Journal of Transportation Engineering, Part A: Systems

In cities with bus rapid transit (BRT) systems, median bus-only lanes (MBLs) may extend to a circular intersection of two arterials. Making the MBLs traverse the central island is advantageous to BRT vehicles with less damage to the aesthetic advantage of the circular intersection. Hybrid signal priority and full actuation (HySOFA) is developed for isolated large four-leg circular intersections with MBLs. The objective of HySOFA is to expedite the operating speed of BRT vehicles with little negative impact on general vehicles. With appropriate application of transit and traffic facilities, the central island is traversable to BRT vehicles and the right-of-way is assigned in an upgraded version of the concurrent entering mode. Traffic detectors are placed at specific positions to detect BRT vehicle arrivals and sense general vehicle demand. Signals for general vehicles are fully actuated. Green extension, early green, and phase insertion are incorporated into the fully actuated logic. According to the simulation results during peak periods, HySOFA was quite effective in reducing the delay and number of stops for BRT vehicles at the circular intersection. The resulting cost to general vehicles was not large. After implementation of HySOFA, the circular intersection outperformed its conventional intersection alternative in serving general vehicles and pedestrians with less delay. BRT vehicles would not perceive substantially less delay and number of stops at the conventional intersection than at the circular intersection. When the MBLs extended to a large four-leg circular intersection, it would be a sensible decision for traffic engineers to make traffic improvements on the circular intersection instead of converting it to a conventional intersection.

Annual average daily traffic (AADT) is an important element for maintenance, safety, environmental analysis, finance, engineering economics, and performance management. Most previous studies were conducted to estimate AADT on the road segment instead of considering the intersection, and did not well consider the possible difference of AADT between the major road and minor road at intersections. The present research was conducted to develop a model that can estimate AADT for minor roads at intersections using available Highway Performance Monitoring System (HPMS) data owned by state departments of transportation and Census data. The performance of multiple linear regression, random forest, and neural network were compared in the study. Multiple regression analysis was selected to develop an estimation function for the minor road AADT. The AADT on the major road, the functional class of the major road and minor road, and the number of traffic lanes on the major road and minor road were selected as the input of the regression model, which was based on the statistical analysis. A multiple regression model with logarithmic transmission was selected for AADT estimation. The cross-validation showed the high accuracy of the developed model. The equation generated in this paper can be easily used by transportation agencies for AADT estimation on minor roads at intersections.

Vehicle headways in work zones are disaggregated into two types in this study—truck-involved (e.g., car-truck) and nontruck-involved (e.g., car-car) headways. Considering the possible effects of vehicle-following patterns and unobserved heterogeneity, we have developed a multivariate distribution model with random coefficients for each headway type using the work zone headway data from Singapore. Five headway distribution types, including the lognormal, normal, gamma, log-logistic, and Weibull, are considered. The lognormal distribution is found to be the best pattern for both headway types. Results show that, for any given distribution pattern, the distribution model with random coefficients could provide better goodness-of-fit than the model with fixed coefficients. It is further found that there is a bigger effect of work intensity on the truck-involved headways than the nontruck-involved headways. In addition, both types of headways decrease as the traffic flow or truck percentage increases. The likelihood ratio test results confirm the necessity of building a separate distribution model for each headway type in work zones.

Vertical alignment profiles are generally governed by geographical constraints; however, vertical alignment design is important, as it may significantly affect operating decisions such as travel time and tractive and braking energy consumption. This paper seeks to optimize vertical railroad alignment considering the costs of user, operation and construction. A genetic algorithm and a simulation model are jointly used to find solutions for vertical alignment characteristics and operating speed, and numerical cases are presented. The model developed can be applied to analyze both the operating efficiency of existing railroads and the feasibility of new railroads.

A hurricane can cause a lot of damage to highway networks and significantly affect the evacuation trip operations on the roads. A good understanding of network vulnerability can improve preparedness for an emergency evacuation. This paper presents a game theory–based approach to the analysis of network vulnerability under a hurricane evacuation. A zero-sum game was constructed between a router, which seeks the minimum-cost paths for the evacuation trips, and a tester, which maximizes the travel cost by disturbing the links. The distribution of the evacuation demand was elastic because the probability of selecting an evacuation destination was determined by the path risk and travel cost. In addition, the congestion effect was also considered. A solution strategy based on the method of successive averages (MSA) was adopted. Over a sample network, the proposed method was compared with three other methods for the network vulnerability analyses. The method was applied to the vulnerability analysis of a realistic evacuation network in the Mississippi Gulf Coast area.

This paper aims to model and analyze the changes in daily driving patterns of taxis in a disrupted market due to the boom in e-hailing services. This is accomplished by mining large-scale trajectory data sets obtained from a major taxi company in Shanghai. The taxi data set includes more than 0.8 billion trajectory points associated with over 12,000 taxis obtained in a period of 10 days (5 continuous weekdays in 2012 and 2016, respectively). The raw data were efficiently processed with the acceleration of high-performance computing. Creatively, the concept of information entropy together with principal component analysis were adopted to spatially delineate the gridded daily taxi driving trajectories. This helps describe the disordered taxi traces in comparable profiles across different spatial zones. Then, distinct patterns were extracted using the $k$-means clustering method. The proposed analysis pipeline has built a stable way of comparing driving patterns between different time periods after relaxing concerns about potential spreading of demand over time. By comparing statistical features associated with the identified clusters, the changes in daily taxi driving patterns in the context of the wide popularization of e-hailing services were quantitatively unveiled. This will be informative for taxi service providers revamping their business models when facing the opportunities brought by e-hailing apps and competition from other ride-sourcing vehicles in urban areas.