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Urban rail transit is an integral part of public transit, and has been extensive built in China. Previous studies have proved that the spatial features are closely related to rail transit ridership, considering a fundamental role of short-term passenger flow forecast in the urban rail operation, it is meaningful to explore how these factors affect the prediction accuracy. This study aims to find a way to improve prediction accuracy by considering spatial features of stations based on deep learning. Therefore, a CNN-LSTM model capturing the spatial and temporal features was applied and Suzhou (China) was choosing as a case study to explore the influence of three spatial features, namely relative position, location, and land use, on the prediction accuracy. The predict model used can extract spatiotemporal features and accurately predict the citywide stations, and the results show that, for the relative position, the inbound and outbound flow prediction errors of transfer stations and middle stations are the lowest, respectively. As for locational features, the more distant the station is from the city center, the more accurate the results are. For stations where land use is dominated by work and living services, the predictions are more accurate. The error rate is higher for stations whose services are mainly tourism, transportation, and leisure services. This study's results can help operators predict the short-term passenger flow of target stations based on different demands and optimize their services on this basis. [ABSTRACT FROM AUTHOR]

Accurately understanding the travel demand of urban rail transit (URT) systems is crucial for effective operational management. Despite the recognition that the diversity in human activity patterns results in different travel demands, few studies have thoroughly investigated the heterogeneity among passengers and its impact on URT ridership. This study utilizes smart card data collected from the Beijing Subway to categorize passengers into four groups: tourist passengers, flexible commuters, regular commuters, and life‐oriented passengers, based on their spatiotemporal travel patterns. Furthermore, a Multiscale Geographically Weighted Regression (MGWR) model is employed to examine the relationship between station‐level ridership of URT and its determinants, including the built environment and station properties, for each passenger group. The results indicate that the influence of these determinants on station‐level ridership varies across passenger groups and spatial scales. For instance, regular commuters exhibit lower sensitivity to accessibility on workdays, whereas those unfamiliar with the URT network are more concerned about the bus accessibility in pedestrian‐ or bicycle‐unfriendly areas. Notably, for tourist and life‐oriented passengers, the stations significantly affected by population density are concentrated in areas with a higher proportion of elderly individuals. Conversely, for flexible and regular commuters, these stations are predominantly situated in areas associated with internet technology and scientific research. These findings are valuable for policymakers in designing strategies tailored to different passenger groups to balance trip demand and capacity, thereby improving URT services and promoting a sustainable urban environment. [ABSTRACT FROM AUTHOR]

Public transport systems worldwide experienced significant declines in usage during the COVID-19 pandemic due to lockdowns and work-from-home mandates. While numerous studies have examined these phenomena, there is still a need for empirical evidence that not only documents what occurred but also provides actionable insights for future transport planning. This study aims to enhance understanding of public transport passengers' mobility behaviors during different stages of the pandemic, using the Metropolitan Area of Porto, Portugal, as a case study. Automated Fare Collection data from 2020 were analyzed and compared with data from the pre-pandemic year of 2019. The analysis included temporal, spatial, spatio-temporal, and sociodemographic dimensions. Key patterns and trends identified include a rapid recovery of ridership post-restriction easing, homogenized daily travel patterns, varied impacts on different transport modes, and significant shifts in demographic travel behaviors. These findings highlight the resilience of public transport demand and suggest that adaptive scheduling, enhanced safety measures, targeted support for vulnerable groups, promotion of off-peak travel, investment in bus infrastructure, and encouragement of multi-modal transport are essential strategies. Implementing these strategies can help improve public transport planning and mitigate the adverse effects of future crises. [ABSTRACT FROM AUTHOR]

Station-level ridership is an important indicator for understanding the relationship between land use and rail transit, which is crucial for building more sustainable urban mobility systems. However, the nonlinear effects of the built environment on metro ridership, particularly concerning temporal heterogeneity, have not been adequately explained. To address this gap, this study proposes a versatile methodology that employs the eXtreme gradient boosting (XGBoost) tree to analyze the effects of factors on station-level ridership variations and compares these results with those of a multiple regression model. In contrast to conventional feature interpretation methods, this study utilized Shapley additive explanations (SHAP) to detail the nonlinear effects of each factor on station-level ridership across temporal dimensions (weekdays and weekends). Using Shanghai as a case study, the findings confirmed the presence of complex nonlinear and threshold effects of land-use, transportation, and station-type factors on station-level ridership in the association. The factor "Commercial POI" represents the most significant influence on ridership changes in both the weekday and weekend models; "Public Facility Station" plays a role in increasing passenger flow in the weekend model, but it shows the opposite effect on the change in ridership in the weekday model. This study highlights the importance of explainable machine learning methods for comprehending the nonlinear influences of various factors on station-level ridership. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Due to its flexibility and door-to-door service, taxis are an integral part of the urban transportation system. They have become an essential solution to the first/last mile problem. Even though much research has been conducted on the effects of built environment variables on taxi passengers' travel behaviors, few have accounted for the spatial heterogeneity embedded in multiscale spatial processes. This study applies multiscale geographically weighted regression (MGWR) to investigate the associations between taxi ridership and spatial contexts to address the gaps. The MGWR considerably improves modeling fit compared to the global OLS model by capturing the spatially varying processes at different scales. The results demonstrate the existence of strong spatial non-stationarity in the various built environment factors affecting the spatial distribution of taxi pick-ups and drop-offs. Specifically, increased residential density induces more taxi demand in areas with less access to public transportation than their surrounding units. Increasing bus coverage where bus coverage is relatively low may attract more commuters to adopt taxi plus bus mode for commuting. Road network density has a more substantial effect on taxi ridership in the south end of the city than in the north. The former is characterized by lower road density. This study reveals the complex relationships between the built environment and the distribution of taxi ridership at different spatial scales and provides valuable insights for transport planning, taxi resource allocation and urban governance. [ABSTRACT FROM AUTHOR]

A reasonable land use development around subway stations can balance the utilization rates of the subway system during peak and off-peak hours, thereby enhancing its service levels and operational efficiency. Analyzing the temporal distribution patterns of passenger flow and their influencing factors is crucial for determining the optimum ratio of each land use type surrounding metro stations. Thus, this paper employs principal component analysis (PCA) at first to investigate the temporal distribution of metro ridership, and identify their main patterns and factor loadings. Then, using geographically weighted regression, the study examines the spatial dependencies between the main component proportions and influencing factors, focusing on Xi'an subway stations. The results indicate that the temporal distribution of passenger flow can be decomposed into three principal components: the first representing commuting characteristics, and the second and third representing regulating functions. The overall distribution is a composite of these components in varying proportions. Residential and educational land uses primarily drive morning and evening peak flows, with residential land use in the city center and peripheral areas having a more pronounced effect compared to transitional areas. Conversely, commercial & office, healthcare, and recreational & park land mitigate peak flows and increase off-peak flows. External hub enhances passenger flow throughout the day, while industrial land use has negligible impact. [ABSTRACT FROM AUTHOR]

Airborne infections pose significant challenges to public transportation systems which can result in significant decline in ridership levels and financial stress for operators. This systematic review presents a comprehensive overview of measures and strategies employed by ground public transportation agencies to protect passengers and staff while ensuring the uninterrupted operation. This study also conducted a bibliometric analysis to provide insights into key topics, publication patterns, and major contributors in the field of airborne transmission research in public transportation. We have included studies published from January 2003 to June 2024, which reported measures and recommendations for managing public transportation to reduce virus transmission. Of the 2848 initially identified studies, 69 met our eligibility criteria. Our review identified four key strategies to prevent virus transmission in public transportation, including air quality improvement, cleaning, mask-wearing, and social distancing in vehicles and stations. While social distancing poses a significant challenge to public transportation, the integration of crowd management techniques and technology-driven information dissemination can provide effective strategies for managing capacity. The adoption of technology-driven solutions, such as efficient filtration systems, automated mask detection mechanisms, ultraviolet disinfection devices, and real-time passenger information, is required to implement these strategies effectively. Transportation agencies can utilize an airborne infection risk calculator during pandemics and beyond to assess and mitigate the risk of airborne transmission in various modes of transportation. Lessons from the Covid-19 pandemic underscored the need for developing advanced technologies to enhance passenger and staff safety in public transportation vehicles and stations. • Systematically reviewed countermeasures for airborne infection in public transport. • Highlighted the need for integrating advanced technologies to minimize infection transmission. • Key strategies included air quality improvement, sensitization, mask wearing, social distancing, and crowd management. • Future research on cost-benefit analysis and technological feasibility is necessary. [ABSTRACT FROM AUTHOR]

Understanding the associations between demographic groups' metro travel behaviors and the built environment is crucial for addressing automobile dependence and promoting transportation equity and reasonable urban construction. This study examines the nonlinear relationships and threshold effects of the built environment on the metro travel patterns of three groups (i.e., commuters, seniors, and students) by applying smart card data in Kunming, China. We select the optimal machine learning model—gradient boosting decision trees (GBDTs)—and consider various built environment attributes. Our findings indicate that: 1) built environment attributes universally have nonlinear and threshold effects on metro travel for all groups; 2) the collective contributions of density and diversity differ greatly across groups compared to other attributes; and 3) only a few built environment attributes have similar effect directions and degrees across all three groups, while most have unique effects on each group. The findings suggest metro station area planning strategies to promote metro use and transportation equity for different groups. • Analyze the relationship between the built environment and different metro travelers. • Apply machine learning methods to evaluate nonlinearity. • Built environment attributes universally have nonlinear effects on all groups' metro travel. • A few built environment variables have similar effect directions and degrees for all three groups. • The effects of most variables vary across groups. [ABSTRACT FROM AUTHOR]

There is no doubt that accurate short-term inbound metro ridership forecasting has always been a significant part in terms of intelligent transportation. Based on the excellent performance of deep learning models in dealing with processing time series data, a large number of researchers have adopted deep learning methods for short-term inbound metro ridership prediction. However, considering the non-linear and cyclical nature of inbound metro ridership, the prediction accuracy using only deep learning models is limited. To solve this problem, a hybrid prediction model combining Ensemble Empirical Mode Decomposition (EEMD), subsequences recombination, empirical wavelet transform (EWT) and long short-term memory (LSTM) network for inbound metro ridership which can greatly reduce the complexity of the original inbound metro ridership is proposed in this article. To prove the superiority of the proposed EEMD-EWT-LSTM-based model, we compare it with recent main forecasting models on three real datasets from the 2019 TIANCHI competition. The experimental results demonstrate that the reduction in WMAPE of the hybrid model almost reach to 10 percent compared to the non-hybrid models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The development of urban rail transit systems is progressing rapidly, and an increasing number of cities worldwide are either in the process or planning to construct urban rail transit systems. However, questions arise as to whether all cities truly require such systems and how to prevent these transit systems from becoming decorative rather than essential to urban development. This paper collates various antecedent conditions that may influence ridership in rail transit across 31 cities in China with operating rail transit. To this end, it uses two main methods, including necessary condition analysis and fuzzy-set qualitative comparative analysis methodologies. In this way, it explores the interactive effects of multiple factors such as the level of urban development, trends in urban development, ground-level road network congestion, urban spatial patterns (centrality), coverage of rail transit, and government focus on rail transit. The results show that no single antecedent condition can be a necessary condition for high ridership in rail transit. Rather, the effective combination of various antecedent conditions has elevated postimplementation ridership in a different means, same end manner. Moreover, the government's focus on rail transit development or its effective planning can increase postimplementation ridership in cities lacking some objective resource conditions. Furthermore, ridership in most cities remains stable after the initiation of rail transit. The findings imply that the development of urban rail transit systems requires a comprehensive urban development plan, which covers urban growth trends, congestion levels, and spatial patterns. [ABSTRACT FROM AUTHOR]

The growing relevance of promoting a transition of urban mobility toward more sustainable modes of transport is leading to efforts to understand the effects of the built environment on the use of railway systems. In this direction, there are challenges regarding the creation of coherence between the locations of metro stations and their surroundings, which has been explored extensively in the academic community. This process is called Transit-Oriented Development (TOD). Within the context of Latin America, this study seeks to assess the influence of the built environment on the metro ridership in the metropolitan area of Valparaíso, Chile, testing two approaches of influence area definition, one of which is a fixed distance from the stations, and the other is based on the origin and destination survey of the study area. The analysis is based on Ordinary Least Squares regression (OLS) to identify the factors from the built environment, which affects the metro's ridership. Results show that the models based on the area of influence defined through the use of the origin and destination survey explain the metro ridership better. Moreover, this study reveals that the metro system in Greater Valparaíso was not planned in harmony with urban development. The models demonstrate an inverse effect of the built environment on ridership, contrasting with the expected outcomes of a metro station designed following a Transit-Oriented Development approach. [ABSTRACT FROM AUTHOR]

The implementation of fare-free public transport (FFPT) in Tallinn (Estonia) in 2013 sparked international media, policy, and academic interest in best practices, funding structures, and ridership. Initial studies showed marginal effects on modal shift, but increased travel by low-income households, and by younger and older passengers. Yet, the assumed social impact of FFPT has since been under-researched. Based on qualitative research with 22 transport-dependent users, including two semi-structured interviews and a seven-day travel diary, this study examines the daily experiences, mobility constraints, and travel practices of care mobilities, i.e., journeys made to care for others or a household. Findings indicate that the absence of fares, although a relative variable in modal choice, allows carers expanded activity spaces, independence from car ownership, and easier coordination of care tasks. I propose to frame accessibility as a relational process emerging from passengers' encounters and the practices adopted to navigate shared spaces. With this, I argue that understanding public transport use and experiences at a micro-level offers an intersectional and justice lens to commuter-oriented transport and neoliberal urban planning policies. [ABSTRACT FROM AUTHOR]

Bus service in emerging countries faces significant challenges such as inferior quality, limited appeal, and declining ridership, which is further aggravated with increased affordability of commuters. The conventional approach of providing a standardized service to all commuters has proven ineffective due to the diverse perception and requirements of commuters. Unlike traditional approaches that categorize commuters solely based on car ownership, the present study employs a comprehensive approach to segment commuters based on their service quality expectations and identifies improvement areas specific to each segment. The methodology uses integrated Importance Performance Analysis-Machine learning (IPA-ML) based framework to identify segments and improvement areas. While demonstrating the methodology in an Indian metro city, two segments, namely 'tolerant users (low quality acceptors)' and 'quality seekers (high quality seekers)', are identified using complete-linkage-hierarchical clustering with Naïve Bayes Machine Learning classifier. Intervention areas specific to these segments are identified using revised-IPA with Kernel Initialization technique giving due considerations to the segment wise factor structure and management schemes. The findings indicated the impact of multiple socio-economic factors on segment formation emphasizing the need for a holistic approach beyond car ownership when segmenting commuters. The significant differences in perceived improvement areas between the two segments highlight their distinct perceptions and expectations, indicating the necessity for segmenting the bus service to cater to their specific needs. While study provides case-specific findings for the improvement of bus service, the methodology and experience give a new direction to develop bus as a sustainable demand management instrument in a broader context • An IPA-Machine Learning framework is presented to segment the commuters based on service quality expectations. • Two distinctly different segment of commuters namely tolerant users and quality seekers are identified. • Intervention areas specific to both segments are identified considering factor structure and management scheme. • Design of bus service with segmentation is recommended for emerging economies. [ABSTRACT FROM AUTHOR]

Understanding mobility patterns of taxi ridership is important for transport planning. However, there is still room for a thorough understanding of the role of the built environment for taxi ridership across different spatial–temporal patterns. This paper proposes an analytical framework that combines non-negative CANDECOMP/PARAFAC (NCP) decomposition for pattern extraction and the light gradient boosting machine (LightGBM) for modeling the relationship between the built environment and taxi ridership. The case study was conducted in Shenzhen, China. We identified four spatial–temporal patterns by evaluating the root mean square error of the decomposition result and the representativeness of patterns. Based on the LightGBM method, we examined the nonlinear associations between taxi ridership for different patterns and the built environment. The results show that demographic characteristics are important across space. Housing-price is mainly associated with taxi ridership in western Shenzhen. Among all types of POIs, finance and entertainment are the most prominent, affecting taxi ridership in southern Shenzhen. The effects of influencing factors exhibit a high degree of localization, and mixed effects may result from localization. All variables show significant nonlinear and threshold effects on taxi ridership, and these effects could guide transport planning in different areas of the city. [ABSTRACT FROM AUTHOR]

As public transportation faces increasing ridership demand, metrics such as the number of passengers denied boarding become important for measuring the service quality of transit systems. Many studies in the past have used automated fare collection (AFC) (also known as smart card data) and automated vehicle location data to infer the probability distributions for commuters that experience unable-to-board (UTB) events in metro systems, but few have studied UTB events for buses. In this paper, we demonstrate that the probability distribution of UTB commuters inferred from AFC data can be modelled by a truncated binomial distribution under certain assumptions. This model is then validated against synthetic UTB events generated using simulations and against actual UTB events recorded from ground surveys. Finally, we apply our model on real AFC data of commuters in the Singapore bus network to serve as a case study. Our method enables transport planners and operators to identify bus stops and time intervals where overcrowding and UTB events is prevalent, so that appropriate measures can be taken to mitigate such occurrences. [ABSTRACT FROM AUTHOR]

Big mobility data (BMD) have shown many advantages in studying human mobility and evaluating the performance of transportation systems. However, the quality of BMD remains poorly understood. This study evaluates biases in BMD and develops mitigation methods. Using Google and Apple mobility data as examples, this study compares them with benchmark data from governmental agencies. Spatio-temporal discrepancies between BMD and benchmark are observed and their impacts on transportation applications are investigated, emphasizing the urgent need to address these biases to prevent misguided policymaking. This study further proposes and tests a bias mitigation method. It is shown that the mitigated BMD could generate valuable insights into large-scale public transit systems across 100+ US counties, revealing regional disparities of the recovery of transit systems from the COVID-19. This study underscores the importance of caution when using BMD in transportation research and presents effective mitigation strategies that would benefit practitioners. [ABSTRACT FROM AUTHOR]

The line-based hybrid transit (LHT) system is a public transit mode that improves transit performance by integrating the fixed-route transit service with a demand-responsive transit service. To facilitate the implementation and operation of such a hybrid transit system, this paper analyzes and compares the operating performance of the LHT system in two different road network structures: a rectangular structure and circular structure. To conduct a meaningful comparison, the generalized cost of LHT systems for both structures is formulated using the same analytical framework based on the continuous approximation approach. After constructing various time cost indicators related to system performance under two structures, the optimal design model is formulated as a nonlinear program, and an iterative enumeration method with vectorized programming is designed to solve it. A case study is performed to compare various cost components corresponding to the two structures, and sensitivity analysis is conducted. The results suggest that the LHT system is more suitable for operating in rectangular structures under low demand densities, while the system in circular structures is more suitable for operating under high demand densities. However, the performance of the LHT system in a circular structure is superior to that in a rectangular structure at most demand density levels for testing scenarios with high service area size or value of time. [ABSTRACT FROM AUTHOR]

Bikeshare, as a convenient transport mode, can address the first- and last-mile travel needs of metro trips while generating many environmental benefits, such as reducing the use of environmentally unfriendly transport modes and lowering the carbon emissions of the urban transportation system. This paper takes bikeshare as a feeder mode of metro stations (BS-FMMS) as the research object and compares the spatial and temporal differences in the carbon emission reduction benefits of BS-FMMS on workdays and non-workdays by using the framework of BS-FMMS carbon reduction benefit analysis and the methods of time-series analysis, spatial aggregation analysis, and box plot analysis. The results show that the carbon emission reduction benefit of bikeshare has obvious morning and evening peaks on workdays, while it tends to be stable without obvious peaks during the day on non-workdays. From the perspective of spatial distribution, the carbon emission reduction benefits of BS-FMMS are more significant in the metro station areas in the south of Baoan district, the west of Nanshan district, the central of Longhua district, and the south of Futian district in Shenzhen city, and the metro stations where the carbon emission reduction benefits of the non-workday are greater than those of the workday are mainly concentrated in Nanshan district, Futian district, and Luohu district. There is a significant positive correlation between BS-FMMS ridership and carbon emission reduction. These findings can provide clear policy implications for the decarbonization development of urban transportation systems. [ABSTRACT FROM AUTHOR]

The decline of bus ridership is increasingly prevalent in cities in both developed and emerging economies around the world. While researchers have made many efforts to explain why choice riders switch to other transportation modes in single-city cases, why cities vary in the performance of maintaining bus ridership is still largely unknown. This study examines the changes in bus ridership among 175 medium and small-sized cities without urban rail transit from 2010 to 2019 in China and links a few determining factors to such changes by deploying fixed effects panel models and piecewise regression models. Both heterogeneity and nonlinear relationships in the dynamics of bus ridership changes are confirmed. The results demonstrate that cities at different development stages perform diversely in maintaining bus ridership, due to the variations in travel demand and travel choice preferences in those contexts. Increased private car ownership and widening income inequity both contribute to bus ridership decline while an expansion of bus fleet sizes is associated with more bus patronage. These relationships are all displayed as nonlinear formats; the effects can diminish when influencing factors reach a certain level during the growth of cities. The research outcomes may inform policymakers that cities at different development stages need to adopt context-based strategies instead of simply copying from elsewhere. Policies such as controlling private car ownership might not work equally well in cities with diversified characteristics. • Shed light on the bus ridership decline challenges faced by 175 medium and small-sized cities in China. • Explore the interaction and nonlinear effect from influencing factors on bus ridership. • Focus on medium and small-sized cities' bus ridership decline phenomenon. • Emphasize the importance of understanding city-specific dynamics to formulate effective transit strategies. [ABSTRACT FROM AUTHOR]

The assessment of transport systems has traditionally focused on congestion and ridership as its core performance measures. These perspectives fail to account for the actual service people seek from the transport system—the ability to reach destinations. Recent studies have shifted to focus on accessibility as a performance indicator, but do not address the question whether the observed accessibility is sufficient for meeting people's daily needs. This paper contributes to the accessibility literature by (1) applying a people-centered approach to the performance assessment of transit systems and (2) exploring the factors explaining the differences in performance between regions. The paper proposes the Accessibility Sufficiency Index (ASI) as a performance standard. The ASI is based on a sufficiency threshold representing an accessibility level that is assumed to enable adequate access to destinations. The paper uses neighborhood transit job accessibility data to calculate separate ASI scores for different sufficiency thresholds for 49 large US metropolitan areas. Regression analyses show that transit system performance is shaped most strongly by transit vehicle revenue miles, mixed land uses, and activity centering. Importantly, the size of these effects varies by the employed sufficiency threshold, suggesting that transportation and land use factors affect transit performance at different spatial scales. The results have implications for the ways we evaluate transport and transit systems and for our understanding of the factors that affect their performance. [ABSTRACT FROM AUTHOR]

With the growth of cars and car-sharing applications, commuters in many cities, particularly developing countries, are shifting away from public transport. These shifts have affected two key stakeholders: transit operators and first- and last-mile (FLM) services. Although most cities continue to invest heavily in bus and metro projects to make public transit attractive, ridership in these systems has often failed to reach targeted levels. FLM service providers also experience lower demand and revenues in the wake of shifts to other means of transport. Effective FLM options are required to prevent this phenomenon and make public transport attractive for commuters. One possible solution is to forge partnerships between public transport and FLM providers that offer competitive joint mobility options. Such solutions require prudent allocation of supply and optimised strategies for FLM operations and ride-sharing. To this end, we build an agent- and event-based simulation model which captures interactions between passengers and FLM services using statecharts, vehicle routing models, and other trip matching rules. An optimisation model for allocating FLM vehicles at different transit stations is proposed to reduce unserved requests. Using real-world metro transit demand data from Bengaluru, India, the effectiveness of our approach in improving FLM connectivity and quantifying the benefits of sharing trips is demonstrated. [ABSTRACT FROM AUTHOR]

Background: Community-based mask wearing has been shown to reduce the transmission of SARS-CoV-2. However, few studies have conducted an economic evaluation of mask mandates, specifically in public transportation settings. This study evaluated the cost-effectiveness of implementing mask mandates for subway passengers in the United States by evaluating its potential to reduce COVID-19 transmission during subway travel. Materials and methods: We assessed the health impacts and costs of subway mask mandates compared to mask recommendations based on the number of infections that would occur during subway travel in the U.S. Using a combined box and Wells-Riley infection model, we estimated monthly infections, hospitalizations, and deaths averted under a mask mandate scenario as compared to a mask recommendation scenario. The analysis included costs of implementing mask mandates and COVID-19 treatment from a limited societal perspective. The cost-effectiveness (net cost per averted death) of mandates was estimated for three different periods based on dominant SARS-CoV-2 variants: Alpha, Beta, and Gamma (November 2020 to February 2021); Delta (July to October 2021); and early Omicron (January to March 2022). Results: Compared with mask recommendations only, mask mandates were cost-effective across all periods, with costs per averted death less than a threshold of $11.4 million (ranging from cost-saving to $3 million per averted death). Additionally, mask mandates were more cost-effective during the early Omicron period than the other two periods and were cost saving in January 2022. Our findings showed that mandates remained cost-effective when accounting for uncertainties in input parameters (e.g., even if mandates only resulted in small increases in mask usage by subway ridership). Conclusions: The findings highlight the economic value of mask mandates on subways, particularly during high virus transmissibility periods, during the COVID-19 pandemic. This study may inform stakeholders on mask mandate decisions during future outbreaks of novel viral respiratory diseases. [ABSTRACT FROM AUTHOR]

This article examines the ten-line cable car network of Tbilisi, Georgia, constructed by the Soviet government between 1953 to 1988, then decommissioned in the 1990s after independence and partially reactivated since 2012. During the Soviet era, Tbilisi's cable cars played an important role in the city's mass mobility, particularly in areas of steep geography. They also functioned ideologically, supporting Soviet ambitions toward public transportation and access to recreational space for the working proletariat. We argue that today's aerial network reflects new state ideological goals where cable cars lure global capital and facilitate the tourism commodification of Tbilisi's historic Old City. Tbilisi's cable car network can, therefore, be understood as embodying changes in government stances toward labor, leisure, and the direction of future development, while further reflecting the mobility politics of the city. The article describes the various ideological shifts that took place, exploring how Tbilisi's cable car network now links to contemporary changes in government urban development priorities and tourism promotion. The findings are based on interviews, document analysis, transit ridership/City Hall data, as well as field observations that collectively provide an overview of Tbilisi's cable car network as it has transformed since the 1950s. [ABSTRACT FROM AUTHOR]

Cities worldwide are promoting bicycling as a sustainable mode of transportation. However, bicycle theft remains a significant deterrent for potential riders, and also influences the behaviors of existing cyclists. Understanding the impact of theft on bicycling behaviors provides a foundation for developing strategies to address the negative impacts of bicycle theft. Our goal is to characterize if and how bicycle theft changes individual bicycling behavior. We gathered responses from 1821 individuals in a survey focused on bicycle theft in North America. We employed bivariate analysis and binary logistic regression models to explore the relationships between demographic factors, bicycle attributes, and pre-theft behavior to explain post-theft bicycling behavior. The results show that 45% of survey respondents reduced or ceased bicycling post-theft, while 6% increased their bicycling. Additionally, 40% transitioned from bicycling to unsustainable modes of transportation for their post-theft trips. Also, 69% of people eventually replaced their stolen bicycles, of which 46% selected models of equal/higher value. Pre-theft bicycling activity emerged as the most influential factor on ridership behavior after a bicycle theft, with occasional riders experiencing the most negative impact, compared to frequent riders, who remained committed to bicycling. Recovery of the stolen bicycles, e-bicycle usage, number of bicycles owned, and income levels were also predictors of future bicycling patterns. The insights from this research can inform targeted interventions for populations most at risk to reduce the negative impact of bicycle theft, such as secure parking for new and low-income bicyclists. [ABSTRACT FROM AUTHOR]

To coproduce better policy outcomes, governments and citizens need to work together. However, information asymmetry between the two parties influences the coproduction adversely. Nowadays, the multiplicity of information and its potential incongruence add to information asymmetry and make the impact of information on coproduction trickier than ever. This study examines the effects of political message, policy, and factual information on citizens' coproduction activities. Analyzing the effects of federal and state leaders' tweets, New York City's COVID-19 policies, reported COVID-19 cases and deaths, and the city's visits and public transportation ridership, the findings show that politicians' message, congruent or not, did not influence citizens' coproduction activities as measured by visits and public transit ridership. Policy implementation information improved coproduction, and the perceptions of factual information contributed to intended coproduction. [ABSTRACT FROM AUTHOR]

We delve into the temporal dynamics of public transportation (PT) ridership in Seoul, South Korea, navigating the periods before, during, and after the COVID-19 pandemic through a spatial difference-in-difference model (SDID). Rooted in urban resilience theory, the study employs micro-level public transportation card data spanning January 2019 to December 2023. Major findings indicate a substantial ridership decline during the severe COVID impact phase, followed by a period in the stable and post-COVID phases. Specifically, compared to the pre-COVID phase, PT ridership experienced a 32.1% decrease in Severe, followed by a reduced magnitude of 21.8% in Stable and 13.5% in post-COVID phase. Interestingly, the observed decrease implies a certain level of adaptability, preventing a complete collapse. Also, contrasting with findings in previous literature, our study reveals a less severe impact, with reductions ranging from 27.0 to 34.9%. Moreover, while the ridership in the post-COVID phase exhibits recovery, the ratio (Post/Pre) staying below 1.0 suggests that the system has not fully returned to its pre-pandemic state. This study contributes to the urban resilience discourse, illustrating how PT system adjusts to COVID, offering insights for transportation planning. [ABSTRACT FROM AUTHOR]

The Covid-19 outbreak changed travel behaviour in many ways. This paper evaluates these changes in Tehran, Iran, from a transportation sustainability perspective. It uses data from travel surveys before and during the pandemic to evaluate changes in travel activity and their impacts on public transportation (PT) system costs and revenues, air quality and traffic crashes. A structural equation model (SEM) is used to assess the potential impact of passengers' characteristics, details of each transportation mode and the severity of Covid-19 on travel behaviour. Hypothesis testing is used to compare the changes that occurred in air quality and traffic crashes. SEM outputs indicate that the frequency of trips previously made by sustainable modes and changes in regular trips have the highest impact on the sustainability of trips made during the Covid-19 pandemic. Air quality declined in many city districts. The pandemic caused reductions in PT ridership and an increase in private car use, which threaten the long-term sustainability of public transit services. Crash fatalities declined slightly, particularly during periods of movement restrictions. However, motorcycle fatal crashes increased in comparison with the reduction in motorcycle trips, apparently due to increased traffic speeds. The government did not use the pandemic as an opportunity to promote sustainable and low-contagion modes. This analysis infers that the Covid-19 outbreak reduced the overall transportation sustainability in Tehran. [ABSTRACT FROM AUTHOR]

This study explores the impact of public safety emergencies on the preferences of women riders within Shanghai's real-time crowdsourcing logistics platform. It employs quantitative research methods, utilizing LDA topic modeling and ERNIE categorization model for data analysis. The research identifies six key topics influencing riders' preferences: Tip Order Information, Sharing and Volunteering, Epidemic Delivery Rules, Quality of Work and Life, Epidemic Control Measures, and Liability Exemption and Reward. The study reveals a cognitive bias among riders towards positive utilities, indicating a generally optimistic emotional state which influences their utility preferences. The findings suggest that the riders prioritize social interests and responsibilities during the pandemic, demonstrating adaptability to new work environments and appreciation for supportive measures by platforms. The study provides insights into the nuances of women riders' preferences, emphasizing the need for targeted strategies by platforms and authorities to enhance job satisfaction and address challenges faced by women riders. [ABSTRACT FROM AUTHOR]

Numerous studies have explored associations between bicycle network characteristics and bicycle ridership. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership. We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated (length of the bicycle network, betweenness centrality, degree centrality, network density, network coverage, intersection density and average weighted slope) and Bayesian spatial models were used to explore associations between these network characteristics and bicycle ridership. We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region. These findings challenge the utility of approaches based on spatially modeling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. Further efforts are required to be able to quantify network characteristics that reflect the myriad of factors that influence comfort and safety for people of all ages and abilities. [ABSTRACT FROM AUTHOR]

While the COVID-19 pandemic upended many aspects of life as we knew it, its effects on U.S. public transit were especially dramatic. Many former transit commuters began to work from home or switched to traveling via private vehicles. But for those who continued to work outside the home and could not drive—who were more likely low-income and Black or Hispanic—transit remained an important means of mobility. However, most transit agencies reduced service during the first year of the pandemic, reflecting reduced ridership demand, increasing costs, and uncertain budgets. To analyze the effects of the pandemic on transit systems and their users, we examine bus ridership changes by neighborhood in Boston, Houston, and Los Angeles from 2019 to 2020. Combining aggregated stop-level boarding data, passenger surveys, and census data, we identify associations between shifting travel patterns and neighborhoods. We find that early in the pandemic, neighborhoods with more poor and non-white households lost proportionally fewer riders; however, this gap between high- and low-ridership-loss neighborhoods shrank as the pandemic wore on. We also model ridership change controlling for multiple factors. Ridership in Houston and LA generally outperformed Boston, with built environment and demographic factors accounting for some of the observed differences. Neighborhoods with high shares of Hispanic and African American residents retained more riders in the pandemic, while those with higher levels of auto access and with more workers able to work from home lost more riders, all else equal. We conclude that transit's social service role elevated during the pandemic, and that serving travelers in disadvantaged neighborhoods will likely remain paramount emerging from it. [ABSTRACT FROM AUTHOR]

Public transit agencies face a transformed landscape of rider demand and political support as the COVID-19 pandemic recedes. We explore people's motivations for returning to or avoiding public transit a year into the pandemic. We draw on a March 2021 follow-up survey of over 1,900 people who rode transit regularly prior to the COVID-19 pandemic in Toronto and Vancouver, Canada, and who took part in a prior survey on the topic in May 2020. We investigate how transit demand changes associated with the pandemic relate to changes in automobile ownership and its desirability. We find that pre-COVID frequent transit users between the ages of 18–29, a part of the so-called "Gen Z," and recent immigrants are more attracted to driving due to the pandemic, with the latter group more likely to have actually purchased a vehicle. Getting COVID-19 or living with someone who did is also a strong and positive predictor of buying a car and anticipating less transit use after the pandemic. Our results suggest that COVID-19 may have increased the attractiveness of auto ownership among transit riders likely to eventually purchase cars anyway (immigrants, twentysomethings), at least in the North American context. We also conclude that getting COVID-19 or living with someone who did is a positive predictor of having bought a car. Future research should consider how having COVID-19 transformed some travelers' views, values, and behaviour. [ABSTRACT FROM AUTHOR]

Advances in information technologies and vehicle automation have birthed new transportation services, including shared autonomous vehicles (SAVs). Shared autonomous vehicles are on-demand self-driving taxis, with flexible routes and schedules, able to replace personal vehicles for many trips in the near future. The siting and density of pick-up and drop-off (PUDO) points for SAVs, much like bus stops, can be key in planning SAV fleet operations, since PUDOs impact SAV demand, route choices, passenger wait times, and network congestion. Unlike traditional human-driven taxis and ride-hailing vehicles like Lyft and Uber, SAVs are unlikely to engage in quasi-legal procedures, like double parking or fire hydrant pick-ups. In congested settings, like central business districts (CBD) or airport curbs, SAVs and others will not be allowed to pick up and drop off passengers wherever they like. This paper uses an agent-based simulation to model the impact of different PUDO locations and densities in the Austin, Texas CBD, where land values are highest and curb spaces are coveted. In this paper 18 scenarios were tested, varying PUDO density, fleet size and fare price. The results show that for a given fare price and fleet size, PUDO spacing (e.g., one block vs. three blocks) has significant impact on ridership, vehicle-miles travelled, vehicle occupancy, and revenue. A good fleet size to serve the region's 80 core square miles is 4000 SAVs, charging a $1 fare per mile of travel distance, and with PUDOs spaced three blocks of distance apart from each other in the CBD. [ABSTRACT FROM AUTHOR]

This study delves into the nuanced patterns of shock and recovery in transit ridership during and after the COVID-19 pandemic, aiming to illuminate the resilience exhibited by various geographic areas. This resilience is measured by the ability of transportation systems to withstand, adapt to, and bounce back from unforeseen shocks. In this research, smart card big data were exploited to track real-time mobility dynamics and economic activity within the city of Seoul, Korea. The approach employed multivariate two-dimensional functional data analysis and a hierarchical clustering method to examine both boarding and alighting patterns, taking into account multi-scalar temporal units, monthly and hourly demand fluctuations. The findings present distinct varied shock-and-recovery patterns across areas in transit ridership based on the socioeconomic characteristics of specific areas. These characteristics encompass factors such as industry and land-use composition, income levels, population density, and proximity to points of interest. Additionally, this methodology proves effective in identifying abnormal surges in demand linked to local large-scale development projects.

This document is a summary of a special issue on transportation big data and its applications. The issue explores various topics related to big data in transportation, including architectures, processing methods, and utilization for tasks such as traffic pattern discovery, collision identification, and operational efficiency optimization. The issue includes sixteen papers covering a range of subjects, such as maritime traffic evaluation, urban traffic intersection extraction, and risk measurement and forecasting. The authors highlight the transformative potential of big data in transportation and express gratitude to the contributors for their insights. [Extracted from the article]

Few studies have examined the relationship at the microscopic spatial scale. In this study, multiple sources of data including mobile phone signal data, automatic fare collection system data, geo-information data, and street-view image data are combined to measure metro ridership and built environment at the plot or block scale. The Random Gradient Boosting Decision Tree was used to explore relationship between the built environment and ridership. The results show the following: (1) the relationship between built environment and ridership shows different types of curves. (2) The path distance to the metro station and the visual perception of road space have more significant impacts on ridership than road network density. (3) The location of the grid also affects grid-level metro ridership. The results suggest that planners should consider the locational factors, pay attention to the different effective thresholds of different variables on ridership and the longitudinal landscaping of non-motorized urban roads. [ABSTRACT FROM AUTHOR]

Our study compared observed traffic volumes in the Bus Rapid Transit (BRT-Light) corridor to those predicted using two different quasi-experimental methods. The first quasi-experimental design, referred to as interrupted time series, assumes the trends in traffic on BRT alignment from 2013 to 2017 continue through to 2019 after BRT-Light is in place. Making that assumption, traffic volume on the BRT alignment is 2514 vehicles per day lower (−7.38%) than one would expect based on the preexisting trend. The second quasi-experimental design is called a before-after design with a control group, which assumes that traffic volume on the BRT route would increase by the same percentage as traffic on parallel streets to the BRT line, that is 2.30 percent or 645 VPD between before and after. Also, BRT alignment's traffic volume was reduced by 1249 VPD or 4.78 percent more rather than outside of BRT corridor, and 17,632 VPD or 9.33 percent considering trip generation. Meanwhile, transit ridership in the corridor increased by 8687 passengers per day (122.66%) more than expected with the introduction of BRT, which helps account for the effective reduction of vehicular traffic on the streets that comprise the BRT alignment. Through the section analysis, we found that dedicated right-of-way reduced traffic effectively. Our estimates suggest that BRT-Light will result in an annual reduction of 17,886,229 pounds of CO2 emissions and 914,614 gallons of gasoline consumption. Based on these results, we conclude that BRT-Light had a positive effect on easing vehicle traffic volume and emissions in the Provo-Orem metropolitan area. • BRT-Light, which is the lower limit of BRT, had a positive effect on easing vehicle traffic and emissions on BRT alignment. • BRT-Light reduced traffic not only on the BRT alignment but also on the entire streets in the half miles-buffered area. • The dedicated right-of-way reduced traffic effectively. • BRT-Light provoked significant nearby land-use development and increased trip generation. • Significantly increased transit ridership in the corridor explained to eased vehicular traffic. [ABSTRACT FROM AUTHOR]

Many of the larger US metropolitan regions promote polycentric development as a way of fostering livability, accessibility, and sustainability. Polycentric urban structures can increase transit ridership, promote active transportation, and decrease vehicle miles traveled (VMT) and CO2 emissions. Although many regions include ambitious polycentric aspirations in their plans, only a few follow up with rigorous implementation and see their efforts come to fruition. The topic of implementation is also widely omitted from scholarly inquiry. This research aims to explore three examples of successful implementation of urban polycentricity: Portland, Oregon; Seattle, Washington; and Denver, Colorado. Each region employs a very distinct polycentric development model, but each relies heavily on its regional governance organization for direction, guidance, and even command in the implementation process. To understand specific strategies and methods used by each region, the authors conducted interviews with metropolitan planning organizations, central cities, and transit agencies in the three regions and used qualitative techniques to analyze the interview transcripts and collected documents. As regional governance organizations play a crucial role in implementing regional plans, their policies and practices were also investigated by the authors. Based on collected data and insights, we conclude that the three regions are great examples of an advanced implementation of polycentric development. This research can be helpful to other US metropolitan regions that wish to promote polycentric development. The lessons learned from the three case studies can provide guidance and possible paths to successful implementation. [ABSTRACT FROM AUTHOR]

The sharing economy, mobile Internet, and smartphones have been widely utilized in recent years to promote the development of bike-sharing services. Bike-sharing serves as a first/last mile travel mode to connect to public transit, which improves trip efficiency, alleviates traffic problems, improves environmental quality, and promotes public health. However, the substitution of public transit by bike-sharing and the decline in public transit ridership have raised concerns among city managers regarding the coopetition between shared mobility services and public transit. To understand the impact of bike-sharing on the decline in public transit and to formulate reasonable synergistic development policies, it is crucial to identify the coopetition relationships between the two. This paper uses a combination of database search and backward snowballing to review existing research. Three research themes were identified: macrolevel studies on bike-sharing and public transit interaction, studies on actual coopetition behaviors based on bike-sharing user surveys, and studies on potential coopetition relationships based on bike-sharing transaction data. The three categories of studies reveal the effect of bike-sharing usage on public transit ridership, the emergency function of bike-sharing in the event of unexpected transit shutdowns, and the substitution and connection relationships between bike-sharing and public transit and the factors influencing them. Finally, this study suggests many directions for future research. This review helps clarify the understanding of the coopetition relationships between bike-sharing and public transit, provides theoretical support to promote the synergistic development of both, and points out ways to deepen the research on the coopetition relationship between the two. [ABSTRACT FROM AUTHOR]

Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020–22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative. [ABSTRACT FROM AUTHOR]

Technology advancements in the last two decades have changed several aspects of public transit service, particularly related to fares. Transit agencies seek to benefit from new technologies to improve the customer experience by launching mobile fare payment applications ("apps") and adopting more sophisticated fare policies such as fare capping ("caps"). However, there is a limited understanding of the impacts of these two fare innovations on bus ridership. Therefore, this study seeks to quantify the impacts of mobile fare payment applications and fare capping policies (both daily and monthly) on bus ridership. Staggered difference-in-difference techniques were used to evaluate system-level bus ridership for the 50 largest transit agencies in the United States. This approach considers the effect on multiple treated units that adopted apps or caps at different times; it also considers heterogeneity of the treatment effect between treated agencies and over time. The results suggest that the launch of mobile fare payment applications and the adoption of daily fare capping policies did not have significant impacts on system-level ridership. On the other hand, monthly fare capping policies were associated with significant ridership gains. Transit systems that adopted monthly fare capping policies for more than one year experienced an average increase in annual bus ridership ranging from 3.6% to 4.1%; these results were heterogenous and increased over time. These findings can help to inform transit agencies across the United States as they consider different strategies to increase ridership and reverse recent bus ridership declines. Perhaps most important, the staggered difference-in-difference methodology used in this study could potentially be applied to evaluate a wide range of transportation technology and policy innovations with staggered (gradual) rollouts. • Technology innovations have transformed public transit fare collection and policy. • Mobile fare payment apps and fare capping are increasingly common in the USA. • Staggered Diff-in-Diff was used to study these innovations impacts of on ridership. • Monthly fare capping policies increased annual bus ridership by 3.6%–4.1%. • Monthly fare capping policies impact was heterogenous and increased over time. [ABSTRACT FROM AUTHOR]

Researchers have applied a series of global models to investigate the link between the built environment and ride-hailing ridership based on ride-hailing data from one specific transportation network company (TNC). However, these research designs inadequately represent real ride-hailing demand within a specific spatial range and cannot reflect spatiotemporal heterogeneity in the link. For the first time, this study collects all demand data of TNCs in Nanjing and analyzes their relationship with the built environment. The effect of taxi demand is considered. We adopt a multiscale geographically weighted regression model to account for the spatial non-stationarity and the multiscale effect of each built environment variable. The findings reveal spatiotemporal heterogeneous relationships of the built environment with ride-hailing ridership. Although the relationship between taxi and ride-hailing ridership varies across spatial locations, ride-hailing always acts as a cooperator for traditional taxis. The findings provide implications for policy making, urban planning, and travel demand management of ride-hailing. [ABSTRACT FROM AUTHOR]

Worldwide, most previous studies based on the transit-oriented development (TOD) concept focused heavily on the three Ds (3Ds) application in TOD (e.g., urban density, design, and land use diversity). Along with the 3Ds, three other TOD dimensions of transportation characteristics, such as destination accessibility, local distance to metro station, and travel demand management (TDM), and sociodemographics, which is the seventh dimension, significantly contribute to decision-making in TOD policies. It is hard to infer the actual travel behavior of transit riders when assessing transit nodes on foot from the static land use distribution that is assigned in the master plan. The novelty of this study is that an aggregated TOD index equation is formulated that uses an expert questionnaire, which involves 31 respondents and a spatial multiple criteria analysis (SMCA) tool, to assign weights and ranks to three identified TOD criteria that strongly influence pedestrian accessibility to metro stations. This study has examined all seven dimensions of TOD along with the impact of seasonal variation on short-distance walking to transit stations in the grid-layout planned Indian city of Noida (Uttar Pradesh, India), which used a Global Positioning System (GPS)-based metro station survey questionnaire. The path walkability evaluation of typical pedestrian routes that use GPS devices identifies typical pedestrian barriers in the urban planning of Noida that dissuade pedestrian accessibility for all. Three suitable TOD criteria, such as distance to the metro station, pedestrian destination accessibility, and availability of two-wheeler parking areas near a metro station, have a statistically significant association with walking to transit nodes. The TOD policy for brownfield urban areas should look at improving pedestrian accessibility for all and revise pedestrian guidelines for Indian cities to address inequitable walking environments. The integration of urban development with the public transit system offers a promising approach to encourage higher transit ridership and discourage citizens from using their private automobiles, especially for daily short and medium trip lengths. The future growth in the transit system when reshaping urban development around metro stations cannot be achieved until pedestrian-accessible streets and sidewalks are enhanced to easily assess transit stations on foot. A portable GPS tracking device and a smart Android phone could be used along with other state-of-the-art technologies to analyze the travel behavior of transit users and the physical evaluation of the actual walkable routes taken by them to reach their nearest metro station. The free smart mobile apps allow researchers and nonresearchers, especially in developing nations, to conduct field surveys that use smart Android phones with GPS location facilities and cameras to capture live images. [ABSTRACT FROM AUTHOR]

• Longitudinal analysis of pedelec (and bicycle) crashes from 2013-2021. • Differences and similarities between pedelec and bicycle crashes were mostly stable throughout the years. • Higher injury severity for pedelec riders compared to cyclists throughout the years. • Consistently higher proportion of single vehicle crashes for pedelec riders. • Decrease in mean age of the involved pedelec riders over time. Introduction : The continuous growth in the use of e-bikes (in Germany mostly pedelecs that support pedaling up to 25 km/h) raises questions about the use of historic crash data for the development of road safety measures. The aim of this study was to address this issue, by conducting a longitudinal analysis of pedelec and bicycle crash data over a period of nine years to identify trends and to clarify whether such trends are specific to pedelecs. Method : We analyzed 95,338 police reported pedelec and bicycle injury crashes from 2013 through 2021. The dataset consisted of crashes from three federal states of Germany: Brandenburg, Hesse and Saxony. Data were analyzed with respect to sex and age distribution, time, location and type of accident, conflict partner, cause of crash and injury severity. Results : Many of the analyzed variables showed a considerable degree of temporal stability, with differences as well as similarities between the two bicycle types staying quite consistent over the years. One notable difference was the mean age of the involved riders, with crashed pedelec riders being significantly older than conventional cyclists. At the same time, however, the mean age of these pedelec riders has decreased by eight years over time. Single vehicle crashes were consistently more common for pedelec riders than for cyclists. Similarly, pedelec rider crashes went with a higher injury severity over all the years. Conclusions and practical applications : While, on a more detailed level, we found differences between the two bicycle types, overall crash characteristics were remarkably similar and consistent over time. Our findings provide no clear argument for road safety measures that are specifically designed to target pedelecs. Instead, the stable crash total, and the increases in ridership of both bicycles and pedelecs, highlight the demand for new, innovative solutions to improve cycling safety in general. [ABSTRACT FROM AUTHOR]

Maintaining passenger loyalty to Public Transportation (PT) services is an effective way to sustain ridership of this eco-friendly transport mode. Although numerous studies have investigated how passengers' perception of service quality influences their loyalty and proposed key determinants to secure their PT loyalty, it remains unclear whether these relationships are independent of the methods used for linear or non-linear analysis in different contexts. Given the impact of this question on relevant policy-making, this study simultaneously investigated the relationships between passengers' perceived service quality of specific attributes and their overall loyalty using both linear (Structural Equation Modeling, SEM) and non-linear (Gradient Boosting Decision Tree, GBDT) methods. To interpret the results of the two models, the Total Accumulated Local Effects (TALE) method, a variant of the Accumulated Local Effects (ALE) method, was proposed as a model-independent approach. The case study was conducted in Xiamen, China. The results showed that TALE had an advantage over the conventional ALE method because it considered the impact of the actual score distribution of the samples on the results, making the interpretation more realistic. Furthermore, it was found that there were no significant differences in the results of the normalized TALE in GBDT and SEM for most explanatory variables, both indicating similar non-linear trends. Therefore, regardless of the employed methods, the natural existence of non-linear relationships between increased perceived service quality and improved overall PT loyalty was observed. After determining the relationship between the perceived service quality and overall loyalty, the Importance-Performance Analysis (IPA) method was combined with the TALE of each explanatory variable to suggest key determinants that would enhance passengers' overall loyalty. • The non-linear relationship between PT service quality and passenger loyalty is analyzed. • A new model-independent interpretable machine learning method, TALE, is proposed. • TALE has advantages over conventional ALE by considering sample distribution • Non-linear effects can be found in both SEM and GBDT. • IPA combined with GBDT can find the vital PT service attributes for loyalty improvement. • Bus operational stability is the most vital PT service attribute [ABSTRACT FROM AUTHOR]