Highway Congestion Research Articles

On-road trust and perceived risk in Level 2 automation

To encourage appropriate use of driving automation, we need to understand and monitor driver’s trust and risk perception. We examined (1) how trust and perceived risk are affected by automation, driving conditions and experience and (2) how well perceived risk can be inferred from behaviour and physiology at three levels: over traffic conditions, aggregated risk events, and individual risk events.30 users with and without automation experience drove a Toyota Corolla with driving support. Safety attitude, subjective ratings, behaviour and physiology were examined.Driving support encouraged a positive safety attitude and active driver involvement. It reduced latent hazards while maintaining saliently perceived risks. Drivers frequently overruled lane centring (3.1 times/minute) and kept their feet on or above the pedals using ACC (65.8% of time). They comfortably used support on curvy motorways and monotonic and congested highways but less in unstable traffic and on roundabouts. They trusted the automation 65.4%, perceived 36.0% risk, acknowledged the need to monitor and would not engage in more secondary tasks than during manual driving.Trust-in situation reduced 2.0% when using automation. It was 8.2% higher than trust-in-automation, presumably due to driver self-confidence. Driving conditions or conflicts between driver and automation did not affect trust-in-automation.At the traffic condition level, physiology showed weak and partially counter-intuitive effects. For aggregated risk events, skin conductance had the clearest response but was discernible from baseline in < 50%. Pupil dilation and heart rate only increased with strong braking and active lane departure assist. For individual risk events, a CNN classifier could not identify risk events from physiology. We conclude that GSR, heart rate and pupil dilation respond to perceived risk, but lack specificity to monitor it on individual events.

Reinforcement Learning Versus PDE Backstepping and PI Control for Congested Freeway Traffic

We develop reinforcement learning (RL) boundary controllers to mitigate stop-and-go traffic congestion on a freeway segment. The traffic dynamics of the freeway segment are governed by a macroscopic Aw-Rascle-Zhang (ARZ) model, consisting of 2 x 2 quasi-linear partial differential equations (PDEs) for traffic density and velocity. The boundary stabilization of the linearized ARZ PDE model has been solved by PDE backstepping, guaranteeing spatial L² norm regulation of the traffic state to uniform density and velocity and ensuring that traffic oscillations are suppressed. Collocated proportional (P) and proportional-integral (PI) controllers also provide stability guarantees for allowable control gains and are always applicable as model-free control options through gain tuning by trial and error, or by model-free optimization. Although these approaches are mathematically elegant, the stabilization result only holds locally and is usually affected by the change of model parameters. Therefore, we reformulate the PDE boundary control problem as an RL problem that pursues stabilization without knowing the system dynamics, simply by observing the state values. The proximal policy optimization (PPO), a neural network-based policy gradient algorithm, is employed to obtain RL controllers by interacting with a numerical simulator of the ARZ PDE. Being stabilization-inspired, the RL state-feedback boundary controllers are compared and evaluated against the rigorously stabilizing controllers in two cases: 1) in a system with perfect knowledge of the traffic flow dynamics and then 2) in one with only partial knowledge. We obtain RL controllers that nearly recover the performance of the backstepping, P, and PI controllers with perfect knowledge and outperform them in some cases with partial knowledge. It must be noted, however, that the RL controllers are obtained by conducting about one thousand episodes of iterative training on a simulation model. This training cannot be performed in a collision-free fashion in real traffic, nor convergence guaranteed when training. Thus, we demonstrate that the RL approach has learning (i.e., adaptation) potential for traffic PDE systems under uncertain and changing conditions, but RL is neither simple nor a fully safe substitute for model-based control in real traffic systems.

Algorithm Design for On-ramp Traffic Flow Controlling of Expressway Based on Toll Gates

On-ramp control is one of the most widely used and most effective methods for relieving highway congestion in foreign countries. In this paper, the principle and common algorithms of the on-ramp control are summarized. Combining the reality of China, an practical algorithm for on-ramp traffic flow controlling of expressway based on toll gates controlling is proposed. That is, the traffic flow model and queuing model between the toll gates and the traffic flow are established based on the demand-capacity control theory, so as to realize the flow control by controlling the toll gates. Finally, the algorithm is verified by taking Guangzhou Airport Express as an example.

Ramp Metering Strategies: A Literature Review

Ramp metering (RM) is a control tool that allows keeping the flow in freeways main stream stable (occupancy below critical values), in order to delay the onset of congestion. A proper application guarantees a lower Total Travel time Spent in the network (TTS) (travel time in the main stream and waiting time at on-ramps), to varying degrees depending on the application. The benefits of RM arise from preventing congestion and/or from preventing queue spillback. In this paper, the state of the art is surveyed, ultimately selecting twenty-one relevant documents reporting case studies that compare the performance of two or more RM strategies. These documents are classified by the strategy used and reported gains (reduction in TTS). Regardless of the strategy, the authors recommend applying RM to highly congested freeways with similar TTS among ramps. Bottlenecks should be clearly identified to establish the place where the control must be carried out. Control parameters should be set using historical and real-time data and updated according to a receding horizon. Detrimental factors that could exacerbate initial conditions of congestion and spillback include: incorrect input parameters, reactive instead of proactive approaches, and queue formation only on some coordinated ramps.

Model-based dynamic toll pricing scheme for a congested suburban freeway with multiple access locations

In this paper we propose a novel approach for alleviating traffic congestion in freeways with multiple access locations through the use of dynamic toll pricing. The pricing strategy is determined using model-based feedback control, with the underlying model derived using a combination of both traffic flow modeling and driver behavior. The traffic segment we focus on is a suburban freeway with multiple access locations. A model derived from the cell transmission method was utilized to develop the traffic flow model, with past traffic information from on-road sensors utilized for determining the model parameters. The behavior of the driver with respect to the choice of whether or not to enter the freeway segment is modeled using utility theory and the Value of Time (VOT) relative to the toll value. The proposed toll-pricing scheme is tested with traffic data from Portuguese freeway A5 and with different hypothesis on the driver’s VOT distribution, showing a significant improvement of the overall traffic conditions. The algorithm developed here provides an opportunity to improve on existing toll policy by guaranteeing more stable traffic conditions for the freeway users and optimizing the overall traffic throughput.

Explaining Drivers’ Support for Tolls and their Toll Avoidance Behavior

There is growing reliance on road tolls for revenue and for highway congestion management. However, there is limited research, in the U.S. context, on how drivers change their behavior in response to tolls. But experiences in Europe and Asia find that drivers respond to tolls through a variety of behavioral shifts. Such behaviors have implications for the revenue generating capabilities of tolls as well as the effectiveness of tolls as a traffic management tool. This research applies the Theory of Planned Behavior to examine drivers’ behavioral responses to tolls. We connect a preference for tolls as a funding source, ability to pay, and perceptions of toll benefits to the support for tolls, and, subsequently, to a number of toll avoidance responses. We examine the following toll avoidance behavior: changing home or employment location; changing commute; avoiding travel to destinations that require tolls; and telecommuting or working from home. Using data from a 2014 survey of drivers in the Hampton Roads region of Virginia, conducted nine months after toll implementation, we find that a higher willingness to pay decreases the likelihood of toll avoidance behavior and that the preference for tolls over taxes and the timing of travel impact willingness to pay, as well as race of the driver. Age, gender, education, and race influence toll avoidance behavior. Our findings suggest social justice be considered when developing a toll system, especially the impact on short-term and long-term toll avoidance behaviors.

Analysis &amp; Prediction of Road Accident Data for NH-19/44

Increased traffic congestion in highways and urban areas with an increase in traffic accidents on road networks—which were never intended to handle the volumes and types of traffic they are now required to carry—are results of growing urbanisation and the rise in the number of vehicles in many developing countries. Unplanned urban growth has also resulted in a wide range of confrontations between vehicles and pedestrians, as well as incompatible land users. Many new urban residents are unaccustomed to such high traffic levels as a result of the migration from rural to urban areas. As a result, there has frequently been a considerable deterioration in driving conditions, as well as an increase in risks and rivalry between various groups of road users. Additionally, poor road upkeep, poorly designed junctions, and insufficient provisions for pedestrians frequently make the inherent dangers worse. The quantity of traffic on the roadways has been increasing recently, which has raised the likelihood of traffic accidents. Road traffic accidents are the fifth most major cause of mortality worldwide, according to evidence from both industrialised and developing nations, and they are on the rise. They are responsible for a sizable part of injuries, fatalities, and disabilities among the population. The route of NH 19/44 between Mathura and Agra has been chosen as the study area. The study span is 36 kilometres long in total. Aside from vehicles owned by the Transport Corporation, the majority of heavy vehicles that travel between Mathura and Agra are trucks transporting industrial items, containers, and vehicles used to transport construction materials. There are steel plants, textile mills, foundries, schools, colleges, banks, gas bunks, etc. on both sides of the stretch at regular intervals. Along the stretch under consideration, there were nine police stations. Information about the road inventory was gathered directly from the study stretch. The entire 67 km route was divided into three sections with various segment lengths based on the number of police stations and jurisdiction. Police stations are scattered throughout these parts, spaced 10 to 12 kilometres apart.

Mitigating urban motorway congestion and emissions via active traffic management

Due to unavoidable expansion of car-ownership, traffic congestion and emissions in developing countries keep on increasing. Meanwhile, high construction costs and statutory restrictions hinder traditional road infrastructure expansion and improvement projects. Thus, exploring cost-effective traffic management solutions to relieve traffic congestion and emissions becomes one of the most significant challenges faced by transportation authorities, especially in developing countries. Active traffic management (ATM) systems that are environmentally sustainable and relatively low-cost are particularly advantageous. In this study, we develop an integrated ATM system to mitigate urban motorway congestion and emissions. The proposed system can be used directly with existing road facilities, which provides a cost-effective way for traffic management and decarbonization in developing countries. The simulation results show that the developed system can reduce travel time spent (TTS), carbon dioxide (CO2) and nitrogen oxides (NO2) by 7.5%, 21.1% and 10.7%, respectively.

The Oxidative Potential of Fine Particulate Matter and Biological Perturbations in Human Plasma and Saliva Metabolome.

Particulate oxidative potential may comprise a key health-relevant parameter of particulate matter (PM) toxicity. To identify biological perturbations associated with particulate oxidative potential and examine the underlying molecular mechanisms, we recruited 54 participants from two dormitories near and far from a congested highway in Atlanta, GA. Fine particulate matter oxidative potential ("FPMOP") levels at the dormitories were measured using dithiothreitol assay. Plasma and saliva samples were collected from participants four times for longitudinal high-resolution metabolic profiling. We conducted metabolome-wide association studies to identify metabolic signals with FPMOP. Leukotriene metabolism and galactose metabolism were top pathways associated with ≥5 FPMOP-related indicators in plasma, while vitamin E metabolism and leukotriene metabolism were found associated with most FPMOP indicators in saliva. We observed different patterns of perturbed pathways significantly associated with water-soluble and -insoluble FPMOPs, respectively. We confirmed five metabolites directly associated with FPMOP, including hypoxanthine, histidine, pyruvate, lactate/glyceraldehyde, and azelaic acid, which were implications of perturbations in acute inflammation, nucleic acid damage and repair, and energy perturbation. The unique metabolic signals were specific to FPMOP, but not PM mass, providing initial indication that FPMOP might constitute a more sensitive, health-relevant measure for elucidating etiologies related to PM2.5 exposures.

Do Ride-Hailing Services Worsen Freeway Congestion and Air Quality? Evidence From Uber in California

Do Ride-Hailing Services Worsen Freeway Congestion and Air Quality? Evidence From Uber in California

Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data

Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 min into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25–100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables.

Erratum to: Congestion in highways when tolls and railroads matter: evidence from European cities

Erratum to: Congestion in highways when tolls and railroads matter: evidence from European cities

Multi-Class Freeway Congestion and Emission Based on Robust Dynamic Multi-Objective Optimization

In order to solve the problem of traffic congestion and emission optimization of urban multi-class expressways, a robust dynamic nondominated sorting multi-objective genetic algorithm DFCM-RDNSGA-III based on density fuzzy c-means clustering method is proposed in this paper. Considering the three performance indicators of travel time, ramp queue and traffic emissions, the ramp metering and variable speed limit control schemes of an expressway are optimized to improve the main road and ramp traffic congestion, therefore achieving energy conservation and emission reduction. In the VISSIM simulation environment, a multi-on-ramp and multi-off-ramp road network is built to verify the performance of the algorithm. The results show that, compared with the existing algorithm NSGA-III, the DFCM-RDNSGA-III algorithm proposed in this paper can provide better ramp metering and variable speed limit control schemes in the process of road network peak formation and dissipation. In addition, the traffic congestion of expressways can be improved and energy conservation as well as emission reduction can also be realized.

Traffic-Aware Lane Change Advance Warning System for Delay Reduction at Congested Freeway Diverge Areas

This paper presents an on-board advance warning system for vehicles based on a probabilistic prediction model that advises them on when to change lanes to reach a highway diverge on time. The system is based on a model that estimates the probability of reaching a goal state on the road using one or multiple lane changes. This estimate is based on several traffic-related parameters such as the distribution of inter-vehicle headway distances as well as driver-related parameters like lane change duration. For an upcoming diverge, the advance warning system uses the model to continuously calculate the probability of reaching it and advise the driver to change lanes when the probability dips below a certain threshold. To evaluate the performance of the proposed system in reducing traffic delay at highway diverges, it was used on a segment of a four-lane highway to advise vehicles taking an off-ramp on when to change lanes. Results show that using the proposed system reduces average delay up to 6% and maximum delay up to 16%, depending on traffic flow and the ratio of vehicles taking the off-ramp.

The Oxidative Potential of Fine Particulate Matter and Metabolic Perturbations in Plasma and Saliva

BACKGROUND AND AIM: Exposure assessment and health impact evaluation of particulate matter (PM) mixtures remain very challenging. Fine particle oxidative potential (FPMOP) has been considered as a key health-relevant particulate parameter. We measured FPMOP exposures in a recent panel study and corresponding metabolic perturbations to evaluate its potential epidemiologic value and examine molecular mechanisms underlying PM-related health impacts. METHODS: We recruited 54 participants from two dormitories in Atlanta, GA near and far from a congested highway. Indoor or outdoor FPMOP levels at the dormitories were measured using dithiothreitol (DTT) assay. Plasma and saliva samples were collected from participants at four time points during 12 weeks. Liquid chromatography coupled with high-resolution mass spectrometry was used to profile the participants’ metabolome. We used mixed effect models to examine associations between metabolic features and FPMOP, controlling for potential confounders including age, gender, race, and body mass index. Significant metabolic features meeting false positive discovery rate at 20% were used for pathway enrichment analysis and metabolite annotation. RESULTS:The 96h-mean water soluble FPMOP levels at the near and far dormitories prior to biosample collection were 26.3 and 22.9 nmol/min/μg, respectively. In total, we extracted 20,766 metabolic features from plasma samples and 29,013 from saliva samples. Purine metabolism, N-glycan biosynthesis, and beta-alanine metabolism were most strongly associated with 5 or more FPMOP-related measurements in plasma, while vitamin E metabolism, leukotriene metabolism, and glycosphingolipid metabolism were found associated with FPMOP in saliva. We confirmed 6 metabolites directly associated with FPMOP measurements including hypoxanthine, histidine, pyruvate, (s)-lactate/glyceraldehyde, azelaic acid, and petroselinic acid/elaidic acid/oleate, which were implications of perturbations in amino acid, carbohydrate, nucleotide and lipid metabolism. CONCLUSIONS:We identified metabolites and pathways perturbations in plasma and saliva following by higher FPMOP exposure in panel-based setting. Perturbations in amino acid, carbohydrate, nucleotide and lipid metabolism may elicit PM-related health impacts. KEYWORDS: Fine Particle Oxidative Potential, Metabolomics, Air Pollution, Dithiothreitol Assay

Congestion in highways when tolls and railroads matter: evidence from European cities

Abstract Using data from the 545 largest European cities, we study whether the expansion of their highway capacity provides a solution to the problem of traffic congestion. Our results confirm that in the long run, and in line with the ‘fundamental law of highway congestion’, the expansion in cities of lane kilometres causes an increase in vehicle traffic that does not solve urban congestion. We disentangle the increase in traffic due to the increases in coverage and in capacity. We further introduce road pricing and public transit policies in order to test whether they moderate congestion. Our findings confirm that the induced demand is considerably smaller in cities with road pricing schemes, and that congestion decreases with the expansion of public transportation.

FUZZY LOGIC BASED IMPROVED MEDIUM ACCESS CONTROLLER FOR CONGESTION CONTROL AND PREDICTION THE SYSTEMS IN WSNS

in resource-constrained networks, particularly those with limited bandwidth to manage high-volume data transmission, network congestion is a major issue, resulting in poor quality of service, including packet loss and delay throughput. Due to self-contained batteries that limit sensor node lifetime, this issue is important in wireless sensor networks (WSNs) with limitations and restrictions, such as limited processing power, memory, and transmission. By determining a path that avoids congested highways, the network can be extended. As a result, we present a WSN route determination architecture that is congestion-aware. The architecture is divided into three stages: In a top-down hierarchical structure, the first path is created. Energy-aware assisted routing for route derivation. Exponential smoothing is used to forecast congestion, but the final parameters for route determination are not taken into account. We use fuzzy logic systems to evaluate proper weights for a variety of factors, including shop count, remaining energy, buffer occupancy, and forwarding rate, as well as a bat algorithm to optimize the weight over the membership functions. Eventually proposed model shows the high throughput, low packet loss, save energy, and extending network lifetime.&#x0D;

Developing the guidelines for managing autonomous vehicle flows on congested highways: A case study of MD-100

Recognizing the need for responsible highway agencies to effectively manage emerging autonomous vehicles (AV) flows in contending with daily recurrent congestion, this study presents a systematic procedure for understanding the impacts of AV flows on traffic conditions under different AV behavioral mechanisms (i.e., car-following and lane-changing), and different penetration rates. Research results show that the presence of AV flows, depending on their adopted behavioral mechanisms, may have significant (either positive or negative) impacts on the overall traffic conditions. Hence, it is essential for responsible highway agencies to have proper operational guidelines to manage and coordinate AV flows. To demonstrate the proposed methodology, this study has carried out extensive simulation experiments using a congested segment of the MD-100 network (a multilane highway segment located in Maryland) under various AV penetration rates and observable behavioral patterns. The collected Measures of Effectiveness highlight that at each AV penetration level there exists a set of optimal behavioral patterns for the AV flows to coordinate with non-AV flows via the Vehicle to Infrastructure or Vehicle to Vehicle infrastructure so as to maximize the roadway capacity and minimize the resulting highway congestion.

Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

The integration of the Internet of Vehicles (IoV) in future smart cities could help solve many traffic-related challenges, such as reducing traffic congestion and traffic accidents. Various congestion pricing and electric vehicle charging policies have been introduced in recent years. Nonetheless, the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system. In this paper, we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment. The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations. We propose a token management system that serves as a virtual currency, where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees. The proposed system is designed for Vehicular Ad-hoc Networks (VANETs) in the context of a smart city environment without the need to set up any expensive toll collection stations. Through large-scale traffic simulation in different smart city scenarios, it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

A comparative CO2 emissions analysis and mitigation strategies of short-sea shipping and road transport in the Marmara Region

The Marmara is the most highly trafficked region in Turkey in terms of seaway and highway transport congestion. The transport mode-based CO2 emissions budget for the region needs to take more environmentally friendly measures. In this study, 13 ro-ro and ferry lines (RFLs) are evaluated in the Sea of Marmara to compare ship-generated CO2 emissions with road transport as if the carried vehicles had used the highway instead of transport by RFL. Additionally, this study revealed the management strategies for CO2 emissions reduction for both transport modes under current conditions. The total CO2 emissions budget of 13 RFLs in the Sea of Marmara is higher than the potential CO2 emissions of their carried vehicles. Using the methods of Entec UK Ltd and Trozzi and Vaccaro, the amount of CO2 emissions generated by the RFLs is 204,470.99 and 170,459.85 t/year, respectively. The potential CO2 emissions of road vehicles carried in 2017, 2018 and 2019 are computed by applying Tier 1 methods as 121,690.54, 106,844.89, and 100,921.95 t/year, respectively. It is observed that shifting trucks from the highway to the seaway contributes to a reduction of the yearly CO2 emissions budget generated from the transport sector to provide sustainable transportation management in the region.