Journal of Transportation Engineering, Part A: Systems

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.

To enable the safe and fast formation of connected automated vehicle (CAV) platoons in real-world traffic, a preceding vehicle identification system for mixed traffic (PVIS-mixed) is proposed. PVIS-mixed utilizes the vehicle’s radar measurements and global positioning system (GPS) measurements reported by surrounding connected vehicles to find the communication identity of the preceding vehicle. The design of PVIS-mixed is based on three goals: a low probability of making a wrong identification, a low probability of missing the connected preceding vehicle, and short time consumption of the identification procedure. The proposed PVIS-mixed is evaluated in highway traffic simulated by real vehicle trajectory data from the Next Generation Simulation (NGSIM) program. Evaluation results showed that the performance of PVIS-mixed is not related to the adoption rate of connected vehicles, and 1 m is found to be the required relative positioning accuracy to make 99th percentile time consumption $<10\mathrm{s}$. It was observed that the multipath bias of GPS positioning could affect the usability of CAV platooning. The possible solutions are then discussed as future work.

Connected and automated vehicles (CAVs) are expected to transform future transportation systems. Over time, these vehicles might enhance traffic efficiency and safety, especially at urban intersections. Therefore, it is essential to make adaptations to the traffic analysis models that are currently designed for human-driven vehicles only. This paper aims to assess the impact of CAVs on the entry capacity of roundabouts and develop an approach to adjust the capacity values calculated by the Highway Capacity Manual (HCM) for planning level analysis. Both single- and double-lane roundabouts are studied under various CAV market penetration rates and conflict flow rates in this paper. A specific CAV application, cooperative adaptive cruise control (CACC), is evaluated in this study because it enhances the car-following behavior at the roundabout entrance and has the best potential for improving the entry capacity. The simulation results indicate that the introduction of CAVs can substantially improve the entry capacity as the market penetration rate increases for both single- and double-lane roundabouts. The capacity improvement is more significant in the single-lane roundabout than in the double-lane roundabout. The capacities under different CAV market penetration rates and conflict flow rates are calculated and compared with the capacity results estimated from base models in the HCM to acquire the adjustment factors. Finally, a table of capacity adjustment factors is provided for the future implementation of HCM models.

The adaptive signal control technology (ASCT) is a traffic management strategy that adjusts signal timing parameters to optimize corridor performance based on actual traffic demand. This study used a Bayesian switch-point regression model (BSR) to estimate the mobility benefits of the ASCT. A 5.3-km (3.3-mi) corridor of Mayport Road in Jacksonville, Florida, was used as the case study. The results indicated that the ASCT improved travel speeds by 4% on midweekdays (Tuesday, Wednesday, and Thursday) in the northbound direction. However, in the southbound direction, mixed results were observed that may be attributed to higher driveway density and congestion. Moreover, the BSR model results revealed that there is a significant difference in the operating characteristics between with and without ASCT scenarios. Transportation agencies could use the findings of this study to justify and plan the future deployment of the ASCT.

It has been recognized that significant travel time estimation errors may be introduced using low-resolution GPS-based floating car trajectory data traditionally. Very few studies have been conducted to concentrate on spatial–temporal relationship identification among travel time measurements. In this study, an attention-based spatial–temporal graph convolutional networks on low-resolution data (AGCN-LR) approach was proposed to estimate more accurate travel time in urban traffic roadway networks using low-resolution GPS-based data measured by floating cars. Specifically, three models were developed in this AGCN-LR approach. Hour, day, and week were used to model the dynamic relationship among spatial–temporal traffic flow attributes, respectively. The same structures were adopted for these three models. Two spatial-temporal block (ST-block) models and one temporal convolutional model were included. Furthermore, one spatial graph convolutional model and one temporal attention mechanism model were embedded in a ST-block. AGCN-LR not only improved the efficiency and accuracy of travel time estimation through the framework optimization training process in a spectrum convolution network but also combined the three temporal components. The final estimation value was formed afterward. Experimental tests were conducted using the real data set from low-resolution floating car data in Harbin, China, in 2017. Results indicated that AGCN-LR outperforms the other state-of-the-art algorithms by reducing estimation mean absolute error (MAE) by about 50 s when it captured the relationship among dynamic spatial and temporal data from the data set. The AGCN-LR approach demonstrated great potential to become one of the important urban network-wide traffic management tools using low-resolution floating car data.

Previous studies show that the popularity of bike-sharing systems has a significant impact on public transit ridership, mainly reflected in two aspects: substitution and supplement. However, a mode choice of the users between bike-sharing and bus for the last-mile connection to urban rails has not been sufficiently studied using actual traffic sensor data. Therefore, in this study, the binomial choice behavior between bike-sharing and buses as a last-mile connection to urban rails in Beijing, China, is analyzed and modeled using the feeder subchain information of bus and bike-sharing for 1 week that is extracted from transit smart card data, cycling record data, and station location information data. We specifically focus on the moderate distance (0.5–3 km), within which distance the bike-sharing is a rival and suitable alternative to the bus. A binomial logit model is constructed to examine the mode choice between buses and shared bikes as a last-mile connection to urban rail. In addition to the general explanatory variables (i.e., travel distance, travel time, and cost), built environments such as housing density, land uses, and bike-lane conditions are also considered in our study. Results show that the walking distance from the metro station has a stronger effect on the users’ mode choice than the total subchain distance. A longer travel time increases the preference for taking the bus. We also find that users are most likely to choose the bus in the evening rush hours, followed by offpeak times and the morning rush hours. The existence of bike lanes, especially with isolation belts alongside them, increases the probability that people choose shared bikes. As bus and bike-sharing are the two most popular and economically competitive urban rail egress modes for a moderate distance in China, understanding the determinants of mode choice contributes to better planning and operational strategies for both feeder bus and bike-sharing systems.

Following major hazards and incidents, it is crucial to mitigate the congestion at intersections of urban traffic systems with disruptions; maximize the evacuation, rescue, and recovery efficiency; and prevent a hazard from turning into a disaster. An optimized traffic signal design strategy can effectively contribute to maintaining an efficient traffic system operation despite various disruptions. Most of the existing studies focused on static and generic congestion scenarios during the recovery stage, rather than realistic time-progressive scenarios covering the whole process following a disruption. An adaptive traffic signal control strategy in response to traffic disruptions at a single intersection is proposed by covering both the incident and recovery stages. Dynamic phase selection (DPS) technology is applied to adjust the traffic signal control plan adaptively during the incident stage, while the queue length dissipation (QLD) algorithm is adopted to carry out optimal green time calculation during the recovery stage. The proposed methodology is demonstrated by considering disruptions caused by several typical vehicle crashes at intersections. The proposed DPS+QLD traffic signal strategy is found to improve the resiliency of a typical intersection against disruptions by clearing queues faster, reducing overall traffic loss time, and maintaining stable mobility with superior performance over conventional fixed and actuated traffic signal plans.

Saturation flow rate (SFR) is a vital input for an intersection’s performance evaluation and development of signal control strategy. Several models have been framed under prevailing traffic conditions for homogeneous and for mixed traffic scenarios. However, studies of mixed traffic streams focus on regulated traffic, mainly observed in metropolitan cities. Midsized cities have different demands, driver characteristics, and intersection properties due to the substantial percentage of small vehicles (especially motorized two- and three-wheelers) in the traffic stream. Thus, there is a need for different policy and planning initiatives than in metropolitan cities. This study framed a two-step optimization model (TSOM) for evaluating the SFR at signalized intersections considering the traffic situations in midsized cities. Thirteen intersections from four different midsized cities in India were used to analyze and model SFR. A saturation flow prediction model (SFPM) was developed using various traffic properties. The superiority of the SFPM was checked by comparing the result with those of other developed models proposed in the literature and the Indian design guide. The results showed significantly better performance of the SFPM than of the existing models. Thus, this study illustrates the effectiveness of the proposed methodology for SFR evaluation in complex traffic situations for midsized cities with a higher percentage of small vehicles.

The current state of practice in traffic data quality control features rule-based data checking and validation processes, where the rules are subjective and insensitive to variation inherited with traffic data. In this paper, self-supervised deep learning approaches were explored to leverage the existence of multiple sources of traffic volume data, which permitted cross-checking of one data source against another for improved robustness. Two types of models were developed, aiming at detecting data anomalies at two distinct timescales. Particularly, a novel variational autoencoder (VAE)-based model was formulated for discerning data anomalies at the daily level and four recurrent model structures, including recurrent neural networks (RNN), gated recurrent units (GRU), long short-term memory (LSTM) units, and liquid time constant (LTC) networks, were evaluated for detecting anomalies in finer incremental timescales (i.e., 5-min intervals). The effectiveness of the proposed methods was demonstrated using two independent sources of traffic data from the Georgia Department of Transportation: (1) traffic counts collected by inductive loops as part of the statewide traffic count program, and (2) traffic volumes acquired by a video detection system as part of the Georgia 511, an advanced traveler information system in Georgia. Based on our experiments, the VAE-based model achieved a precision of 0.95, recall of 0.92, and ${\mathrm{F}}_1$ score of 0.94. Among the recurrent models, the fully connected LTC produced the lowest prediction error and achieved a precision of 0.82, recall of 0.88, and ${\mathrm{F}}_1$ score of 0.85.

The classic triangular fundamental diagram describes the functional relationship between flow rate and traffic density for longitudinal movements of homogeneous traffic. This study generalized the classic triangular fundamental diagram in the congestion regime to consider lateral lane-changing and heterogeneous traffic. $F$-test results indicated that the generalized model fit and predicted flow rate better than the classic model. The inconsistency in the literature regarding the impacts of lane changing was reconciled as follows. Lane changes decreased flow rate when traffic was both slightly and severely congested but increased flow rate when traffic was moderately congested. Motivated by this finding, a dynamic lane-changing management strategy was developed to mitigate congestion. Results showed that the proposed strategy improved throughput by 10.3%, average speed by 9.7%, and average delay by 19.2%. The findings from this study advance knowledge of the classic two-dimensional fundamental diagram, reveal the nonlinear effects of lane changes, and provide policy implications for congestion mitigation.

To control driving speed at intersections and assist drivers in making accurate decisions, a vehicle–road collaborative decision-making system for the active safety of intersections was developed. The system is divided into three modules: information collection, decision-making control, and intelligent underground lamps. According to the signal phase of intersections and the speed information of approaching vehicles, the auxiliary decision-making model is established based on an analysis of the dynamic characteristics of the dilemma zone. The state of the dynamic yellow light and intelligent underground lamps are controlled based on judgment results. The intelligent underground lamps can be flexibly switched between the three states, and the driver can be alerted by vision. In addition, the dynamic yellow light time can effectively avoid the risk of intersection accidents, shorten the time delay caused by conservative driving behavior, and improve the overall robustness of the road network.

A one-way traffic strategy is widely used in densely populated urban areas to improve traffic efficiency, but its environmental impacts remain largely unclear. This study investigated the effects of a one-way traffic strategy on vehicle emissions along a busy traffic corridor in Xi’an, China. This was done by integrating the traffic flow model calibrated by onsite traffic data and the emission model. The detour distance estimation method was proposed so that the emission trade-offs between increased vehicle miles traveled (VMTs) and improved traffic efficiency on one-way traffic corridors could be uncovered. Empirical results showed that one-way traffic cut down the total ${\mathrm{CO}}_2$ emissions by 11.8%, and reduced the CO, hydrocarbon (HC), and NOx emissions by 4.7%–25.0%. However, such emission reduction capacity gradually weakened and even completely dissipated with the increase of traffic demand on the corridor. When the road volume-to-capacity ratio (V/C > 0.87), one-way traffic was no longer a greener traffic organization. With the promotion of mitigation policies and technologies, one-way traffic is expected to further reduce ${\mathrm{CO}}_2$ emissions by 4.7%–10.9% and CO, HC, and NOx emissions by 8.4%–41.1%. In addition, there was a total of 456 vehicles detours with an overall detour distance of 316.8 km, which resulted in a doubling of the emissions of detour vehicles. Although the implementation of one-way traffic brings environmental benefits, the detour problem should also be taken seriously. This study provides convincing evidence for understanding the emission changes on traffic corridors caused by one-way traffic conversion to support the promotion of eco-friendly traffic organization for realizing the goal of carbon peaking.