Journal of Transportation Engineering, Part A: Systems

The uncertainty in walking and waiting transfer times at transit stops depends on the environmental conditions, vehicle schedule punctuality, and variations among individuals. Relying only on the mean values ignores such uncertainty and leads to improper transfer coordination. This paper provides a general framework to estimate the time distribution parameters of walking and waiting based on automatic-fare-collection data under the assumption that the distribution function is known. The proposed methodology contributes to the existing literature by providing a stochastic frontier model (SFM) that is adaptable to transit assignment models. The normal/exponential SFM is utilized to estimate distribution parameters based on the maximum-likelihood method. The methodology was applied to smart-card data from Japan to estimate the transfer walking- and waiting-time distributions between bus stops and rail stations. The results indicate a low disturbance in the observed data for walking time and that our assumptions regarding walking-time estimation were reliable. Furthermore, we quantified how the waiting time at a bus stop depends on variations in the service headway. These findings are expected to help in making more informed decisions to optimize transit schedules.

Reducing energy consumption without degrading the normal operation of metro trains and service quality has received increasing attention. Besides, fully automatic operation (FAO), for which no drivers and crew attendants are needed and all functions are controlled automatically, has been applied as a new generation train operation integrated control technology to achieve better performance. In this paper, a two-level energy-efficient optimization approach is proposed based on the characteristics of the FAO system. First, we formulate a single train trajectory optimization model and develop a genetic algorithm to calculate the optimal speed curves with variable running time. The Pareto frontier that represents the relationship between energy consumption and running time can be obtained. Second, we propose a sensitivity analysis method to distribute the total running time among different station segments based on the Pareto solutions in Level 1. Furthermore, the global optimal solution can be obtained. Finally, a case study of the Nanning Rail Transit Line 5 demonstrates that an optimal distribution of running time leads to extra energy savings compared to the original timetable.

Managed lanes (MLs) offer public infrastructure owners a key policy lever for reducing the financial burden of road expansion while managing induced travel demand. ML’s impact varies depending on the operational method adopted. Previous literature has focused on only one option’s optimum toll estimation, operational strategies, or impacts at a time. In this study, we provide the first detailed network-wide comparisons of MLs that include travel time, vehicle-miles-traveled (VMT), general-travel cost, fuel consumption, and emissions. We embed a toll-choice model within a four-step travel demand model considering drivers’ value of travel time (VOTT). The study uses existing high-occupancy-toll (HOT) lanes and the surrounding network in the Dallas-Fort-Worth, Texas, area as a case-study area. We find the following: (1) HOT lanes are the preferred option providing the highest travel-time savings; (2) the all-tolled option performs the best at the corridor level. It reduces corridor travel time by around 20%. However, lower traffic volume on tolled lanes generates lower overall network performance; (3) high-occupancy-vehicle (HOT) performs the worst and is similar to the do nothing option; and (4) both priced options, all-tolled and HOT, generate the smallest total emissions and fuel consumption.

This paper evaluated the effect of the COVID-19 preventive orders on arterial roadway travel time reliability (TTR). A comparative analysis was conducted to examine average travel time distributions (TTD), and their associated TTR metrics, before and during the COVID-19 pandemic. Travel time data for four urban arterial corridors in Nebraska, disaggregated by peak period and direction, were analyzed. It was found that in 2020, the average TTD mean and standard deviation values for all 16 scenarios were reduced by an average of 14.0% and 43.4%, respectively. The travel time index, the planning time index, the level of travel time reliability (LOTTR), and the buffer index metrics associated with these TTDs were reduced, on average, by 14.0%, 19.7%, 3.5%, and 35.0%, respectively. In other words, whether the test corridors were more reliable during the pandemic was a function of which TTR metric was used. The paper concludes by arguing for a fundamental change in how arterial TTR is measured and reported to different user groups.

Collecting traffic data and/or estimating and reporting annual average daily traffic (AADT) is important for planning, designing, building, and maintaining the road infrastructure. However, AADT is not available for most local functionally classified roads (referred to as local roads in this paper), which comprise a major proportion of the roads in the United States. The AADT of a local road depends on geospatial data such as road density, socioeconomic and demographic characteristics, and proximity to the nearest nonlocal road. The suitability of these explanatory variables for modeling local road AADT has not been widely explored, nor have methodological approaches been comprehensively compared in the past. Therefore, the focus of this research is on exploring geospatial and statistical methods and conducting a comparative assessment to estimate local road AADT. The AADT based on traffic counts collected at 12,899 stations on local roads in North Carolina during 2014, 2015, and 2016 was considered in model development and validation. The road, socioeconomic, and demographic characteristics based on the data gathered from the North Carolina Department of Transportation (NCDOT) for 2015 were considered as the explanatory variables. Five different modeling methods were examined and compared to estimate AADT on local road links. They include traditional ordinary least squares (OLS) regression, geographically weighted regression (GWR), and geospatial interpolation methods such as kriging, inverse distance weighting (IDW), and natural neighbor interpolation. The model development and validation results showed that the GWR model performed better compared with the other considered geospatial and statistical methods. The GWR model can better capture the effect of geospatial variations in the data, by geographic location, when estimating local road AADT. Local road AADT estimates help practitioners in planning and prioritizing road infrastructure projects for future improvements and air quality estimates, in addition to Highway Safety Improvement Program (HSIP) and Highway Performance Monitoring System (HPMS) reporting.