Improvement Of Traffic Flow Research Articles

Cooperative MARL-PPO Approach for Automated Highway Platoon Merging

This paper presents a cooperative highway platooning strategy that integrates Multi-Agent Reinforcement Learning (MARL) with Proximal Policy Optimization (PPO) to effectively manage the complex task of merging. In modern transportation systems, platooning—where multiple vehicles travel closely together under coordinated control—promises significant improvements in traffic flow and fuel efficiency. However, the challenge of merging, which involves dynamically adjusting the formation to incorporate new vehicles, remains challenging. Our approach leverages the strengths of MARL to enable individual vehicles within a platoon to learn optimal behaviors through interactions. PPO ensures stable and efficient learning by optimizing policies balancing exploration and exploitation. Simulation results show that our method achieves merging with safety and operational efficiency.

IMTRAC: Image-Based Modeling for Transformative Traffic Control via Foundation Models

This paper introduces the Image-based Modeling for Transformative Traffic Control (IMTRAC) framework, which revolutionizes urban traffic management by integrating image-based representations, foundation models, and dynamic signaling. It transitions from textual to visual data analysis, transforming complex traffic scenes into quantifiable insights. The image-based representation is then analyzed by foundation models to infer refined and effective traffic control policies. Our experimental results show marked improvements in traffic flow and reductions in waiting times, highlighting IMTRAC’s ability to outperform traditional control methods. The proposed IMTRAC promises a significant leap towards smarter, efficient urban traffic systems, leveraging the synergy of advanced imaging techniques and multi-modal foundation models to enhance control decisions.

WIRELESS TRAFFIC AND ROUTING ENHANCEMENT USING EMPEROR PENGUIN OPTIMIZER GUIDED BY CONDITIONAL GENERATIVE ADVERSARIAL NETS

The escalating demand for efficient wireless communication systems has prompted researchers to explore innovative solutions to optimize traffic flow and routing. The existing wireless communication infrastructure faces challenges such as congestion, latency, and suboptimal routing, impeding the seamless transmission of data. Traditional optimization approaches fall short in adapting to dynamic network conditions, necessitating the exploration of advanced methodologies. Despite recent advancements in optimization techniques, a notable research gap exists in the integration of bio-inspired algorithms like the Emperor Penguin Optimizer with machine learning models such as Conditional Generative Adversarial Nets for the purpose of wireless traffic and routing enhancement. Bridging this gap is crucial for achieving adaptive and robust wireless communication systems. This study addresses the challenges posed by the dynamic nature of wireless networks, aiming to enhance their performance through the synergistic application of the Emperor Penguin Optimizer (EPO) and Conditional Generative Adversarial Nets (CGANs). This research leverages the inherent strengths of the EPO, inspired by the collective foraging behavior of emperor penguins, to dynamically optimize the wireless network parameters. Concurrently, CGAN are employed to intelligently learn and adapt routing strategies based on real-time network conditions. The symbiotic integration of these two methodologies creates a powerful framework for adaptive wireless traffic and routing. The results indicate a significant improvement in traffic flow, reduced latency, and optimized routing paths in comparison to conventional methods. The EPO-CGAN framework demonstrates adaptability to varying network conditions, showcasing its potential to revolutionize wireless communication systems.

Adaptive Traffic Signal Timer for A Signal in Chennai Metropolitan City Using Python and OpenCV

Background: The current traffic control system in India’s metropolises is ineffective because of the randomization of traffic density patterns throughout the day. For a predetermined period of time, the traffic signal timers switch traffic’s direction. Vehicles must therefore wait for a long time even when there is little traffic. Objectives: To continuously adjust the traffic signal timer based on the varying real-time traffic density and to significantly lessen traffic congestion. Methods: An adaptive traffic light timer is offered by this research using image processing algorithms. The real-time image data will be analyzed by the Raspberry Pi and OpenCV, which will determine when to regulate traffic flow. Findings: The adaptive timer minimizes vehicle waiting time and increases the number of turns granted to cars in each lane to cross the signal, which results in an improvement in traffic flow. Novelty: The use of image processing algorithms, the Raspberry Pi, and OpenCV makes the proposed system highly efficient and effective in adapting to the real-time traffic conditions. The suggested methodology will be more affordable than the currently used methodologies. Keywords: Adaptive Traffic Signal Timer; Intelligent Traffic Control

An Adaptive Traffic-Flow Management System with a Cooperative Transitional Maneuver for Vehicular Platoons

Globally, the increases in vehicle numbers, traffic congestion, and road accidents are serious issues. Autonomous vehicles (AVs) traveling in platoons provide innovative solutions for efficient traffic flow management, especially for congestion mitigation, thus reducing accidents. In recent years, platoon-based driving, also known as vehicle platoon, has emerged as an extensive research area. Vehicle platooning reduces travel time and increases road capacity by reducing the safety distance between vehicles. For connected and automated vehicles, cooperative adaptive cruise control (CACC) systems and platoon management systems play a significant role. Platoon vehicles can maintain a closer safety distance due to CACC systems, which are based on vehicle status data obtained through vehicular communications. This paper proposes an adaptive traffic flow and collision avoidance approach for vehicular platoons based on CACC. The proposed approach considers the creation and evolution of platoons to govern the traffic flow during congestion and avoid collisions in uncertain situations. Different obstructing scenarios are identified during travel, and solutions to these challenging situations are proposed. The merge and join maneuvers are performed to help the platoon's steady movement. The simulation results show a significant improvement in traffic flow due to the mitigation of congestion using platooning, minimizing travel time, and avoiding collisions.

Inferred vehicular emissions at a near-road site: Impacts of COVID-19 restrictions, traffic patterns, and ambient air temperature

Vehicles are a major source of anthropogenic emissions of carbon monoxide (CO), nitrogen oxides (NOx), and black carbon (BC). CO and NOx are known to be harmful to human health and contribute to ozone formation, while BC absorbs solar radiation that contributes to global warming and also has negative impacts on human health and visibility. Travel restrictions implemented during the COVID-19 pandemic provide researchers the opportunity to study the impact of large, on-road traffic reductions on local air quality. Traffic counts collected along Interstate-95, a major eight-lane highway in Maryland (US), reveal a 60% decrease in passenger car totals and an 8.6% (combination-unit) and 21% (single-unit) decrease in truck traffic counts in April 2020 relative to prior Aprils. The decrease in total on-road vehicles led to the near-elimination in stop-and-go traffic and a 14% increase in the mean vehicle speed during April 2020. Ambient near-road (NR) BC, CO, NOx, and carbon dioxide (CO2) measurements were used to determine vehicular emission ratios (ΔBC/ΔCO, ΔBC/ΔCO2, ΔNOx/ΔCO, ΔNOx/ΔCO2, and ΔCO/ΔCO2), with each ratio defined as the slope value of a linear regression performed on the concentrations of two pollutants within an hour. A decrease of up to a factor of two in ΔBC/ΔCO, ΔBC/ΔCO2, ΔNOx/ΔCO2, and in the fraction of on-road diesel vehicles from weekdays to weekends shows diesel vehicles to be the dominant source of BC and NOx emissions at this NR site. We estimate up to a 70% reduction in BC emissions in April 2020 compared to earlier years, and attribute much of this to lower diesel BC emissions resulting from improvements in traffic flow and fewer instances of acceleration and braking. Future efforts to reduce vehicular BC emissions should focus on improving traffic flow or turbocharger lag within diesel engines. Inferred BC emissions from the NR site also depend on ambient temperature, with an increase of 54% in ΔBC/ΔCO from −5 to 20 °C during the cold season, similar to previous studies that reported increasing BC emissions with rising temperature. The default setting of MOVES3, the current version of the mobile emission model used by the US EPA, does not adjust hot-running BC emissions for ambient temperature. Future work will focus on improving the accuracy of mobile emissions in air quality modeling by incorporating the effects of temperature and traffic flow in the system used to generate mobile emissions input for commonly used air quality models.

Capacity and emission evaluation of new interchange designs using microsimulation

The insufficient capacity of interchange has negative impact on environment in terms of emission production in urban areas. We are limited by dense urban development that force us to look for the new construction designs in terms of the interchange renovation.Recently, the unconventional alternative interchange designs (UAID) become more popular in big cities for their smart design that save us space and citizens benefit not only from the improvement of traffic flow but also from the reduction of emission production. The aim of this paper is to demonstrate the capacity efficiency and emission performance of interchange current state and its two UAIDs using a PTV Vissim software to create microsimulations. The case study is in Brno, Czech Republic at tight diamond interchange (TDI) located close to the city center. The complex traffic survey was carried out to create and calibrate the microsimulations. The two microsimulations of alternative designs Diamond Diverging Interchange (DDI) and Michigan urban diamond interchange (MUDI) were compared with each other and the current state according to its capacity performance and emission production. The capacity was estimated directly from the microsimulation but the emission production of selected Nitrogen Oxides (NOx), nitrogen dioxide (NO2), carbon monoxide (CO), Benzene and particulate matters (PM10 and PM2,5) elements were calculate using MEFA 13 software recommended by the Ministry of Environment in Czech Republic. The results of comparison show delay reduction in both alternatives. The Level of Service (LOS) on main approaches improve from E level at current state to C level on DDI and B on MUDI where was slight deterioration on the minor approaches but the interchange preserve efficiency. The results from emission production comparison show that the DDI alternative has higher reduction in average about 30% comparing to the current state.

Robotic Competitions to Design Future Transport Systems: The Case of JRC AUTOTRAC 2020

Vehicle automation and connectivity bring new opportunities for safe and sustainable mobility in urban and highway networks. Such opportunities are however not directly associated with traffic flow improvements. Research on exploitation of connected and automated vehicles (CAVs) toward a more efficient traffic currently remains at a theoretical level, and/or based on simulation models with limited reliability. Furthermore, testing CAVs in the real world is still costly and very challenging from an implementation perspective. A possible alternative is to use automated robots. By designing and testing both the low- and the high-level controllers of CAVs, it is indeed possible to reach a better understanding of the challenges that future vehicles will need to face. Robotic applications can effectively test these challenges within a wide variety of research communities—for example, via robotic competitions. Along this direction, the Joint Research Centre has organized the first European robotic traffic competition for automated miniature vehicles. Each team participated with four robots and was judged based on a set of indicators that assess the collective behaviors of the vehicles. Results show the suitability of the methodology with different teams proposing completely different approaches to deal with the challenge and thus achieving different results. Future competitions may further raise awareness about the possibility of using CAVs to improve traffic and to engage with a broader community to design systems that are really capable of achieving this goal.

Modeling and Control of Priority Queueing in Software Defined Networks via Machine Learning

Software Defined Networking (SDN) is a new architectural paradigm that enables programmable control of a network to make it more flexible and easier to manage. SDN architectures decouple control and forwarding functionalities, and enable switches and routers to be remotely configurable/ programmable in run-time by a controller. Modeling and optimization of such modern heterogeneous network infrastructures are key factors to achieve better performance, e.g. in terms of traffic flow improvement while reducing bandwidth allocation. Identifying an accurate model of a network device in SDNs (e.g., a switch or a router) is crucial in order to apply advanced techniques such as Model Predictive Control (MPC). However, such a problem is very challenging due to non-linearities and unavailability of internal variables measurements in real devices. To this aim, a promising direction is given by an appropriate integration of System Identification and Machine Learning techniques to obtain predictive models using historical data collected from the network thanks to the SDN paradigm. In this paper we apply a novel data-driven methodology to learn accurate models of the dynamical input-output behavior of a network’s switch device by appropriately combining AutoRegressive eXogenous (ARX) model identification with Regression Trees (RTs) and Random Forests (RFs). The advantage of such model is that it can be directly used to apply MPC (which just requires Quadratic Programming to be solved) to optimally control the queues’ bandwidth of the switch ports within the SDN paradigm.We validate our approach on an experimental emulation setup using the Mininet network emulator environment and a real dataset obtained from measurements of an Italian Internet Service Provider (Sonicatel S.r.l.). To this aim, we first develop a model of a real network switch, then implement MPC using the RYU controller, and finally demonstrate the benefits of the proposed dynamic queueing control methodology in terms of packet losses reduction and bandwidth saving, i.e. in terms of improvement of the Quality of Service.

A decentralized intersection management system through collaborative negotiation between smart signals

Actuated and pre-timed traffic signal controllers have been beneficial to the improvement of traffic flow in cities and dense urban environments around the world. While these methods have been effective in reducing traffic congestion, recent works have shown that incorporation of reinforcement learning (RL) or other artificial intelligence (AI) based optimization techniques may further improve the performance of traffic signal controllers. This work investigates a novel decentralized traffic signal control structure which encourages cooperative signal behavior via repeated negotiations between neighboring intelligent agents. This method capitalizes on emerging inter-infrastructure communications technologies to exercise ‘system-level’ control over a network of connected signalized intersections. The proposed method was tested in a simplified grid-network of 20 intersections. In this network, static arrivals of along east-west lanes and along north-south lanes were supplied. In addition, a simulated shift to along east-west lanes and along north-south lanes was analyzed to provide insights into the presented method’s performance in response to any sudden shifts in traffic patterns. The findings indicated that, when compared to non-negotiating traffic signals, the presented method may improve the service rate of traffic networks under static conditions by on average, reduce emissions by an average of in addition to reducing travel time across a network of intersections. The performance characteristics were captured by a SUMO testbed, and computational efficiency was explored using a suite of simple testbeds developed in MATLAB.

Energy consumption in a new lattice hydrodynamic model based on the delayed effect of collaborative information transmission under V2X environment

A novel lattice hydrodynamic model is proposed based on the delayed effect of synergistic information transmission involving density and flux. The stability condition of the novel model is further analyzed theoretically via the linear analysis. Through nonlinear analysis, the modified Korteweg–de Vries (mKdV) equation near the critical point is obtained. The changing trends of density wave, flux and energy consumption are investigated when a small perturbation is intervened into the uniform traffic flow. The results show that the delayed effect of collaborative information transmission can effectively suppress traffic congestion, reduce energy consumption, and improve the stability of the traffic system. Theoretical analysis and simulation results verify the feasibility and validity of the delayed effect of collaborative information transmission on the improvement of traffic flow.

Using big data to estimate the impact of cruise activity on congestion in port cities

By using big data that are generated by crowdsourced mobility platforms and GPS devices, policymakers can now better understand the relationship between cruise activities and traffic congestion, identify key areas in the road network that need traffic flow improvement, and design appropriate measures to take advantage of cruise tourism while mitigating its effects on urban mobility. This study uses big data and econometric techniques to analyze the relationship between traffic congestion and cruise activity for five of the busiest cruise destinations in South America and the Caribbean: Buenos Aires, Callao, Cartagena, Montevideo, and Rio the Janeiro. Over 80 million observations were collected from automatic identification system (AIS) and crowdsourced traffic data platforms for 2019. Results suggest that cruise activity causes a 12% increase in urban congestion. The analysis of big data presented in this paper facilitates the design of customized interventions to ease congestion according to the temporal dynamics of traffic flows, thus leading towards more efficient traffic management.

Introducing Autonomous Vehicles: Adoption Patterns and Impacts on Social Welfare

Problem definition: Autonomous vehicles (AVs) are predicted to enter the consumer market in less than a decade. There is currently no consensus on whether their presence will have a positive impact on users and society. The skeptics of automation foresee increased congestion, whereas the advocates envision smoother traffic with shorter travel times. We study the automation controversy and advise policymakers on how and when to promote AVs. Academic/practical relevance: The AV technology is advancing rapidly and there is a need to study its impact on social welfare and the likelihood of its adoption by the public. Methodology: We use supply-demand theory to find the equilibrium number of trips for autonomous and regular households. We develop a simulation model of peer-to-peer AV sharing. We compare the socially optimal level of automation with the selfish adoption patterns where households independently choose their vehicle type. Results: We establish that the optimal social welfare is influenced by: (i) the network connectivity, that is, the ability of the infrastructure to serve AVs, (ii) the additional comfort provided by AVs that allows passengers to engage in other productive activities instead of driving, and (iii) the AV sharing patterns that reduce ownership costs, but create empty vehicle trips that increase congestion. Managerial implications: We investigate the impact of AVs in a case study of Toronto and show that partial automation maximizes social welfare. We show that the comfort of AVs may add traffic that compromises social welfare. Moreover, although traffic increases with automation, travel times may decrease because of significant improvements in traffic flow caused by AV connectivity in the network.

Implementation of Different Traffic Management Strategies on Major Roads for the Improvement of Traffic Flow in Lahore City

Traffic congestion causes many socio-economic problems in developing nations. One of the main causes of traffic congestion can be attributed to poor design practices. In the last few years, Lahore city has expanded haphazardly where the allocation of resources was done based on political will instead of feasible design practices. This haphazard growth coupled with a poor design resulted in traffic congestion on the main roads of the city. In this study, simulations using different traffic management design strategies were carried out on major roads to check if traffic delays and Level of Service (LOS) can be improved. The most feasible designs based on the simulated results were proposed for the improvement of the existing infrastructure designs. These findings suggested that Akbar Chowk and Jinnah Hospital Intersection have greatly reduced the traffic delay time by 92.97% and 92.67%, respectively. Policymakers can utilize these simulated results for future design guidelines to accommodate the future growth of traffic on these arterial roads.

Cooperative adaptive cruise control and intelligent traffic signal interaction: a field operational test with platooning on a suburban arterial in real traffic

It is becoming increasingly important to gain real-life insights into the effects of vehicle automation with the continued introduction of cooperative and automated vehicles (CAV). This study reports on the findings of a field operational test (FOT) of cooperative adaptive cruise control (CACC) vehicles on an arterial corridor with other traffic. The FOT demonstrated that that CACC vehicles can operate well under such conditions and can operate in platoons at lower time-headways than human driven vehicles. Platoon disengagement and cut-ins were analysed and showed that although many platoon break-ups are unavoidable, CACC operation was carried out without incident with frequent recoupling of platoons occurring. Most cut-ins occurred near to intersections, where vehicles are required to merge or need to change lanes to turn off the main corridor. It was not possible to derive potential traffic flow improvements from the FOT, due to a limited overall penetration rate and limitations of the intelligent traffic signals. The findings offer greater insights into the performance of CAV technology in a suburban environment and can aid road authorities to prepare infrastructure for the broader introduction of CAVs as well as the development of modelling tools to improve impact analysis of CAVs in urban environments.

Smart Traffic Management System Using IoT and Machine Learning Approach

As we know that due to heavy population in urban areas, our cities are dealing with many problems like pollution, water shortages, traffic jams etc. So, overcome this Situation there is a concept comes in role that is “Smart City”. The main purpose of Smart City is to create a society which can perform effectively and efficiently making effective use of city infrastructures through machine learning and artificial intelligence. It also focuses to optimize city functions and drive economic growth while improving quality of life for its citizens using smart technology. Smart City makes use of Artificial Intelligence, machine learning and Internet of Things (IOT) devices such as connected sensors, lights, and meters to collect and analyze data. The cities then use this data to improve infrastructure, public utilities, services and humans are interact with different devices like Smart homes , smart health , smart vehicles , smart agriculture etc. Machine learning will help the power for control the autonomous vehicles or self-driving vehicles to reduce delays in traffic and to reduce pollution emission by using e-vehicle. IOT based Intelligent Transportation Systems make the exchange of information possible through cooperative systems that broadcast traffic data to enhance road safety. Traffic light assistance systems in particular utilize real-time traffic light timing data by accessing the information directly from the traffic management center. To test the reliability of a traffic light assistant system based on networked inter vehicular interaction with infrastructure, we present in this paper an approach to perform theoretical studies in a lab-controlled scenario. The proposed system retrieves the traffic light timing program within a range in order to calculate the optimal speed while approaching an intersection and shows a recommended velocity based on the vehicle’s current acceleration and speed, phase state of the traffic light, and remaining phase duration. Results show an increase in driving efficiency in the form of improvement of traffic flow, reduced gas emissions, and waiting time at traffic lights after the drivers adjusted their velocity to the speed calculated by the system.

Simulation Study of Autonomous Vehicles’ Effect on Traffic Flow Characteristics including Autonomous Buses

Autonomous vehicles are expected to revolutionize the transportation industry. The goal of this research is to study the heterogeneity in traffic flow dynamics by comparing different penetration rates of four different types of vehicles: autonomous cars (AC), autonomous buses (AB), manual cars (MC), and manual buses (MB). For the purpose of this research, a modified cellular automata (CA) model is developed in order to analyze the effect of heterogeneous vehicles (manual and autonomous). Previously, studies have focused on manual and autonomous cars, but we believe a gap in perception and analysis of mixed traffic still exists, as inclusion of other modes of autonomous vehicle research is very limited. Therefore, we have explicitly examined the effect of the AB on overall traffic flow. Moreover, two types of lane changing behavior (aggressive lane changing and polite lane changing) were also integrated into the model. Multiple scenarios through different compositions of vehicles were simulated. As per the results, if AB is employed concurrently with AC, there will be a significant improvement in traffic flow and road capacity, as equally more passengers can be accommodated in AB as AC is also anticipated to be used in carpooling. Secondly, when the vehicles change the lanes aggressively, there is a substantial growth in the flow rate and capacity of the network. Polite lane change does not significantly affect the flow rate.

Simulation study on the effect of automated driving in a road network environment

Automated driving, which is considered to be able to reduce driving workload, enhance driving safety and improve traffic efficiency, has become a research hotspot in recent years. It is believed that traffic flow will consist of manual vehicles and automated vehicles at different automation levels in the near future. Researchers have carried out many studies on mixed traffic; most of them focus on the highway scenario. However, the majority of traffic occurs in urban/suburban road networks, which contains many different scenarios, such as expressways, merging/diverging areas, signalised intersections, trunk roads, branch roads, etc. To evaluate the effect of automated driving in a more realistic way, the authors first take adaptive cruise control (ACC) and cooperative ACC as representatives of automated driving. Then, the authors make simulations at the road network level and investigate macroscopic fundamental diagram in different scenarios. Results show that compared to a highway, the improvement of traffic flow is significantly limited in the road network.

Development of Decision Support System for Integrated Active Traffic Management Systems Considering Travel Time Reliability

A new decision support system (DSS) using travel time reliability was developed for integrated active traffic management (IATM) including freeways and arterials. The DSS consists of recommendation and evaluation of response plans. The DSS also includes three representative traffic management strategies: variable speed limits, queue warning, and ramp metering. The recommendation of response plans for recurring traffic congestion was generated from the logics of the three strategies. The evaluation of response plans was conducted by travel time reliability through the prediction of traffic conditions with response plans. The near-future prediction of traffic conditions with control strategies was conducted through METANET for freeways and arterials. The developed DSS was evaluated under three types of traffic congestion: extreme, heavy, and moderate. According to the evaluation results, the developed DSS recommended an IATM strategy with the highest synergistic relationships in real time and contributed to enhancing the effectiveness of the IATM strategies. It was confirmed that arterials should have the allowable residual capacity for the improvement of traffic flow of the entire corridor network. Furthermore, the DSS demonstrated a more balanced traffic condition between freeways and arterials.

Altruistic Control of Connected Automated Vehicles in Mixed-Autonomy Multi-Lane Highway Traffic

We consider the problem of altruistic control of connected automated vehicles (CAVs) on mixed-autonomy multi-lane highways to mitigate moving traffic jams resulting from car-following dynamics of human-driven vehicles (HDVs). In most of the existing studies on CAVs in multi-lane settings, vehicle controller design philosophy is based on a selfish driving strategy that exclusively addresses the ego vehicle objectives. To improve overall traffic smoothness, we propose an altruistic control strategy for CAVs that aims to maximize the driving comfort and traffic efficiency of both the ego vehicle and surrounding HDVs. We formulate the problem of altruistic control under a model predictive control (MPC) framework to optimize acceleration and lane change sequences of CAVs. In order to efficiently solve the resulting non-convex mixed-integer nonlinear programming (MINLP) problem, we decompose it into three non-convex subproblems, each of which can be transformed into a convex quadratic program via penalty based reformulation of the optimal velocity with relative velocity (OVRV) car-following model. Simulation results demonstrate significant improvements in traffic flow via altruistic CAV actions over selfish strategies on both single- and multi-lane roads.