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Evaluating arterial operational performance plays a pivotal role in arterial system management and enhancement. As a fundamental reference guide for traffic engineers, the Highway Capacity Manual (HCM) provides a series of analysis methodologies for evaluating multimodal operations on urban streets, highways, freeways, and off-street pathways. However, the efficient use of the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM can be restricted by a large amount of traffic volume collections at signalized and access point intersections, roadway segment geometric data inputs, and complicated calculation procedures on urban street segments. Further, the use of alternative tools, such as vehicle trajectory data, remains to be explored to supplement the urban street facility calculation framework in the HCM. To bridge the gap, this research utilizes vehicle trajectory data as an alternative tool and proposes a combined vehicle trajectory data and HCM calculation framework to implement and assess the existing performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. A case study was carried out on three major signalized arterials in Sparks, Nevada, for HCM methodology implementation and performance evaluation. Based on evaluation results, it was found that vehicle trajectory data can be utilized as an alternative tool to implement the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. It is recommended to combine arterial through-vehicle trajectories, the time-space diagram, arterial level of service (LOS), and the poorest segment LOS for arterial operational performance evaluation and further enhancement. The proposed automobile traveler perception score (ATPS) in the HCM was found to be insensitive to reflecting traveler’s perception of service quality. Additional traveler perception-based factors are recommended to be included for further ATPS development and enhancement. [ABSTRACT FROM AUTHOR]

Evaluating arterial operational performance plays a pivotal role in arterial system management and enhancement. As a fundamental reference guide for traffic engineers, the Highway Capacity Manual (HCM) provides a series of analysis methodologies for evaluating multimodal operations on urban streets, highways, freeways, and off-street pathways. However, the efficient use of the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM can be restricted by a large amount of traffic volume collections at signalized and access point intersections, roadway segment geometric data inputs, and complicated calculation procedures on urban street segments. Further, the use of alternative tools, such as vehicle trajectory data, remains to be explored to supplement the urban street facility calculation framework in the HCM. To bridge the gap, this research utilizes vehicle trajectory data as an alternative tool and proposes a combined vehicle trajectory data and HCM calculation framework to implement and assess the existing performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. A case study was carried out on three major signalized arterials in Sparks, Nevada, for HCM methodology implementation and performance evaluation. Based on evaluation results, it was found that vehicle trajectory data can be utilized as an alternative tool to implement the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. It is recommended to combine arterial through-vehicle trajectories, the time-space diagram, arterial level of service (LOS), and the poorest segment LOS for arterial operational performance evaluation and further enhancement. The proposed automobile traveler perception score (ATPS) in the HCM was found to be insensitive to reflecting traveler’s perception of service quality. Additional traveler perception-based factors are recommended to be included for further ATPS development and enhancement. [ABSTRACT FROM AUTHOR]

With the development of autonomous driving, a mixed traffic flow state composed of connected automated vehicles (CAVs) and human-driven vehicles (HVs) will last for an extended period. The abundant computing resources and CAVs with high compliance in the intelligent connected environment provide a good situation for variable speed limit control on highways, which helps even the traffic flow and improves traffic efficiency and safety. In this paper, we propose a variable speed limit control method for mixed traffic flow based on deep-reinforcement learning. First, the variable speed limit control problem is abstracted into a Markov decision process and the factors of real-time CAV penetration rates and predictions are considered in the state description. Different from variable message signs (VMS), CAVs are taken as the executive objects of the controller so that the variable speed limit control for mixed traffic flow is realized indirectly through the interaction with HVs. Next, double deep Q network (DDQN) is introduced to calculate the optimal speed limit in different states. Finally, the empirical study on US101-S proves the effectiveness of the proposed model. The results show that the variable speed limit control model based on the DDQN algorithm can effectively improve the efficiency and environmental benefits of mixed traffic flow. Moreover, the multi-objective reward function can achieve a better control effect than the single objective. Besides, the proposed model outperforms other models in this paper and predictive factors can further improve proactive control performance. In addition, with the increasing penetration of CAV, the proposed model achieves a better control effect. [ABSTRACT FROM AUTHOR]

The information exchange among vehicles and road infrastructure with wide sensing range can be accomplished by roadside units (RSUs). Communication delays occur and are influenced by different approaches of deployment of RSUs, which increases the complexity of vehicle automatic control in a mixed traffic flow. Therefore, a speed control strategy was proposed for mixed traffic flow considering RSU deployment distance, interaction radius, and communication delay, aimed at enhancing traffic safety and efficiency. The characteristics of RSU deployment were modeled as functions, where an adaptive efficiency function was proposed to evaluate different RSU deployment plans. An improved speed control model was then developed based on the characteristics of the RSU in the mixed traffic flow. In addition, numerical analysis and simulation were carried out to evaluate vehicle stability and traffic throughput based on the improved speed control model. The analysis results show that the proposed model is stable under different updating frequencies, with the best stability achieved when the velocity update frequency is 500 and the velocity update time interval is 0.01 s. Moreover, it was found that the spatial occupancy rate increases with the increment of penetration rate of autonomous vehicles, which improves the rate by at least 20% compared to the no-control scenario. Additionally, the average speed is also increased by 30% while under the influence of communication delays. This paper investigated the relationship between characteristics of RSUs and traffic performances, which is essential for DOTs to develop an effective RSU plan to adapt to the mixed traffic flow. [ABSTRACT FROM AUTHOR]

To reduce life threats and financial losses under hurricane weather and traffic conditions, stakeholders need to make a sequence of decisions (e.g., enforcing traffic control and/or broadcasting travel advisory) with the presence of various uncertainties, which could be formulated as a Markov decision process (MDP). To effectively solve the MDP for optimal operation of hurricane-impacted transportation networks, a risk-informed decision-support framework is proposed consisting of both a decision-making environment and tool. The decision-making environment involves essentially three coupled modules of hurricane hazard, transportation infrastructure, and traffic flow, where the uncertainties from natural environment, built environment, and human behavior are examined. Specifically, multiple correlated hazards for each specific storm are capsulated in the hurricane hazard module, representative hurricane-vulnerable infrastructure components and associated moving vehicles are evaluated in the transportation infrastructure module, and network-level traffic relations are addressed in the traffic flow module. In view of the complexity and intractability of a hurricane-transportation infrastructure-traffic system through full model representation, the efficient low-dimensional modeling approaches including both physics-based analytical and data-driven surrogate models are utilized in all these three simulation modules. Considering an explicit probabilistic model for the decision-making environment (and hence state transition function) is not available, the operation optimization (involving competing objectives of traffic safety and mobility) is accomplished through a deep reinforcement learning-based decision-making tool that learns to act optimally (involving both traffic control and travel advisory actions) by directly interacting with the environment. A hypothetical case study is conducted to verify the applicability and effectiveness of the proposed framework as a testbed for optimal operation of hurricane-impacted transportation networks. [ABSTRACT FROM AUTHOR]

Two-lane rural roads constitute a significant part of the roadway system in India. Geometric characteristics such as facility type, lane width, shoulder width, and horizontal and vertical alignments, are essential parameters that influence vehicle behavior and traffic flow characteristics on two-lane rural roads. Among all geometric characteristics, the magnitude of the grade most substantially impacts the operational characteristics of traffic flow on two-lane rural roads. The present study investigates the effect of grade on the operating speed and capacity of two-lane rural roads under mixed traffic conditions. Traffic video data for eight road sections with grades varying from 1% to 8% were collected under dry weather conditions. The investigation revealed a significant effect of grade on the operating speeds of different vehicle types. The operating speed decreased with an increase in the grade magnitude. The road capacity was derived by calibrating various single regime models. The Northwestern model was deemed appropriate to derive the capacity values, based on theoretical and statistical investigation. The results showed that the capacity decreases by 6.4% with every 1%increase in grade. Further, the effect of grade on passenger car units (PCU) of different vehicle types was investigated. For varying volume-to-capacity (V/C) ratios, it was observed that the PCU of heavy vehicles increases as the magnitude of the grade increases. The present study develops operating speed and capacity prediction models as an essential practical outcome. The developed models can facilitate planners and traffic engineers to estimate operating speed and capacity based on the magnitude of the grade. [ABSTRACT FROM AUTHOR]

Analyzing road risks and developing targeted countermeasures are essential for a safe and orderly traffic flow. However, previous intersection safety analyses were conducted based on crash data. Little research has been conducted on surrogate safety measures based on risky driving behavior. In this study, categorical boosting (CatBoost) and Shapley additive explanation (SHAP) were used to analyze the impact of features on traffic order using a set of multisource data that include roadway geometry, signal control, and land use. The traffic data for intersection entrances in Beijing were collected from navigation systems, field investigations, and application programming interfaces. The model results showed that CatBoost exhibits a prediction accuracy of 83.5%, a recall of 83.5%, and an F1 score of 81.1%. Moreover, the importance, total effects, main effects, and interaction effects of influence factors were analyzed by using SHAP. It was found that the congestion index (CI) has significant negative effects on traffic order. A larger number of lanes and more electronic traffic control were found to have a positive effect on traffic order. Intersection entrances with three-phase signals or off-peak intersection entrances helped increase traffic order. Moreover, a high green ratio for through vehicles can reduce the positive impact of CI on traffic order when the value of CI is 1.1–1.4, and the signal control scheme with a high left-turn green ratio would result in a safe and orderly traffic flow. The results from this study can be used for further studies on improving traffic safety at signalized intersections. [ABSTRACT FROM AUTHOR]

In the conventional human driving environment, due to poor traffic information transmission and the inability to actively control vehicles, variable speed limits (VSLs) and ramp metering (RM) strategies often fail to achieve the desired results when dealing with the merging zone of an expressway under high traffic demand. To improve traffic efficiency, an integrated control method for traffic flow in the merging area of expressways is proposed by combining connected and autonomous vehicle (CAV) active lane change (LC) technology with traditional VSL and entrance RM strategies. This method uses an upgraded cell transmission model (CTM) to estimate the state of the mainline and ramp input traffic volume and then uses the genetic algorithm (GA) to solve for the optimal combination of the mainline speed limit, number of mainline vehicles changing lanes, and ramp regulation under maximized flow in the merging zone. This integrated control strategy incorporating VSL, LC, and RM (VSL-LC-RM) enables synergistic control of the mainline and ramp traffic entering the merging zone in time-space. Finally, simulation experiments are conducted with the mainline two-lane and on-ramp singlelane expressway merging zone scenarios. The results show that compared with the VSL strategy and the strategy incorporating only VSL and RM (VSL-RM), the proposed VSL-LC-RM strategy can enhance the regulation of traffic flow, improve the flow of merging bottlenecks, and make full use of the road space-time resources. [ABSTRACT FROM AUTHOR]

As one key basis of traffic flow theory, the fundamental diagram (FD) has wide application in traffic management and control. However, the FD of mixed human-driven vehicles (HVs) and cooperative adaptive cruise control (CACC) vehicles remains unclear. The deterministic and stochastic FD of mixed traffic was obtained. First, the longitudinal control model (LCM) and connected LCM (CLCM) were selected as the car-following model of HVs and CACCs. Next, in deterministic simulation, the mathematical expression of mixed-traffic FD was conducted considering the CACC penetration rate and platooning intensity. Then, the stochastic distribution of perception and response time calibrated by the vehicle trajectory data set NGSIM was introduced to derive the stochastic FD. After that, in stochastic simulation, taking the variance and computation effort into account, the optimal number of simulated vehicles to obtain steady-state FD was obtained. The impact of CACC penetration rates and platooning intensity was also explored. It revealed that the optimal number of simulated vehicles is 400. CACC can improve the capacity and critical density of highways. In mixed traffic, due to the large variance of HV driver response time, scattering remains unchanged regardless of CACC penetration when there are enough vehicles. Pure CACC traffic has minimum scattering. More importantly, the proposed framework is applicable to other CACC, adaptive cruise control (ACC), and HV car-following models, including further experimental models. [ABSTRACT FROM AUTHOR]

This paper considers the stability of mixed traffic flow and the necessity of speed control to achieve safety and efficiency goals. Therefore, a speed-coordinated control model is proposed for mixed traffic flow based on stability analysis at an expressway merging area. Firstly, stability intervals are obtained by using an intracluster stability analysis in an intercluster. Secondly, a speed-coordinated control model is set up considering the stability characteristics in a merging area. Finally, an objective function is developed, which considers time-to-collision (TTC), dynamic space occupancy (DSO), and vehicle specific power (VSP). A numerical simulation is built up to analyze the traffic conditions and stability by using the proposed model against a null scenario without using the proposed model. The analysis results indicate that the stability of the intracluster has two regions, and the stability of the intercluster is mainly affected by the speed fluctuation and the distance between the merging cluster and the vehicles on the main road. Secondly, the proposed model tends to gradually achieve a steady state when the position reaches 180 m. Meanwhile, the efficiency of the model is found to be the best when the minimum safe-distance interval is set at (18, 27). In addition, the volatility of TTC drops by 5% and the DSO has reached 11.99% in the acceleration lane optimization together with 18.37% in the main lane optimization. The frequency is found to be the highest when the VSP reaches (10, 20), and the optimized efficiency reaches 45%–50%. The model proposed in this paper will effectively analyze the stability characteristics of mixed traffic flow and provides a research foundation for the stability of mixed traffic flow. Moreover, this model builds up a multi-objectiveoriented speed-coordinated control scheme for high-efficiency operations in the merging area and provides technical support for traffic management in the future. [ABSTRACT FROM AUTHOR]

This research evaluated the effect of the COVID-19 social isolation orders on traffic volume, traffic violations and road crashes in the city of Fortaleza, Brazil. Using data from automated traffic enforcement cameras, a reduction in traffic volume between 30% and 50% was observed during the social isolation period. However, even with the traffic volume reduction, the absolute number of speeding and red-light running violations were 13% and 26% higher than prepandemic levels, respectively. When controlling for traffic exposure, the violation rates increased by more than 100%. After social isolation restrictions were lifted and the traffic volumes returned to prepandemic levels, both traffic violations and traffic violation rates remained at elevated levels (14% to 44% higher than prepandemic levels), possibly related to a nationwide decision that delayed the issuing of violation tickets. Using an interrupted time-series approach and segmented Poisson and negative binomial regression models, it was found that the fatal crash rate was 1.66 times greater during the period of social isolation compared to the prepandemic levels but returned to prepandemic levels following the removal of the social isolation restrictions. A significant reduction in injury crash rate was observed during and following the period of social isolation restrictions; however, the authors hypothesize that this is related to injury crash underreporting during the pandemic. [ABSTRACT FROM AUTHOR]

Ramp metering (RM) is a traffic management technique that aims at controlling the flow of traffic entering specific roadways tailored for fast-moving traffic containing separate multilane divided carriageways (such as motorways, highways, expressways, freeways, and turnpikes). The objective of RM is to minimize congestion on the main thoroughfare of the roadway. RM algorithms have evolved significantly since the 1960s and will continue to do so into the future. While the functionalities of the algorithms remain valid through time, the applications of the RM strategies are continually being updated. Unlike previous reviews that focused on the RM methodological aspect, this study details the recent literature regarding the implementation of RM strategies. The aim of this paper is to provide a global perspective on existing RM applications and the algorithms used, for future reference for both academics and practitioners. The paper provides an indicative historical context and characteristics for each reported project, as well as an overview of the evaluation of these schemes. Based on the current understanding of RM strategies, the paper discusses challenges and the potential future of RM technology. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) technology is a key component of an autonomous vehicle’s sensing system. It also has the potential to be used at the roadside as a major infrastructure-based detection for connected and autonomous traffic infrastructure systems, as well as for the general purpose of traffic data collection and performance evaluation. Lane and movement-based traffic volume data collection is a basic function of roadside traffic sensing systems. The accuracy of volume detection is mainly impacted by occlusion for most of the advanced traffic sensing technologies, such as LiDAR, video, and radar. This paper presents research results to quantify the influence of occlusion on LiDAR systems’ traffic volume detection in different traffic demand scenarios. A method for automatic identification and classification of LiDAR specific occlusion was first developed based on the inherent characteristics of LiDAR sensors, which can report occlusion ratios of roadside LiDAR data. Then, the study was extended to accommodate all traffic demand scenarios, traffic levels of service (LOS A to E), and different truck compositions (5% to 30%) by integrating the developed method and traffic simulation. Lastly, a comprehensive case study first verified the accuracy of the simulation results using field data collected from two testbeds, and then at the third testbed, a lane and movement-based traffic volume study was demonstrated. The practical significance of this paper is to help traffic engineers making informed decisions when considering LiDAR as their choice of sensing technology in the field from two aspects: (1) the quantitative relationship between expected occlusion rate and resulted detection accuracy under various traffic conditions; (2) lessons learned from the pilot field implementation on LiDAR, installation strategy, data storage, and communication. [ABSTRACT FROM AUTHOR]