation. Kamrej intersection, a multi-legged junction in Surat, located in the western part of India, is taken as a case study. Traffic data such as traffic volume counts and spot speeds are used to generate and calibrate the present field condition (base model) using PTV-VISSIM software. The calibrated model is then validated using vehicle-class-wise average travel time for each traffic movement (straight and turning) and traffic flow during peak hours. Average delay is used as a measure to assess the effectiveness of selected potential traffic management alternatives. When different demand management scenarios are assessed, it is found that delays for straight and right movements can be reduced substantially, contributing towards improvement in efficiency and traffic operations at selected intersections. The current approach holds promise, thereby contributes to improving traffic operations at selected multi-legged intersections.

An en route capacity optimization model based on air traffic control process.

In order to resolve the imbalance of demand-capacity and airspace congestion, improve the performance of the en route air traffic management, promote the development of air traffic control automation system in the future, this paper proposes an En route air traffic control process model from the perspective of operation requirements. Taking the minimization of operation time, instantaneous density, maximum lateral displacement and air traffic controllers' workload as the optimization objectives, the commonly used air traffic control instructions such as climb and descent and speed restriction are set as constraints, the algorithm is designed based on the air traffic control scheme, and a complete air traffic control process are modeled which outputs instructions for each aircraft. Finally, the model is applied to a case study in the northwest region of China. The simulation results show that compared with the actual operation process, the total operation time is reduced by 18.6%, the variance of the lateral displacement and the vertical separation are efficiently reduced, and the en route air traffic capacity is improved. The proposed model envisages the following two innovations: (ⅰ) the whole process of air traffic controllers' command is considered in the model, especially the control scheme and different types of instructions, and (ⅱ) the en route historical trajectory data of aircraft is used to as the key parameters of the input data to efficiently yield the acceptable results of the model. By quantifying the operation requirements of air traffic control, this model can also balance the distribution of traffic flow density, reduce the utilization rate of horizontal airspace, alleviate flight conflicts on air routes, and lessen the workload of controllers.

Explicit traffic shaping scheme in content centric networking

Content centric networking (CCN) is considered as the prominent future internet architecture. CCN packets are routed based on content name itself, not on IP address and has two kinds of packets such as interest and data. CCN has a security model to secure content pieces, not to secure a transmission session. However, CCN has not widely taken the side effect to the occurrence of network congestion. So, this paper proposes the traffic control scheme in CCN environment to reduce high communication overhead, long content retrieval time, and high resource consumption. The proposed traffic shaping scheme for data packets tries to delay the transmission of data packets during network congestion.

Passenger-oriented traffic management integrating perimeter control and regional bus service frequency setting using 3D-pMFD

Public buses and private vehicles are two main transportation modes for urban road traffic. It has strong flow interactions between the two traffic modes. From data analysis, the vehicle density of a traffic mode not only determine its flow, but also affect the flow of the other mode. With such mixed vehicle flows, the application of traditional traffic management that controls each vehicle flow system separately is limited. Thus, it is beneficial to control both bus and private vehicle flows in an integrated way. Under such bi-modal traffic management, the network efficiency cannot be reflected by vehicle flows since the occupancies of the traffic modes are greatly different. It leads to that the control objective should be necessarily based on passengers. To do so, we adopt bi-modal passenger-based macroscopic fundamental diagram (3D-pMFD) to capture the relations among vehicle accumulations and the total passenger flow in a network. From this aggregate perspective, a passenger-oriented traffic control strategy is proposed by integrating the perimeter control and the regional bus service frequency setting. The characteristics of both traffic modes, especially the operation strategy of buses, are taken into account in the model to describe the bi-modal traffic flow dynamics. A control framework comprised of optimization and simulation is established using model predictive control. The corresponding passenger-based optimization problem is solved by a modified genetic algorithm. With multiple data collected from the network of Shenzhen City in China, numerical experiments are designed to verify the validity and efficiency of the proposed integrated control strategy. Results show that: (1) the proposed strategy can maintain a high level of passenger flow during peak hour with a relatively high demand; (2) the interest of private vehicles is guaranteed even if buses are more recommended in the control strategy; (3) the crowding degree in a bus is reduced properly, which is able to improve the attraction of buses during peak hours; and (4) the proposed passenger-oriented objective results in a comprehensively good performance of the bi-modal network system.

Quantitative Evaluation of the Impacts of the Time Headway of Adaptive Cruise Control Systems on Congested Urban Freeways Using Different Car Following Models and Early Control Results

The impact of driving automation and adaptive cruise control (ACC) on traffic performance has been increasingly studied in recent years. This paper focuses on two widely used ACC car following models and investigates the impact of the time headway parameter on traffic operation and performance on one of the busiest freeway corridors in Ontario, Canada. Using Aimsun microsimulation, we compare two commonly used ACC car following models; the intelligent driver model (IDM) and Shladover’s model which has been recently adopted in Aimsun Next 20. Several experiments have been conducted to evaluate the freeway performance for different desired headway settings and market penetration rates of ACC-equipped vehicles. Simulations results confirm the reported IDM drawbacks of having a slow response leading to headway errors which are less pronounced with Shladover’s model thereby leading to more accurate quantification by the latter. This study further presents a simple on-off ACC-based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that freeway performance is improved. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average network throughput, delay, and speed compared to the case of only manually driven or uncontrolled ACC vehicles.

Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment.

Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.

Short-Term Traffic Flow Forecasting Model Based on GA-TCN

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

Designing Maps for Bicycling Simulator Studies – three practical Approaches

Abstract. Bicycle simulator studies result from attempts of solving various novel problem statements of modern transportation-related research questions. Examples imply the evaluation of novel traffic control strategies for prioritizing urban bicycle traffic, novel bicycle infrastructure (such as bicycle highways) and the interaction and communication of vulnerable road users with automated or autonomous vehicles. As one of classical disciplines of transportation research, namely traffic engineering, and less related to human factors research, automotive research, geography, urban planning or citizen science, we want to point out those bicycle simulator studies design approaches, which are more related to testing novel traffic control strategies for cyclists, experiencing changing traffic-efficiency and –safety-related parameters in ongoing interfaced microscopic traffic flow simulations. We believe that this is a key factor in experiencing various traffic situations and the evaluation of thereof. In this research, we introduce three practical approaches of how to design maps for bicycling simulator studies. This is mainly resulting from manifold practical experiences from already conducted simulator studies beginning from the year 2018.

Spatial-Temporal Traffic Flow Control on Motorways Using Distributed Multi-Agent Reinforcement Learning

The prevailing variable speed limit (VSL) systems as an effective strategy for traffic control on motorways have the disadvantage that they only work with static VSL zones. Under changing traffic conditions, VSL systems with static VSL zones may perform suboptimally. Therefore, the adaptive design of VSL zones is required in traffic scenarios where congestion characteristics vary widely over space and time. To address this problem, we propose a novel distributed spatial-temporal multi-agent VSL (DWL-ST-VSL) approach capable of dynamically adjusting the length and position of VSL zones to complement the adjustment of speed limits in current VSL control systems. To model DWL-ST-VSL, distributed W-learning (DWL), a reinforcement learning (RL)-based algorithm for collaborative agent-based self-optimization toward multiple policies, is used. Each agent uses RL to learn local policies, thereby maximizing travel speed and eliminating congestion. In addition to local policies, through the concept of remote policies, agents learn how their actions affect their immediate neighbours and which policy or action is preferred in a given situation. To assess the impact of deploying additional agents in the control loop and the different cooperation levels on the control process, DWL-ST-VSL is evaluated in a four-agent configuration (DWL4-ST-VSL). This evaluation is done via SUMO microscopic simulations using collaborative agents controlling four segments upstream of the congestion in traffic scenarios with medium and high traffic loads. DWL also allows for heterogeneity in agents’ policies; cooperating agents in DWL4-ST-VSL implement two speed limit sets with different granularity. DWL4-ST-VSL outperforms all baselines (W-learning-based VSL and simple proportional speed control), which use static VSL zones. Finally, our experiments yield insights into the new concept of VSL control. This may trigger further research on using advanced learning-based technology to design a new generation of adaptive traffic control systems to meet the requirements of operating in a nonstationary environment and at the leading edge of emerging connected and autonomous vehicles in general.

Hyperfine-resolution mapping of on-road vehicle emissions with comprehensive traffic monitoring and an intelligent transportation system

Abstract. Urban on-road vehicle emissions affect air quality and human health locally and globally. Given uneven sources, they typically exhibit distinct spatial heterogeneity, varying sharply over short distances (10 m–1 km). However, all-around observational constraints on the emission sources are limited in much of the world. Consequently, traditional emission inventories lack the spatial resolution that can characterize the on-road vehicle emission hotspots. Here we establish a bottom-up approach to reveal a unique pattern of urban on-road vehicle emissions at a spatial resolution 1–3 orders of magnitude higher than current emission inventories. We interconnect all-around traffic monitoring (including traffic fluxes, vehicle-specific categories, and speeds) via an intelligent transportation system (ITS) over Xiaoshan District in the Yangtze River Delta (YRD) region. This enables us to calculate single-vehicle-specific emissions over each fine-scale (10 m–1 km) road segment. Thus, the most hyperfine emission dataset of its type is achieved, and on-road emission hotspots appear. The resulting map shows that the hourly average on-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74.01, 40.35, 8.13, and 1.68 kg, respectively. More importantly, widespread and persistent emission hotspots emerged. They are of significantly sharp small-scale variability, up to 8–15 times within individual hotspots, attributable to distinct traffic fluxes, road conditions, and vehicle categories. On this basis, we investigate the effectiveness of routine traffic control strategies on on-road vehicle emission mitigation. Our results have important implications for how the strategies should be designed and optimized. Integrating our traffic-monitoring-based approach with urban air quality measurements, we could address major data gaps between urban air pollutant emissions and concentrations.