Autonomous vehicles (AVs) represent a new, growing segment of transportation research. While there have been prior studies and deployments of AVs worldwide, full autonomy in bus transit has gained interest among researchers and practitioners within the last decade, which presents an opportunity to synthesize early trends. Therefore, the objective of this paper is to provide a review of the latest research on fully autonomous buses to summarize findings and identify gaps needing future research. Forty studies were reviewed in detail, and five main themes were identified, which are (1) technology deployment; (2) user acceptance; (3) safety; (4) social and economic aspects; and (5) regulations, policies, and legal issues. The results reveal that most prior studies have focused on technology development, and the area of regulation and policy would benefit from additional study. Noteworthy differences between research in Europe and the United States were also identified. In Europe, large funded projects involving real-world deployments have focused on user acceptance, security and safety, costs, and related legal issues, whereas in the United States, research has primarily concentrated on simulation modelling with limited real-world deployments. The results of this review are important for policy-makers and researchers as AV technology continues to evolve and become more widely available.

Recently, it has become common practice for transit operators to provide real-time information (RTI) to passengers about the location or predicted arrival times of transit vehicles. Accompanying this is a growing body of literature that aims to assess the impacts of RTI on transit passenger behaviour and perceptions. The main objective of this research is to compile a literature review of studies that assess the passenger benefits of RTI provision. The results suggest that the primary behavioural changes associated with providing RTI to passengers pertain to decreased wait times, reductions in overall travel time due to changes in path choice, and increased use of transit. RTI may also be associated with increased satisfaction with transit service and increases in the perception of personal security when riding transit. A second objective of this review was to identify areas for future research based on remaining gaps in the literature; two keys areas that were identified are assessing actual behavioural changes of path choice of transit riders and conducting cost–benefit analyses post implementation of RTI systems. The results of this study have immediate implications for public transit operators considering implementation or expansion of RTI systems and researchers seeking topics for future investigation.

In traffic operations, the aim of transportation agencies and researchers is typically to reduce congestion and improve safety. To attain these goals, agencies need continuous and accurate information about the traffic situation. Level-of-Service (LOS) is a beneficial index of traffic operations used to monitor freeways. The Highway Capacity Manual (HCM) provides analytical methods to assess LOS based on traffic density and highway characteristics. Generally, obtaining reliable density data on every road in large networks using traditional fixed location sensors and cameras is expensive and otherwise unrealistic. Traditional intelligent transportation system facilities are typically limited to major urban areas in different states. Crowdsourced data are an emerging, low-cost solution that can potentially improve safety and operations. This study incorporates crowdsourced data provided by Waze to propose an algorithm for LOS assessment on an hourly basis. The proposed algorithm exploits various features from big data (crowdsourced Waze user alerts and speed/travel time variation) to perform LOS classification using machine learning models. Three categories of model inputs are introduced: Basic statistical measures of speed; travel time reliability measures; and the number of hourly Waze alerts. Data collected from fixed location sensors were used to calculate ground truth LOS. The results reveal that using Waze crowdsourced alerts can improve the LOS estimation accuracy by about 10% (accuracy = 0.93, Kappa = 0.83). The proposed method was also tested and confirmed by using data from after coronavirus disease 2019 (COVID-19) with severe traffic breakdown due to a stay-at-home policy. The proposed method is extendible for freeways in other locations. The results of this research provide transportation agencies with a LOS method based on crowdsourced data on different freeway segments, regardless of the availability of traditional fixed location sensors.

Means-based reduced-fare programs can help address transit rider fare equity. The objective of this study is to synthesize the current state of reduced-fare programs for low-income transit riders. The reduced-fare programs of the 50 largest transit agencies in the United States were examined and agencies with programs for low-income riders were compared based on three dimensions: eligibility and enrollment, fare media and discount pricing, and estimated transit expenditure by eligible riders. The results reveal that 17 of the 50 largest transit agencies have low-income reduced-fare programs. Of these, 14 agencies administer the programs themselves, while three use partnerships with social service organizations to administer them. Additionally, nine of the 14 agencies that administer their own programs provide a 50% discount on fares and require participants to have an income at or below 125% to 200% of the federal poverty level for eligibility. Using a method developed to evaluate the “fare burden” of transit riders with different income levels, it was determined that low-income reduced-fare program participants at the income eligibility threshold typically spend an estimated 2% to 6% of their annual income on transit, although very-low-income people may need to spend much higher shares of their incomes on transit fares. These results indicate that agencies may need to reevaluate the structure of their existing low-income reduced-fare programs and implement tiers of discounts to ensure that fare equity is being extended to all riders.

Demand Response Transit (DRT) services (e.g., dial-a-ride) play a crucial part in supporting transportation systems in small urban and more rural areas. However, the exploration of DRT trip patterns and accessibility, particularly the differences tied to demographics such as gender and age, remains an underdeveloped field. This study begins to fill this research gap by statistically and spatially analyzing real-world DRT trip data from Tennessee. DRT trip purposes were identified based on origin and destination land uses. Statistical analysis was conducted to evaluate DRT travel patterns for passengers of different demographic groups, particularly women and the elderly. Spatial analysis identified areas with higher DRT trip demand and limited DRT accessibility as potential essential destination deserts. The results show that women took more DRT trips across all purposes (e.g., Home-Healthcare, Home-Home, and Home-Leisure) except for Home-Work. About 36.8% of the DRT trips were made by the elderly, primarily for Home-Healthcare trips (67%), and they preferred shorter DRT trips. Spatial analysis revealed disparities in potential essential destination deserts for the elderly and females, as well as differences in possible deserts by trip purpose (e.g., healthcare-related trips). This study contributes to the literature by proposing a methodological framework for assessing DRT travel patterns and accessibility, which has been excluded from most of the prior literature on accessibility.