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.
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.
The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.
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