Journal of Transportation Engineering, Part A: Systems

The right-turn flashing yellow arrow (FYA) signal display is still considered a new signal practice in the United States. The Manual on Uniform Traffic Control Devices (MUTCD; 2009) allocates a signal phasing section for the right-turn FYA, which requires a four-section configuration. It supports multiple phase indications that guide the motorists through permissive, protected, or permissive or protected phases. However, there are no right-turn FYA or protected permissive right turn (PPRT) guidelines in place with a focus on operational efficiency. In this paper, we investigated two permissive right-turn FYA phases in various traffic conditions and signal timing plans. The first permissive right-turn FYA phase is the right turn on impeding through (RTOIT) taking place during cross-street through movement. The second permissive right-turn FYA phase occurs during opposing left-turn movement and is thus called right turn on impeding left (RTOIL). We aimed to develop warrants leading to the efficient implementation of permissive right-turn FYA phases based on a microsimulation analysis. The response, the average maximum right-turn throughput (MRTT) per cycle, was categorized into three categorical variables represented as the nonefficient (NE), low efficient (LE), and efficient (E) categories depending on the number of executed right turns per cycle. A multinomial logit model was developed to establish a decision support system that predicts the efficiency attributes of the permissive RTOIT and RTOIL FYA phases that can help traffic management center operators in planning and operational-level applications.

This paper presents the results on the demand survey obtained from owner–operator truck drivers for their willingness to use truck-only toll lanes on Southern California freeways. The stated preference survey method was used to estimate the values placed by them on time, reliability, and safety measures given nine different scenarios geared toward assessing those values. The data was collected by meeting in person with owner–operator truck drivers near the Ports of Los Angeles and Long Beach to understand their perspectives on truck-only toll lanes. The completed 31 survey responses out of 100 were used for analysis of variance and two sample $t\text{-}\mathrm{tests}$. The results showed that 75.27% of the owner–operator truck drivers are willing to pay toll fees when they choose routes. The tolerated average toll fees are $\$13.77/\mathrm{h}$ and $\$12.82/\mathrm{h}$ for weekdays and weekends, respectively. The results showed that owner–operator truck drivers will prefer truck-only toll lanes in four out of six comparisons according to three measures despite sharing a common departure and destination. Drivers are willing to pay the highest toll fee of $\$0.31/\mathrm{mi}$ or $\$18.35/\mathrm{h}$ on any day when the main factor being compared is time measure. The toll fees associated with reliability and safety measures are $\$0.30/\mathrm{mi}$ or $\$8.94/\mathrm{h}$ and $\$0.22/\mathrm{mi}$ or $\$11.01/\mathrm{h}$, respectively. These results are meaningful for legislators and transportation agencies as they will help better understand the contemporary issues and demands in estimating the utility of a truck-only toll lane.

The concept of redesigning public spaces to encourage physical distancing amid the COVID-19 pandemic is being tested around the world. In Canada, municipalities are reallocating underutilized road lanes for active modes of transportation, such as walking and cycling. We evaluated the usage and benefit of these shared spaces to ensure redesign efforts are optimally allocated. We analyzed two sets of closed-circuit television (CCTV) footage before and after the change, covering April 7–13, 2020, at two locations using automated computer vision techniques. We detected and recorded physical distancing violations, traffic safety risks such as midblock crossing, speeds, and traffic conflicts, and generated trajectory maps of all road users. It was found that the redesign was utilized effectively by road users and improved physical distancing compliance without compromising traffic safety. The proposed framework also provides an innovative tool to automatically gather, extract, share, and analyze real-world data to improve response to the COVID-19 pandemic as well as future outbreaks of contagious diseases.

Safety barriers limit the severe consequences to occupants of vehicles leaving the roadway from colliding with obstacles, yet they may significantly reduce the available sight distance ($ASD$). A procedure was devised to accurately recreate highway guardrails in three dimensions (3D), fitting the barriers onto the curve roadsides. The effect of these devices on the reduction of the $ASD$ was thus evaluated in a set of case studies comprising different curve radii overlapped with different vertical alignments and combined each with two safety barriers in two-lane rural roads. The 3D models of highway curves with the selected guardrails were created in a geographic information system. Then, a geoprocessing model was used to compute the $ASD$. The results made it possible to quantify the $ASD$ reduction produced by the barrier with a higher containment level in relation to the highway design parameters, which can assist authorities in developing more comprehensive safety device standards.

Prediction of the extra-peak hourly ridership (EPHR) is directly related to the capacity design of subway station service facilities. In the traditional station-level EPHR prediction process, the predicted value is simply the result of the multiplication of the predicted peak hourly ridership (PHR) value by a unified extra-peak hour factor (EPHF). However, the station-level EPHR predicted by this method may be underestimated because the PHR prediction results are extracted from a line-level prediction value, rather than the station-level value. Moreover, while the existing EPHF is always determined by China’s Code for Design of Metro, it is too simple and unrefined to be applicable. The proposed station-level EPHR prediction approach exhibits significantly improved accuracy and applicability via the introduction of a least absolute shrinkage and selection operator (LASSO)-based feature selection method. The historical ridership and related attribute data of the stations are used to construct relationship models for the peak deviation coefficient (PDC) and the EPHF to make the model more explanatory. As a case study, this approach was evaluated on a real-world, large-scale passenger flow dataset from Xi’an, China, and compared with the results of the traditional method. The results indicate that the EPHR prediction accuracies of 10% to 51% of the stations are improved and the corresponding mean absolute percentage error (MAPE) is reduced by 6%–30%, as compared with the traditional method, suggesting wider applicability and higher precision for station-level prediction. A supplementary comparison with two other feature selection methods further verifies that the LASSO-based approach exhibits higher accuracy and applicability.

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.

Maintaining transit infrastructure in a state of good repair (SGR) is key to improving the sustainability of urban transportation. Insufficient SGR investments lead to the deterioration of transit agency assets and to declining service quality and a loss of ridership. Thus, a significant portion of the benefits of SGR investments are due to travel demand impacts, but the literature on this subject is extremely sparse. As a result, evidence and demand modeling tools to properly determine benefits of transit SGR investments are lacking. This paper quantifies the impact that this gap of knowledge has on SGR investment benefit forecasts. It presents a comprehensive sensitivity analysis to determine first-order and higher-order effects of travel demand variables on the variance in SGR benefit estimates, using 37 hypothetical investment scenarios. The results show that the variance attributable to uncertainty in travel demand forecasts is considerable and may skew investment decisions. It varies by SGR project type and may be reduced by addressing some of the variables with which travel demand forecasts interact.

The present study explores the spatial and temporal variation of rear-end conflicts at signalized intersections under mixed traffic conditions with poor lane discipline (disordered traffic conditions). Vehicular trajectory data was extracted using the videographic field data of three signalized intersections in India. Due to disordered traffic conditions, a simple and robust method is proposed to identify the leader and follower vehicle pairs for identifying rear-end conflicts. Spatiotemporal variation of identified conflicts is investigated using two popular surrogate safety measures (SSMs). A significantly higher number of rear-end conflicts occur at the downstream of intersections compared to the upstream section. The curbside lanes with a higher proportion of smaller vehicles observe more rear-end conflicts than medianside lanes. Different vehicle classes with varying lateral movements affect vehicle following time, resulting in constantly changing leader-follower pairs and a high probability of rear-end conflicts. Dynamic parameters such as speed, acceleration, and spacing maintained by the following vehicles offer detailed insights into identifying conflicting interactions. Finally, rear-end conflict and crash probability at different study locations and vehicle classes is determined using the peak over threshold approach of extreme value theory.