Transit Trips Research Articles

Are older adults living in compact development more active? – Evidence from 36 diverse regions of the United States

With the population of older adults growing globally, this study asks the question: are older adults living in compact developments more active than those living in sprawling developments? Older adults can be deemed more active if they travel more in total or travel more by non-auto travel modes (such as walking, transit). By analyzing disaggregated data from 36 regions of the United States, this study finds that older adults living in compact neighborhoods do not travel more in total but travel more by walking and public transportation than those living in sprawling neighborhoods. In addition, older adults travel less, are more auto-dependent, and make more home-based-nonwork trips, compared to younger adults. Older adults with lower income travel less than those with higher income. Older adults living in compact neighborhoods with the lowest income level generate the highest number of transit trips. It is important for planners and policy makers to not only create built environments that support older adults’ travel needs, but also to avoid social inequity.

Socioeconomics of urban travel in the U.S.: Evidence from the 2017 NHTS

Using the 2017 National Household Travel Survey (NHTS), this study analyzes America's urban travel trends compared with earlier nationwide travel surveys, and examines the variations in travel behaviors among a range of socioeconomic groups. The most noticeable trend for the 2017 NHTS is that although private automobiles continue to be the dominant travel mode in American cities, the share of car trips has slightly and steadily decreased since its peak in 2001. In contrast, the share of transit, non-motorized, and taxicab (including ride-hailing) trips has steadily increased. Besides this overall trend, there are important variations in travel behaviors across income, home ownership, ethnicity, gender, age, and life-cycle stages. Although the trends in transit development, shared mobility, e-commerce, and lifestyle changes offer optimism about American cities becoming more multimodal, policymakers should consider these differences in socioeconomic factors and try to provide more equitable access to sustainable mobility across different socioeconomic groups.

Effects of Built Environment on Bus Trip Rates under Rail Transit Competition

During the past decade, the rapid construction of rail transit in developing countries has had a great impact on bus transport, resulting in the waste of public transport resources and ineffective competition between bus and rail transit. Therefore, it is particularly important to explore ways to promote the integrated development of bus and rail transit by combining both the effects of the built environment and their competition. Using Wuhan city in China as a case study, this study develops gradient boosting decision trees (GBDTs) to examine the quantified threshold range of the built environment impacts on bus transit trip rates and to further explore the competitive impacts of rail transit on bus transit within these threshold ranges. The threshold range results show that when the number of service point of interests (POIs) within a 500-m radius of the bus stop reaches 350 and the distance to the destination is 5 km, the model split of bus transit is the highest. The competition impact analysis shows that rail transit promotes bus transit trip rates in urban fringe areas and locations, including a high proportion of 3- to 5-km middle-distance trips, but restricts bus transit trip rates in high-density central areas and the regions dominated by short-distance trips of less than 3 km or long-distance trips of more than 5 km. Strategies are proposed to optimize land use and the layout of bus and rail transit stations to mitigate ineffective competition and improve the efficiency of public transit to provide theoretical and technical support for optimizing urban spatial layouts through transit-oriented development.

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

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

Exploring alternative bus operations strategies for adaptive and efficient transit systems in the face of disruptions

Disruptions in public transit systems can have significant impacts on agency and passenger needs. Therefore, it is crucial to implement new transit policies to address these disruptions and to ensure efficient and reliable transit services. This study proposes a framework to assess alternative bus operating strategies to adapt with different disruptions to public transit systems. Conventional all-stop systems, visiting all the stops along a bus corridor, are compared against three alternative operational schemes: Skip-stop, express-local schemes with stop-skipping designs, and on-demand service with a fixed route but flexible stopping patterns, are compared to find the most efficient bus operating service under various circumstances. We developed an optimization model based on the total generalized system cost for each operating strategy using continuous approximation techniques and extended the previous models by comparing a wider range of alternative services and model flexibility to evaluate the optimum system in response to different disruptions. Different factors, such as the level of demand, demand patterns, and sensitivity to various components of transit trips for passengers and the operator, including crowding, denied boarding costs, and fleet constraints, are considered. We found that, given a different disruption scenario, demand, and travel patterns, the most efficient service can vary significantly between on-demand, all-stop, skip-stop, and express-local services. As such, it is suggested that the service scheme be chosen more adaptively by employing such frameworks.

EVALUATION OF THE EQUIVALENT TRIP DISTANCE USING MICROMOBILITY AND PUBLIC TRANSIT ACCORDING TO THE TIME CRITERION

The advanced direction of improving the transport systems of the cities is to ensure their sustainability. Transport system sustainability directed to provide its operational efficiency, ensures the city residents transportation needs satisfaction with the appropriate level of quality and the least of the travel time costs, contributes to the economic development of the city and reduces the negative environmental impact of the transport. The concept of sustainable urban development involves widespread alternative transportation modes, in particular micromobility. The benefits of micromobility include cost-effectiveness (lack of dependence on fuel), high capacity of the transport infrastructure, environmental friendliness, and positive impact on user’s health. The main factors that affect users' choice to using micromobility are safety, cost, time and effort. The cost level depends on which one vehicle is used, own or hired through a sharing system. Travel time depends on distance and speed, which is related to the type of vehicle, user experience, time of day, weather conditions, etc. Carrying out long-distance micromobility is associated with significant efforts and usually does not provide time savings. However, the joint use of micromobility with transit services at the first-mile and (or) last-mile of the trip can really compete with private cars. The research proposed an analytical equation of determining the rational spheres micromobility trips choice by users according to the time criterion. This equation involves the definitionn of the equivalent trip distance by scheduled passenger transport and micromobility considering the directness of connections by the specified modes. It is assumed that the directness of the micromobility trips is higher than for public transit. It was established that the equivalent trip distance can vary widely, which is due to the different ratio of the micromobility and public transit trips conditions. Further research will be focused on the study of the separate and combined influence of equivalent trip distance factors and establishing the patterns of its change. Keywords: transport system, micromobility, equivalent distance, sharing system, public transit, directness of the connection.

Planning of integrated mobility-on-demand and urban transit networks

We envision a multimodal transportation system where Mobility-on-Demand (MoD) service is used to serve the first mile and last mile of transit trips. For this purpose, the current research formulates an optimization model for designing an integrated MoD and urban transit system. The proposed model is a mixed-integer non-linear programming model that captures the strategic behavior of passengers in a multimodal network through a passenger assignment model. It determines which transit routes to operate, the frequency of the operating routes, the fleet size of vehicles required in each transportation analysis zone to serve the demand, and the passenger flow on both road and transit networks. A Benders decomposition approach with several enhancements is proposed to solve the given optimization program. Computational experiments are presented for the Sioux Falls multimodal network. The results show a significant improvement in the congestion in the city center with the introduction and optimization of an integrated transportation system. The proposed design allocates more vehicles to the outskirt zones in the network (to serve the first mile and last mile of transit trips) and more frequency to the transit routes in the city center. The integrated system significantly improves the share of transit passengers and their level of service in comparison to the base optimized transit system. The sensitivity analysis of the bus and vehicle fleet shows that increasing the number of buses has more impact on improving the level of service of passengers compared to increasing the number of MoD vehicles. Finally, we provide managerial insights for deploying such multimodal service.

Exploring the Impact of Bike Lanes on Transportation Mode Choice: A simulation-based, route-level impact analysis

Many U.S. cities are investing in making a more bike-friendly environment in hopes of reducing auto-dependency. Studies have shown that improving bike lanes enhance bike users’ perceived safety and comfort, but whether it also shifts mode choice towards more biking remains largely unaddressed. This study proposes a model to examine whether and how bike lanes shift the mode choice towards various non-automobile modes. With the help of more than 110,000 sample trips from travel surveys, hypothetical bike routes are obtained to assess each route's level of bike-friendliness. A mode choice model with four modes – walking, biking, driving, and transit – is developed using this data. The model results suggest that bike lanes increase not only biking trips but also walking and public transit trips. However, the impact on biking trips alone was marginal, suggesting a more comprehensive strategy may be necessary to make a significant transition towards non-auto trips. When the data are segmented by poverty level, model results show that the mode choice of the low-income population is as significantly affected by bike lanes as the general population. In addition, the study results indicate that frequent transit users can greatly benefit from bike infrastructure since walking, biking, and public transit trips are closely associated with each other.

Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms

Building an accurate model of travel behaviour based on individuals’ characteristics and built environment attributes is of importance for policy-making and transportation planning. Recent experiments with big data and Machine Learning (ML) algorithms toward a better travel behaviour analysis have mainly overlooked socially disadvantaged groups. Accordingly, in this study, we explore the travel behaviour responses of low-income individuals to transit investments in Greater Toronto and Hamilton Area, Canada, using statistical and ML models. We first investigate how the model choice affects the prediction of transit use by the low-income group. This step includes comparing the predictive performance of traditional and ML algorithms and then evaluating a transit investment policy by contrasting the predicted activities and the spatial distribution of transit trips generated by vulnerable households after improving accessibility. We also empirically investigate the proposed transit investment by each algorithm and compare it with the city of Brampton’s future transportation plan. While, unsurprisingly, the ML algorithms outperform classical models, there are still doubts about using them due to interpretability concerns. Hence, we adopt recent local and global model-agnostic interpretation tools to interpret how the model arrives at its predictions. Our findings reveal the great potential of ML algorithms for enhanced travel behaviour predictions for low-income strata without considerably sacrificing interpretability.

Inferring origin and destination zones of transit trips through fusion of smart card transactions, travel surveys, and land-use data

This paper presents a data fusion method to infer the origin and destination zones of transit trips from smart card data. The fusion framework has disaggregate mixed multinomial logit models at its core that predict the most probable origin and destination zones of individual transit trips using smart card transaction records, land use data, and transit system characteristics. The logit models are estimated using revealed trip origin and destination responses from a travel survey that are augmented by land use and transit system data to provide contexts about the zones' trip generation and attraction potentials. For empirical analysis, the methodology is applied to the smart card system of the Greater Toronto and Hamilton Area. Specifically, it is tested under different system configurations (tap-on-only and tap-on-and-off) and for networks with substantial shares of automobile and walk access/egress. When applied to transit trips constructed from the smart card transactions, the estimated models successfully capture the spatial distribution of trip origin and destination at the traffic analysis zone level. The empirical analysis also demonstrates that the proposed fusion method can be appropriately used to reconcile information provided by transit smart card and travel surveys to generate up-to-date transit demand data necessary for public transport planning and operations.

Estimation of Transfer Time from Multimodal Transit Services in the Paris Region

A reliable public transport system is beneficial for people traveling in the metropolitan area. Transfer time in multimodal transit networks has been highlighted as one of the measures of public transport service quality. In this paper, we propose a novel method to estimate the passengers’ transfer time between the transit modes (i.e., train, metro, and bus) based on the 2018 Household Travel Survey in the Paris region, France. The transit trips with a single transit leg are primarily studied, wherein average wait time and mode speeds are estimated through an integrated linear regression model. Based on these inferences, transfer time is deduced within the trips of multiple transit legs. The decomposition procedure of journey time facilitates the estimation of the time components, and reveals the transfer variability in mode, time, and space. From the results, we find that the transfer to the railway modes, especially to the metro, costs less time on average than the transfer to the bus in the study area. The transfer patterns in the morning and evening peak hours are different regarding the transfer duration and locations. Lastly, the results’ reliability, method scalability, and potential applications are discussed in detail.

TTSAT: A New Approach to Mapping Transit Accessibility

Transit agencies have never had an accurate indicator of the extent of their service area based on riders’ door-to-door travel time. This is an important gap in knowledge, because travel time is one of the most important factors determining whether or not people will use public transit. This paper presents a powerful new travel time-based method to visualize and analyze transit service coverage—a computer application called the Time-Based Transit Service Area Tool (TTSAT). Unlike other service area metrics, TTSAT incorporates total trip travel time into the transit service area maps it generates. To make these travel-time estimates realistic, TTSAT integrates all segments of a complete, door-to-door transit trip into the trip time calculations. TTSAT’s mapping and analysis capabilities offer numerous potential applications for planners, developers, and members of the public working to create transit-accessible communities.

Trends in Mobile Transit Information Utilization: An Exploratory Analysis of Transit App in New York City

Smartphone applications that provide transit information are now very popular. However, there is limited research that examines when and where passengers use mobile transit information. The objective of this research was to perform an exploratory analysis of the use of a smartphone application known as Transit App, which provides real-time transit information and trip planning (schedule) functionality. Backend data from Transit App were examined by time of day and day of week in the New York City metropolitan area. The results show that the pattern of both the trip planning feature and overall real-time information usage follow the typical pattern of transit ridership, which has morning and evening peaks. Additionally, self-reported household locations of Transit App users in the New York City area were compared with household socioeconomic characteristics (specifically, income, ethnicity, and age) from census data using GIS visualizations and the Pearson correlation coefficient, but they do not appear to be correlated. This implies that passengers use Transit App regardless of household income, race, or age trends in their neighborhood. This exploratory study examined a rich new data source—backend data from a transit information smartphone application—that could be used in many future analyses to help transit agencies better understand how transit riders use information and plan their trips.

Using GIS for Measuring Transit Stop Accessibility Considering Actual Pedestrian Road Network

Bus stops attain their importance to the transit service from being the main points of contact between the passenger and the bus. Considering spatial attributes, both the location and the spacing of bus stops significantly affect transit service performance and passenger satisfaction, as they influence travel time in addition to their role in ensuring reasonable accessibility. Knowing that every transit trip begins and ends with pedestrian travel, access to a bus stop is considered a critical factor for assessing the accessibility of the stop location. In this research, transit stop access coverage is estimated based on the actual pedestrian road network surrounding the stop. Accordingly, new indices are developed to assess a bus stop location on a more spatial basis. These indices measure the accessibility of a bus stop through the surrounding road network in addition to the ratio of actual access coverage to the ideal access coverage of a stop.

Impacts of Express Bus Service on Passenger Demand

The Region of Waterloo, Ontario, is a rapidly-growing metropolitan area approximately 100 km west of Toronto. In 2005, the Region’s transit operator, Grand River Transit, introduced an express bus service, known as iXpress, along the central north-south corridor of the Region. This paper explores the impact of the iXpress service on transit user costs and passenger attraction. We employ a methodology to quantify the generalized cost (including waiting time, in-vehicle and transfer times) of transit trips between key destinations in the Region before and after the implementation of iXpress. We also develop a methodology to identify those customers who benefit from the reduced cost of the iXpress. Finally, we present the change in ridership (boardings) in the corridor pre- and post-implementation. From these demand and cost data, we compute transit elasticity of demand with respect to generalized cost.

Are Printed Transit Information Materials a Significant Barrier to Transit Use?

This study investigated the extent to which the lack of ability to use printed transit information materials correctly to plan transit trips is a barrier to transit use. A total of 180 people were asked to undertake two transit trip-planning assignments, each requiring the use of a system map, two route maps, and two schedules.The study found that only 52.5 percent of the sample, composed of both transit users and nonusers, was able to plan a transit trip successfully using standard printed information materials. The main problems existed at the latter stages of the trip-planning process involving schedule use (55.6% success rate). Although printed information materials were the most popular trip-planning media for transit users, more than half stated that they did not use this method to plan their trips.Additional questioning suggested that a relationship between transit trip-planning ability and transit use does exist. However, it was also found that while lack of information material comprehension is a problem, it is not a primary barrier to transit use—none of the nontransit users cited lack of transit trip-planning ability as their main reason for not using transit. Furthermore, a wide range of other information resources is available for transit users to choose from if they are uncomfortable with printed media.

Can Trip Planner Log Files Analysis Help in Transit Service Planning?

Transit trip planners are now found on most transit authority websites. This feature gives transit users a full itinerary from a point of origin to a destination. The web server on which the trip planner is installed usually stores usage logs on a daily basis. Log files contain data on origins, destinations, calculated paths, and other website entries. The main purpose of this article is to determine whether the analysis of trip planner log files can help to improve transit service by providing better knowledge on transit users. A website-oriented analysis and a transit-oriented analysis based on four years of observations on the Montreal Transit Commission website are presented. Results show that, even though not all transit users have access to the Internet or use the planner regularly, log files can be useful for identifying new locations to be accessed by a transit system, for better understanding user behaviors, and for guiding updates of the geographic information system (GIS) and the trip planner itself.

Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications

Many global high-density cities have embraced transit-oriented development (TOD) strategies around metro stations in a strong push toward promoting transit trips. However, the general TOD principles must be adapted to a variety of local features. Thus, conducting a baseline study that deciphers the locality-specific differences and similarities in the influence of TOD on metro ridership is of paramount importance. Using the case of Shanghai in China, this paper demonstrated a novel approach to unveiling the locality-specific influence of TOD on metro ridership that integrated the node-functionality-place model with interpretable machine learning. By exploring a wide range of explanatory variables, we discovered that the relative importance of TOD structural factors (e.g., node, place and functionality) and neighborhood sociodemographics as well as their interactions presented great spatial and temporal heterogeneity for boarding and alighting on weekdays and weekends. Among the TOD structural factors, functionality was of the highest importance, and a greater contribution was observed within the central districts during peak hours. Regarding the interactions among the TOD structural factors, the interaction between node and functionality presented the highest relative importance, followed by that between place between functionality and that between node and place. Additionally, neighborhood sociodemographics also accounted for a noticeable contribution, especially in the outskirts. Based on the locality-specific estimations, the affinity propagation clustering algorithm was further used to cluster the TODs into difference groups as nuanced representations of the TOD-metro ridership relationships. The findings refreshed the knowledge base concerning the spatiotemporal heterogeneity in the nonlinear influence of TOD on metro ridership and provided new insights into spatial planning. The proposed approach showed high computational feasibility with strong theoretical underpinnings and thus offered broad potential applications into other high-density cities worldwide.

Measuring the impacts of dockless micro-mobility services on public transit accessibility

Dockless micromobility services have potential as a fast and flexible solution to short-distance trips and public transit's first-mile/last-mile (FM/LM) access problem; however, these services also have limitations, including uneven spatial distribution, low capacity, and user out of pocket expense. This can impact on the ability of micromobility to enhance public transit accessibility. We introduce accessibility increment measures – the amount by which public transit accessibility improves due to micromobility services. We apply these measures to hypothetical trips using public transit and micromobility data from Columbus, Ohio, USA. We find dockless scooters can increase accessibility by multimodal public transit trips, with increments in the first mile significantly outweighing last mile accessibility increments. Accessibility increments are highly concentrated in the city center due to the distributions of scooters and bus stops. We also find that scooters' accessibility increment contribution is highly unequal: a small number of scooters contribute most of the accessibility increments. Monetary cost simulations show that the first-mile accessibility increment will rapidly decrease and last-mile increment slightly increase with lower willingness to pay. Capacity simulations show a group of users' accessibility increment will rapidly decrease as the group size increases, but this depends on whether they are competing or collaborating for scooters. Our results show that despite showing promising potentials, vendors and policymakers still need to address these issues to make collaboration between public transit and dockless micromobility sustainable and equitable. The paper provides measures and evidence for future transit and micromobility planning for scooter vendors and transit authorities.

Understanding the ground access and airport choice behavior of air passengers using transit payment transaction data

We investigate the ground access and airport choice behavior of individual air passengers that use rail transit in their ground transport to airports. Using a novel dataset of payment transaction records of rail transit trips, we calibrate a binomial logit model of individual's choice between two airports in Beijing, China to examine air passengers' preferences for ground rail service attributes including travel time, travel cost and number of transfers and compute the implied value of travel time and transfer penalty. We further visualize the spatial distribution of access and egress air ridership across the city at the rail transit station level with a set of heat maps and develop a gravity model to understand factors that affect the air ridership at individual stations. Our findings have significant implications for airport planner and transit authorities in assessing the potential benefit of airport rail link projects and improving the efficiency of regional airport systems.