Journal of Transportation Engineering, Part A: Systems

Only a small minority of road incident detection algorithms (IDAs) have been designed for use in urban road networks. A review of the literature conducted by the authors revealed that approximately 10% of published papers on novel IDAs are designed specifically for urban networks. Urban networks present many challenges that are not faced on highways, such as signalized junctions causing queuing similar to that of an incident. This paper reviews the progress made in urban incident detection research and highlights where improvements are needed. It is found that few algorithms have been implemented or tested on real-world data, and so few have been sufficiently evaluated or compared. Those that have been implemented often find difficulty in differentiating disruption from incidents and contextual factors such as sporting events or public holidays. This has caused unnecessary false alerts to be triggered, leading to dissatisfied operators. Progress could be made if more research data and IDAs were published, allowing for thorough evaluations and comparisons of algorithm performance on real-world data. Further research is required to improve IDAs’ ability to differentiate between incidents and contexts, particularly in complex urban networks.

This paper proposes a ramp metering and variable speed limit (RM-VSL) control strategy to prevent and recover from capacity drop at freeway merge bottlenecks. Using bounded acceleration kinematic wave theory, we propose a strategy designed to prevent disruptive lane-changing maneuvers at the merge that cause capacity drop. This strategy gives more flexibility to control the ramp queue length by transferring some congestion to the freeway while maximizing total system discharge rate. Simulation experiments showed that the proposed RM-VSL system outperforms both individual components in isolation for preventing or curtailing traffic breakdown. The proposed strategy can be implemented with current technology.

Connected-and-automated vehicles (CAV) hold the potential for substantial improvements to traffic safety, travel time reliability, driver comfort, roadway capacity, environmental impacts, and users’ overall travel experience. Numerous modeling and simulation studies have been conducted to evaluate these impacts. However, model accuracy and simulation assumptions limit the validity of evaluation results. These factors have resulted in the wide range of differences in effectiveness among studies examining the same CAV applications available in the literature. In this study, we propose a bundled CAV application that involves platoons of equipped vehicles governed by an integrated set of cooperative adaptive cruise control (CACC), cooperative merge, and speed harmonization applications. We implemented the bundled application in a fleet of five vehicles at the Saxton Transportation Operations Lab of the Federal Highway Administration. Experiments were conducted to collect and compare data on CAV and human-driven behavior. Based on the real experimental data, our results show that the performance of the CAV operations, including platooning and cooperative merging under varying Infrastructure-to-vehicle speed commands, demonstrate string stability. The results also present key behavioral parameters of the vehicles and strings. This will eventually help the research community, particularly the modelers, to come up with models with realistic performance to further understand the CAV impacts on traffic. The results can also serve as references for transportation agencies to make informed decisions on infrastructure and traffic management decisions.

The purpose of this study is twofold: to assess prehospital emergency medical service performance and to improve the traffic crash survival rate on Korean freeways. To achieve these objectives, this study utilized a logit regression model to predict the likelihood of a severe crash in every Korean administrative district at the local level and compared the spatial clusters of severe crash locations with prehospital emergency medical service (EMS) performance. The results showed that the spatial proximity of freeway-exclusive EMS units and the number of definitive EMS facilities were statistically significant factors contributing to severe crash occurrence. Three cities in the southeastern, southwestern, and capital states of Korea were found to have a concentration of severe crash occurrences, longer average EMS response times, and a lower number of prehospital EMS facilities. The recommendations derived from the study include the provision of more freeway-exclusive EMS units and new definitive EMS facilities near local freeways. These recommendations are expected to inform future policymaking and decision-making on EMS planning and programming.

The paper investigates the causes of high-rate rail/weld failure on a heavy haul track using real life rail weld fracture data. The results show that despite the significant progress in rail manufacturing, and rail welding technology, the failure rate has not dropped significantly. Thus, it is postulated with an analysis that the failure rate may be linked with the current high value of allowable bending fatigue stress and current track maintenance standard; the analysis gives insights on the requirement of construction and maintenance of a heavy haul track to a high standard. The analysis would also be helpful to establish heavy haul track maintenance and operation strategy with rail wear and deteriorating track stiffness. The paper sheds light on some causes of rail/weld failure and suggests remedial measures. Finally, further research is suggested toward a new rail section/s for heavy haul operation.

To evaluate the travel reliability of an urban rail transit (URT) in normal operation, three travel reliability indicators are proposed from passengers’ travel. The connectivity reliability, travel time reliability, and capacity reliability indicators are computed from the weighted average of the number of tolerable travel paths, the ratios of 95–50 quantiles of travel times, and effective residual capacity coefficients between pairs of stations, respectively. The weight of an origin-destination (OD) station pair in a certain time period is the ratio of the passenger trips of this station pair to the passenger trips entering the origin station during that period. The tolerable coefficient, which is the ratio of passengers’ tolerable travel time to shortest possible travel time, is proposed to determine the tolerable travel paths. The effective residual capacity coefficient between an OD station pair is calculated with residual capacities and capacities of all tolerable paths between them. The travel reliability indicators are used to evaluate the travel reliability of Chengdu’s URT stations and network during different time periods and for different tolerable coefficients. The correlations among the travel reliability indicators of stations are analyzed. The results show the travel reliability of a URT in different time periods and indicate how that reliability is related to the tolerable coefficient. A station with better connectivity reliability often has higher capacity reliability also. Because passengers may choose different tolerable travel paths when multiple tolerable travel paths exist between station pairs, a station with high connectivity reliability may correspond to low travel time reliability.

Conventional intersection designs are known to be problematic and unreliable when handling the complexity associated with the heavy traffic volume and travel demand on today’s roadways. Alternative innovative and safer designs such as the restricted crossing U-turn (RCUT) intersection can address these complex problems. However, there is still a gap in the literature concerning the safety performance analysis of RCUT intersections. Consistent with this goal, a comprehensive search was performed to identify RCUTs in the US and collect the relevant data needed to create appropriate safety performance functions and crash modification factors/functions for RCUTs. As such, different safety performance function (SPF) and crash modification factors/function (CMF) models were developed for signalized and unsignalized RCUTs with a focus on all crashes or fatal and injury crashes only, which span from complex models to relatively simple and easily implementable models. This can aid in creating a flexibility for safety agencies or officials that can prefer more complex models when sufficient data are available. Findings indicate that RCUTs have the potential to reduce the number of fatal and injury (F&I) crashes substantially at problematic locations and specifically illustrate that the selection of RCUT location depends significantly on the traffic volumes of major and minor approaches as well as their ratios. The developed SPFs and CMFs can be successfully used by transportation agencies to make informed decisions on the evaluation and justification of the installation of RCUTs.

The heterogeneous driving behaviors from different travelers are not considered in current advanced traveler information systems (ATIS) such as Google Maps and 511 systems, which leads the systems to generate the same travel time for everyone who inputs the same origin and destination. This paper explores the modeling of individualized travel time based on the individual behavior of each driver as opposed to average traffic information, with the ultimate goal of enabling individualized traffic information provision for the ATIS and subsequently reducing travel-time prediction errors. A back propagation neural network model was built to quantitatively estimate the driving behavior differences (i.e., the delta) between individual drivers and the surrounding traffic, with both roadway geometrics and dynamic traffic conditions considered in the modeling process. A travel-time estimation algorithm is then proposed to derive link-level traffic information that considers individual behavioral difference. Finally, individualized route travel time is computed for each traveler based on the derived link-level traffic information and individual behavioral difference. The proposed model is implemented and tested on an open-source Next Generation Simulation (NGSIM) dataset, which demonstrated the feasibility and effectiveness of the proposed model. The proposed model has the potential of being directly applied to enhance existing ATIS travel-time prediction accuracies.

A majority of the past studies on bus dwell time have been carried out in the context of developed countries, and the dwell time prediction models established in literature adopted the fixed-parameter assumption. The variations due to human factors such as passenger and driver behavior are not reflected under a fixed-parameter framework. This paper presents a methodology for rational estimation of passenger boarding and alighting time in the context of an emerging country using both fixed-parameter and random-parameter models. The methodology was demonstrated with a case study in the Kolkata metro area of India. The results established that the boarding and alighting time varies significantly across bus type, in-vehicle crowding, and size of the passenger group. In this context, random-parameter models were found to outperform fixed-parameter models in capturing the heterogeneity in bus and demand characteristics. The models were validated successfully with statistically acceptable errors.

Traffic signal plan designs become more complex with developments in the fields of sensing and communication and the introduction of features, such as transit priority or pedestrian actuation. Traffic signal optimization programs were developed mostly for the basic parameters of pretimed traffic signal plans using analytical or simple traffic models. Simulation-based optimization for other parameters related to actuated traffic signals has been very limited due to the high computational time associated with detailed traffic simulation models. This paper presents the overall structure and the various components of a simulation-based system to optimize the parameters of complex actuated traffic signal plans. The system framework incorporates a connection between a mesoscopic traffic simulation, MESCOP, traffic signal control, and a genetic algorithm as the optimization method. The integrated system is demonstrated with applications to a fully actuated signalized intersections with vehicle and pedestrian actuations and transit priority in Haifa, Israel.

In this paper, a Markov-based time series model (MTSM) framework is developed to predict traffic network conditions by integrating archived and real-time data under various external conditions, including weather, work zones, incidents, and special events. The model considers a Markov process to explicitly characterize the probabilistic transitions between traffic states with external conditions. Environmental observations (e.g., weather) and external events (e.g., incidents and work zones) are grouped into different scenarios based on archived traffic data, and Markov transition matrices for different scenarios are calculated to predict traffic state evolutions under different scenarios. In the proposed MTSM framework, short-term and long-term time series models are combined to forecast the traffic conditions of normal cases to consider weekly/daily trends, and the outputs are further adjusted with the Markov processes for external conditions. The evaluation results show that the error rates under normal cases are about 6%, and the error rates under external conditions are around 10%. The prediction results of the proposed model are compared with the benchmark (i.e., single short-term series model and weighted time series model without the Markov process). The performances of the proposed framework are superior to the benchmark models for both cases with and without external conditions. The proposed model is able to accurately capture recurring and nonrecurring traffic congestion. It provides a reliable prediction of traffic speeds for traffic management centers to efficiently deploy proactive traffic management strategies.

In this paper, we present a modeling framework and approach to capture vacant taxi drivers’ route choice behavior learning process and simulate their changes of routing decisions over time due to updated experiences of the traffic and passenger’s information. Efforts to unveil their behavioral learning process were rather limited, although some researchers focused on the modeling of routing behavior. We focused on the street-hailing of vacant taxi drivers, who selected a route to minimize the search time for picking-up a waiting customer along the road, which was determined by the traffic information and customer arrival rate. At the end of each learning cycle, or “learning day,” taxi drivers updated their knowledge on the traffic and passengers based on their newly gained experience, and made corresponding changes to their route choice at the next learning day until an optimal route had been found. Both analytical and numerical analysis were conducted on the Taipei traffic simulation network. The case study results showed that the proposed model was able to reasonably capture taxi drivers’ changes of route choice.

Bus signal priority (BSP) is an effective strategy to enhance the quality of public transit service, but unfortunately, it also deteriorates the performance of general vehicles. However, the tandem intersection with midblock presignals can increase the intersection discharge capacity. The study aims to integrate BSP and presignal methods at the intersection to improve bus performance and relieve congestion. The phase scheme is designed, and signal states are classified into four categories. The control flow under each signal-state category is developed by considering the coordination between presignals and main signals. The algorithm is further compared with a tandem intersection without BSP and a traditional intersection with BSP based on simulations. The results show that (1) Integrating BSP and presignal at the intersection can provide huge benefits to buses. Compared with the average delay of general vehicles, the average delay of buses can be decreased by 70.7%. (2) Under medium and heavy traffic scenarios, the developed algorithm can decrease average intersection delay of general vehicles by 30.1%, thus, highlighting its potential to relieve traffic congestion.

In this paper, a model for transit signal priority control is presented. The model is based on an adaptive signal control called self-organizing logic (which evolved from fully actuated control) with advanced features, including a platoon-based secondary extension that allows signals along a corridor to maintain coordination. Utilizing advance bus detection, the proposed model grants look-ahead transit signal priority under self-organizing control for a longer time horizon of bus arrival to the intersection. Priority is provided based on a tradeoff between buses and platoons of cars under specific conditions. The model was tested in a simulation network of the national connected vehicle testbed in Anthem, Arizona. The simulation experiments showed that the model, when compared to four other signal control systems, was able to reduce average transit vehicle delay and travel times without having a significant negative impact on regular passenger cars. The developed approach increased the reliability of bus operation by decreasing the standard deviation of travel time. The results showed that both the reduction in bus travel time and the increase in reliability were statistically significant. A sensitivity analysis on demand level indicated that the model outperformed the competing approaches by a promising margin.

Both bus dwell time and bus headway adherence are critical to the serviceability of the bus transit system. It is acknowledged that passengers with physical disabilities may greatly impact bus dwell time and bus headway adherence due to the increased time needed to safely board the bus. However, the mitigating effects of securement systems and ramp features on bus dwell time and bus schedule adherence have not yet been examined in the literature. This study investigates the impacts of the combination of bus boarding conditions and passengers who used wheeled mobility devices on bus dwell time and headway adherence. The results indicate that bus dwell time drops by 30% for the most supportive boarding conditions. Results also demonstrated that the deviations of bus headways from the scheduled headways regarding the most favorable conditions were much smaller than the traditional design of the buses under the light traffic condition. There is a 47% reduction in headway adherence coefficient under the light traffic condition with favorable ramp slopes, interior bus configuration, and securement conditions.

Simple circular curves have been proposed by the road specifications to be implemented in horizontal alignment. In these curves, the centrifugal force is applied suddenly to the vehicles at the beginning of the curve and remains constant at its maximum value until the end of the curve. The current research intends to introduce a new curve for horizontal alignment. This curve is a simplified form of the general transition curve and since it has a Symmetrical Parabolic Curvature, it is called the SPC curve. This curve applies the centrifugal force to the vehicles in a gradual manner. By using the SPC curve, the edge profile matches the curvature and the superelevation would be applied more efficiently to the roadway. Compared to the simple circular curve, the SPC curve has a lower horizontal side offset and applies a smoother vertical tire force to the outer edge of the pavement. The shorter total length of the alignment comprising the SPC curve leads to decreased road construction and maintenance costs. Also, the mechanical simulation shows that vehicles negotiating the SPC curve seem to be less prone to rollover.

The research work presented is originated with the intent of studying driving behavior, especially in a lateral direction under the influence of construction work activities under prevailing heterogeneous traffic conditions. Initially, a midblock road section is selected, and then macroscopic traffic characteristics were evaluated. Later, Mumbai metro-rail construction work was started on the same road section. As a result, it was planned to study the variation in traffic-flow characteristics for road sections with-and-without construction work zone at microscopic as well as macroscopic levels. For this purpose, video graphic surveys were conducted, and macroscopic characteristics were evaluated for both study sections (with-and-without construction work zone). The results showed that at given flow levels, the stream speeds were dropped from 70 to 50 kph, followed by a drop in efficiency in terms of per-lane capacity. To sense this impact more deeply, vehicular trajectory data were developed over road sections at three different traffic-flow levels. The driving behavior is investigated using different variables, such as longitudinal following behavior, following times, lateral amplitude, and lateral placement, followed by lateral direction correlation analysis. From the investigation, the results show that the following-hysteresis plots for vehicles are found to be slender in a construction work-zone, revealing the conservative approach in driving behavior. Furthermore, following-time analysis also supports the same interpretation. Given the smaller size of vehicles, the vehicle tends to possess extra lateral freedom and hence is responsible for the seeping phenomenon through a porous media in the traffic stream. As an impact of reduced road width, capacity (per-lane) values are found to be higher in the base section than the section having a construction work zone. Further, based on the conceptualized framework, safety in the traffic stream over the study sections was evaluated. Finally, the importance of cautioning drivers the construction work activity in advance was well revealed from the safety analysis of this work.

In prior studies, the transit service reliability has been looked at from the passenger’s perspective in terms of time spent by the passengers in waiting for their bus to arrive at a stop. The operators’/agencies’ perspective based on analyzing day to day travel time variability (TTV) gives a clear picture about transit service reliability but is not a well-researched area, partly because of limited access to comprehensive data sets of bus travel times. The present study used citywide global positioning system (GPS) data of bus rapid transit system (BRTS) operating in Ahmedabad city of India to carry out the travel time reliability analysis. A three-level TTV analysis was carried out in the present study (i.e., segment, route, and network level). The route level analysis dealt with observing the day to day and within the day TTV. Whereas, in the segment level analysis, the BRTS routes were divided into segments based on criteria of shared and unshared routes. Two travel time variability (TTV) models (considering different dependent variables) were then developed using 770 observations to analyze the factors causing variability in travel time (TT). The model was developed considering the linear regression technique, and the significant variables in the suggested model were selected based on the backward stepwise selection method. The TTR model with T90–T10 (90th minus 10th percentile travel time) as dependent variable was showing a better adjusted $R^2$ value (i.e., 0.73). The model revealed that the independent variables like length, bus stops, and number of intersections affect TTR to a larger extent. The third part of the TTR analysis was based on developing level-of-service (LOS) criteria for comparing TTR of transit systems. Transit’s network level data was used to propose a revised LOS based on weighted delay index (WDI) which is an improvement over the conventional LOS criteria. K-mean clustering was used to classify WDI into groups wherein each group corresponded to a certain transit service level. The mean of the silhouette coefficient was estimated to be 0.5 which highlighted that the structure of the clusters was reasonable.