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Bus electrification plays a crucial role in advancing urban transportation sustainability. Battery Electric Buses (BEBs), however, often need recharging, making the Problem of Locating and Allocating Charging Equipment for BEBs (PLACE-BEB) essential for efficient operations. This study proposes an optimization framework to solve the PLACE-BEB by determining the optimal placement of charger types at potential locations under the stochastic charging demand. Leveraging the existing stochastic location literature, we develop a Mixed-Integer Non-Linear Program (MINLP) to model the problem. To solve this problem, we develop an exact solution method that minimizes the costs related to building charging stations, charger allocation, travel to stations, and average queueing and charging times. Queueing dynamics are modeled using an M/M/s queue, with the number of servers at each location treated as a decision variable. To improve scalability, we implement a Simulated Annealing (SA) and a Genetic Algorithm (GA) allowing for efficient solutions to large-scale problems. The computational performance of the methods was thoroughly evaluated, revealing that SA was effective for small-scale problems, while GA outperformed others for large-scale instances. A case study comparing garage-only, other-only, and mixed scenarios, along with joint deployment, highlighted the cost benefits of a collaborative and a comprehensive approach. Sensitivity analyses showed that the waiting time is a key factor to consider in the decision-making.

This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming, we generate bus blocks that accommodate electric vehicles (EVs), ensuring successful completion of each block while considering recharging requirements between blocks and during off-hours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP, and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints. A case study conducted reveals that nearly 100% electrification for Chicago, IL and Austin, TX transit buses is viable yet requires 1.6 EVs at 150-mile range per diesel vehicle., Comment: 23 pages

A sustainable solution to negative externalities imposed by road transportation is replacing internal combustion vehicles with electric vehicles (EVs), especially plug-in EV (PEV) encompassing plug-in hybrid EV (PHEV) and battery EV (BEV). However, EV market share is still low and is forecast to remain low and uncertain. This shows a research need for an in-depth understanding of EV adoption behavior with a focus on one of the main barriers to the mass EV adoption, which is the limited electric driving range. The present study extends the existing literature in two directions; First, the influence of the psychological aspect of driving range, which is referred to as range anxiety, is explored on EV adoption behavior by presenting a nested logit (NL) model with a latent construct. Second, the two-level NL model captures individuals' decision on EV adoption behavior distinguished by vehicle transaction type and EV type, where the upper level yields the vehicle transaction type selected from the set of alternatives including no-transaction, sell, trade, and add. The fuel type of the vehicles decided to be acquired, either as traded-for or added vehicles, is simultaneously determined at the lower level from a set including conventional vehicle, hybrid EV, PHEV, and BEV. The model is empirically estimated using a stated preferences dataset collected in the State of California. A notable finding is that anxiety about driving range influences the preference for BEV, especially as an added than traded-for vehicle, but not the decision on PHEV adoption., Comment: 27 pages, 3 figures, 5 tables

Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger equipment costs were found to be the most significant component in the objective function, leading to a substantial reduction in cost when decreased. VOT costs exhibited a significant decrease with rising energy costs. An increase in VOT resulted in a notable rise in the number of fast chargers. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible.

With the prospect of next-generation automated mobility ecosystem, the realization of the contended traffic efficiency and safety benefits are contingent upon the demand landscape for automated vehicles (AVs). Focusing on the public acceptance behavior of AVs, this empirical study addresses two gaps in the plethora of travel behavior research on identifying the potential determinants thereof. First, a clear behavioral understanding is lacking as to the perceived concern about AV safety and the consequent effect on AV acceptance behavior. Second, how people appraise the benefits of enhanced automated mobility to meet their current (pre-AV era) travel behavior and needs, along with the resulting impacts on AV acceptance and perceived safety concern, remain equivocal. To fill these gaps, a recursive trivariate econometric model with ordinal-continuous outcomes is employed, which jointly estimates AV acceptance (ordinal), perceived AV safety concern (ordinal), and current annual vehicle-miles traveled (VMT) approximating the current travel behavior (continuous). Importantly, the co-estimation of the three endogenous outcomes allows to capture the true interdependencies among them, net of any correlated unobserved factors that can have common impacts on these outcomes. Besides the classical socio-economic characteristics, the outcome variables are further explained by the latent preferences for vehicle attributes (including vehicle cost, reliability, performance, and refueling) and for existing shared mobility systems. The model estimation results on a stated preference survey in the State of California provide insights into proactive policies that can popularize AVs through gearing towards the most affected population groups, particularly vehicle cost-conscious, safety-concerned, and lower-VMT (such as travel-restrictive) individuals., Comment: The initial version with the primary results is presented at Transportation Research Board 98th Annual Meeting Transportation Research Board

The COVID-19 pandemic is an unprecedented global crisis that has impacted virtually everyone. We conducted a nationwide online longitudinal survey in the United States to collect information about the shifts in travel-related behavior and attitudes before, during, and after the pandemic. The survey asked questions about commuting, long distance travel, working from home, online learning, online shopping, pandemic experiences, attitudes, and demographic information. The survey has been deployed to the same respondents thrice to observe how the responses to the pandemic have evolved over time. The first wave of the survey was conducted from April 2020 to June 2021, the second wave from November 2020 to August 2021, and the third wave from October 2021 to November 2021. In total, 9,265 responses were collected in the first wave; of these, 2,877 respondents returned for the second wave and 2,728 for the third wave. Survey data are publicly available. This unique dataset can aid policy makers in making decisions in areas including transport, workforce development, and more. This article demonstrates the framework for conducting this online longitudinal survey. It details the step-by-step procedure involved in conducting the survey and in curating the data to make it representative of the national trends.

The COVID-19 pandemic has impacted billions of people around the world. To capture some of these impacts in the United States, we are conducting a nationwide longitudinal survey collecting information about activity and travel-related behaviors and attitudes before, during, and after the COVID-19 pandemic. The survey questions cover a wide range of topics including commuting, daily travel, air travel, working from home, online learning, shopping, and risk perception, along with attitudinal, socioeconomic, and demographic information. The survey is deployed over multiple waves to the same respondents to monitor how behaviors and attitudes evolve over time. Version 1.0 of the survey contains 8,723 Wave 1 responses that are publicly available. This article details the methodology adopted for the collection, cleaning, and processing of the data. In addition, the data are weighted to be representative of national and regional demographics. This survey dataset can aid researchers, policymakers, businesses, and government agencies in understanding both the extent of behavioral shifts and the likelihood that changes in behaviors will persist after COVID-19.

Accident detection is a vital part of traffic safety. Many road users suffer from traffic accidents, as well as their consequences such as delay, congestion, air pollution, and so on. In this study, we utilize two advanced deep learning techniques, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), to detect traffic accidents in Chicago. These two techniques are selected because they are known to perform well with sequential data (i.e., time series). The full dataset consists of 241 accident and 6,038 non-accident cases selected from Chicago expressway, and it includes traffic spatiotemporal data, weather condition data, and congestion status data. Moreover, because the dataset is imbalanced (i.e., the dataset contains many more non-accident cases than accident cases), Synthetic Minority Over-sampling Technique (SMOTE) is employed. Overall, the two models perform significantly well, both with an Area Under Curve (AUC) of 0.85. Nonetheless, the GRU model is observed to perform slightly better than LSTM model with respect to detection rate. The performance of both models is similar in terms of false alarm rate., Comment: 13 pages, 4 figures,2 tables