Highway Congestion Research Articles

Do Ride-Hailing Services Worsen Freeway Congestion and Air Quality? Evidence from Uber’s Entry in California

Do Ride-Hailing Services Worsen Freeway Congestion and Air Quality? Evidence from Uber’s Entry in California

Analysis of congestion key parameters, dynamic discharge process, and capacity estimation at urban freeway bottlenecks: a case study in Beijing, China

ABSTRACT Recurring bottlenecks significantly contribute to urban freeway congestion, making their analysis essential. This study examines six bottlenecks on Beijing’s Ring Road using multi-day data, identifying them via Dynamic Time Warping and Fuzzy C-Means Clustering (DTW+FCM). Key parameters—free-flow speed, critical speed, critical density, and jam density—are calibrated using fundamental diagram models. The Weibull distribution analyzes flow and speed patterns during congestion phases. The DTW+FCM method effectively identified bottlenecks and congestion levels. Severe congestion lasting over 10 hours on the West Second and Third Ring Roads averaged speeds of 15 km/h. The S3 model best fits data for the West Ring Roads, while the Van Aerde model suits the North Ring Roads. Different methods yield varying traffic capacity estimates, highlighting the need for nuanced approaches in urban expressway planning to maintain traffic quality and comfort. These findings offer valuable guidance for research and practical traffic management solutions.

Close Look into the Spatial-Temporal Distribution of Speed, Lane Changes, and Heavy Vehicles in a Congested Freeway Weaving Section

Close Look into the Spatial-Temporal Distribution of Speed, Lane Changes, and Heavy Vehicles in a Congested Freeway Weaving Section

Using frequency domain analysis to elucidate travel time reliability along congested freeway corridors

Travel time reliability (TTR) is an essential measure of service for traffic performance management, especially for congested freeway corridors. This paper proposes a systematic analytical framework to analyze the predictable and unpredictable variations in route TTR in corridor networks. More specifically, the predictable variation in route TTR is estimated with a deterministic fluid-based polynomial arrival queue (PAQ) model, while the unpredictable variation in route TTR is analyzed through the residual induced by the PAQ estimation model. Based on the output of the PAQ estimation model, a frequency-domain approach is proposed to decompose the observed time-domain travel time into underlying unobserved factors in different temporal resolutions. With the discrete-time Fourier transform method and the Butterworth low-pass filtering technique, it is capable of analytically uncovering different temporal scales of predictable variations in TTR, including the trend of day-to-day variations, the dynamics of within-day variations, as well as the stochasticity of within-period variations. Connecting with the unpredictable variations represented by the residual due to the PAQ approximation model, we can comprehensively elucidate TTR for congested freeway corridors. Case studies are conducted to show the effectiveness of the proposed frequency-domain approach in elucidating and measuring different contributing elements in TTR, and more specifically, the four components, i.e., the trend of day-to-day variations, the dynamics of within-day variations, the stochasticity of within-period variations, and the residual induced by the PAQ estimation model, account for 64.9 %, 20.2 %, 1.2 %, 13.7 % of variations in the route travel time, respectively, based on a case study with the PeMS dataset.

Freeway congestion management on multiple consecutive bottlenecks with RL‐based headway control of autonomous vehicles

AbstractAdaptive cruise control (ACC) is the core building block of future full autonomous driving. Numerous recent research demonstrated that Autonomous Vehicles (AVs) adopting shorter headways generally increase road capacity and may relieve congestion at bottlenecks for moderate demand scenarios. However, with high demand scenarios, bottlenecks can still be activated causing capacity breakdown. Therefore, extra control measures as dynamic traffic control near bottlenecks is necessary. The challenge is harder on urban freeways with consecutive bottlenecks which affect each other. This paper aims to improve the performance of ACC systems in a high demand scenario. A multi‐bottleneck dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts headways to optimize traffic flow and minimize delay is proposed. The controller dynamically assigns an optimal headway for each controlled section, based on state measurement representing the current traffic conditions. The case study is a freeway stretch with three consecutive bottlenecks which is then extended to include eight bottlenecks. Three different RL agent configurations are presented and compared. It is quantitatively demonstrated that the proposed control strategy improves traffic and enhances the system delay by up to 22.30%, and 18.87% compared to shortest headway setting for the three‐bottleneck and the eight‐bottleneck networks, respectively.

Estimation and prediction of freeway traffic congestion propagation using tagged vehicle positioning data

Effective estimation and prediction of freeway congestion propagation is the basis for formulating a traffic management strategy. A new estimation and prediction model for congestion propagation based on coordinated vehicle positioning data collected from fixed detectors is proposed. A shockwave-based method is established to model the propagation trajectory of congestion in time and space using data filtered by the Savitzy-Golay. Bayesian ridge regression is applied to determine the probability range of the propagation path of the congestion. The proposed method was tested with Wi-Fi positioning data from a section of the Beijing-Kunming Freeway in China. The results of our method were compared with filed traffic conditions estimated by loop data. The results show a difference of approximately 200s between the start time of the estimated congestion and the observed time on the selected road section. The predicted congestion start time differs from the observed results by around 100s.

Case Study Evaluation of a New Approach to Price Metropolitan Highways for Congestion Relief, Sustainability and Equity

This paper addresses the practically important challenge of devising efficient and politically feasible congestion pricing policies in the context of metropolitan highways. Congestion on metropolitan highways continues to grow while governments struggle to fund alternative modes of travel. The purpose of this paper is to explore the viability of a new approach to address highway congestion while also accommodating the mobility needs of those who don’t drive. It involves creating congestion-priced lanes on limited access highways without adding new lanes. The lanes would be taken from general use and reserved for high-occupancy vehicles (HOV), transit vehicles and toll-payers, with cash rewards paid to HOV and transit users to attract solo drivers to shared travel. Variable tolls charged to lower-occupancy vehicles on the dedicated lanes would limit traffic demand on the lanes, keep traffic flowing, and fund the cash rewards. Rewards would be high enough to attract a sufficient number of drivers to ride as passengers instead so that congestion would be eliminated on the toll lanes and reduced on the remaining toll-free lanes through mode shifts. The policy-level analysis using a real-world case study of a radial highway segment with directional peaking suggests that this congestion pricing/cash rewards strategy could generate surplus revenues and provide financial support for bus rapid transit operating on the congestion-free lanes.

Proximal policy optimization learning based control of congested freeway traffic

AbstractIn this paper, a delay compensation feedback controller based on reinforcement learning is proposed to adjust the time interval of the adaptive cruise control (ACC) vehicle agents in the traffic congestion by introducing the proximal policy optimization (PPO) scheme. The high‐speed traffic flow is characterized by a two‐by‐two Aw Rasle Zhang nonlinear first‐order partial differential equations (PDEs). Unlike the backstepping delay compensation control,23 the PPO controller proposed in this paper consists of the current traffic flow velocity, the current traffic flow density and the previous one step control input. Since the system dynamics of the traffic flow are difficult to be expressed mathematically, the control gains of the three feedback can be determined via learning from the interaction between the PPO and the digital simulator of the traffic system. The performance of Lyapunov control, backstepping control and PPO control are compared with numerical simulation. The results demonstrate that PPO control is superior to Lyapunov control in terms of the convergence rate and control efforts for the traffic system without delay. As for the traffic system with unstable input delay value, the performance of PPO controller is also equivalent to that of backstepping controller. Besides, PPO is more robust than backstepping controller when the parameter is sensitive to Gaussian noise.

Dynamic speed harmonization for mixed traffic flow on the freeway using deep reinforcement learning

AbstractIn the vicinity of weaving areas, freeway congestion is nearly unavoidable due to their negative effects on the continuous freeway mainline flow. The adverse impacts include increased collision risks, extended travel time, and excessive emissions and fuel consumption. Dynamic Speed Harmonization (DSH) has the potential to dampen traffic oscillation during congestion. However, its effectiveness is typically limited by the low compliance rates of drivers and delays in information access. The integration of Connected and Automated Vehicles (CAVs) into intelligent transportation systems aims to enhance various measures of effectiveness. This research investigates the effects of DSH in mixed traffic flow involving human‐driven vehicles and CAVs on the freeway. A deep reinforcement learning‐based strategy is developed to better understand how CAVs can improve operational performance. A holistic evaluation is conducted to quantify the impacts under different penetration rates of CAVs in multiple simulated scenarios. The results reveal that the proposed method can enhance mobility and achieve co‐benefits with safety, and environmental sustainability could be improved under higher penetration rates. Spatiotemporal features of bottleneck speed demonstrate that DSH powered by CAVs can smooth speed variations for partial areas. Sensitivity analysis of headways indicates that high‐level CAVs can further improve performance.

Ramp metering to maximize freeway throughput under vehicle safety constraints

Ramp Metering (RM) is one of the most effective control techniques to alleviate freeway congestion. We consider RM at the microscopic level subject to vehicle following safety constraints for a freeway with arbitrary number of on- and off-ramps. The arrival times of vehicles to the on-ramps, as well as their destinations are modeled by exogenous stochastic processes. Once a vehicle is released from an on-ramp, it accelerates towards the free flow speed if it is not obstructed by another vehicle; once it gets close to another vehicle, it adopts a safe gap vehicle following behavior. The vehicle exits the freeway once it reaches its destination off-ramp. We design traffic-responsive RM policies that maximize the throughput. For a given routing matrix, the throughput of a RM policy is characterized by the set of on-ramp arrival rates for which the queue sizes at all the on-ramps remain bounded in expectation. The proposed RM policies operate under vehicle following safety constraints, where new vehicles are released only if there is sufficient gap between vehicles on the mainline at the moment of release. Furthermore, the proposed policies work in synchronous cycles during which an on-ramp does not release more vehicles than the number of vehicles waiting in its queue at the start of the cycle. All the proposed policies are reactive, meaning that they only require real-time traffic measurements without the need for demand prediction. However, they differ in how they use the traffic measurements. In particular, we provide three different mechanisms under which each on-ramp either: (i) pauses release for a time interval before the next cycle begins, or (ii) adjusts the release rate during a cycle, or (iii) adopts a conservative safe gap criterion for release during a cycle. The throughput of the proposed policies is characterized by studying stochastic stability of the induced Markov chains, and is proven to be maximized when the merging speed at all the on-ramps equals the free flow speed. Simulations are provided to illustrate the performance of our policies and compare with a well-known RM policy from the literature which relies on local traffic measurements.

ROAD ACCIDENTS AND THE NATIONAL ECONOMY: LEGAL ANALYSIS

The present publication aims to conduct a legal analysis of the mutual influence of road accidents and the national economy, which is manifested at different levels of relevant public relations. The research subject is road accidents and the national economy. The publication’s methodology relies on an organic combination of well-known laws and techniques of dialectics (the transition of quantitative changes into qualitative, unity and struggle of opposites, and the negation of negation), general scientific methods of research (techniques of the logical method, comparative method, techniques of the sociological method, systemic and structural-functional methods), and special legal methods (legal statistics, formal-legal method, methods of comparative jurisprudence and legal modeling). Results. Road safety is inextricably linked with economic processes taking place in a particular state. In addition, as economic indicators affect the number of road accidents so the dynamics and consequences of road accidents significantly affect the national economy. The most important economic factors affecting the number of road accidents are as follows: underdeveloped road infrastructure, outdated vehicle fleet, poor financial and logistic support of law enforcement agencies, insufficient number of traffic enforcement cameras, and other innovative means of preventing road accidents. The priority in traffic accident prevention is the maximum automation of means ensuring road safety: coverage of all key traffic intersections with traffic enforcement cameras, equipment of modern cars with automatic speed limit systems and alcohol intoxication testers, which automatically block the ignition system, the development of a fleet of AI-based self-driving cars and automated traffic control systems. Other economic and administrative means of road accident prevention are equally important, i.e., financial incentives for compliance with traffic rules, development and improvement of public transport efficiency, and investments in multi-level road infrastructure to reduce traffic congestion in available highways and transport interchange.

Optimization of Consecutive On-Ramp Control for Urban Freeways: An Application of the Store-and-Forward Approach

Highway congestion is a common problem worldwide, and different management strategies have been proposed and implemented. In Taiwan, commuter trips in urban areas tend to use freeways for daily trips. One particular problem in Taiwan is consecutive on-ramps within metropolitan areas, such as Taipei, Taichung, and Kaohsiung. Three to four on-ramps bring more vehicles to the freeways, and coordinating with an integrated ramp control strategy is critical to avoiding congestion in metropolitan areas. Based on the concept of the store-and-forward method, this study proposes a linear optimal control model to optimize integrated on-ramp control strategies. The store-and-forward method is chosen because freeway congestion is usually observed in these areas, and freeway efficiency depends on avoiding queue length. The developed mathematical program can be solved efficiently through mathematical optimization packages. The objective of the formulation is to minimize the total queue length within considered areas. In the Taipei metropolitan area, numerical experiments were conducted based on three consecutive on-ramps, including Yuanshan, Taipei, and Sanchun ramps. Several scenarios were designed to illustrate the possible benefits of the proposed model.

Optimizing Locations of Energy Storage Devices and Speed Profiles for Sustainable Urban Rail Transit

Urban growth and the resulting highway congestion is driving up demand for rail transit. Rail, a significant component of transportation infrastructure, is critical to economic efficiency and is one of the least energy-intensive modes. However, the scale of operations results in high energy consumption, atmospheric pollution, and operating costs. Fortunately, some of the braking energy can be harvested and either used to power a simultaneously accelerating train or stored to power subsequent accelerations. The objective of this research was to optimize the number of locations of the energy storage devices and speed profiles. First, kinematic equations were applied to simulate energy consumption. Then, a genetic algorithm (GA) was developed to optimize the speed profiles that minimize the energy consumption with and without a wayside energy storage unit (WESS) for a rail transit line. Finally, a model was developed to optimize the WESS locations that maximized the net benefit. A case study was conducted to examine the model in a real-world setting and to demonstrate its effectiveness. The results indicate that about 980 MWh of electrical energy, or an additional 5%, could be saved by optimizing the WESS locations over only applying speed profile optimization. In addition to significant energy savings, environmental emissions could be mitigated using these methods.

A Data-Driven Approach to W-Beam Barrier Monitoring Data Processing: A Case Study of Highway Congestion Mitigation Strategy

In this paper, a data-driven approach is used to process W-Beam Barrier monitoring data, expecting to achieve online estimation of the number of trucks and accurate identification of barrier impact events. By analyzing the data features, significant noise was found in the original data, hiding the useful information, so this paper proposes an improved wavelet thresholding algorithm to achieve data denoising. As there is no study of the same application, this paper compares three commonly used data fault diagnosis algorithms: Principal Component Analysis (PCA), Partial Least Squares (PLS) and Fisher Discrimination Analysis (FDA). By designing and conducting comparison experiments, the results show that the PCA model is more suitable for estimating the number of trucks and the FDA model is more suitable for identifying barrier impact events. The data processing results are shared with the highway operation management system as a trigger condition to enable the strategy of forbidden truck overtaking. Through long-term application, the results show that highway capacity is improved by 12.7% and the congestion index and emissions are slightly reduced after adopting this paper’s method.

Freeway Congestion Management With Reinforcement Learning Headway Control of Connected and Autonomous Vehicles

Adaptive cruise control (ACC) systems are increasingly offered in new vehicles in the market today, and they form a core building block for future full autonomous driving. ACC systems allow vehicles to maintain a desired headway to a leading vehicle automatically. Recent research demonstrates that (1) shorter headways lead to higher throughput, and (2) the effective use of ACC can improve traffic flow by adapting the desired time headway in response to changing traffic conditions. In this paper we show that, although shorter headways result in higher capacity, flow breakdown still occurs if traffic densities at bottlenecks are allowed to exceed the critical density. Therefore, dynamic traffic control near bottlenecks is still necessary to avoid bottleneck activation and capacity loss. We propose an adaptive reinforcement learning (RL) headway controller that uses ACC headways to optimize traffic flow and minimize delay. Based on state measurements, the controller dynamically assigns an optimal headway value for each freeway section within a control cycle. In a freeway simulation example, we first demonstrate that different nondynamic headway assignment strategies failed to avoid congestion and traffic breakdown. We then present a dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts the desired headway according to the changing traffic conditions on both the freeway and the ramp to effectively maximize traffic flow and minimize system delay. We quantitatively demonstrate that our DRL dynamic headway control strategy improved traffic and reduced system delay by up to 57% compared with the examined nondynamic headways.

Minimally Disruptive Cooperative Lane-Change Maneuvers

A lane-change maneuver on a congested highway could be severely disruptive or even infeasible without the cooperation of neighboring cars. However, cooperation with other vehicles does not guarantee that the performed maneuver will not have a negative impact on traffic flow unless it is explicitly considered in the cooperative controller design. In this letter, we present a socially compliant framework for cooperative lane-change maneuvers for an arbitrary number of CAVs on highways that aims to interrupt traffic flow as minimally as possible. Moreover, we explicitly impose feasibility constraints in the optimization formulation by using reachability set theory, leading to a unified design that removes the need for an iterative procedure used in prior work. We quantitatively evaluate the effectiveness of our framework and compare it against previously offered approaches in terms of maneuver time and incurred throughput disruption.

Anticipating Spatial–Temporal Distribution of Regional Highway Traffic with Online Navigation Route Recommendation

Detailed anticipation of potential highway congestion is becoming more necessary, as increasing regional road traffic puts pressure on both highways and towns its passes through; tidal traffic during vacations and unsatisfactory town planning make the situation even worse. Remote sensing and on-site sensors can dynamically detect upcoming congestion, but they lack global and long-term perspectives. This paper proposes a demand-network approach that is based on online route recommendations to exploit its accuracy, coverage and timeliness. Specifically, a presumed optimal route is acquired for each prefecture pair by accessing an online navigation platform with its Application Programming Interface; time attributes are given to down-sampled route points to allocate traffic volume on that route to different hours; then different routes are weighted with the origin–destination traveler amount data from location-based services providers, resulting in fine-level prediction of the spatial–temporal distribution of traffic volume on highway network. Experiments with data in January 2020 show good consistency with empirical predictions of highway administrations, and they further reveal the importance of dealing with congestion hotspots outside big cities, for which we conclude that dynamic bypassing is a potential solution to be explored in further studies.

Study on Freeway Congestion Propagation in Foggy Environment Based on CA-SIR Model

The visibility in a foggy environment has a significant impact on driver behavior and traffic flow status, especially for whole closed highways with long distances between entrances and exits. Foggy days are very likely to cause congestion and even secondary traffic accidents, which seriously affect the reliability of freeway operation. In order to explore the influence of a fog environment on freeway traffic jams, firstly, this paper was based on the analysis of the impact of visibility on foggy days. Light fog, medium fog and heavy fog were classified as one scenario, while dense foggy weather was set separately as an extreme scenario without considering lane change. Furthermore, it used the SIR model of infectious disease for reference, and combined with the cellular automata (CA) model, the car-following model and lane-changing rules in different scenarios were set based on safe driving distance and speed for two scenarios. Finally, the key parameters of CA-SIR were calibrated, such as congestion propagation, recovery probability, vehicle braking, and lane-changing probability. The simulation analysis showed that with the decrease in visibility and vehicle speed, the phenomenon of congestion propagation was more prominent, but the causes of queuing phenomenon were different. A low speed limit was the main reason for traffic jams in the light fog condition. In the medium fog condition, the frequency of traffic jams was related to the random braking probability of the visibility. In heavy fog conditions, the congestion area gradually moved upstream with the passage of time. Moreover, in the dense fog condition, the congested area gradually moved upstream with the passage of time; however, vehicles were more likely to accompany each other, and the congested area traveled downstream synchronously with the passage of time and did not dissipate easily. Therefore, in a foggy environment, the best speed limit should be better established under different visibilities, the flow of highway traffic should be strictly controlled if necessary, and in worse situations than high-density traffic in low visibility, to avoid the spread of congestion, the intermittent release of different lanes is suggested to be implemented.

Guardrails on priced lanes: Protecting equity while promoting efficiency

We examine the fairness of congestion pricing from two perspectives. First, using data from California’s six largest urban areas, we empirically estimate the share of the population that is both economically vulnerable and likely to be impacted by freeway congestion charges. Our estimates suggest that 13 % of households would fall into this category. Second, we consider ways to mitigate burdens for this group. In particular, we compare freeway use to use of other metered network infrastructure, like electricity grids and water systems. We suggest that assistance programs from these utilities provide useful lessons for protecting low-income drivers from road prices, and argue that policymakers would be less constrained in progressively redistributing congestion toll revenue than they would be in redistributing utility revenue.

GPS-Based Traffic Conditions Classification Using Machine Learning Approaches

This paper addresses the problem of accurately estimating traffic conditions based on sparse GPS information. GPS data have limited spatial-temporal availability, particularly at a regional scale. Therefore, it lacks reliability to accurately estimate traffic conditions of a transportation network. This study proposes a novel methodology to address this problem. First, instead of estimating traffic conditions on a geographic road segment, traffic conditions are estimated for trip segments, which span multiple road segments. Second, machine learning methods are applied to classify traffic conditions. In this study, traffic conditions are defined as the combination of congestion level and road type. This study develops two machine learning models—a random forest (RF) model and a supervised clustering method—to classify traffic conditions, using trip characteristics such as average speed and acceleration. The two models are compared in relation to their accuracy and computational efficiency. Results show that speed-related trip characteristics, such as average instantaneous speed, are the most important variables for classifying traffic conditions in both methods. In addition, the proportion of idling in a trip is essential in distinguishing the Congested Highway and Uncongested Urban traffic conditions when applying the supervised clustering method. The comparison shows that the RF model has a higher estimation accuracy (81%) than the supervised clustering method (72%). Overall, this study shows that traffic conditions can be determined efficiently even in cases of limited GPS data.