Transit Vehicles Research Articles

What determines modal substitution between bike-sharing and public transit? Evidence from Columbus, Ohio during the COVID-19 pandemic

This study explores the relationship between bike-sharing and public transit trips before and after COVID-19 lockdowns, focusing on Columbus, Ohio. Using CoGo Bike Share trip data together with Automatic Passenger Counter (APC) data from the Central Ohio Transit Authority (COTA), we first identify bike-share trips and public transit ridership patterns during this period and classify bike-share trips as substitutive and complementary to public transit trips. We then employ binary logit models to analyze the determinants of substitutive trips. Some key findings are as follows. First, the COVID-19 outbreak caused decreases in bus ridership while increases in bike-sharing trips. Second, bike-sharing may compete with public transit for short distance trips. Third, bike-sharing trips are likely to substitute public transit within more intensive and congested public transit networks after controlling for various other factors. Fourth, trips made in different neighborhoods and land use reveal significant relationships with modal types. Lastly, lockdowns due to the COVID-19 pandemic led to substitutive trips substantially, particularly on weekdays.

Resolving the accessibility dilemma: Comparing cumulative and gravity-based measures of accessibility in eight Canadian cities

There is a lack of agreement regarding the theoretical framework that practitioners should use for accessibility assessment – a measure of transport and land-use systems performance. Cumulative measures are simple and easy to interpret, while gravity-based measures are more sophisticated, resourceful, and less intuitive approaches. As such, this study aims to investigate whether the estimates of a simple cumulative opportunity measure are significantly different from those made using advanced gravity-based measures to understand if the former can be a substitute for the latter in practice and if a certain threshold of travel time can be recommended for different regions. We estimated cumulative and gravity-based accessibility using decay-probability density functions, decay-cumulative density functions, Gaussian, and a Log-Logistic decay-cumulative density functions using census commuting flows, car congested travel time and public transit schedules from eight metropolitan regions across Canada – Toronto, Montreal, Vancouver, Edmonton, Quebec City, Winnipeg, London, and Halifax. These measures of accessibility were tested for correlation, and we found that a coefficient of approximately 0.90 is reached when the threshold to calculate cumulative opportunities accessibility is set to the average commute time for both low- and non-low-wage jobs accessibility analyses by transit and motor vehicles. The paper provides evidence to support the reliability of cumulative accessibility, facilitates its broader adoption for evaluation of transport and land use interactions in North American cities, as well as opens opportunities to advance the equitable distribution of transport system benefits.

Estimating road traffic impacts of commute mode shifts.

This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting.

Numerical simulation of heavy rail transit vehicle collision and analysis of collision force characteristics

The collision force of a heavy rail transit vehicle hitting a bridge pier is affected by many factors. In order to investigate the characteristics of the collision force when a heavy rail transit vehicle hits a bridge, the finite element software LS-DYNA is used to simulate the impact of a heavy rail transit vehicle on a bridge. The influence of the vehicle speed of heavy rail transit vehicles, the strength of bridge pier concrete, the spacing of pier stirrups, the ratio of pier stirrup reinforcement, and the eccentric distance between the vehicle and the bridge pier on the characteristics of the collision force. The simulation analysis results show that the total deformation frequency around the impact point of the bridge pier and the heavy rail transit vehicle decreases as the distance from the impact point increases, and the deformation frequency in the vicinity of the impact point is the largest. The speed of the heavy rail transit vehicle, the concrete strength of the bridge pier, the reinforcement ratio of the pier stirrup, the larger the eccentric distance between the vehicle and the bridge pier, and the smaller the spacing between the stirrups, the greater the collision force when the heavy rail transit vehicle hits the bridge pier.

Influence of Centrality Indices of Urban Railway Stations: Social Network Analysis of Transit Ridership and Travel Distance

This empirical study calculated the network centralities of urban railway stations in Seoul using Social Network Analysis (SNA) and analyzed the effect of this value on the number of passengers and average travel distance at each station. Network centrality can be calculated using various methodologies. In this study, reach, betweenness, and closeness were used as network centrality indicators. A regression model was used with the characteristics of building use in the station catchment areas as control variables. This methodology was compared to previous studies’ methods for obtaining stations’ topology values. The adjusted R2 values increased by 0.272 and 0.220 for the respective models, explaining the number of passengers and the average travel distance, respectively; the number of significant variables also increased in both models. These results indicate that defining the network centrality obtained through SNA as the station’s topology could improve explanations of the number of passengers and the average travel distance by 27.2% and 22.0%, respectively, compared to previous studies. While this methodology is not new, this study demonstrated the advantages of using SNA to determine the centrality indices of urban railway stations.

Will transit recover? A retrospective study of nationwide ridership in the United States during the COVID-19 pandemic

Although the COVID-19 pandemic highly impacted transit ridership as people reduced or stopped travel, these changes occurred at different rates in different regions across the United States. This study explores the impacts of COVID-19 on ridership and recovery trends for all federally funded transit agencies in the United States from January 2020 to June 2022. The findings of this analysis show that overall transit ridership hit a 100-year low in 2020. Changepoint analysis revealed that June 2021 marked the beginning of the recovery for transit ridership in the United States. However, even by June 2022, rail and bus ridership were only about two-thirds of the pre-pandemic levels in most metropolitan statistical areas (MSAs). Only in a handful of MSAs like Tampa and Tucson did rail ridership reach or exceed 2019 ridership. This retrospective study concludes with a discussion of some longer-term changes likely to continue to impact ridership, such as increased telecommuting and operator shortages, as well as some opportunities, such as free fares and increased availability of bus lanes. The findings of this study can help inform agencies about their performance compared to their peers and highlight general challenges facing the transit industry.

Travel behavior of unbanked, underbanked, and ultra-low-income transit riders in Ohio under covid-19

This research focuses on unbanked, underbanked and ultra-low-income (household income under $10,000) transit riders and their travel frequency before and during the COVID-19 pandemic. 1608 transit riders from Ohio and northern Kentucky were surveyed between October 2020 and April 2021. The study examines which demographic and socioeconomic factors are associated with being unbanked or underbanked, and whether the change in weekly trips per respondent after the onset of COVID-19 varied according to banked and income status. Logistic regression analysis revealed that the unbanked respondents are most likely to have household income less than $40,000, possess no high school degree, or live in public housing. Based on the OLS regression results, the unbanked and underbanked riders reduced their total weekly trips by about 17% and 22% respectively during COVID, compared to a 35% reduction by banked riders. All income groups in our sample reduced non-work trips to a greater extent than they did work trips. However, the responsiveness of weekly work trips within the ultra-low-income group (16%) during COVID was one of the smallest among all income groups. The analysis also revealed a significant interaction between banked status and income: unbanked riders earning between $10,000 and $30,000 cut back on work and medical trips by a significantly lower proportion, compared to banked riders in the same income categories. In line with existing research, the predicted decrease in trips during COVID compared to pre-COVID was generally smaller for the lower income groups in our sample, especially for work trips. These disparities in trip frequency after the onset of the pandemic suggest that different groups of transit users may have been impacted differently by service reductions and shutdowns.

下车信息对公交乘客精细化分类影响研究

The widely used bus one ticket system lacks off boarding information, and the passenger classification based on the dataset of one-ticket bus system needs to be refined to evaluate the classification effect. This study uses the large-scale bus ticket data with alighting information to compare and analyze the transit rider classification models with and without the alighting information to explore the impact of the missing alighting information on the fine classification of transit riders. The classification indexes and combined clustering method are adopted to refine the classification of bus passengers. A case study of Beijing conventional buses which contain boarding and alighting data of 28 consecutive days is presented and used to analyze the impact of bus alighting data and classification indexes on the transit rider classification results. The result indicates that both models successfully complete the fine classification of the riders and reflect the spatio-temporal characteristics of each type of riders. The model with alighting information is able to identify the distance-concerned type with few samples such as the long-distance transit riders accounting for 0.25%. The standard deviation of the average proportion of categories in the model without alighting information is much smaller than that in the model with alighting information. The results obtained from the model without alighting information are more balanced, and the regularity of various categories of passengers in multiple dimensions is significantly different, which can better reflect the macro characteristics of classification.

Evaluation of the short- and long-term impacts of the COVID-19 pandemic on bus ridership in Miyazaki City, Japan

We used a Bayesian structural time series (BSTS) model to evaluate the short- and long-term impacts of the coronavirus disease 2019 (COVID-19) pandemic on transit ridership. We accessed smart-card data from Miyazaki City, Japan. We defined attributes based on card types (commuters, students and elders) and aggregated attributes (high-frequency users and “frequently used bus-stop pairs”) and analyzed the differences between all users and the extracted groups. Among card types, the short-term impact on elders was almost identical to that of all users, however, the short-term impact of the pandemic on commuters was much smaller and that of students was much larger than that of all users. The long-term trend of commuters was less fluctuated than that of all users. The long-term ridership recovery of students was higher than that of all users. Among aggregated attributes, the short-term impact was smaller on “high-frequency users” than on all users: the decrease in ridership immediately after the appearance of COVID-19 was smaller among “high-frequency users” than among all users. The long-term recoveries in the riderships of the extracted subsets were slower than the recoveries of riderships of all users.

An Autonomous Modular Mobility Paradigm

In the United States, public transit vehicles have a very low average load factor (10.1–12.4%), resulting in an excessive waste of seat capacity and poor fuel economy per passenger mile served. This problem is gravely exacerbated by the COVID-19 pandemic, which at its peak had caused more than 70% reduction in transit ridership nationwide. On the other hand, the rapid uptake of e-commerce, also accelerated by the pandemic, has put tremendous pressure on last-mile delivery. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">co-modality</i> system that integrates transit services with last-mile logistics offers a promising solution to better utilization/sharing of vehicle capacity and supporting infrastructure. Here we show such a system may be implemented based on Autonomous Modular Vehicle Technology (AMVT). At the core of AMVT is the ability to operate a fleet of modular autonomous vehicles or pods that can be moved, stationed, joined, and separated in real time. Coupling modularity with autonomy is poised to enable co-modality and beyond. We describe an AMVT bimodality system that provides integrated public transit and last-mile logistics services with a fleet of pods and discuss relevant research challenges and opportunities, research approaches, and real-world adoption issues.

Deep Q‐Learning Network Model for Optimizing Transit Bus Priority at Multiphase Traffic Signal Controlled Intersection

When multiple bus vehicles send priority requests at a single intersection, the existing fixed‐phase sequence control methods cannot provide priority traffic request services for multiphase bus vehicles. In view of the conflict of multiphase bus priority requests at intersections, the priority vehicle traffic sequence is determined, which is the focus of this study. In this paper, a connected vehicle‐enabled transit signal priority system (CV‐TSPS) has been proposed, which uses vehicle‐infrastructure communication function (V2I) technology to obtain real‐time vehicle movement, road traffic states, and traffic signal light phase information. By developing a deep Q‐learning neural network (DQNN), especially for optimizing traffic signal control strategy, the public transit vehicles will be prioritized to improve their travel efficiency, while the overall delay of road traffic flow will be balanced to ensure the safe and orderly passage of intersections. In order to verify the validity of the model, the SUMO traffic analysis software has been applied to simulate real‐time traffic control, and the experimental results show that compared with the traditional timing signal control, the loss time of vehicles is reduced by nearly 40%, and the cumulative loss time per capita is reduced by nearly 43.5%, and a good control effect is achieved. In the case of medium and low densities, it is better than the solid scheduled traffic control scheme.

Feeder bus network design with modular transit vehicles

Modular transit vehicles have the potential to transform public transport systems. Indeed, the possibility of dynamically adjusting capacity by assembling and disassembling multiple modular pods allows for improved transit service flexibility and efficiency, reducing operator and passenger costs. As such, modular buses are considered particularly advantageous for settings with large variations in passenger demand, permitting en-route capacity adjustment and seamless transfers. In this context, this study presents a model for the design of a feeder bus network operated by autonomous modular buses, accounting for en-route transfers, and a Genetic Algorithm to solve the non-linear mixed integer programming problem arising. Results for a case study in Athens, Greece for an area served by three metro lines are presented and discussed.

轨道交通车辆基地防洪防涝措施研究

轨道交通车辆基地防洪防涝措施研究

Design of Intelligent Operation and Maintenance System for Urban Rail Transit Vehicles Based on IoT-5G Technology

Design of Intelligent Operation and Maintenance System for Urban Rail Transit Vehicles Based on IoT-5G Technology

Environment Pollution Analysis on Smart Cities Using Wireless Sensor Networks

One of the numerous independent sensing devices in this sector, WSNs can monitor physical and environmental parameters and thousands of applications in other disciplines. A large environmental change has numerous harmful repercussions on human beings, such as air pollution. Using a cloud that communicates through wireless sensor networks (WSNs), environmental factors such as pollution can be monitored with greater precision. If cities are considered smart, they will have to address air pollution, a significant environmental problem in cities. It negatively impacts human health, discouraging individuals from relocating to cities, resulting in a lack of economic development. This means that WSN nodes could monitor the pollution levels in and around the city and along major thoroughfares. In this paper, wireless sensor network-based environmental pollution analysis (WSN-EPA) has been suggested to reduce air pollution in a smart city. WSN nodes have been installed to continuously monitor the city’s air quality levels and the movement of public transit vehicles. Passenger vehicles and public transit buses return to their original locations after passing through stationary nodes across the city with data on air pollution particles, such as gases, smoke, and other pollutants, collected by sensors onboard. Nodes on public transportation, buildings, and automobiles wirelessly gather data from stationary nodes. Once the nodes returned to the pollution monitoring system, the data would be processed. The findings show that the suggested system is a visually effective environmental monitoring system.

Advancing investigation of automated vehicle crashes using text analytics of crash narratives and Bayesian analysis

Vehicle automation, manifested in self-driving cars, promises to provide safe mobility by reducing human errors. While the testing of automated vehicles (AVs) has improved their performance in recent years, automation technologies face challenges such as uncertainty of safety impacts in mixed traffic with human-driven vehicles. This study aims to examine the gaps in AV safety performance and identify what will be required on a preferential basis for AVs to guarantee an acceptable level of safety performance, especially in mixed traffic, by conducting a thorough analysis of crashes involving levels 2–3 AVs. Based on 260 AV collision reports from California from 2019 to 2021, this study extracts crash-related variables from crash records in a standardized form, crash locations, and, notably, crash narratives reported by AV manufacturers. This study untangles the complex interrelationships among pre-crash conditions, AV driving modes, crash types, and crash outcomes by applying a path-analytic framework with the frequentist and Bayesian approaches. Results show that 51.9 percent of crashes were rear-ends. Particularly, AVs become more vulnerable to rear-end collisions in the automated driving mode than in the conventional mode, given a crash. Additionally, the automated driving mode would not significantly affect the chance of a sideswipe collision, injury, or AV damage levels. Another interesting finding is that manual disengagement is more likely to happen when an AV interacts with a transit vehicle right before a crash occurs while having a negative relationship with injury crashes. Moreover, to reduce injury crashes, AVs would need more thorough testing to adapt to the critical roadway and infrastructure features such as intersections, ramps, and slip lanes; and roadway infrastructure would require improvements to support transportation automation. The risk factors identified in this study can be considered in AV safety assessment scenarios and future operations of mixed traffic. This study demonstrates that AV crash narrative data can be leveraged to improve knowledge of AV safety in mixed traffic.

Investigation of modal integration of bike-sharing and public transit in Seoul for the holders of 365-day passes

Bike-sharing has a mixed relationship with public transit—characterized by modal integration and modal substitution—and affects the public transit ridership. In previous studies, to investigate the relationships between public transit and bike-sharing, analyses based on either surveys or analysis based on trip record data have been conducted; thus, there have been several limitations to obtaining accurate and large data to reveal the relationships between public transit and bike-sharing. Unlike previous studies, owing to the mileage program of Seoul Bike, this study investigates the modal integration of bike-sharing and public transit using only trip records of bike-sharing integrated with public transit for the holders of 365-day Seoul Bike passes in detail using explanatory data and regression analyses. Additionally, considering that the relationship between bike-sharing and public transit might vary according to the mode of public transportation, this study investigates the spatio-temporal patterns of modal integration between bike-sharing and public transit, and the factors that influence the modal integration at the station level using binomial regression. The results indicate that the effects of various factors on the modal integration depend on the distance to the nearest subway station and the time when the trip occurs (weekdays or weekends and certain periods during the day). This study also suggests that the relationship between bike-sharing and public transit may vary according to the public transit mode. Although shared bikes seemed to be used in conjunction with both the subway and bus, the proportion of modal integration between bike-sharing and public transit among all trips using 365-day passes is likely to be affected more by the subway system than the bus system, especially for regular commutes. Furthermore, it is observed that shared bikes can be used to avoid overcrowded buses in connection with subways.

Structural optimization design of a bolster based on a simulation-driven design method

Abstract A simulation-driven design method which uses multiple optimization methods can effectively promote innovative structural design and reduce the product development cycle. Meanwhile, the sub-model technology which has more detailed simulation and optimization analysis can enormously improve the efficiency of modelling and solving. This study establishes a general workflow of structural optimization for a stainless-steel metro bolster by combining the simulation-driven design method and sub-model technology. In the sub-model definition phase, the end underframe sub-model which contains the bolster is obtained based on the whole car body finite element (FE) model, and the effectiveness of the end underframe sub-model is also proved. In the conceptual design phase, the topology path inside the bolster is obtained by the topology method and the optimized structure of the inner ribs inside the bolster is determined according to manufacturing processes and design experiences. In the detailed design phase, the thickness of each part of the bolster is determined by size optimization. The simulation analyses indicate that the requirements of static strength and fatigue strength are fulfilled by the optimized bolster structure. Besides, the weight can be reduced by 11.18% and the weld length can be decreased by 17.79% compared with the original bolster structure, which means that not only the lightweight design goal is achieved, but also the welding quantity and manufacturing difficulty are greatly reduced. The results show the effectiveness of the simulation-driven design method based on the sub-model technology in the structural optimization for key parts of rail transit vehicles.

Optimal selection of vehicle types for an electric bus route with shifting departure times

Transition to electrified transit vehicles has attracted a great public attention to achieve a greener public transport service. This work develops a methodology for multi-type electric buses (EBs) accommodating spatio-temporally imbalanced passenger demand to improve significantly the operating efficiency. However, a new complexity of this multi-type EB scheme in contrast to conventional diesel buses occurs because multi-type EBs are characterized by different capacities, limited driving ranges, decisions on recharging time and/or locations and high initial investment costs. This work proposes a new, integrated timetabling and vehicle scheduling problem with shifting departure time to attain an even-load timetable using different types of EBs at a route’s max-load stop, considering the use of fast/opportunity charging strategy. A genetic algorithm associated with right shifting of departure time has been developed to solve the resulting formulation, which is shown to be an NP-hard problem. A numerical example is used to illustrate the developed methodology, and a case study based on a scenario in the city of Dandong, China shows that the scheme of combining multiple vehicle types for a bus route not only can reduce the total cost but also bring out greater benefits than the single vehicle-type operation. From the operator viewpoint, it reduces passenger load surplus cost by approximately 11.2% for small Type A and 14.8% for large Type B. Moreover, the value of leftover pax unit cost has a significant effect on the selection of vehicle types, but has little effect on the number of trips or departures. This work shows that the higher the leftover pax unit cost is, the higher the number of large vehicle types is.

The impact of ridesourcing on equity and sustainability in North American cities: A systematic review of the literature

Research on ridesourcing has grown exponentially in recent years. This study details the results of a systematic review of 161 publications on ridesourcing that explore environmental sustainability and equity in North American cities. We identify five main areas of research. First, ridesourcing is associated with two modal shifts: a decline of the taxi industry and a more complicated association with public transit ridership. Second, several studies have documented workers' rights and the challenges of contingent labor. Third, demographic studies show associations between ridesouring and affluent riders, and spatial analyses indicate that ridesourcing may exacerbate existing patterns of inequity in cities. Fourth, ridesourcing has dubious claims to improve environmental sustainability: it has only a small effect on vehicle ownership, but it increases emissions associated with deadheading, and pooled services do not reduce vehicle miles traveled. Fifth, ridesourcing companies also tend to oppose regulatory responses and sharing data with potential regulators. The review concludes with suggestions on areas for future research.