Journal of Transportation Engineering, Part A: Systems

In recent years, connected vehicle (CV) technology has reached a level of maturity and can be beneficial for traffic control at signalized intersections. Enriched information provided by CVs could be utilized to perform a more efficient signal control. This paper proposes a methodology to dynamically adjust the offsets to provide progression bands for multiple critical paths in a CV environment. The critical paths refer to the routes connecting origin-destination (OD) pairs with the highest volumes and are determined based on the CV trajectory. A real-time optimization model is constructed to design a coordination plan, and the control objective is to provide maximum green bandwidth along the determined critical paths by optimizing the offsets of all intersections along an arterial. To solve this model, a solution algorithm based on dynamic programming is proposed. A real-world arterial in Salt Lake City, Utah, is modeled in commercially available software to evaluate the effectiveness and efficiency of the proposed control strategy. Simulations are conducted to compare the proposed system with a fixed coordination strategy. The results reveal that the travel times of those critical paths by the proposed coordination strategy are lower the ones by the fixed coordination strategy. The average delay and average number of stops of critical paths are reduced by about 16.82% and 5.94%, respectively, compared with the fixed coordination strategy. Moreover, results also indicate that the proposed coordination strategy outperforms the fixed coordination strategy by reducing 7.84% and 5.79% of the average delay and average stops, respectively.

Safety in highway geometric design is one of the main goals to be achieved. However, deterministic design criteria do not provide information concerning the risk associated with the design outputs proposed. To yield consistent safety levels, the sag vertical curve design model on undivided highways was calibrated using a reliability-based framework, which allows one to incorporate the uncertainty associated with the model variables. The sag curve design model contemplates the features of vehicle front lighting systems to compute the headlight sight distance (HSD), which must be equal to or greater than the stopping sight distance (SSD). A dataset of 34,238 case studies was generated. Each case study was associated with two values of the risk level, designated as the probability of noncompliance ($P_{nc}$), one per driving direction. A Monte Carlo simulation was selected to calculate the values of $P_{nc}$. Through a series of interpolating surfaces of the cloud of points, contour graphs and calibrated charts were depicted. The paper provides a new methodology to verify, design, and compare sag vertical curves, evaluating the risk level with the $P_{nc}$.

The exit lane for left-turn (EFL) intersection is a newly proposed unconventional intersection that can alleviate left-turn traffic congestion at intersections. The EFL design characteristic lies in that a part of exit lanes can be provisionally used as left-turn lanes to provide additional capacity for left-turn traffic. The inappropriate lane selection of human drivers decreases the effectiveness of such design. This paper establishes an optimal trajectory planning model for left-turn vehicles at EFL intersections under the automated driving environment. The longitudinal acceleration and lane changing variables are optimized simultaneously. The optimization model is formulated as a mixed-integer linear model, which ensures the computational efficiency. The effectiveness of the proposed model is explored under various traffic, geometric, and signal control conditions. The results show that the model can provide a suitable control strategy for left-turn vehicles. On average, traffic delay can be reduced by 47.3%. The proposed model has a promising control effect under high volume conditions. Moreover, the proposed model performs stably under the various length of the mixed-usage area.

As a typical type of adverse weather, heavy rainfall can result in congestion and productivity loss in the urban transportation system, which has been a severe problem for cities and attracted much attention from scholars. However, little attention has been paid to the effects of the combination of rainfall and rain-induced waterlogging on urban road traffic. In this paper, first, an experiment based on a high-fidelity driving simulator is conducted to collect the drivers’ behavior data in the scenario of rainfall and waterlogging where 30 groups of scenarios with different levels of rainfall intensity and water depth are included. Second, different car-following models are calibrated in the light of the drivers’ behavior database from the experiment, and the best one is chosen to simulate the traffic flow on urban road sections. Lastly, different traffic flow models that depict the relationship between flow, velocity, and density are calibrated based on the simulation results. The results show that the increase of both the rainfall intensity and water depth can result in decreased free flow speed and maximum flow. The results also show that the depth of water is more critical as an influencing factor on the characteristics of traffic flow compared with the rainfall intensity. The results from this study can facilitate a deeper understanding of the influence of rainfall and waterlogging on traffic flow and improve the response capacity of traffic management departments when faced with rainfall and waterlogging.

Detailed knowledge about the times at which links in a network suffer travel time instability is of great significance for route decision making and traffic management. With a data set of 1,170 links in a large-scale urban road network, this paper introduces advanced algorithms to fit the travel time volatility (TTV) and analyze its characteristics. TTV is prone to clustering, and its distribution exhibits leptokurtosis and a heavy tail. These characteristics suggest iterative cumulative sums of squares (ICSS) and the autoregressive conditional heteroskedasticity family models (ARCHs) are applicable to analyze TTV. ICSS finds the structural change points to locate the time and determine the intensity of the travel time variances fluctuate. ARCHs are employed to fit the TTV series, and the best model is determined by comparison. ICSS and ARCHs are combined to improve the fitting model. With TTV, TTR is analyzed from a dynamic and temporal perspective at intervalwise and linkwise levels.

To ensure improvements are made in the parts of the city currently most prone to crashes, this study conducted a 2009–2016 longitudinal comparison of the traffic safety in Shanghai at the level of the traffic analysis zone (TAZ). The police-reported crashes occurring in 2009 and 2016 within the downtown areas of Shanghai were examined to acquire a basic knowledge of the traffic safety development and spatial distribution characteristics of crashes. Considering that different zones within a city show significant heterogeneity in socioeconomic indicators, road characteristics, traffic patterns, and land-use features, a macrolevel traffic safety model was developed to capture the relations between the aggregate number of crashes and the various influencing factors. The study also identified hot zones for the two years to demonstrate the zonal changes in crash frequency. Results show that crash frequency had shifted significantly in suburban areas of Shanghai, with many hot zones having moved from the central areas of the city to suburban areas. Urban planners and decision makers can use this study to better understand the changes in crash distribution and contributing factors, permitting better targeted safety countermeasures.

Truck platooning has several benefits over traditional truck mobility. Platooning improves safety and reduces fuel consumption up to 15%, depending on platoon configuration. Although platooning benefits are quantifiable, platooning routes are not identified. Many factors are relevant to identifying these routes. For efficient platooning, a truck platoon needs to travel at a constant high speed for an extended distance. In addition, platoon integrity should be preserved from interfering vehicles and frequent ramps that may compromise the robustness and safety of the operation. This study presents an easy to implement approach to determine platoonable routes based on platoon configuration, speed, roadway volume/capacity, and number of highway exit and entry conflicts. Based on this approach, each roadway section is assigned a level of platoonability from one to five, where one is the most platoonable. This approach was used to analyze the highway network in Illinois. According to this approach, 89% of the interstates and freeways in Illinois are platoonable under average traffic conditions.

With social progress and economic development, urban rail transit systems have been developed to satisfy the growing travel demands in China. Undoubtedly, it is necessary to evaluate the operation efficiency of urban rail transit systems and analyze their influence factors, which are highly significant for promoting management performance. In the present paper, an integrated approach is proposed, which consists of a data envelopment analysis (DEA) model, a Malmquist productivity index, and a Tobit regression model. For evaluating the operation efficiency of urban rail transit systems in China, the DEA model and the Malmquist productivity index provide an evaluation metric, and the Tobit regression model is used to carry out influential factor analysis. The test data are collected from six typical cities at different stages of rail transit system development from 2013 to 2017, in order to investigate the effectiveness of the proposed methodology. The experimental results demonstrate the operation efficiency of different rail transit systems and the effects of several influencing factors. The conclusions from this paper can guide policymaking and provide references for enterprises to improve the operation efficiency of rail transit in China.

Deteriorated track infrastructure can lead to significant consequences, such as derailment incidents, loss of revenue-service hours, reduced train operation speed, and even failure of the business. The Federal Railroad Administration (FRA) requires thorough and timely rail track inspections to keep trains operating safely. Approaches using advanced and emerging technologies have become more accessible and broadened their applications. Unfortunately, for Class II (regional) and III (short line) railroads (referred to as local railroads hereafter), there are many obstacles for deployment of these advanced technologies, such as budget constraints and lack of workforce skills. Local railroads are actively seeking cost-effective and efficient approaches to replace conventional methods of manual inspection. This paper, for the first time, aims at identifying the key challenges for local railroads regarding the demand for reliable and timely rail track inspection, lack of advancement of emerging technologies, and unique constraints faced by the local railroads. Through a comprehensive review of the common rail and track geometry defects and state-of-the-art track inspection techniques, this paper critically analyzes the knowledge gaps from four key perspectives from the perspective of local railroad operation: finance, technology, standards, and operation. It is found that automated nondestructive evaluation (NDE) techniques and inexpensive alternatives to the Automated Track Inspection Program (ATIP) may still be cost-feasible and effective for local railroads with future technological and operational upgrades.

The intrazonal modeling parameters of distance, duration, and generalized cost (GC) are conventionally estimated from a fixed-fraction of interzonal parameters or using linear equations. In this study, the intrazonal GC that was developed in the National Transport Authority’s (NTA) regional modeling system using a conventional fixed-fraction method (base) was compared to intrazonal GCs for six travel modes [passenger cars, bus, cycle, walk, taxi (passenger) and light goods vehicles] that were estimated using zonal GC equations adopted from literature. The zone-specific distance and duration values for the parameters of the six equations were developed using a regression modeling approach. At first, National Household Travel Survey (NHTS) data, Open Street Map, and traffic zone data were used to generate regression models. Then the reported trip duration and distance of NHTS datasets were then replaced with the corresponding time and coordinate defined distances and durations from the Google Maps Journey Time and Distance Estimates (GMJTDE) records, and the estimated equations from this process produced better validation performance, and thus integrated with the six equations. In the comparison stage, intrazonal GC values were estimated for all six modes using two different analysis and compared against the base dataset. In the first analysis, average distances and duration were obtained for all zones containing intrazonal trips and were directly applied in the six equations to estimate intrazonal GC. In the second analysis, intrazonal GC were estimated using models based on the aforementioned NHTS-GMJTDE datasets. Results indicate that the intrazonal GC estimated using the second analysis matched the intrazonal GC from the conventional method, and provided an indication of real-world situations to some extent. This study confirms that intrazonal parameters can be developed separately from the survey data, minimizing the efforts of calibration with sufficient accuracy that cannot be obtained directly from the survey data.

The lack of multistakeholder cooperation is one of the main challenges faced by emergency medical services (EMS). Especially in the ambulance routing process, inactive traffic operators fail to provide coordination to prioritize the ambulance, while ignoring the choice of hospitals will lead to inevitable patient transfer between hospitals. To provide efficient decision support for EMS, this paper considers daily ambulance routing problems in a network with high spatial resolution in which two advanced technologies are introduced: prehospital screening that provides patient injury diagnosis and lane preclearing that ensures the predefined driving speed of ambulances. Three different types of ambulances are used to transport and offer first aid to patients based on the screened results. To manage the ambulance fleet properly, a mixed-integer linear programming (MIP) model is proposed to assign vehicles to the injured and plan routes with the shortest travel time. A semisoft time window constraint is incorporated to reflect the late arrival penalty onsite and at hospitals. Because high-quality EMS responds to the call in seconds, a real-world case in Shenzhen, China, is presented to validate the computational performance by a commercial solver: the general algebraic modeling system (GAMS). In the case study, we further analyzed the effect of different stakeholders’ involvement, like the hospitals and traffic operators. This information proves the efficiency of multistakeholder participation in ambulance routing.

Inadequate understanding of driver heterogeneity and characteristics creates challenges building models in microscopic traffic simulation tools that accurately represent merging behavior in real traffic. To further understand merging behavior, this study, unlike previous studies, considers age group and other driver characteristics under varying weather, traffic, and geometric conditions. A pilot study was conducted using a driving simulator to simulate merging scenarios on four-lane and six-lane freeway segments for Level of Service (LOS) A and B under clear and foggy weather conditions. A total of 100 individuals voluntarily participated in the study and their time taken to complete merging maneuvers (or merging time) was used as the performance measure. The collected data were analyzed using ANOVA and log-linear regression models, and results show that although there were statistically significant differences among age groups, “number of lanes” was the most significant predictor variable in the model because drivers required longer time merging to the four-lane freeway segment than to the six-lane freeway segment. Also, some driver characteristics and self-reported driving abilities were found to influence the merging time of drivers.

Two-lane rural highways represent the absolute majority of road networks globally, but there is a noticeable lack of research on this type of road for traffic planning purposes. This paper presents the relationship between average flow speed on two-lane rural highways and traffic volume and composition, and roadway horizontal and vertical geometric characteristics, expressed in the form of a volume–delay function. A new volume–delay function was developed through a combination of field research and traffic microsimulation. Field research was conducted on the main road network in Bosnia and Herzegovina. In total, more than 1 million simulations of different traffic situations were performed in an automated process. The results showed that a combination of traffic composition and geometric features (bendiness and longitudinal grade) has a crucial impact on average flow speed. The new proposed model predicts average flow speed in various traffic situations with high reliability ($R^2=0.77$), compared with $R^2=0.26$ for the widely used Bureau of Public Roads (BPR) volume–delay function.

Driver perception-reaction time and ability to sustain attention decrease as drivers become drowsy. The consequences of drowsy driving crashes are often worse on interstate highways due to the relatively high travel speeds typical on such facilities. To address the problem of driver fatigue, rest areas are often located along interstates and other long-distance highways to serve as public facilities at which motorists can take a break from driving. This paper explores the relationship between a range of crash factors and the injury severity outcomes of drowsy driving crashes on interstates in Alabama. The crash outcomes were examined in relation to interstate rest area locations. Findings from the study establish that severe injury crashes were more likely to be recorded on interstates that did not have any public rest areas. In addition, drowsy driving crashes that occurred more than 16.1 km (10 mi) away from a public rest area and those that occurred after a driver passed a rest area were more likely to record severe injuries. Other factors that were found to be associated with drowsy driving crash outcomes include unlit roadway conditions, driver gender and age, seatbelt use, location attributes of the crash site, and some temporal factors. Drowsy driving crashes involving out-of-state drivers were also found to have higher chances of recording severe injuries. Increased driver education and public campaigns are recommended as strategies to reduce drowsy driving–related crashes. While drivers should be encouraged to utilize rest areas, state officials should ensure adequate security and sanitation as well as provide amenities to make rest areas more attractive to motorists.

Station overcrowding negatively impacts passenger safety and travel efficiency. To deal with the overcrowding in the urban rail transit transfer station, this paper proposes a novel multiposition joint control (MPJC) model. Its objective is to meet all the safety limits in key areas of the transfer station and improve passenger travel efficiency. The model makes full use of the space, with the control positions jointly set on the moving routes of passenger flow. To make better use of the capacity of the trains to improve travel efficiency, the outflows at multiple control positions are optimized considering the nonlinear characteristics of passenger flow. Simulation experiments were carried out on GuoMao Station in the Beijing subway. Extensive experimental results demonstrated that the joint control scheme with the MPJC model can increase the average travel efficiency of passengers by 3.9% and keep the numbers of passengers in key areas under safety limits, thus effectively relieving the transport pressure in the station.

Due to variations in freight traffic arising in daily operations, railways need to develop a distribution plan to determine the actual movements of empty cars every day. This paper investigates the daily empty car distribution problem in the Chinese railway system, which is a scheduled railway system but one where scheduled train services in the timetable are not fixed. To make current empty car distribution more market-oriented, this paper takes the timeliness requirement of empty car demands into account. A time-space network that divides the planning horizon into multiple decision-making stages is constructed to describe the process of empty car distribution. With consideration of car types and the network capacity shared by loaded and empty cars, an integer programming model is proposed to minimize total cost in the distribution process. A Lagrangian relaxation heuristic algorithm is designed to solve this problem. The effectiveness of the proposed model and algorithm is demonstrated by numerical experiments based on realistic data from the Haoji Railway, a rail freight corridor. Finally, sensitivity analyses indicate that unit shortage costs and delay penalties can be set relatively high and that many train paths should be scheduled in the timetable to ensure that stations receive empty cars on time.

Nowadays, a large percentage of people use smartphones frequently. The mobile phone signaling data contains various attributes that can be used to infer when and where the user is. Compared with other big data sources (e.g., social media and GPS data) for the human movement, mobile phone signaling data demonstrate the advantages of a high coverage of population, strong temporal continuity, and low cost of collection. Taking advantage of such mobile phone signaling data, this work aims to identify tourists and locals from a large volume of mobile phone signaling data in a tourism city and analyze their spatiotemporal patterns to better promote tourism service and alleviate possible disturbance to local residents. In this paper, we present a framework to differentiate these two types of people by the following procedure: first, the hidden behavior characteristics of users are extracted from mobile phone signaling data; and then, the K-means clustering method is adopted to identify tourists and locals. With the identification of both tourists and local residents, we analyze the distribution and interaction characteristics of tourists and locals in an urban area. An experimental study is conducted in a famous tourism city, Xiamen, China. The results indicate that the proposed method can successfully identify the most popular scenic spots and major transportation corridors for tourists. The feature extraction, identification, and spatiotemporal analysis presented in this paper are of great significance for analyzing the urban tourism demand, managing the urban space, and mining the tourist behavior.

To address the transfer connection problem for urban rail transit (URT) and bus bridging in the case of unscheduled operation disruption, an integrated optimization model for bus bridging timetables and vehicle scheduling is proposed that considers the surge characteristics of transfer passenger flows. First, we analyze the transfer connections between the URT system and the bus bridging system and introduce the concept of passenger tolerance to determine whether a connection is maintained. A bilevel programming model is formulated, in which the upper level addresses timetable optimization with the aim of minimizing the passenger waiting time and the number of transfer failures, and the lower level addresses vehicle scheduling with the aim of minimizing bus operation cost. To solve the proposed model, an improved simulated annealing (SA) algorithm is developed. Finally, a case study of the Shanghai Rail Transit Line 10 is analyzed. The results show that, compared with the even headway timetable, the proposed model results in a 13.7% reduction in total cost, a 14.1% reduction in passenger waiting time, and a 54.9% reduction in the number of failed transfers.

Dilemma zone could be an effective surrogate safety measure of a signalized intersection because its length and location are significantly correlated with right-angle and rear-end conflicts and vary from one site to another. Two popular methods, Zegeer’s method and time-temperature indicator (TTI) based method, are available in transportation literature to quantify Type II dilemma zones. The existing methods, however, have limitations to quantify the dilemma zone because the former requires extensive data collection and analysis efforts, and the latter is solely dependent on a single variable (i.e., speed). The objective of the present study is to develop effective methods of quantifying Type II dilemma zones using important site-specific characteristics of signalized intersections (e.g., the operating speed, the approach grade, and the amount of truck traffic). The present study also seeks to address the methodological shortcoming of the TTI-based method. Driver behavior at 46 high-speed signalized intersection approaches where the posted speed limit is 80.47 km/h ($50\mathrm{mi}/\mathrm{h}$) or higher was analyzed to see if there is a relationship between the dilemma zone and the intersection site-specific characteristics. The results show that the approach grade, the operating speed, and the amount of truck traffic at the signalized intersection have strong correlations with the dilemma zone. It was also found that the dilemma zone length is longer, and its location is farther from the stop bar if the approach grade is the steeper (toward negative values), if the operating speed is higher, or if more truck traffic operates at the intersection approach. The developed models were compared with the TTI-based method, which uses 2.5 and $5.5\mathrm{s}$ of the travel time to the intersection stop bar (TTI) to determine the dilemma zone start and endpoints. The analysis showed that the developed site-specific dilemma zone models outperform the TTI-based method and well fit the observed data with pseudo ${\mathrm{R}}^2\ge 0.9$ and $p$ value $\le 0.05$.

Freight train accidents can damage infrastructure and rolling stock, disrupt operations, and possibly cause casualties and harm the environment. Understanding accident risks associated with major accident causes is an important step toward developing and prioritizing effective accident prevention strategies. This paper developed a negative binomial regression model to estimate freight-train derailment frequency on Class I railroad mainlines, accounting for derailment accident cause, traffic exposure, railroad, and season. The primary focus is to quantitatively measure the seasonal effect on freight-train derailment frequencies given traffic exposure. For model illustration, the analysis focused on three common derailment causes on freight railroads: broken rails, broken wheels, and track buckling, using the empirical Federal Railroad Administration (FRA)-reportable freight railroad derailment data on mainlines gathered between 2000 and 2016. The modeling results show that it tends to have high derailment rates in winter due to broken rails and broken wheels (double that of summer), whereas summer has the highest likelihood of buckling-caused derailment of all of the seasons (e.g., 6 times that of spring and 10 times that of fall). These analytical results can contribute to the risk-based optimization of rail and wheel inspection frequency. The statistical modeling methodology developed in this paper can be adapted to other types of train accidents or accident causes, ultimately supporting the optimal allocation of train safety improvement resources.

Providing public transportation with quality service is critical to attracting more passengers to the system. However, high-demand routes are prone to the so-called bus bunching—a tendency of buses to group as a consequence of variations in travel times and demands. Bus holding is applied to overcome this effect. In this study, we present a novel method for bus holding in which the control law is based only on the buses’ position using a computationally efficient rolling horizon approach. The method uses similar inputs as linear control approaches while not increasing significantly the computational time. On the other hand, the method overcomes a key weakness of the linear control approach thanks to the explicit constraint handling that always ensures the control action effectiveness. Simulation experiments in a validation case and a model-specific for a bus rapid transit line in Curitiba, Brazil, showed a reduced holding time and improved resilience, delivering more than 20% reduction in delay time accounting for the on-board and station delays.

The coronavirus disease 2019 (COVID-19) pandemic has had a rapid and significant effect on human mobility because of the travel restriction to slow the spread of the infectious disease. However, the impact caused by COVID-19 is not the same for all modes of transportation. In previous studies, public transport has shown the greatest decline compared with other modes, and bike-sharing systems have been less affected by COVID-19 than public transport. This study aims to investigate the impact of COVID-19 on bike-sharing systems in detail over a longer period than previous studies to determine the changes in the ridership and usage patterns of bike-sharing systems depending on the circumstances related to COVID-19. This study found that bike rentals for leisure purposes rather than for means of transportation have increased during the COVID-19 pandemic, which tends to be more distinct during outbreaks. Moreover, it was also shown that the status of COVID-19 and the strong social distancing affected bike rentals, and the effects of some factors related to bike-sharing ridership on bike rentals have significantly changed because of the change in the mobility patterns.