Transit Vehicles Research Articles

Influence of real and video-based experiences on stated acceptance of connected public transportation and autonomous vehicles in a transit mall: A hybrid choice modeling approach

Innovative technologies such as autonomous and connected vehicles can create a better transit mall, a street where only public transit vehicles, bicycles, and pedestrians are allowed. However, successful introduction of such technologies depends on user acceptance. To assess the potential acceptance of these technologies in the context of a new transit mall environment, it is crucial to provide an opportunity to experience them before they roll out. This study explores how real and video-based experiences of new technologies can affect public acceptance of new transit mall options in Hiroshima City, Japan, where trams and buses are significant modes of public transportation. We conducted a survey on the stated acceptance of new transit mall options with three groups of respondents: real experience, video-based experience, and no experience groups. The real-experience group self-selected themselves for the experiment, which potentially caused bias. Thus, we used a hybrid choice model with attitudinal variables (ethical, legal, and social concerns regarding emerging technologies, as well as risk-related factors) introduced to control for this bias. We found that both real and video-based experiences improved overall acceptance of new transit mall options, but there were differences in preferences between those who had only video-based experiences and those who had real experiences. We also found that prior knowledge of new transport services and infrastructure had a considerable influence on stated acceptance.

Smart Remote Transit Vehicle Monitoring with Emission Alert and Secured Access Using IOT

Abstract: The research paper focuses on addressing automobile theft by proposing a framework utilizing the Internet of Things (IoT), GPS, and GSM technologies. This system allows vehicle owners to track and monitor their vehicles at any time from anywhere, improving security against professional thieves. The IoT sends information about the vehicle's status to the guardian or owner, while GPS provides the vehicle's current location. In case of theft, the fuel supply can be gradually cut off by sending a message, disabling the vehicle, and updating data to the Google Firebase server. An RFID card is provided to start the vehicle, and an Android app is developed using the MIT AppInventor tool for monitoring maps and notifications. Additionally, emission levels are measured and updated to the cloud, and a panic button is included for driver assistance in emergencies. This proposed system enhances existing anti-theft measures while offering innovative features for better vehicle security and management.

A Study on the Viability of Adopting Battery Electric Vehicles in Bus Rapid Transit in Brazil Using the AHP Method

Public transport is essential to provide urban mobility, and cities need to offer a fast, comfortable, secure, and low-pollutant public transport mode to attract passengers and reduce private car use. Despite metros, trains, and light rail systems being desirable, the cost of implementation and low flexibility make bus services the best option for many cities. One solution for improving bus service systems is the implementation of bus rapid transit systems that are composed of buses running in segregated lines with pre-payment fares and level boarding platforms in stations. However, the main challenge of bus systems is the use of engines fueled by diesel, which is extremely polluted. One possible solution is the use of battery buses; but are they really feasible regarding daily operation cost? In this paper, we investigate the adoption of battery buses in comparison to diesel engines in a BRT system using municipal data from São Paulo, Brazil, as a reference. We collected operational data from the Sao Paulo city government transport agency and data from the literature to produce an analytic hierarchy process (AHP) model, which allowed us to compare both systems. The AHP model considered a triple-bottom-line perspective using social, economic, and environmental impact criteria. The result showed that the initial costs of acquisition can be a barrier, but in the long run, cities adopting battery buses in their BRT system can benefit from the effects of reduction in gas emissions and longer lifetimes of electric bus components. Moreover, the results showed that the adoption of battery buses in BRT systems depends on local government subsidies being implemented.

Transit Safety System Evaluation and Hotspot Identification Empowered by Edge Computing Transit Event Logging System

This paper discusses the importance of near-crash events and associated metadata as valuable sources for smart transit applications, such as surrogate safety measures for transit safety research. The STAR Lab at the University of Washington, sponsored by the Federal Transit Administration, has developed an edge computing system that processes onboard videos for near-crash detection. This paper builds on previous work by addressing two research questions: first, how to leverage the near-crash detection system to synthesize rich data sources on transit vehicles, and second, how to use the smart data hub to support transit operation and safety studies. The proposed procedures for event-based transit data collection, evaluation of commercial collision avoidance warning systems (CAWS) technologies, and transit safety hotspot identification are detailed. CAWS’ performance was benchmarked on four transit buses that were operated for almost a year in Pierce County, WA, U.S. Furthermore, the meta-information of near-crash events enables hotspot analysis and the identification of several exemplar clusters that can be explained by driver behavior and roadway geometries. The results of the experiments demonstrate the system’s promising performance and its applicability to addressing various transit operation questions.

Where can you go on the go? An Assessment of Public Restroom Access along Transit in San Diego

For people “on the go” like public transit riders, restroom access can be a valued amenity, especially as cities seek to curb greenhouse gas emissions by reducing auto-dependence. However, little research exists on public restroom access along transit lines. This study: 1) visualizes the distribution of available restrooms along trolley lines in San Diego, California; and 2) assesses the basic health-supportive features of available transit-adjacent restrooms. Results show low restroom availability, especially at stations with the highest volumes of riders. Results also reveal limited access and low quality of the restroom facilities that are available.

A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks

With the advancement of urban rail transit towards intelligence, the demand for urban rail transit communication has increased significantly, but the traditional urban rail transit vehicle–ground communication system has been unable to meet the future vehicle–ground communication requirements. To improve the performance of vehicle–ground communication, the paper proposes a reliable low-latency multipath routing (RLLMR) algorithm for urban rail transit ad hoc networks. First, RLLMR combines the characteristics of urban rail transit ad hoc networks and uses node location information to configure a proactive multipath to reduce route discovery delay. Second, the number of transmission paths is adaptively adjusted according to the quality of service (QoS) requirements for vehicle–ground communication, and then the optimal path is selected based on the link cost function to improve transmission quality. Third, in order to enhance the reliability of communication, a routing maintenance scheme has been added, and the static node-based local repair scheme is used in routing maintenance to reduce the maintenance cost and time. The simulation results show that compared with traditional AODV and AOMDV protocols, the proposed RLLMR algorithm has good performance in improving latency and is slightly inferior to the AOMDV protocol in improving reliability. However, overall, the throughput of the RLLMR algorithm is better than that of the AOMDV.

Getting groceries during the pandemic: How transit remained important despite the rise of e-delivery

BackgroundWe model the use of public transit to reach grocery stores and the use of online delivery services to get groceries, before and during the COVID-19 pandemic among people who used transit regularly prior to the crisis. MethodsWe draw upon a panel survey of pre-pandemic transit riders in Vancouver and Toronto. We conduct multivariable two-step tobit regression models that predict the likelihood of a respondent using transit as their primary mode for getting groceries before the pandemic (step 1) and then during the pandemic (step 2). Models are conducted for two survey waves, May 2020 and March 2021. We also conduct zero-inflated negative binomial regression models predicting the frequency respondents ordered groceries online. ResultsTransit riders over the age of 64 were more likely to use transit to reach groceries before the pandemic and more likely to continue to do so during the pandemic (wave 1, OR, 1.63; CI, 1.24–2.14; wave 2, OR, 1.35; CI, 1.03–1.76). Essential workers were more likely to continue using transit to reach groceries during the pandemic (wave 1, OR, 1.33; CI, 1.24–1.43; wave 2, OR, 1.18; CI, 1.06–1.32). Walking distance to the nearest grocery store was positively associated with using transit to get groceries pre-pandemic (wave 1, OR, 1.02; CI, 1.01–1.03; wave 2, OR, 1.02; CI, 1.01–1.03), and in May 2020 (wave 1, OR 1.01; (1.00–1.02). During the pandemic, people who stopped using transit to get groceries were less likely to have made zero online grocery purchases (wave 1, OR, 0.56; CI, 0.41–0.75; wave 2, OR, 0.62; CI, 0.41–0.94). DiscussionPeople still physically commuting to work were more likely to still use transit to get groceries. Among transit riders, older adults and those living far walking distances from grocery stores are more likely to use transit to get groceries. Older transit riders and those with higher incomes were also more likely to use grocery delivery services, while female, Black, and immigrant riders were less likely to do so.

Dial-a-ride problem with modular platooning and en-route transfers

Modular vehicles (MV) possess the ability to physically connect/disconnect with each other and travel in platoon with less energy consumption. A fleet of demand-responsive transit vehicles with such technology can serve passengers door to door or have vehicles deviate to platoon with each other to travel at lower cost and allow for en-route passenger transfers before splitting. A mixed integer linear programming (MILP) model is formulated to solve this “modular dial-a-ride problem” (MDARP). A heuristic algorithm based on Steiner-tree-inspired large neighborhood search is developed to solve the MDARP for practical scenarios. A set of small-scale synthetic numerical experiments are tested to evaluate the optimality gap and computation time between exact solutions of the MDARP using commercial software and the proposed heuristic. Large-scale experiments are conducted on the Anaheim network with up to 75 vehicles and 150 requests considering 378 candidate join/split nodes to further explore the potentials and identify the ideal operation scenarios of MVs. The results show that MV technology can save up to 52% in vehicle travel cost, 41% in passenger service time, and 29% in total cost against existing on-demand mobility services in the scenarios tested. Results suggest that MVs best benefit from platooning by serving “enclave pairs” as a hub-and-spoke service.

Assessment of Satisfaction Level for Existing Public Transport Systems using Machine Learning: A Case of Bhopal (India)

Most people are now highly dependent on private motorize travel because of the low Satisfaction level of public transit modes. This leads to poor public transit share in most of the cities in India. This study is intended to identifying the influencing parameters; those affect the satisfaction level of a PT mode, and also develop a model for assessing the Satisfaction Level of these public transport modes in Bhopal, India. A Machine Learning-based approach is used to analyze 1189 responses from the user. It classifies data in various dimensions that shape customers satisfaction among each mode of public transport (i.e. BRTS, Mini-Buses and Magic-Van service) and develops a model for evaluating the overall satisfaction of these PT modes in Bhopal. An On-board opinion surveys were done for identifying the parameters which influence the Satisfaction of these PT modes and also develop a model for evaluating the Satisfaction of these modes. Based on the literature, Delphi survey and opinion survey, eight parameters have been identified that influence the satisfaction level of these modes in Bhopal. Further by correlation matrix, most influencing parameters (key parameters) were considered amongst them for evaluating the satisfaction level for these modes. To determine the coefficient values acting from each of the respective elementary scores, we used a trained linear regression, multilinear regression, considering the classification of customers who assessed the performance of their satisfaction. This model will help to increase the Satisfaction of these PT Modes which result to increase the transit ridership in Bhopal. Adopted methodology in this study can help decision-makers to improve public transport services so that transit ridership can be improved.

Frequent public transit users views and attitudes toward cycling in Canada in the context of the COVID-19 pandemic

Several Canadian cities observed a shift from public transit use to private cars and active transport modes during the COVID-19 pandemic. At a moment where pre-pandemic public transit users are reconsidering their travel options, studies describing their attitudes toward cycling are lacking. Because most trips in urban areas involve short- and mid-range travel, cycling is seen as a promising environmentally sustainable means of transportation. This study aims to describe how pre-pandemic public transit users in Toronto and Vancouver view cycling, including their comfort with available infrastructure, cycling frequency, and perceived barriers to adoption. Data from the Public Transit and COVID-19 Survey, a web-based panel survey of over 3,500 regular transit riders in Toronto and Vancouver administered in May 2020 and April 2021 were analysed. Applying Geller's typology, 70% of participants could be classified as interested but concerned and one fifth as no way no how regarding their comfort levels toward cycling. Women were more likely to be no way no how cyclist type. Weather, lack of safe routes, and having to carry things were the main barriers to cycling in both cities. Our results give insight on who should be targeted by city initiatives aiming to promote changes toward more active modes of transportation. Further studies with a causal design are required to identify possible mitigating strategies to the main barriers to cycling.

DRL-based adaptive signal control for bus priority service under connected vehicle environment

Transit Signal Priority (TSP) strategy gives public transit vehicles privileges to pass through the intersection without stopping. Most previous studies have adopted the compulsory TSP strategy that considers to maximize the utility of public transportation, which is likely to reduce the efficiency of social vehicles. In this paper, we propose an Adaptive Transit Signal Priority (ATSP) model that considers the efficiency of both buses and social vehicles. This model has the Single Request Adaptive Transit Signal Priority (SR-ATSP) module and the Multi-Request Adaptive Transit Signal Priority (MR-ATSP) module. First, the intersection network is divided into grids based on the Discrete Traffic State Encoding (DTSE) idea to obtain the spatial information of vehicles. Then, in the SR-ATSP module, the Dueling Double Deep Q-learning Network (D3QN) algorithm is introduced to determine whether to implement the TSP strategy or not, considering the goal of minimizing the total passenger waiting time of buses and social vehicles. Based on the SR-ATSP, the MR-ATSP module introduces some rules to tackle the conflict from multiple priority requests of different buses. Simulation experiments based on an intersection in Nansha District, Guangzhou City are conducted on SUMO software. The results show that the proposed ATSP model can realize the priority treatment for of buses while reducing the waiting time of social vehicles by . It has superior performance for reducing the waiting time of buses and social vehicles than other widely-used TSP models.

Analysis Platform of Rail Transit Vehicle Signal System Based on Data Mining

Analysis Platform of Rail Transit Vehicle Signal System Based on Data Mining

Perimeter control in a mixed bimodal bathtub model

Perimeter control involves monitoring network-wide traffic and regulating traffic inflow to alleviate hypercongestion. Implementation of transit priority with perimeter control measures, which allow transit into a controlled area without queuing at the perimeter boundary, is an effective strategy in bimodal transportation systems. However, travelers’ behavior changes in response to perimeter control strategies, such as shifts in their departure times and transportation modes, have not been fully investigated. Therefore, important questions remain, such as the use of transit during perimeter control with transit priority. This paper examines the travelers’ behavior changes in response to perimeter control with transit priority in a mixed bimodal transportation system with cars and flexible route transit (FRT) vehicles. We model departure time and transportation mode choices in such a transportation system with hypercongestion and discomfort in FRT (called the mixed bimodal bathtub model). Initially, we investigate the properties of dynamic user equilibrium without perimeter control. Then, we study the equilibrium patterns during perimeter control with transit priority. Unlike existing works, we find that the number of FRT passengers decreases with time toward the desired arrival time and that FRT may not be used around the peak of rush hour. Furthermore, transit priority may not be sufficient to promote the use of FRT, and additional incentive such as subsidy for lower fares may be required to encourage FRT use during perimeter control. Finally, we show that operating many FRT vehicles does not always decrease the equilibrium cost, even under perimeter control with transit priority.

Video Observations and Large Accessible Transit Vehicle Operator Evaluations of Two Wheelchair Securement Systems

Wheelchairs need to be secured properly during transit to reduce the risk of passenger injury. New wheelchair securement systems have been developed in an attempt to provide increased usability and independence for passengers who need to use them. This field study evaluated newer wheelchair securement system usage through a retrospective review of on-board video surveillance recordings and large accessible transit vehicle (LATV) operators’ perceived usability of two newer wheelchair securement systems on fixed-route LATVs. Results showed that nonuse and misuse of securement systems were more prevalent with trips using the 3-point, forward-facing (3P-FF) tiedown system compared with trips using the semi-automated, rear-facing (SA-RF) system. While a majority of nonuse and misuse of the 3P-FF system was because of LATV operator error, the reasons for nonuse and misuse of the SA-RF system were more complex and mostly involved system design and transit policy issues. Operators most preferred using the 3P-FF system on the LATV rather than the SA-RF system. Findings support the need for further development and implementation of wheelchair securement with improved usability, innovative securement systems that can be operated independently by wheelchair users, and improved securement training for bus operators and wheelchair passengers.

The correlation between the city bus accessibility and transit ridership in Surabaya City, Indonesia

Suroboyo Bus and Trans Semanggi Suroboyo are public modes in Surabaya City, should be one way to serve city mobility in high number. However, the load factor of the city bus is below 50%. Therefore, it is necessary to evaluate the provision of public transportation, such as the city bus accessibility. This study uses the Public Transport Accessibility Levels (PTAL) method to analyze the city bus accessibility. The analysis result shows that most of the city bus accessibility levels are in the very poor category. This means that the city bus system could cover the city area at low level. Following that, the relationship between the accessibility level and the transit ridership is analyzed using correlation analysis. The results show that there are a significant correlation between the variable number of passengers and the bus stop accessibility index; frequency with the purpose of travel; accessibility index with the distance variable (distance of the departure stop from the origin location; distance of the destination stop to the destination location; distance traveled from the departure stop to the destination stop and total mileage); the frequency with the socioeconomic variables (age; average monthly income and driving license ownership). Based on these results, some recommendations are proposed to increase the accessibility level for influencing the demand of city bus.

Understanding the land use function of station areas based on spatiotemporal similarity in rail transit ridership: A case study in Shanghai, China

Understanding the land use function of station areas based on spatiotemporal similarity in rail transit ridership: A case study in Shanghai, China

Assessing Public Acceptance of Autonomous Vehicles for Smart and Sustainable Public Transportation in Urban Areas: A Case Study of Jakarta, Indonesia

This study examines the benefits and drawbacks of autonomous public transit vehicles among 210 Indonesians. Some 25% of respondents knew nothing about driverless vehicles, whereas 14% did. The average 5-point Likert scale response was 3.12 (SD = 1.05), indicating intermediate expertise. Some 42% of respondents used autonomous vehicle sources such as public transport, 47% had no experience, and 11% were doubtful. The survey items’ Cronbach’s alpha score is 0.873, indicating strong internal consistency and reliability. Most respondents supported the deployment of autonomous road vehicles for public transportation and said they would improve public transportation quality and accessibility. Technical issues and legal liabilities worried responders. The mean scores for the seven autonomous car benefits were similar, showing that respondents did not strongly prefer any benefit. After assessing the socioeconomic status and concerns, the study indicated that people who saw greater benefits were more tolerant of autonomous vehicles. Most respondents also wanted a clearer explanation of their legal responsibilities in case of an accident, thought human operators should play a major role in the future, and supported government trials of autonomous vehicles before their widespread usage. The study’s findings can help policymakers and stakeholders increase public acceptance of new transportation solutions such as autonomous vehicles, and improve future mobility safety and sustainability.

Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility

The unprecedented COVID-19 outbreak has significantly influenced our daily life, and COVID-19’s spread is inevitably associated with human mobility. Given the pandemic’s severity and extent of spread, a timely and comprehensive synthesis of the current state of research is needed to understand the pandemic’s impact on human mobility and corresponding government measures. This study examined the relevant literature published to the present (March 2023), identified research trends, and conducted a systematic review of evidence regarding transport’s response to COVID-19. We identified key research agendas and synthesized the results, examining: (1) mobility changes by transport modes analyzed regardless of government policy implementation, using empirical data and survey data; (2) the effect of diverse government interventions to reduce mobility and limit COVID-19 spread, and controversial issues on travel restriction policy effects; and (3) future research issues. The findings showed a strong relationship between the pandemic and mobility, with significant impacts on decreased overall mobility, a remarkable drop in transit ridership, changes in travel behavior, and improved traffic safety. Government implemented various non-pharmaceutical countermeasures, such as city lockdowns, travel restrictions, and social distancing. Many studies showed such interventions were effective. However, some researchers reported inconsistent outcomes. This review provides urban and transport planners with valuable insights to facilitate better preparation for future health emergencies that affect transportation.

Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study

COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea – a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators – from perspectives of trip frequency, spatial diversity, and travel range – to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors’ mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors’ transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.

Multimodal Intersection Signal Timing Considerations Framework, Performance Measures, and Case Study

Signal retiming is a cost-effective practice to improve intersection operations through periodic updating of the timing plans. Traditionally, signal retiming has focused on vehicular-based performance (e.g., reducing vehicle delay or number of stops), while often unintentionally ignoring the needs of other users, such as pedestrians, cyclists, and transit vehicle passengers. This unintended omission is further exacerbated by the signal timing tools used in the industry, which tend to primarily report vehicular metrics. To address these challenges, this research developed a multimodal signal timing framework along with easy-to-use performance measures. These performance measures include (1) vehicle delay, (2) crosswalk delay, (3) crossing stress, and (4) bus delay. To assess the approach effectiveness, the proposed framework and the developed tools were applied to 12 intersections along the North Glebe Corridor, in Arlington, VA, for the weekday a.m. peak. Analysis results showed that the proposed framework and tools can incorporate most multimodal considerations and address the limitations of the traditional vehicle-based approaches by quantifying multimodal tradeoffs at signals. With the proposed tools, the alternative timing plan developed for the a.m. peak yielded a 4.5% improvement in performance along the corridor, versus a degradation of 3.7% if only vehicle delay was considered in the analysis. Additional sensitivity analysis conducted along the corridor using various timing plan scenarios showed that the calculated values of the measures are accurately reflecting the anticipated effects of signal timing changes that aim to improve crossing stress for non-motorized users and/or reduce their delay at the intersection crosswalks.