Journal of Transportation Engineering, Part A: Systems

Connected and automated vehicles (CAVs) require proper infrastructure for safer and more reliable operations. Many state and local planning agencies have developed multiyear capital programs to provide such infrastructure in a timely manner within their limited budgets. Meanwhile, the traffic environment will evolve over time as CAV technologies become available (i.e., toward the mixed environment of CAVs and human-driven vehicles), which requires infrastructure plans specific to different planning terms (i.e., short-, medium-, and long-term) to accommodate changing infrastructure needs. To develop an effective multiyear infrastructure plan, planning agencies need to understand changing infrastructure needs with time, identify alternative infrastructure options for different planning terms, and select the most appropriate ones based on their long-term vision. This study performed a systematic literature review to develop a knowledge base for multiyear infrastructure planning for CAVs. To be more specific, the literature review aims to develop the following knowledge areas: (1) identification of existing and future infrastructure options for the operation of CAVs, (2) understanding the role of infrastructure to support different functions of CAVs to realize safety, mobility, and environmental benefits, and (3) integration of the aforementioned findings into planning agencies’ multiyear infrastructure plans for CAVs. Based on the review, this study categorizes different CAV infrastructure into existing infrastructure and future infrastructure options while considering five system functions of CAVs (i.e., cooperative merging, platooning, intersection movement, dynamic routing, and cooperation and connected functions) to illustrate the role of these infrastructure options under different traffic scenarios. The implementation of the developed knowledge base is demonstrated through a case study of two selected state agencies’ long-term infrastructure planning for CAVs.

Autonomous vehicle (AV) technology is considered to be the future of transportation and has gained much focus from academia, vehicle industries, transportation agencies, and the general public. To contribute to the existing literature, this study used three surveys carried out by the Puget Sound Regional Travel Study in 2015, 2017, and 2019 to identify the segments of the population based on AV adoption interest, the characteristics of such population segments, and the dynamics of AV adoption interest over time. Using $k$-means clustering, three distinct levels of AV adopters in the increasing order of adoption interest were found: (1) skeptics, (2) indifferents, and (3) enthusiasts. Multinomial logit models of the clusters identified sociodemographic, household, residential, and travel-related attributes as significant predictors of the level of AV adopters. The AV adoption interest was found to increase gradually over time despite the increase of public AV concerns. Ways to increase AV adoption interest were discussed based on the study results.

In this paper, we presented a novel adaptive traffic control strategy for urban signalized interchanges based on traditional sensors and emerging connected and automated vehicle (CAV) technologies. The signalized interchanges in this paper refer to those controlled by one traffic signal controller while vehicles must cross two or more stop lines to cross. Examples included diamond interchanges (DIs), diverging diamond interchanges (DDIs), and single-point urban interchanges (SPUIs). With the expansion of urban areas, such interchanges are increasingly common and often become mobility bottlenecks. The traffic signal optimization in this paper was derived from the cumulative vehicle counting curves ($A\text{-}D$ curves). An assumption of the $A\text{-}D$ curves for control delay estimation is that vehicles are no longer restricted once they cross the stop line. However, at a signalized interchange, vehicles may stop multiple times before completely cross. This situation cannot be effectively reflected with the standard cumulative vehicle counting curves. The phasing sequence is also challenging due to the limited space within the interchange. To address these issues, we proposed a new adaptive traffic control framework based on a linear traffic control model, referred to as a phase-time network. The objective of this framework was to dynamically fine-tune control splits and optimize the phasing sequence according to the vehicle arrival counts (from infrastructure sensors) and turning movement ratios (from CAV technologies). The optimization problem was first formulated into a mixed-integer linear programming (MILP) formulation and validated through offline examples. Then, an online search algorithm was presented and evaluated within a microscopic traffic simulation environment. The proposed MILP formulation and algorithm were assessed in both offline and online experiments. The results of all numerical experiments validated the formulation and show promise for real-world implementations.

Accurate traffic state estimation is essential for the successful application of intelligent transportation systems (ITS). In the past, traffic state estimation methods based on the macro traffic flow model and data assimilation technology have been widely developed. Based on the data collected from video image detectors and the freeway charging system, this paper proposed a dynamic method to estimate the traffic state at an arbitrary cross section of the freeway. Firstly, the original static method was briefly described, including the vehicle average speed calculation, travel time estimation on road segments, and allocation of vehicle travel time. Then, congestion analysis and dynamic vehicle travel time allocation were introduced to compensate for the inapplicability of the static method to the change of the traffic state. Finally, the traffic volume at an arbitrary cross section in any period was directly derived. The proposed multisource data-based dynamic method was validated by real data and tested on different days. The results showed that the proposed dynamic method outperformed the original static method in traffic state estimation, especially in the case of congestion. In addition, the effect of setting different time intervals on the results was analyzed, and the analysis results suggested that the performance of the proposed method can be significantly improved when the time interval is set to 5 min.

On mountainous freeways, some tunnels are located adjacent to each other, composing a tunnel group where the safety conditions are more challenging compared to a single tunnel. However, most of the existing studies have focused on single tunnel safety, and the research efforts to investigate traffic safety, especially the injury-severity analysis of the tunnel group crashes, is scarce. Therefore, the present study employed a random parameter logit model to examine the factors affecting the injury severity of the freeway tunnel group crashes. The analysis is based on a five-year of police-reported data set of 377 crashes collected from six tunnel groups in Hunan Province, China. The results indicate that the daytime, weekdays, entrance zone, downgrades, elder drivers, speeding, fatigue driving, and rollover collisions are positively associated, while winter, curves, and sideswipes are negatively associated with severe crashes and have signs consistent with engineering intuition. More importantly, due to the complex driving environment of the tunnel groups, the summer, access zone, connecting zone, and drivers with less driving experience increases the probability of severe crashes. Also, the effects of the access zone, elderly drivers, speeding, and sideswipe collisions were found to be best modeled with random parameters in this study. Multiple countermeasures are provided to improve tunnel groups traffic safety, including the provision of variable message signs to provide information to the drivers regarding the speeding limits and distance to the tunnel, periodic maintenance of the illumination according to the lighting guidelines in the tunnel groups, implementation of the automatic section speed control for speeding, and public awareness about the complex driving environment of the tunnel groups.

Passenger flow predictions are of great significance to bus scheduling and route optimization. In this paper, a novel algorithm, namely the Spatial–Temporal Graph Sequence with Attention Network (STGSAN), was proposed to predict transit passenger flow. The algorithm mainly focused on the following three aspects: (1) a graph attention network (GAT) was used to capture the spatial correlation of various bus stops; (2) to make full use of the historical and real-time data, a bidirectional long short-term memory and attention mechanism was conducted to extract the temporal correlation of historical ridership at bus stations; and (3) external factors that affect passenger choices were taken into account. We conducted an experiment using field data collected in Urumqi, China. After comparison with five other models, the proposed model was proven to have excellent performance prediction.

Adaptive signal control technology (ASCT) is a traffic management strategy that optimizes signal timing based on actual traffic demand. Although meant to improve the operational performance of signalized intersections, such mobility enhancements may translate into substantial safety benefits. This study examined the safety effects of ASCT using an observational before–after empirical Bayes (EB) approach with a comparison group. The proposed approach was used to develop crash modification factors (CMFs) for total crashes, angle crashes, rear-end crashes, and specific crash severity levels [fatal or injury (FI) and property damage only (PDO) crashes]. The analysis included 42 treatment intersections with ASCT and their corresponding 47 comparison intersections without ASCT. Findings from the study indicated the potential of ASCT to improve the safety of signalized intersections since the CMFs for both ASCT systems (InSync and SynchroGreen) showed significant reductions in crashes. On average, the deployment of ASCT was found to significantly reduce the total, rear-end, FI, and PDO crashes by 5.2%, 12.2%, 4.2%, and 5.7%, respectively. These results were consistent between the two systems. Also, a 2.1% reduction in angle crashes was observed, although the reduction was not statistically significant at a 95% confidence level. The findings from this study provide researchers and practitioners with an effective means to quantify the safety benefits of ASCT and conduct an economic appraisal of ASCT systems.

Capacity is an important traffic parameter, extending nonnegligible influence on road network planning, traffic management, and traffic state prediction. The stochasticity of capacity is widely accepted considering the stochastic nature of traffic flow. Previous studies studied stochastic capacity based on long-term observations, lasting for months or even years, at one single site. On the other hand, data-driven methods were applied by researchers to evaluate the impacts of external factors on capacity, in which, however, capacity was always viewed as deterministic. To fully exert the advantages of data-driven methods, this paper proposes a Weibull-distribution-based neural network for capacity estimation on freeways, considering both stochastic nature and external factors. Extremely long-term observation at one single site is no longer essential because this method considers different scenes at the same time and is able to integrate the information automatically. Furthermore, the model has a certain generalization performance. No matter which influencing factor is adjusted, a new distribution can be obtained. The model is verified by open-source data from the California Department of Transportation Performance Measurement System (PeMS) in this paper. Eight easily-fetched explanatory variables are introduced into the model. The mean absolute percentage error between predicted median capacities and observed ones is 0.29 and 70%–80% of observed median capacities are within the prediction band.

This paper presents a study that aims to investigate the role of experience in the mode choice of using ridesourcing services. Factors that impact users’ mode choice between traditional modes (i.e., private vehicles and public transit) and ridesourcing modes (i.e., exclusive and shared rides) were examined using stated preference (SP) survey data. This study focuses on understanding the differences between two groups (those that used and never used ridesourcing services) in terms of their attitudes and perceptions toward ridesourcing services and how these attitudes might influence their mode choice behavior. Two separate error component nested logit modes were applied for individuals with and without ridesourcing experiences. Model results supported our hypothesis that people with and without ridesourcing experience have different attitudes and perceptions toward these services. Individuals without ridesourcing experience displayed more concerns about using ridesourcing and were more attached to their vehicles than those with experience. In particular, trust issues with traveling with strangers, higher travel time concerns, and being a fan of private vehicles significantly reduced the probability of using ridesourcing services for those without ridesourcing experience. Car dependency and preferences for private vehicles could also be addressed by exposing travelers to ridesourcing services through promotion strategies and free-trial programs. The suggested policies and strategies could better address travelers’ needs and concerns, encourage them to switch from private vehicles to shared mobility, and promote more sustainable and environmentally friendly transportation.

Pushbutton control is ideal for midblock crossings with low pedestrian and vehicle demand, but it causes significant interruptions to traffic flow with frequent pedestrian crossing requests. Therefore, we propose an adaptive midblock crossing control (AMCC) that minimizes the impact of the pushbutton on traffic flow while maintaining a reasonably short pedestrian wait time (PWT). We regard the midblock crossing and two adjacent intersections as an integrated system and propose two types of AMCCs—AMCC-band and AMCC-vehicle—based on different types of real-time information. AMCC-band seeks the best PWT at the midblock crossing to minimize the green band loss with downstream intersections using the signal control status of adjacent intersections. Alternatively, AMCC-vehicle leverages real-time vehicle location information [e.g., obtained from vehicle-to-infrastructure (V2I) communication, connected vehicles (CVs), or advanced sensors] to minimize the estimated number of affected vehicles. Our study tests AMCC in the software Simulation of Urban MObility (SUMO) with a two-intersection traffic network. Results show that using AMCC at a midblock crossing significantly reduces vehicle delay under a wide range of traffic conditions compared to using a fixed phase and timing (Fixed) control or a pedestrian light-controlled (Pelican) crossing. The average pedestrian delay of AMCC is slightly above Pelican but much lower than Fixed. In addition, the two types of AMCCs work equally well in reducing vehicle delay, but the AMCC-vehicle has a considerably lower pedestrian delay. The results demonstrate the advantages of AMCC in reducing vehicle and pedestrian delay and vehicle stops, improving traffic efficiency at the arterial. Furthermore, the sensitivity analysis shows that the AMCC approach is adaptive to a broad range of traffic demands. Our method extends the application scope of common pushbutton control methods. We conclude that AMCC contributes to a more traffic-efficient, more pedestrian-friendly, and safer transportation system.

Congestion detection is one of the key steps in reducing delays and associated costs in traffic management. With the increasing use of global positioning system (GPS)-based navigation, promising speed data are now available. This study used extensive historical probe data (year 2016) in Des Moines, Iowa. We used Bayesian change point detection to segment the speed signal and detect temporal congestion. The detected congestion events were then classified as recurrent congestion (RC) or nonrecurrent congestion (NRC). This paper thus presents a robust statistical, big-data-driven expert system and a big-data-mining methodology for identifying both recurrent and nonrecurrent congestion.

This paper evaluated the congestion reduction benefits over a 6-year period from the implementation of an adaptive traffic control system (ATCS) in a congested area of Manhattan, New York, bounded by 2nd and 6th Avenues and 42nd and 57th Streets—known as the Midtown-in-Motion (MIM) area. A methodology using taxi Global Positioning System (GPS) sensors was used to measure traffic speed. Traffic speeds were calculated for May weekdays before the ATCS was installed (May 2011) and for each subsequent year (May 2012 to May 2016). Within 1 to 2 years after the implementation of the ATCS, the area’s traffic speed increased on avenues and cross streets. This gain, however, could not be sustained in subsequent years because of intervening changes in key factors impacting traffic congestion. These factors included reduction in roadway space/capacity for motor vehicles, lack of effective traffic enforcement to maintain/protect available roadway capacity, increased vehicle miles traveled (VMT) from transportation network company vehicles, and increasing volume of bicycle trips sharing street space with vehicles/pedestrians. This paper’s two key findings are, first, traffic speed gains initially seen in the MIM area after 1 year of ATCS implementation could not be sustained because intervening external factors reduced capacity and increased VMT. However, during the same analysis period, the rest of the Midtown Core area (without ATCS deployment) experienced a greater speed loss than the MIM area, indicating the effectiveness of ATCS deployment in minimizing losses in traffic speed. Second, to protect street network capacity and to minimize VMT growth, midtown Manhattan requires adopting proactive, collaborative, and coordinated strategies by the three key agencies involved with traffic management in New York City (NYC): the NYC Department of Transportation (traffic control system technology), the NYC Police Department (traffic enforcement), and the Mayor’s office (traffic demand mitigation policies).

In recent times, the ubiquity of wireless technology has encouraged researchers to collect travel time data using Wi-Fi media access control scanners (WMS). This notion inspired the current work, which analyzed data from an in-house–developed WMS for potential intelligent transportation system (ITS) applications. First, the WMS was tested against a commercial sensor to validate its performance for travel time data collection. Results showed that the in-house–developed WMS was equivalent to or performed better than the commercial counterpart, with a price reduction of about 80%. Subsequently, the travel time data collected using the developed WMS was used to forecast future travel times and their prediction intervals (PIs). An autoregressive integrated moving average (ARIMA) model was used for the same. The results of the forecasts were evaluated for five selected routes in Chennai, India. In all the analyzed routes, the mean errors in point estimates ranged from 20% to 23%, and for interval predictions, the prediction interval coverage probability (PICP) ranged between 0.78 and 0.84, suggesting good performance.