Traffic Congestion Mitigation Research Articles

Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road

How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00–8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance.

A platoon-based eco-driving control mechanism for low-density traffic flow

Forming traffic into platoons on the road has the potential for mitigation of traffic congestion and improvement in fuel economy, and can be more readily achieved by leveraging emerging connected and automated vehicles. This paper proposes a platoon-based eco-driving control mechanism that forms small-sized platoons and guides them to move on low-density traffic corridors. To facilitate this investigation, a low-density traffic corridor is divided into an initial road segment plus a series of subsequent road segments. To ensure computational efficiency, an approximate model based on the predefined function is employed. Simulation shows that traffic flow of small-sized platoons can bring considerable benefits in fuel consumption compared to that of large-sized platoons. This proposed control mechanism is shown to be significant in fuel economy.

A Flow-Based Centralized Route Guidance System for Traffic Congestion Mitigation

A Flow-Based Centralized Route Guidance System for Traffic Congestion Mitigation

A Highway On-Ramp Control Approach Integrating Percolation Bottleneck Analysis and Vehicle Source Identification

Identifying the bottleneck segments and developing targeted traffic control strategies can facilitate the mitigation of highway traffic congestion. In this study, we proposed a new method for identifying the bottleneck segment in a large highway network based on the percolation theory. A targeted on-ramp control approach was further developed by identifying the major vehicle sources of the bottleneck segment. We found that the identified bottleneck segment played a crucial role in maintaining the functional connectivity of the highway network in terms of meeting the required level of service. The targeted on-ramp control approach can more effectively enhance the service level of the highway network.

Deep spatial-temporal travel time prediction model based on trajectory feature

Research on travel time prediction shows its importance in the rational planning of travel arrangements and traffic congestion mitigation. The scale of taxi and online ride-hailing users is huge, and accurate travel time prediction is convenient for passengers to reasonably arrange travel planning. Unlike highways and buses, the trajectory of taxis is complex. At the same time, the travel time prediction of the taxi is affected by many factors. Existing models lack effective trajectory feature. They have low accuracy in predicting travel time in urban areas. This will affect the travel arrangement of passengers. In this paper, we propose a trajectory feature learning method based on the image processing method and time series prediction. Traffic congestion is quantified as the congestion value, and a variety of external factors are considered. The experimental results show that this model has advantages over some classical models in predicting travel time.

A Survey on Traffic Modeling and Mitigation Techniques

Abstract—The growing road traffic congestion in India is becoming more prevalent nowadays, the worst hit being the metropolitan and developed cities. Its ever-increasing nature poses a threat to development and requires quick action to help ease the troubles faced by people on a day-to-day basis. Modern technology offers a great aid to these efforts by providing mechanisms to model the travel patterns of a region and provide quick congestion relief by introducing adaptive traffic signals that operate based on the current traffic density of the road. In this paper, we present a survey of the most popular techniques involving travel demand modeling and adaptive signal control and compare the various methods employed by researchers to tackle both of these issues separately. The survey focuses on analyzing these methods from the perspective of implementation in an Indian scenario and offering a theoretical solution that involves the use of both these techniques to ease the problem of traffic congestion. Index Terms—road traffic congestion mitigation, adaptive traf- fic signal, travel demand modeling,

Modeling Car-Following Behavior with Different Acceptable Safety Levels

In normal car-following (CF) states, the minimum safe braking distance (MSBD) is virtually an unmeasurable variable, mainly due to the diversity of drivers’ reaction times and vehicles’ braking performance. The average MSBD regarding the reaction time and decelerations as constant values is sometimes greater than the distance used for safe braking of the following vehicle when the leading vehicle applies an emergency brake, which is named the short-distance CF behavior. The short-distance CF conveys that drivers adopt strategies of lower acceptable safety levels, which can be applied to intelligent connected technology (ICT). The objective of this paper was to extend the CF model to accommodate manual driving behavior on the state of different safety levels, and to analyze road traffic flow in the environment from manual driving to high-level intelligent driving with different delays. First, the cognitive bias variable was defined as the ratio of the actual braking distance available to the average MSBD to indirectly analyze different safety levels. Second, the Gipps model was extended, depending on the cognitive bias variable threshold and the duration length of the short-distance CF state, to reproduce driving behaviors with different acceptable safety levels more accurately by numerical simulation. Finally, using models to numerically simulate the impact of vehicles on road traffic flow was carried out. CF behaviors with lower acceptable safety levels under manual driving conditions increase traffic efficiency, and road capacity and safety are significantly improved due to ICT enabling a shortened reaction time. The short-distance driving applied to ICT is expected to be a strategy for traffic congestion mitigation.

Dynamic adaptive vehicle re-routing strategy for traffic congestion mitigation of grid network

Dynamic adaptive vehicle re-routing strategy for traffic congestion mitigation of grid network

Model predictive control policy design, solutions, and stability analysis for longitudinal vehicle control considering shockwave damping

Longitudinal vehicle control models, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), have been the core component of many Automated Driving Systems (ADS) or Advanced Driver Assistance System (ADAS). ACC and CACC systems make vehicles drive with faster reaction time and smaller following distance than manual vehicles, alleviating the congestions and attenuating traffic disturbances. Many existing ACC and CACC control models focus on three strategies that can indirectly mitigate congestions and damp traffic shockwaves: time headway suppression, stability improvement, and adaptive mode-switching control based on traffic conditions. Although congestion mitigation and shockwave damping are achievable with those control strategies, the improvement is often limited to the by-product effect of their main control objectives of headway suppression and system stability. Many adaptive models also need to calibrate many parameters for different road conditions.This paper proposes a new ACC and CACC framework that directly integrates the congestion shockwave damping and traffic congestion mitigation into the objective function of a dynamic control framework. A Model Predictive Control (MPC) based dynamic optimization framework is proposed to balance the trade-offs among multiple objectives, including safety, efficiency, shockwave, elasticity, and driver’s comfort. The proposed framework is applied in both ACC and CACC platoons mixed with manual vehicles, and five different control models are explored. The stability conditions of the proposed models are derived analytically and analyzed numerically. Three experimental studies, including platoon, ring-road, and traffic simulation studies, indicate promising results of proposed models in reducing shockwave propagation speed and mitigating traffic congestion under different environments compared with existing ACC and CACC models.

Robust and resilient equilibrium routing mechanism for traffic congestion mitigation built upon correlated equilibrium and distributed optimization

With the rapid development of wireless communication, mobile computing, and GPS technologies, drivers’ route decisions nowadays rely more on navigation services, such as Google or Waze. However, these navigation services don't always come with improved traffic conditions. Individual drivers often make independent and selfish route decisions that are not systematically favorable and thus often result in severe congestions. This study aims to alleviate such problems by exploiting the information gaps between individuals and the central planner (CP). Specifically, we develop a correlated equilibrium routing mechanism (CeRM) for the CP, which drives a group of vehicles’ route choices to an equilibrium with a systematically optimal traffic condition while still satisfying individuals’ selfish nature. Participating drivers would only be better off by following the suggested routing guidance than navigating on their best responses to real-time traffic information. The CeRM is modeled as a nonconvex and nonlinear program involving a large-scale of users. A distributed Augmented Lagrangian algorithm (D-AL) is developed to efficiently solve the CeRM to provide online real-time navigation service, taking advantage of the on-board computation resources of individual vehicles. Considering the D-AL relies on the wireless communications between vehicles and the CP, we proved the convergence robustness of the D-AL against random communication failures and derived the convergence rate upper bound as a function of the communication failure probability. It is noticed that the convergence rate of the D-AL degrades dramatically as the communication failure probability increases, which hampers the applicability of implementing the CeRM in practice. To improve the solution algorithm's resilience in the computation performance, we further designed and proved an acceleration scheme aided D-AL (aD-AL) to expedite the convergence rate under the high likelihood of communication failures. Numerical experiments conducted on the Sioux Falls city network confirmed the D-AL's convergence properties, robustness against random communication failures, and the resilience of the aD-AL to solve the CeRM. The experiments also show that the CeRM results in better system performance (have less system cost) compared with the existing Independent Routing (IR) mechanism and user-oriented Equilibrium Routing (uoER) mechanism.

Time-of-day pricing for toll roads under traffic demand uncertainties: A distributionally robust simulation-based optimization method

Road pricing has received increasing attention for traffic congestion mitigation, but traffic demand uncertainties cause a profound impact on its effectiveness. This study proposes a link-based time-of-day pricing distributionally robust simulation-based optimization (DRSO) model to minimize the worst-case expected total travel time (TTT). This model aims to answer where and how much to charge under origin–destination (OD) traffic demand uncertainties that are described by an ambiguity set of probability distributions, and is solved by a DRSO method with two stages. The first stage searches an infill sample after building two stochastic kriging models (i.e., SKG-I and SKG-II) to map the relation between the traffic demand uncertainty parameter and the average TTT for each decision sample, and that between the decision variable and the worst-case average TTT, respectively. The second stage introduces the optimal computational budget allocation (OCBA) method to distribute more simulation replications to more promising combination samples, which helps to improve the prediction accuracy of SKGs in promising regions. The DRSO method is first tested with an M/M/1 queuing problem, and numerical results show its outperformance compared with one counterpart algorithm. After that, it is applied to address the link-based time-of-day pricing DRSO problem on Anaheim network and two solution algorithms are also added for comparison. Numerical results show that the robust toll plan solved by the DRSO method outperforms no toll plan and two other toll plans solved by the comparison algorithms in handling traffic dynamics and different levels of traffic demand uncertainties. In conclusion, the DRSO method is promising to address the costly simulation-based optimization (SO) problems under uncertainties characterized by an ambiguity set of probability distributions.

Optimal information perturbation for traffic congestion mitigation: Gaussian process regression and optimization

Information perturbation is an information provision strategy which strategically alters the traffic information sent to drivers, aiming to mitigate network congestion caused by selfish routing decisions. Previous works have integrated information perturbation into coordinated routing mechanisms and theoretically proved that the system performance improvement due to information perturbation is more significant than the corresponding average user optimality loss. However, they do not give direct guidance as to which perturbation level should be implemented to manage the competing effects of system performance improvement and user optimality loss most efficiently. Accordingly, the goal of this work is to uncover the optimal perturbation that should be included in the information sent to vehicles under a coordinated routing scheme under some known network condition. The optimal perturbation problem considers the tradeoff between system performance improvement and user optimality loss due to perturbation, while ensuring that no exorbitant individual user optimality loss occurs. The relationships used in the optimization models are obtained through gaussian process regression, which are trained using the output of computer experiments. We pay special attention to the input features of these models, which impact the system performance and user optimality loss in our model the most—namely the perturbation, demand, and routing competition levels. We model route competition levels through a new parameter called weighted concentration, and present a novel approach for sampling weighted competition values for a single demand level. We present several candidate gaussian process specifications which are used in the optimization, including single and multiple output. Finally, we demonstrate the merit of our methodology through a sensitivity analysis using the Sioux Falls network as a test case. Here, we find that our optimization approach can find robust solutions (in terms of their proximity to local optimums) relatively high rate, with a satisfied solution time.

Robust and Resilient Equilibrium Routing Mechanism for Traffic Congestion Mitigation Built Upon Mixed Strategy Correlated Game and Distributed Optimization

Robust and Resilient Equilibrium Routing Mechanism for Traffic Congestion Mitigation Built Upon Mixed Strategy Correlated Game and Distributed Optimization

Connected Vehicles Versus Conventional Traffic Congestion Mitigation Measures: An Operational Economic Analysis

This paper conducted an operational, economic analysis to assess alternative solutions to traffic congestion. They involved integrating adaptive traffic signal control (ATSC) with connected vehicle technology (ATSC-CV) and the application of various conventional and unconventional solutions. The studied conventional scenarios include signal timing optimization, signal actuation, and upgrading existing intersections to interchanges. There were unconventional scenarios involving converting two intersections to interchanges and the third to a continuous green-T intersection (CGTI). Different unconventional alternatives involved deploying ATSC-CV-based systems assuming varying market penetration rates (MPRs). The operational performance of each alternative was analyzed using VISSIM microsimulation software. To model the driving behavior of CVs, Python programming language was used through the COM interface in VISSIM. One-way analysis of variance (ANOVA) and post-hoc testing results indicate that implementing any suggested alternative would substantially decrease the mean vehicular travel time compared to the fixed signal control strategy currently implemented. Specifically, the ATSC-CVbased systems yielded notable travel time reductions ranging from 9.5% to 21.3%. Also, ANOVA results revealed that the highest benefit-to-cost ratio among all alternatives belonged to scenarios in which the MPRs of CVs were 100%. It was also found that ATSC-CV-based systems with MPRs of 25% and 50% would be as feasible as converting signalized intersections to underpass interchanges.

Evolution of road traffic congestion control: A survey from perspective of sensing, communication, and computation

Road traffic congestion can inevitably degrade road infrastructure and decrease travel efficiency in urban traffic networks, which can be relieved by employing appropriate congestion control. According to different developmental driving forces, in this paper, the evolution of road traffic congestion control is divided into two stages. The ever-growing number of advanced sensing techniques can be seen as the key driving force of the first stage, called the sensing stage, in which congestion control strategies experienced rapid growth owing to the accessibility of traffic data. At the second stage, i.e., the communication stage, communication and computation capability can be regarded as the identifying symbols for this stage, where the ability of collecting finer-grained insight into transportation and mobility reality improves dramatically with advances in vehicular networks, Big Data, and artificial intelligence. Specifically, as the pre-requisite for congestion control, in this paper, existing congestion detection techniques are first elaborated and classified. Then, a comprehensive survey of the recent advances for current congestion control strategies with a focus on traffic signal control, vehicle route guidance, and their combined techniques is provided. In this regard, the evolution of these strategies with continuous development of sensing, communication, and computation capability are also introduced. Finally, the paper concludes with several research challenges and trends to fully promote the integration of advanced techniques for traffic congestion mitigation in transportation systems.

Basic Study on Expressway Congestion Mitigation using Cellular Automata

Traffic congestion has become a serious social problem in Japan. In particular, traffic congestion causes enormous time and economic losses on expressways, which are intended to facilitate smooth traffic flow. It also causes environmental problems and a decrease in logistics efficiency, so efforts to eliminate or reduce traffic congestion are essential. The elimination and mitigation of traffic congestion on highways is a factor in reducing traffic accidents and fatalities. In recent years, with the improvement of computing capabilities, research on traffic congestion reduction and mitigation has been conducted using traffic simulation. Traffic simulation allows us to reproduce and evaluate traffic phenomena that occur on roads on a computer. The Kakuto Tunnel is located on the border between Ebino City in Miyazaki Prefecture and Hitoyoshi City in Kumamoto Prefecture. The Kakuto Tunnel has insufficient traffic capacity and is frequently congested on holidays. In this study, we created a traffic simulator assuming the Kakuto Tunnel. In the simulation, we examined the effectiveness of increasing the number of lanes as a method of reducing traffic congestion.

Research Paper on Performance Analysis and Recommendations of Traffic Congestion Strategies in Cities of Haryana

This experimental study uses national regulations and survey reports to identify short, medium, and long-term traffic congestion strategies in Haryana's cities. The current study looked into a variety of successful road congestion mitigation techniques, ranging from expanded road capacity to the use of roadways, to see which ones were the most cost-effective. Using an examination of quantitative regression, interviews with transportation policy and decision makers, and alternate matrix criteria, I ranked each traffic congestion mitigation approach from least to most cost efficient based on three cost factors. I discovered that ramp measuring was both the most cost-effective and the most difficult method. Meanwhile, I discovered that expanding transit capacity was the least cost-effective of the solutions I looked into.

A Trial-and-Error Toll Design Method for Traffic Congestion Mitigation on Large River-Crossing Channels in a Megacity

In this study, we addresse traffic congestion on river-crossing channels in a megacity which is divided into several subareas by trunk rivers. With the development of urbanization, cross-river travel demand is continuously increasing. To deal with the increasing challenge, the urban transport authority may build more river-crossing channels and provide more high-volume public transport services to alleviate traffic congestion. However, it is widely accepted that even though these strategies can mitigate traffic congestion to a certain level, they are not essential approaches to address traffic congestion. In this study, we consider a channel toll scheme for addressing this issue. Additional fares are applied to private vehicles, that an appropriate number of private vehicle drivers are motivated to take public transport or switch to neighboring uncongested river-crossing channels. To minimize the toll surcharge on both neighboring channels, while alleviating the traffic flow to a certain level, in this study, we provide a bi-objective mathematical model. Some properties of this model are discussed, including the existence and uniqueness of the Pareto optimal solution. To address this problem, a trial-and-error method is applied. Numerical experiments are provided to validate the proposed solution method.

An Implementation of a Route Reservation Architecture

Route Reservation Architecture seems to be a reliable and durable traffic congestion mitigation mechanism that can significantly reduce drivers’ travel-times. In this framework, a Route Reservation controller is responsible for coordinating drivers’ departure times and routes to follow so as to arrive at their destination at the earliest time. Despite the great efficiency of the Route Reservation Architecture, its real-life implementation is a significant challenge considering the size and the complexity of urban networks. This work investigates and develops all the modules required to realize the Route Reservation Architecture as a real-life traffic and demand management mechanism. In doing so, this work proposes all the needed communication protocols and algorithmic methods that ensure its fast and reliable application and addresses any architecture’s scalability issues. The developed modules are evaluated over a large-scale urban area where a microscopic simulation serves as a realistic transportation network.

Built Environment Correlates of the Propensity of Walking and Cycling

Walking and cycling are not only frequently-used modes of transport but also popular physical activities. They are beneficial to traffic congestion mitigation, air pollution reduction, and public health promotion. Hence, examining and comparing the built environment correlates of the propensity of walking and cycling is of great interest to urban practitioners and decision-makers and has attracted extensive research attention. However, existing studies mainly look into the two modes separately or consider them as an integral (i.e., active travel), and few compare built environment correlates of their propensity in a single study, especially in the developing world context. Thus, this study, taking Xiamen, China, as a case, examines the built environment correlates of the propensity of walking and cycling simultaneously and compares the results wherever feasible. It found (1) built environment correlates of the propensity of walking and cycling differ with each other largely in direction and magnitude; (2) land use mix, intersection density, and bus stop density are positively associated with walking propensity, while the distance to the CBD (Central Business District) is a negative correlate; (3) as for cycling propensity, only distance to CBD is a positive correlate, and job density, intersection density, and bus stop density are all negative correlates. The findings of this study have rich policy implications for walking and cycling promotion interventions.