Journal of Transportation Engineering, Part A: Systems

This research was undertaken to comparatively assess the unprecedented travel and activity conditions related to the onset of coronavirus disease of 2019 (COVID-19) in the US in the first half of 2020. In this effort, roadway traffic volumes were used to relate government directives for social separation and COVID-19 case progression in ten diversely populated and located states. Among the key contributions of the research were its illustration of the amount and time scale of public response to activity restrictions across the country and the general finding that overall, governmental directives, as reflected in rapid traffic decreases, likely served their purpose. Another key finding was that by June 1st, no state had completely returned to routine levels of travel. Combined, the results of this study illustrate the effect of governmental action with respect to the course of the virus, including how varied timings of responses reflected outcomes based on the levels of threat and characteristics of individual locations. It is expected that this paper will be of use to practitioners, governmental, and researchers to assess and develop plans for future similar major events and emergencies.

Traffic operations on two-lane rural roads differ substantially from those on divided carriageways due to vehicular interactions between traffic flows in the opposite directions. With the presence of grades and mixed (heterogeneous) traffic, traffic operations on two-lane highways become even more complex and challenging. The present study developed level-of-service (LOS) criteria for assessing the performance of two-lane rural roads with grades. Eight two-lane undivided study sections with grades varying from 1% to 8% were selected. The suitability of well-established performance measures such as percent time spent following (PTSF), number of followers per capacity (NFPC), follower density (FD), average travel speed (ATS), and percent of free-flow speed (PFFS) was evaluated. The results showed that the foregoing performance measures were not practically applicable for characterizing the operational LOS for two-lane rural roads with grades. A new performance measure termed density ratio (DR) was developed in the present study. The ATS, PFFS, and FD measures for different grades revealed no significant difference when visualized at similar DR ranges. Therefore, DR can be considered an effective measure for developing LOS criteria for such roads. The criteria were first developed using DR, ATS, and FD; subsequently, a design LOS was derived.

The coronavirus disease (COVID-19) pandemic has significantly disrupted transportation and travel patterns across the US and around the world. A significant driving factor in the significant reduction in travel in the US was the declaration of varying state-, county-, and city-level stay-at-home orders with varying degrees of reduction. However, it is still not clear how significantly any one of those orders contributed to the reduction in travel. This article looks at continuous count data from the Minneapolis–St. Paul, Minnesota, area to quantify the disruption in terms of reductions in traffic volume as well as the abnormality of the disruption to travel patterns. A nearly 50% reduction in total traffic volume is found, and regional trends both in reductions and the gradual recovery toward normal travel patterns are identified. Furthermore, key dates are identified that led to significant reductions in travel, and this disruptive event is compared with other significantly disruptive events in Minnesota for context. It is found that although the stay-at-home order was a significant milestone in the fight against COVID-19, traffic volumes had already reduced significantly before the order went into effect, and traffic volumes had recovered significantly before the order expired. These findings will be helpful in understand the impact of stay-at-home orders on future outbreaks of COVID-19 or other pandemics.

The operating speed model (OSM) is essential for geometric consistency analyses, but the formulation of OSMs for a complicated highway geometry is a challenging task. Highways passing through hilly terrain consist of a few geometric elements that differ from most elements because they are designed to tackle the topographical challenges. The existing practice for the development of OSMs cannot capture such distinctive elements because the data might be significantly biased to the geometric elements constituting the major part of the alignment. This paper aims to develop an OSM considering the interaction between the vehicle and complex highway geometry. The interaction between the vehicle and highway geometry can be captured through proper horizontal curve clustering. This study classifies horizontal curves into eight categories based on the turning direction of the horizontal curves (left and right) and the type of superimposed vertical alignment—hog, sag, upgrade, and downgrade. In this process, it is necessary to consider the bias in the highway geometry data resulting from the clustering. The present study develops the operating speed models considering the selection bias and heteroscedasticity in the data collected from a two-lane undivided rural road passing through hilly terrain. The findings from this study indicate that the consideration of curve clustering and selection bias resulted in improved operating speed models. Gradient at approach tangent ($G_1$), curvature change rate ($CCR$ and ${CCR}_S$), length of the vertical curve ($L_V$), length of approach and exit tangents ($L_{at}$ and $L_{et}$), and the interaction of radius with lane width ($R\times LW$) were found to be significant in the operating speed modeling. The design consistency analysis highlights that there exists a statistically significant difference in the geometric design consistency estimated using the proposed approach compared to the ordinary least-square method.

The emerging connected vehicle (CV) technology has introduced the opportunity to improve traditional traffic signal operation. Real-time vehicle trajectory information (location, speed, and heading) from CV technology can provide information about the nearby traffic conditions which potentially can be utilized for enhanced traffic signal control operation. However, implementation of CV technology still is impractical due to the lower penetration rate of CV-enabled vehicles on the road and the limited deployment of vehicle-to-infrastructure (V2I) communications. This paper developed an approach to use vehicle trajectory data with traditional traffic signal controllers to improve intersection operational performance, even with the limited use or absence of V2I communications. Two signal control algorithms, the delay-based algorithm (DBA) and the weighted delay-based algorithm (WDBA), were developed to demonstrate delay optimization at a signalized intersection. The intersection was modeled in Vissim microsimulation, and simulation scenarios were tested for various traffic demands. Analysis results showed that both proposed algorithms outperformed existing free timing operation, and statistically significant improvement was observed in terms of vehicle delay, stop delay, and queue length.

This study evaluated the mobility performance of pedestrian-bicycle crossing alternatives at continuous flow intersections (CFIs). CFI crossing types were compared with a standard intersection designed to provide an equivalent volume-to-capacity ratio. Three CFI crossing alternatives were tested, namely traditional, offset, and midblock crossings. A total of 12 alternative scenarios were generated by incorporating two bicycle path types and two right-turn control types. These scenarios were analyzed through microsimulation on the basis of stopped delay and number of stops. Simulation results revealed that the offset crossing alternative incurred the least stopped delay for all user classes, including motorized traffic. The traditional crossing generated the least number of stops for most route types. The midblock crossing can be considered as a supplement to the offset and traditional crossings depending on the specific origin–destination patterns at the intersection. The exclusive bicycle path performed better than the shared-use path in most cases. When compared with an equivalent standard intersection, aggregated results showed significant improvement for all CFI crossing types with respect to stopped delay, but the standard intersection had an equal or fewer number of stops for most routes investigated. Regarding the effect on vehicular movement, the lowest volume-to-capacity ratio and control delay at the main intersection was incurred by the offset crossing. Future research should incorporate pedestrian-bicyclists’ safety, comfort, and the relative effects of these crossing alternatives on additional vehicular performance measures.

High-occupancy toll (HOT) lanes have increasingly been adopted as a strategy to reduce congestion. While numerous studies have focused on the operations of HOT facilities, little is known about their safety performance. This study used a Bayesian network model to evaluate the safety performance of HOT facilities by identifying factors contributing to single-vehicle (SV) and multiple-vehicle (MV) crashes at these facilities. The study utilized 3 years (2012–2014) of data from four HOT facilities in California. Concrete barrier separation, wet road surface condition, nighttime condition, and weekend are major contributing factors for SV crashes. MV crashes are associated with pylon separation, weekdays, and daytime conditions. The maximum possible probability (79%) of a SV crash is expected to occur over the weekend, during nighttime, and on a wet road surface located in a rolling/mountainous terrain having double solid white line separation. Meanwhile, the maximum probability (93%) of a MV crash is expected to occur over the weekend, during the daytime, and on a dry road surface located in rolling/mountainous terrain having pylon separation. The study results can assist transportation officials in implementing policies that will improve the safety performance of HOT facilities.

In this paper, we evaluate the performance of a spatial multiresolution analysis (SMA) method, that behaves like a variable bandwidth kernel density estimation (KDE) method, for hazardous road segments identification (HRSI) and crash risk estimation(expected number of crashes). The use of spatial analysis for HRSI is well documented in the literature, especially with KDE methods. The proposed SMA, which is based on the Haar wavelet transform, is similar to the KDE method with the additional benefit of allowing the bandwidth to be different at different road segments depending on how homogenous the segments are. Furthermore, the optimal bandwidth at each road segment is determined solely based on the data by minimizing an unbiased estimate of the mean square error for Poisson data called Poisson’s unbiased risk estimate (PURE). We compare SMA with the state-of-the-practice crash analysis method and the empirical Bayes (EB) method, in terms of their HRSI ability and their ability to predict future crashes. The results indicate that SMA may outperform EB, at least with the crash data of the entire Virginia interstate network used in this paper. The SMA is computationally fast, does not require any data other than crash counts and their location, and is implemented in an Excel spreadsheet freely available for download. Therefore, it can be used for quick large-scale network screening before a more complex analysis that complements crash counts with other crash explanatory variables, such as traffic volume, is used for selected areas of interest.

Reducing the energy consumption of trains plays an important role in sustainable development of the subway system. When timetables are generated mainly considering passenger satisfaction, there are still some margins to optimize them to improve the energy efficiency. This paper slightly reoptimizes the planned timetable to reduce the energy consumption by improving the utilization of regenerative braking energy, while not changing the number of the subway trains. To this purpose, the authors innovatively propose a periodic or off-peak subway timetable optimization method to coordinate the arrivals and departures of all trains located in the same power supply section, so that the regenerative energy from braking trains can be more effectively utilized by other trains. First, we built a timetable optimization model with constraints derived from the planned timetable, which takes dwell time, the time shift of downbound timetable, and headway as decision variables, aiming to minimize the total energy consumption. Second, we designed a timetable optimization algorithm with two levels and high computational efficiency. In the upper level, the multiresolution-based traversal algorithm (MBTA) is proposed to search the feasible sets of time shift of the downbound timetable and the headway. And in the lower level, the decomposition coordination algorithm (DCA) is designed, in which the dwell times of the trains in the same power supply section are optimized in an optimization subproblem. Simulations are given using the real data of Beijing Metro Line 4 to evaluate the proposed method, and the results show that the proposed timetable optimization method can reduce energy consumption by 11.12%. In the random disturbance simulations, the proposed method showed robustness, which makes it possible to apply this method to real subway operations.

Automation in road public transportation has been facilitated by the emergence of infocommunication and vehicle technologies. The widely accepted definitions of automation level focus solely on the driving aspects of vehicles. However, automation covers even more fields: service planning and management, vehicle and traffic control, and passenger-handling functions. Therefore, the main outcome of this paper is a method to be applied for the handling of complex automation levels, which gives a more comprehensive assessment of new transport technologies and mobility services. Functions and function categories are all described. The paper defines four levels of automation, with a detailed description of each level and each function. The method can be applied efficiently to the analysis and comparison of services, thereby highlighting possible fields of development. Mobility services can be described by values only in a general and simplified manner. To demonstrate the applicability of the proposed method, existing services are assessed. Dial-a-bus service, taxi, ride sourcing, and car sharing are evaluated using the novel method, which provides an objective and comprehensive comparison of these services.

This study aimed to describe the operation of left-turn vehicles under the impact of exit lanes for left-turn (EFL) control. A microscopic traffic flow model was proposed, in which a multivariate logit model and a lane-change model were combined in the car-following model for lane selection and lane changing, respectively. The results of the plausibility validation showed that the model can describe car-following behavior in different road segments, lane selection behavior between normal left-turn lanes and lanes in the mixed-usage area, and the double stop-and-go phenomenon due to the existence of presignal control. The impacts of geometrical, traffic, and driver-proficiency conditions on vehicular delays, lane usage, and variance of velocity were explored. Effectiveness in improving running efficiency increases when drivers have higher proficiency in EFL.

In recent years, there have been research efforts to develop intelligent control approaches for connected and automated vehicles (CAVs) at merging highways to reduce traffic jams and the consumption of fuel. This paper addresses the problem of optimal coordination of CAVs at highway on-ramps to reduce fuel consumption. To formulate the problem of online optimal vehicle coordination, two optimization functions (acceleration and jerk) are analyzed to obtain a closed-form solution of each vehicle at a highway on-ramp. Real-world field traffic data from an on-ramp were used as the input of the optimization algorithm to validate the optimal fuel consumption of three different merging behaviors by considering different merging positions along the acceleration lane. The different merging behaviors considered were the merging vehicle entering the highway after the leader and follower vehicles, between the leader and follower vehicles, and in front of the leader and follower vehicles. To illustrate the performance of the proposed method, simulation results were given to find the optimal merging behavior with the least fuel consumption. The results showed that the optimal control functions improved fuel consumption by 55% compared with the base scenario.

Traffic congestion monitoring is a long-term concern in urban areas. However, due to the complex structure of urban road networks and large amounts of traffic data, it is necessary to find an efficient way to identify traffic congestion in urban areas. In the big data era, more and more researchers are using traffic data to model traffic road networks and to identify traffic dynamics. Through the grid mapping method, this paper proposes an efficient abstraction approach to simplify the structure of a road network and then to identify urban traffic congestion. Based on the probe vehicle trajectory data, the intersection nodes between trajectories and grid boundaries are clustered through the method of density-based spatial clustering of applications with noise (DBSCAN). Then, a new traffic performance index is established by the principal component analysis (PCA) method based on the traffic characteristics in the node network. With the case study in Beijing, the proposed method effectively identifies urban traffic congestion in spatial and temporal dimensions. The proposed method is map-independent because it is only based on the probe vehicle data without a digital map. The method is highly efficient for a large urban road network in practice because all the calculations are basic operations based on the cells. Moreover, the proposed method can distinguish the expressway and the frontage roads. The mean absolute error (MAE) is about $10\mathrm{km}/\mathrm{h}$ and the root-mean-square error (RMSE) is lower than $14\mathrm{km}/\mathrm{h}$. This method is expected to provide valuable spatiotemporal information for traffic engineers and managerial personnel to identify and relieve the traffic congestion problem.