Transit Vehicles Research Articles

Changes in Fuel Prices and the Use of Public Transport: Insights from the European Union following the Invasion of Ukraine

Rising fuel prices are known to affect public transport (PT) demand. However, the impact of the oil market inflation and subsequent energy crisis following the 2022 Russian invasion of Ukraine on mobility behaviour in Europe has received little attention. This study examines shifts in fuel prices and PT mobility across 25 Europe-an Union countries between February and May 2022. Weekly variations in PT mobility were correlated with diesel and gasoline prices, and a multiple linear regression was conducted to assess the influence of contextual factors in explaining variations in PT usage. The results indicate a significant increase in PT mobility across all countries after February 2022, which was positively associated with rising fuel prices. Approximately 84% of the variance in mobility was explained, with more pronounced increases observed in countries characterised by lower telecommuting prevalence, higher housing costs relative to household income, greater price differentials between gasoline and diesel, lower adoption of alternative fuels, and higher motorisation rates. These findings suggest that the surge in fuel prices, driven by the 2022 energy crisis, may have stimulated an increase in transit ridership.

Forecasting public transit ridership amidst COVID-19: a machine learning approach

Forecasting public transit ridership amidst COVID-19: a machine learning approach

Research on friction stir welding technology of urban rail transit car body

Abstract In this paper, the principle and technical characteristics of friction stir welding are introduced. According to the form of the side wall of urban rail transit vehicles, a friction-stir welding joint structure with convex is proposed. In order to verify the performance of the joint, the joint tensile strength test and the static strength test of the car body are carried out. The results show that the designed friction stir welding joint has high smoothness and excellent performance, fulfilling the requirements for the standard EN12663.

Analysis of the Causes of Failures of the Air Supply System of a Ford Transit Vehicle with a Euro 5 Diesel Engine

Analysis of the Causes of Failures of the Air Supply System of a Ford Transit Vehicle with a Euro 5 Diesel Engine

The influence of ultrasonic vibration on the microstructure and properties of laser-cladded Fe-Ni-Ti composite coatings

To enhance the service life of brake discs for rail transit vehicles, an experimental platform for ultrasonic-assisted laser cladding was established. Single-layer and multi-layer overlapping Fe-Ni-Ti coatings were prepared on the surface of 316 L steel with and without ultrasonic assistance. The study investigated the influence of ultrasonic vibration on the microstructure and residual stress of composite coatings, and conducted tests on hardness, frictional wear, and corrosion resistance of the coatings. Results showed that ultrasonic vibration can refine grain structures.Under ultrasonic assistance, residual tensile and compressive stresses of the multi-layer cladding decreased by 12 % and 13 %, respectively. Under ultrasonic assistance, the average microhardness of single-pass samples increased to 515 HV, while the average microhardness of multi-pass overlapping samples increased to 460 HV. After adding ultrasonic vibration, the wear rate of single-layer samples decreased by 18.1 %, and that of multi-layer samples decreased by 12.9 %. Electrochemical impedance spectroscopy shows that under the action of ultrasound, the composite coating has better corrosion resistance performance.

Application of Historical Comprehensive Multimodal Transportation Data for Testing the Commuting Time Paradox: Evidence from the Portland, OR Region

There have been numerous theoretical and empirical transportation studies contesting the stability of commuting time over time. The constant commuting time hypothesis posits that people adjust trip durations, shift across modes, and sort through locations, so that their average commuting time remains within a constant budget. There is a discrepancy between studies applying aggregate analysis and those using disaggregate analysis, and differences in data collection may have contributed to the varying conclusions reported in the literature. This study conducts both aggregate and disaggregate analyses with two travel surveys of the Portland region. We employ descriptive analysis and t-tests to compare the aggregate commuting times of two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level to examine whether the stability of the commuting time remains after substantial changes in the transportation and land use systems. Our study indicates that the average commuting time, along with the average commuting distance, increased slightly, as the mode share shifted away from driving during the examined period. The growth in shares of non-driving modes, which are slower than driving, coupled with an increased travel distance, contributed to the small increase in the average commuting time. Our analysis also indicates that the average travel speed improved for transit riders as well as drivers, contradicting earlier research that claims that public transit investment has worsened the congestion in Portland.

Pantograph fault prediction of urban rail transit vehicles based on edge feature extraction and detection

Nowadays, as an indispensable part of urban infrastructure, urban rail transit (URT) vehicles have also developed rapidly. A large amount of manpower, material resources, and financial resources need to be invested in the construction process of URT. For URT vehicles, research on more accurate fault prediction methods can save a lot of maintenance costs and improve the reliability of URT construction. As an important electrical equipment for urban rail transit vehicles to obtain electric energy from the catenary, the operation of rail transit vehicles puts forward higher performance requirements for the pantograph. For solving the problems of low accuracy of fault prediction, over reliance on practical experience and high cost of fault prediction in the application of traditional URT vehicle pantograph fault prediction model. Combining sensor network and artificial intelligence algorithm, this paper analyzed the traditional rail transit vehicle pantograph fault prediction model, and verified it through comparative experiments. Through the comparative analysis of the experimental results, this paper can draw a conclusion that compared with the traditional rail transit vehicle pantograph fault prediction model, the rail transit vehicle pantograph fault prediction model has higher fault prediction accuracy, less model response time, lower risk of pantograph failure, higher model application satisfaction, and the accuracy of fault prediction increased by about 6.6%. The rail transit vehicle pantograph fault prediction model can effectively improve the accuracy of vehicle pantograph fault prediction, which can greatly promote the safety of URT and promote the intelligent process of URT.

A physical‒data-driven combined strategy for load identification of tire type rail transit vehicle

Tire load is one of the basic input parameters required for vehicle design and safety evaluation. Identifying the tire load with high accuracy is of great significance. However, the direct measurement of tire load is often high-cost and complex. Meanwhile, load identification solely based on physical measurement or data has great limitations of low accuracy and low robustness. This paper proposes a physical‒data-driven combined load identification strategy that includes an extended Kalman filter (EKF) and a data-driven correction model. These two models are configured in series. Derived from a physical state-space model of the vehicle dynamics, the EKF is used for the preliminary identification of the load. The data-driven model extracts the spatial and temporal characteristics of signals through the convolutional neural network and bidirectional gated recurrent unit, and then predicts the errors of the extended Kalman filter and corrects the identified results. The presented strategy is applied to an APM300 rubber wheel vehicle for load identification. The results have shown that the physical‒data-driven combined strategy can reduce the influence of parameter perturbation and improve the identification accuracy (6.4%). Thanks to organically combining the physical- and data-driven methods and integrating the rules and experience of the entire system, the strategy has strong generalization performance. Compared with traditional algorithms, the presented strategy could effectively reduce the error of load identification, improve adaptability under different operating conditions, and handle the measurement error of different noise levels, which are of practical application value in the engineering field.

No Longer Seat-Less in Seattle: The Role of Coordinated Transportation and Land Use Planning in Sustaining Transit Ridership through the Pandemic Recovery Period

Transit ridership had been decreasing in major cities across the United States prior to the Covid-19 pandemic, with Seattle as a notable exception. I examine the relationship between travel behavior and Seattle’s land use planning program in conjunction with transit improvements. I use econometric methods to analyze multiple waves of the Puget Sound Regional Council (PSRC) Household Travel Survey from 2014 to 2021. Living in one of Seattle’s Urban Villages is significantly associated with a higher likelihood of taking transit. This relationship holds during the pandemic time period and when controlling for self-selection.

Logit and Probit Models Explaining Mode Choice and Frequency of Public Transit Ridership among University Students in Krakow, Poland

The predictors of urban trip mode choice and one of its important components, public transit ridership, have still not been thoroughly investigated using case studies in Central Europe. Therefore, this study attempts to clarify the correlates of mode choices for commute travel and shopping, and entertainment travel to distant places, as well as the frequencies of public transit use of university students, using a wide range of explanatory variables covering individual, household, and socio-economic attributes as well as their perceptions, mobility, and the nearby built environment. The correlation hypothesis of these factors, especially the role of the street network, was tested by collecting the data from 1288 university students in Krakow and developing Binary Logistic and Ordinal Probit models. The results show that gender, age, car ownership, main daily activity, possession of a driving license, gross monthly income, duration of living in the current home, daily shopping area, sense of belonging to the neighborhood, quality of social/recreational facilities of the neighborhood, and commuting distance can predict commute and non-commute mode choices, while gender, daily activity, financial dependence from the family, entertainment place, quality of social/recreational facilities, residential self-selection, number of commute trips, time living in the current home, and street connectivity around home are significantly correlated with public transit use. Some of these findings are somewhat different from those regarding university students in Western Europe or other high-income countries. These results can be used for policy making to reduce students’ personal and household car use and increase sustainable modal share in Poland and similar neighboring countries.

Analysis of Fault Events in Rail Transit Vehicle Traction Systems Based on Knowledge Graph Reasoning

Fault text records provide detailed information on faults and handling steps, which are valuable for fault analysis. However, the different individuals’ recording styles can lead to ambiguities, and it is challenging to uncover potential fault associations in complex systems. To address these issues, this paper proposes a novel method for fault information extraction and analysis. Firstly, to tackle the problem of ambiguous boundaries between entities in fault texts, an integration algorithm is employed to accurately recognize fault entities considering contextual semantic features to establish fault knowledge graph (FKG). Then, a Relational Graph Convolutional Networks (RGCN) is improved with Graph Attention Networks (GAT) for sparse nodes caused by specific types of faults, to dynamically adjust the weight distribution of node learning, inferring potential links within the graph. The proposed method was validated using actual fault records from the traction system of rail transit vehicles, and contributes a reference for the mining and analysis of fault records in complex systems.

Predicting transit ridership using an agent-based modeling approach

Accurate ridership estimation is pivotal in the advancement of sustainable transit systems, be it for proposed or existing transit networks. A multitude of methods, including travel demand models, direct ridership models, and regression models, have been employed by practitioners and researchers to estimate ridership at both station and network levels. However, travel demand models, frequently utilized for new transit lines, exhibit intrinsic limitations due to their aggregate nature and complexity based on their types. Researchers have also identified deficiencies, such as the incapacity to capture small spatial resolutions and specific station characteristics, as these models are predominantly designed for large-scale analyses.This study aims to overcome these limitations by introducing a novel approach that utilizes three microscopic agent-based models to develop a travel demand modeling suite, providing a policy-sensitive forecasting tool. The suite comprises three agent-based models: SILO-MITO-MATSim. Validation of the model against previous year data is conducted, and projections are made for future years. The model is applied to estimate network-level ridership for the proposed ‘Purple Line,’ a light rail transit line planned by MDOT, MTA, Maryland, which will integrate with the Washington D.C. Metro, the fourth largest transit system in the USA, boasting an average daily ridership of half a million. The study’s findings indicate an anticipated ridership of approximately 31,230 passengers in the inaugural year of 2027. The proposed model offers a robust and policy-sensitive solution empowering decision-makers to make informed choices to support a sustainable transportation system.

Use of Galvanic Skin Response in a stated preference survey to assess factors influencing college student use of transit

ABSTRACT The COVID-19 pandemic significantly impacted all forms of travel. Transit ridership decreased drastically and has rebounded slowly compared to highway travel in the United States. This research explored a unique method to gain insight into the reasons behind the slow return to transit. Students were surveyed regarding their use of transit before, during, and after the pandemic. The study was conducted in a lab with a Galvanic Skin Response device, a computer with a webcam, and software to measure both recorded and self-reported stress levels in students as they answered questions regarding their transit use, stress levels, and fear of COVID-19. The Fear of COVID-19 and Perceived Stress Scale showed a significant influence on the change in transit use for a small proportion of students. The proposed methodology showed that these psychological scales and collecting GSR data may be valuable in predicting a traveler’s return to transit.

Short and mid-term effect of the streetcar on vehicle-vehicle (and vehicle-pedestrian) crash rate on the adjacent street

IntroductionIntroducing new public transit systems impacts the surrounding built environment, and changes in the built environment can affect travel behavior. Prior research has yet to thoroughly conduct a comprehensive exploration of the influence of new investments in public modes of transit, particularly streetcars, on motor vehicle crashes occurring on adjoining streets, considering other related factors. In particular, the difference between short-term and mid-term impacts of streetcars considering initial break-in periods has yet to be thoroughly conducted. This study focuses on the short-term and mid-term effects of the streetcar on total, injury, and pedestrian-involved vehicle crash rates on the adjacent street, considering traffic volume, traffic speeds, and traffic conflicts (transit ridership, pedestrian volume, and traffic policy). Data & MethodThis paper used the Utah Department of Transportation’s (UDOT) crash count, annual average daily traffic (AADT), iPeMS data, Utah Transit Authority’s (UTA) ridership, manually calculated pedestrian volume from Google Street View, and conducted interviews with UDOT’s experts. In the method, we used three quasi-experimental research designs: (1) before-after without a control group, (2) interrupted time series, and (3) before-after with a control group. In addition, to identify the cause of this impact, we examined multiple dimensions, including traffic volume, traffic speeds, transit ridership, pedestrian volume, and adjustments in traffic policy changes. ResultsAs a result, the establishment of the S-Line streetcar eventually led to a significant decrease in total (short: 11 %, mid: −15 %), injury (short: −9%, mid: −41 %), and pedestrian-involved (short: −25 %, mid: −43 %) crash rates on the adjacent street, especially after the streetcar was fully established (3 years after). In particular, injury and pedestrian-involved crash rates decreased significantly. Also, we found that increased drivers’ awareness and vitality of the street due to the increased transit ridership (short: 43 %, mid: 50 %), increased pedestrian volume (short: 35 %, mid: 75 %), and improvement of traffic signal on the adjacent street can be the main causes. Practical ApplicationsThe outcomes of this study are considered to help establish short-term and mid-term traffic policies that consider public transit improvements such as streetcars.

Evaluating temporal variations in access to multi-tier hospitals using personal vehicles and public transit: Implications for healthcare equity

Understanding healthcare accessibility, or the ability to access healthcare services, has significant implications for both individual well-being and community equity. However, existing studies seldom account for temporally varying factors such as traffic conditions and hospital schedules, resulting in miscalculation of accessibility. This study addresses this gap by introducing a framework that evaluates accessibility to multi-tier hospitals, factoring in both spatial and temporal aspects, using public transit (PT) and personal vehicles (PVs), and assesses its impact on horizontal and vertical equity. Implemented in Shanghai, China, we employ the Gaussian two-step floating catchment area method for accessibility quantification and utilize map APIs for dynamic travel time data. Our analysis reveals: (i) notable temporal fluctuations in healthcare accessibility, especially for PT, and their significant impact on both horizontal and vertical equity due to varying travel times and hospital schedules; (ii) larger disparities in higher-tier hospital accessibility compared to lower-tier ones; (iii) greater horizontal equity using PV-based accessibility and higher vertical equity using PT-based accessibility. These findings highlight the need to offer customized transit to healthcare facilities, expand telehealth services, incorporate equity in healthcare resource allocation, incentivize healthcare professionals to work in underserved areas, and develop outreach programs to improve accessibility and equity.

Evaluating accessibility benefits and ridership of bike-transit integration through a social equity lens

Integration of bike usage and transit services is an effective way to enhance accessibility for both transportation modes. Using high-resolution transit data, the study analyzes bike-transit multimodal accessibility and usage patterns through a social equity lens. Two types of accessibility increment are studied: bicycle increment to public transit – the benefits of using bicycle for transit riders, and transit increment to cycling – the merits of using transit for cyclists. Results show that bike-transit integration benefits both public transit riders and cyclists, expanding their accessible opportunities by up to 70% and enabling longer trips for cyclists while providing continuous benefits for public transit users. Meanwhile, better infrastructure significantly improves multimodal accessibility, resulting in more increment for public transit riders but less increment for cyclists. The paper also shows the spatiotemporal patterns of multimodal ridership. The research highlights disparities in bike-transit activities for Black communities due to inadequate local biking infrastructure. Black people majority neighborhoods enjoy less increment compared to other neighborhoods for shorter and very long trips; they also have disproportionately lower multimodal ridership despite much higher transit ridership and better transit access. Enhancing biking infrastructure in these areas can improve physical accessibility increment and promote social equity. The paper provides practical insights for transit planning, emphasizing the importance of connecting bike lanes and creating safer streets for cycling.

Incorporating equity into transit performance measures: A disaggregated bus route level approach

Marginalized communities usually experience inadequate public transport services in North America. Within these communities, buses play a vital role in everyday activity participation. Unfortunately, transport services that may advance equity by improving coverage and service for disadvantaged population groups and areas are often overlooked in transit planning since they usually underperform in ridership-based metrics.To address this problem, the Toronto Transit Commission (TTC) introduced an equity service policy in 2019 to enhance bus services in vulnerable neighbourhoods known as Neighbourhood Improvement Areas (NIAs). This policy involves augmenting observed ridership counts for buses serving NIAs to improve or protect services for these communities. Although this proposed measure can improve bus services in NIAs, its current form might exclude other equity-deserving riders (EDRs) living outside NIAs, increasing their accessibility barriers.Our study uses quantitative and spatial analytical methods to better understand the equity impacts of this service policy change. We propose moving from the aggregate neighbourhood-based approach using NIAs to a more comprehensive and disaggregated person-based approach. First, we create an alternate method that identifies EDRs at the bus stop level using a fusion of census and travel survey data. We then explore the differences in augmented ridership levels created by the two approaches and evaluate whether the augmented ridership levels are likely to result in meaningful service changes using existing TTC service standards.We found that 2 out of 3 transit riders in Toronto are equity-deserving, with 72% residing outside NIAs. Our approach reveals increased ridership on routes with many EDRs, which receive minimal augmentation under the NIA-based method. Conversely, if all riders in NIAs are considered EDRs, our approach shows a 19% ridership overestimation equivalent to 35,245 users. Based on these results, we recommend creating a dedicated equity tool with an individual-centric focus, identifying areas with low transit service and high EDR percentages. This tool could then be integrated as an equity perspective into annual bus service planning.

Are public transit investments based on accurate forecasts? An analysis of the improving trend of transit ridership forecasts in the United States

Historically, forecasts of travel demand on public transit infrastructures have been found to be optimistically biased. However, there has been a lack of data available for statistically significant analysis of factors affecting the accuracy. This paper analyzes the overall trend of transit ridership forecast accuracy in the US and contextualizes it with ridership trends based on the largest yet database of 164 large-scale transit infrastructure projects in the US. We find that transit ridership is about 24.6 % lower than forecast on average with about 70 % of the projects over-predicting ridership. Forecast accuracy varies by mode, service area characteristics, familiarity with transit, ramp up period, and time span. The accuracy has been getting better over the years, particularly after 2000 with the introduction of new analytical and evaluation tools as part of the Capital Investment Grants program. Projects that have been forecasted since 2000 have average ridership about 22 % lower than forecast, compared to about 52 % lower from pre-2000. The steadily improving accuracy, however, is offset by the unexpected decline in transit ridership since 2012. Advent of ride-hailing services and improved socio-economic trends that support auto-oriented cities have prompted this decline in ridership and have affected their forecasts as well. Despite the improving trend, we find that there remains substantial deviation in the outcomes from their forecasts. This points to the need of better scrutiny of model inputs and specifications and how they interact with the built environment to unearth the underlying reasons for inaccuracy. Planners and policymakers may make use of our results to advocate for considering the uncertainty around forecasts for any project and funding decision.

Effects of AR-HMD Interactive Interface Layout on Usability and Obstacle Avoidance in Subway Overhauls

The maintenance and overhaul of rail transit vehicle frames are vital for ensuring the safe and reliable operation of equipment. AR-based maintenance has been proven to be cost-reducing and effective. Few studies explored the application of AR-based maintenance on subway overhaul. In this study, 74 participants were selected to conduct the underground maintenance AR recognition and obstacle avoidance experiment. And two-factor ANOVA, independent samples t-test, and non-parametric test were used for data statistics. The results show that a high-opacity AR interface has better recognition but affects obstacle avoidance. Facing different types of obstacles, AR interfaces with different layouts have significant differences in terms of recognizability and obstacle avoidance effects. Compared with the list layout, the matrix layout AR interface will make more effort for workers. The findings provide useful references for designing the visual information layout of AR interfaces and conducting usability testing of AR interfaces in complex scenarios such as underground maintenance. Highlights This study assessed the effects of the AR-HMD interface on workers’ recognition of and ability to safely avoid obstacles in a subway maintenance environment. A high-opacity AR interface leads to better recognition but affects obstacle avoidance. List AR interfaces lead to better recognition but increase the probability of obstacle avoidance failure by 50% under block obstacles. For linear obstacles, layout has no significant effect on recognition or obstacle avoidance. A matrix layout AR interface is more effortful than the list layout AR for the workers.

Modal shift, environmental benefits and population exposure assessment on contingent transport policies

Transport policies targeting emission reduction and air quality improvement typically discourage private car use and promote shifts to mass transit (bus, metro). However, possible increase of travelers’ exposure while adapting to those policies have often been overlooked, which is addressed in this work via a case study of Heavy Pollution Episodes (HPEs) in Chengdu, China, using city-scale traffic camera and transit ridership data. By investigating traffic fluctuation and modal shift under prolonged license plate rationing for private cars and free or discounted fare for mass transit, we show that the reduction of small car traffic was marginal (up to 2.3 % in 24-hour periods), which, environmental benefits wise, is negligible according to air quality modelling. Meanwhile, over 3.23 % of car users switched to mass transit during Orange-level alerts, with a higher degree (by 19 %–39 %) of exposure to PM2.5. Our findings highlight the importance of exposure-centric decision making during HPEs.