Public Transport Trips Research Articles

Analyzing the reporting error of public transport trips in the Danish national travel survey using smart card data

AbstractHousehold travel surveys have been used for decades to collect individuals and households’ travel behavior. However, self-reported surveys are subject to recall bias, as respondents might struggle to recall and report their activities accurately. This study examines the time reporting error of public transit users in a nationwide household travel survey by matching, at the individual level, five consecutive years of data from two sources, namely the Danish national travel survey (TU) and the Danish smart card system (Rejsekort). Survey respondents are matched with travel cards from the Rejsekort data solely based on the respondents’ declared spatiotemporal travel behavior. Approximately, 70% of the respondents were successfully matched with Rejsekort travel cards. The findings reveal a median time reporting error of 11.34 min, with an Interquartile Range of 28.14 min. Furthermore, a statistical analysis was performed to explore the relationships between the survey respondents’ reporting error and their socio-economic and demographic characteristics. The results indicate that females and respondents with a fixed schedule are in general more accurate than males and respondents with a flexible schedule in reporting their times of travel. Moreover, trips reported during weekdays or via the internet displayed higher accuracies compared to trips reported during weekends and holidays or via telephone interviews. This disaggregated analysis provides valuable insights that could help in improving the design and analysis of travel surveys, as well accounting for reporting errors/biases in travel survey-based applications. Furthermore, it offers valuable insights underlying the psychology of travel recall by survey respondents.

Smarter but more unequal transport? How socioeconomic and digital inequalities hinder adoption of mobility apps in the Global South

All over the world, cities are becoming “smarter.” The improved use of data generated by the transport system (e.g. smart cards data), combined with GPS trackers and citizens’ mobile data enabled the development of a new generation of smarter mobile apps (e.g., Waze) and several local real-time public transport services and trip planners. These technologies have the potential to reduce transport inequalities via two mechanisms. First, governments can develop more efficient and evidence-based public transport plans. Second, citizens have access to better, real-time information about public transport for trip planning and, thus, can make better—i.e., more efficient or cheaper—decisions regarding their everyday mobility. To assess whether citizens’ appropriation of smart mobility—as with their appropriation of any other digital service—depends strongly on having a certain set of socioeconomic and digital attributes, we analyzed both a mobility survey and a digital inequalities survey based on representative samples of adult Uruguayans living in the capital Montevideo. We find that while the use of mobility apps increased in the second half of the 2010s, the adoption of these tools was stratified by traditional digital inequalities, such as type of internet access and digital literacy, and by variables that predict inequalities in both digital and transport fields, such as age, gender and education. As a result, mobility continues to be stratified in favor of those who are more knowledgeable (i.e., tech-savvy) and have greater access to digital technology. Thus, “smart mobility” has failed to ameliorate—and may even exacerbate—transport inequality. Policy implications of these findings are discussed.

The Impacts of Remote Work and Attitudinal Shifts on Commuting Reductions in Post-COVID Melbourne, Australia

This paper analyses the commuting frequencies and modal choices of travellers in Melbourne, using a dataset reflecting travel behaviour before and after COVID-19. A factor analysis of 63 latent variables identified seven key factors, which were used in cluster analysis to examine the relationships between latent constructs, land use, and socio-demographic variables, as well as commuting behaviours. The analysis categorised white-collar employees into four groups based on their remote work engagement, with socio-demographics and industry type as key factors. The analysis shows that female clerical and administrative workers who worked from home during the pandemic are now returning to the office, raising gender equality concerns within society. Meanwhile, the education and training sector mandates office attendance despite the feasibility of remote work, as universities prioritise in-person attendance to attract more international students, impacting societal norms around telecommuting. The analysis revealed that saving on commute costs, reducing travel time, and spending more time with family are the among the primary factors influencing travel behaviour among white-collar employee’s post-pandemic. The study found that the decrease in public transport trips is associated with increased telecommuting rather than service dissatisfaction, especially among Central Business District (CBD) employees who still rely on public transport. This trend suggests that the CBD sector’s growing acceptance of remote work is reducing daily commutes, which puts additional pressure on public transport providers to sustain and improve their services. A decline in service quality could further reduce ridership, highlighting the need for consistent, high-quality public transport. Furthermore, the study found that increased telecommuting is likely to reduce car trips in the future, especially among healthcare and social workers who prefer driving due to public transport’s unreliability for their demanding schedules. By examining variables like the advantages and disadvantages of working from home, convenience, accessibility, and the efficiency of public transport, this study enhances the understanding of transport behaviour and underscores the need to improve public transport reliability to support sustainable cities as remote work grows.

Is public transport a promising strategy for increasing physical activity? Evidence from a study of objectively measured public transport use and physical activity

BackgroundGreater public transport use has been linked to higher physical activity levels. However, neither the amount of physical activity associated with each daily public transport trip performed, nor the potential total physical activity gain associated with an increase in trips/day, has been determined. Using objective measures, we aimed to quantify the association between public transport use, physical activity and sedentary time.MethodsA longitudinal study of Australian adults living in Hobart, Tasmania, who were infrequent bus users (≥ 18 years; used bus ≤ 2 times/week). The number of bus trips performed each day was determined from objective smartcard data provided by the public transportation (bus) provider across a 36-week study timeframe. Accelerometer measured steps/day (primary outcome), moderate-to-vigorous physical activity (min/day), and sedentary time (min/day) were assessed across four separate one-week periods.ResultsAmong 73 participants across 1483 day-level observations, on days that public transport was used, participants achieved significantly more steps (β = 2147.48; 95%CI = 1465.94, 2829.03), moderate to vigorous physical activity (β = 22.79; 95% CI = 14.33, 31.26), and sedentary time (β = 37.00; 95% CI = 19.80, 54.21) compared to days where no public transport trips were made. The largest increase in steps per day associated with a one-trip increase was observed when the number of trips performed each day increased from zero to one (β = 1761.63; 95%CI = 821.38, 2701.87). The increase in the number of steps per day was smaller and non-significant when the number of trips performed increased from one to two (β = 596.93; 95%CI=-585.16, 1779.01), and two to three or more (β = 632.39; 95%CI=-1331.45, 2596.24) trips per day. Significant increases in sedentary time were observed when the number of trips performed increased from zero to one (β = 39.38; 95%CI = 14.38, 64.39) and one to two (β = 48.76; 95%CI = 25.39, 72.12); but not when bus trips increased from two to three or more (β=-27.81; 95%CI=-76.00, 20.37).ConclusionsGreater public transport use was associated with higher physical activity and sedentary behaviour. Bus use may yield cumulative increases in steps that amount to 15–30% of the daily recommended physical activity target. A policy and public health focus on intersectoral action to promote public transport may yield meaningful increases in physical activity and subsequent health benefits.

A framework to include socio-demographic characteristics in potential job accessibility levels in low-car and car-free development areas in the Netherlands

Car-free development has become popular in recent years due to concerns regarding transport-related health issues in urban areas as well as a growing trend toward sustainability and environmentally friendly living. Although car-free development is regarded as progress to promote active transport modes and healthier cities, the accessibility impacts for its residents remain unclear. To address this knowledge gap, this paper proposes a job accessibility assessment framework that integrates individual and household socio-demographic characteristics into a job accessibility assessment, making it possible to account for commuting preferences of different population groups in accessibility analyses. For this purpose, a stated choice survey was conducted in existing low-car areas in the Netherlands to determine transport use and perception of public transport trip characteristics. Then, the influence of socio-demographic characteristics on trip perceptions was analyzed using a Latent Class Logit (LCL) regression model and Monte Carlo simulations. Finally, a multi-modal transport network combining walking and public transport trips was used to assess potential job accessibility levels of different population groups in a car-free development area. The proposed framework was implemented in a case study in the province of Utrecht (the Netherlands). Results show notable differences between the job accessibility levels within different population groups, reflecting distinct perceptions toward commuting trip characteristics based on socio-demographic characteristics and demonstrating the suitability of the applied approach to assess accessibility levels in car-free development areas. Compared to the sample average distribution, more than 15% lower accessibility levels were observed for starters (age 18-35) in some urban areas, indicating the aversion to longer and more expensive commuting trips. Contrarily, increased accessibility levels for families (>2 persons in household) were observed, demonstrating the acceptance to experience longer commuting travel times and additional costs. No differences were observed between accessibility levels of the sample average and senior adults (age >50).

Scenarios for New Mobility Policies and Automated Mobility in Beijing

In this study, we consider the introduction of new mobility services and technologies into the megacity of Beijing, China, as per developed strategy and action plans, in order to investigate their potential contribution to sustainable mobility. This includes population relocation (decentralization), the construction of new rail lines, the introduction of shared bike services as a feeder to subway stations, the electrification of passenger vehicles and the adoption of automated and shared vehicles. The well-established, system dynamics-based MARS model is adapted to Beijing and further improved via the inclusion of these new services, technologies and policies. We find that decentralization can have a profound effect on overall sustainability if not considered in conjunction with other policies and that new rail lines and shared bikes may only have benefits in specific zones. Shared and automated vehicles could increase VKT by 60% and reduce active and public transport trips by a quarter. As such, nuanced integrated policy approaches will be required that are similar to those currently in place, such as imposed car shedding and taxi fleet control.

The influence of individual physical capabilities for cycling adoption: Understanding its influence and mode-shift potentials

We show the importance of individual physical capabilities for cycling in Switzerland, as well as introducing a routing-tool that integrates these capabilities into the estimation of cycling demand potentials. A binary logit model to predict daily cycling was estimated, using data from the Swiss Health Survey. It is found that the frequency of physical exercise explains most of the variability of the choice to cycle or not. Since previous work in the literature has shown a clear link between exercise frequency and physical strength, one valid, although so far not directly observable hypothesis is that more physical power leads to faster speeds and higher competitiveness of cycling against other modes. The higher power output of E-Bikes and S-Pedelecs further increase cycling potentials in this context. We therefore use data from the Swiss Health Survey to impute cycling potentials for individuals who participated in the Swiss Mobility Microcensus and evaluate mode-shifts potentials to bikes based on travel times of public transport and car trips. Because our router makes use of an elevation-aware physical model, which takes into account bike and cyclist characteristics, such as input power and weight, we also model pedelecs and speed-pedelecs and show how such bike types increase the mode-shift potentials towards bicycles.

New challenges for sustainable urban mobility

Micromobility provide intelligent, fast, and sustainable trips for tourist from accommodation facilities to attraction locations. However, some points of interest may be located away from the accommodation i.e. in a different city of a metropolitan area. In this case, cycling to the attractions may not be possible and multimodal bike and public transport trips can be considered for more sustainable tourism. Therefore, it is essential to identify how adequate a city transport infrastructures and services are for these types of trips. In this paper, we propose a cycling indicator for tourism accessibility with the aim of defining the level of multimodal bike-public transport friendliness through numerical values related to a specific zone in the main city of a metropolitan area. The indicator was applied to the metropolitan area of Bari (Italy) and results showed that the presence of bike lanes and bike-sharing plays an important role.

Mobility-as-a-Service and unsustainable travel behaviour: Exploring the car ownership and public transport trip replacement side-effects of the MaaS paradigm

Car-centred development adds significantly to the climate change threat our planet faces. Mobility-as-a-Service (MaaS) is a concept looking to generate modal shift via the digital and physical integration of alternative on-demand transport services. Recent research raises concerns, however, that contrary to the hype, MaaS may result in unsustainable travel behaviour. On the one hand, future MaaS users that are now primarily drivers may not be ready to shift away from their cars, while, on the other hand, people regularly using public transport may substitute some of these journeys with MaaS-enabled car-sharing, ride-sharing and ride-hailing services. Herein, we study the effects of MaaS on intentions to own a car and to substitute public transport trips with car-centric services, via a UK-based quantitative survey. We use Principal Component Analysis and Ordinal Regression Modelling to see how MaaS-related perceptions referring to Added Value, Car Use Convenience and Enjoyment, Human Element Externalities, Trust in Functionality, Cost Incentives, Car Use Morality, Trust in Enabling Technology and Car Ownership Necessity affect usage intentions. Our results highlight some, typically less discussed, grey areas of MaaS and inform the design of policies, including car service disincentivitisation, responsible user benefits, MaaS rebranding and public transport investment prioritisation.

The examination of the relationship between Internet use at home, on the move and trip frequencies of public transport and private vehicle before and during the COVID-19 pandemic

ABSTRACT The Internet has significantly changed the way people travel. It allows people to reorganize activities in time and space. Due to its important policy implications, a body of empirical research has emerged to examine the relationship between Internet use and travel behaviour, spanning several decades. One of the less researched areas is where people use the Internet and how this relates to the use of different transport modes. Recently, this relationship might have become more complicated due to the coronavirus pandemic changing the extent to which people use the Internet and travel. Since future outbreaks are highly likely to happen, understanding the changes in this relationship during this unprecedented time is critical to make more resilient transport plans. This study utilized three-years of National Travel Survey data in the UK and statistical models to address two research question: (1) Does using the Internet at home or on the move for non-work purposes generate additional non-commute trips by car or public transport?; and (2) did this relationship changed during the COVID-19 pandemic? Our results showed that, although the total number of trips significantly reduced in 2020 due to the COVID-19 pandemic, frequent Internet users generated more trips, and the location of Internet use mattered, even during the pandemic. People who frequently used the Internet at home generated more trips by car, while those who used the Internet on the move had more public transport trips. However, people who used the Internet more often at home or on the move during the COVID-19 pandemic generated more trips in total, and more car trips for non-commuting purposes compared to those who used the Internet less frequently. Our results revealed an important role of ICTs in travel demand during the pandemic and raises potential social exclusion issues.

Exploring the Resilience of Public Transport Trips in the Face of Urban Violence from a Gender Perspective

Public transport systems that ensure safe and efficient mobility are essential to promote sustainability in cities. However, public transport is susceptible to violence. Additionally, men and women have distinct perceptions of security, which can lead to different reactions in the face of danger. Therefore, considering this situation from a gender perspective, the goal of this study is to assess the levels of resilience in trips made by public transport users when exposed to violence. Data were collected from 763 individuals (women: 60.8%; men: 39.2%) within the academic community of a university campus in Rio de Janeiro, Brazil, through an online questionnaire. The information obtained included the participants’ socioeconomic details, security perceptions, and changes in travel patterns due to security concerns. The results of the Cronbach’s alpha test (0.842) indicated a good internal consistency within the data. Chi-squared tests of independence were applied, and calculations for effect size measures were conducted to evaluate the possible association between gender and other variables. Regarding the level of resilience of the trips made, evidence was found that the perception of safety and the behavior of public transport users are influenced by their gender. Regarding the gender of the respondents, an association was found with the resilience levels of trips. Furthermore, it was found that women are more vulnerable to violence in public transport than men, with risks of feeling unsafe during walks to or from stations and on buses being 1.1 and 1.5 times higher, respectively. Additionally, it was observed that the behavior of public transport users is significantly influenced by past victimization experiences, prompting measures for greater protection to be sought. The results of this study allow for a better understanding of how men and women feel exposed to violence when using public transport and can contribute to the creation of public policies to promote safety. Additionally, they can assist security authorities in directing and concentrating police efforts more effectively.

Introducing shared, electric, autonomous vehicles (SAEVs) in sub-urban zones: Simulating the case of Vienna

Shared, autonomous electric vehicles (SAEVs) are expected to enter the market in the coming decades. Using MATSim, we simulate a use case where SAEVs are introduced in multiple suburban zones at the outskirts of Vienna (Austria), which are characterized by relatively low population density, but have access to at least one rail-based public transport stop. For all combinations of different fleet sizes and fare levels, we find that a relatively small share of car trips by residents of these zones (7%–14%) are replaced by SAEVs, generating CO2 emissions reductions of 5%–11%. Moreover, 23%–35% of trips previously undertaken by foot or bicycle are replaced by SAEVs, as well as 10%–20% of public transport trips. The potential of SAEVs to reduce the use and ownership of private vehicles in suburban areas therefore seems to be rather limited, which is also reflected in our finding that one SAEV usually replaces only 2–4 private vehicles. The potential becomes somewhat larger when the usage and ownership of private cars is assumed to become more expensive, leading to 17%–20% of car trips being replaced by SAEVs and generating CO2 emissions reductions of up to 32%.

Application of an Improved A* Algorithm for the Path Analysis of Urban Multi-Type Transportation Systems

The modern urban transportation service network could be split into unrestricted and restricted networks depending on whether travelers face limitations in route selection. Along with the continuous expansion of the city, it is difficult for travelers to find a more reasonable travel solution when confronted with such a complex transportation service network, which combines both unrestricted and restricted networks, especially for the park-and-ride (P&R) travel mode. This paper addresses the issue of route analysis in modern urban transportation service systems to provide travelers with reasonable travel solutions based on multiple types of transportation services. An improved A* algorithm is proposed to address the optimal path analysis for restricted networks to provide reasonable travel solutions for public transportation trips. Furthermore, by establishing the topological relationship between restricted and unrestricted networks, this paper presented an improved A* algorithm based on hybrid networks that solves the optimal path analysis problem for P&R trips, bringing convenience to many urban travelers.

Modeling package delivery acceptance in Crowdshipping systems by Public Transportation Passengers: A latent class approach

The subject of this research is to model the probability of accepting package delivery tasks in Crowd-Shipping (CS) systems focusing on shipping packages by Public Transportation (PT) passengers. Real-world implementation of such a PT-based CS initiative requires a proper grasp of the behavior of crowd shippers, package receivers, and senders, among which crowdshippers are at the center of attention. Given survey data collected from 2,208PT passengers in the Sydney metropolitan area, this study constructs a model for package delivery task acceptance among PT passengers who express a willingness to participate as crowdshippers. A Latent Class (LC) choice model is developed and estimated to explore different classes of participant characteristics and preferences for accepting CS delivery tasks, under different levels of offered incentive, package weight, and required detour distance at the destination. As a key finding, the model identifies three classes, namely “leisurely”, “avid”, and “skeptical” users with class sizes of 19%, 53%, and 28%, respectively. Leisurely users tend to be majorly employed with relatively higher ages and lower PT trip frequency per month. This class shows a potential desire to take CS tasks as long as they are not logistically burdensome. Avid users, forming the largest class of the population, are mostly younger PT passengers who are less sensitive to task difficulty and might accept even CS tasks with minimum incentives (around AU$3). Skeptical users, displaying high PT trip frequency per month and high income, are unwilling to accept the CS tasks unless get compensated well.

Predicting modal choice for urban transport using an algebraic equation

Demand estimation and forecasting is an essential step in urban passenger transport planning. Relating the factors that influence the modal choice behavior of individuals facilitates demand estimation. In this study, we develop machine learning models that consider individuals' demographic, socioeconomic, and travel characteristics to justify their mode choice. Two datasets are used to train and validate the models. We use logistic regression and multilayer perceptron models to classify public or private transportation trips. It was observed that a multilayer perceptron model with a low number of parameters could predict modal selection with an accuracy exceeding 90%. We derive an algebraic equation from this result to perform modal selection prediction. Our results show that the models can effectively predict the mode of transportation of individuals based on their demographic and travel characteristics.

Estimation of the aggregation degree of public transport use among elderly people based on urban built environment

With the accelerated aging process, the increased willingness of elderly people to travel, and the concessionary bus travel policy implementation, public transport, especially buses, has become one of the important ways for China's elderly people to participate in social activities. Meanwhile, the increased use of public transport by elderly people results in serious conflicts with commuters. In order to understand details about when, where and why public transport trips of the elderly become large, this study investigates the aggregation degree of public transport use among the elderly as well as its determinants mainly from the viewpoint of spatial characteristics. Based on Smart card data and Baidu Map API, the aggregation degree of public transport use in elderly people is rated on a scale of 1 to 5. And then its correlation to the microscopic built environment is analyzed by using negative binomial regression. The results indicate that public transport use by elderly people is highly aggregative in both spatial and temporal dimensions. It is also found that the urban built environment has a significant effect on the aggregation degree of public transport use in the elderly and significant variables for four time periods on both weekdays and weekends are quite different. The proximity of housing estates, elderly activity centers, universities for the elderly, and medical and shopping facilities near bus stations increases the aggregation degree of public transport use by elderly people. This study will provide a reference basis for optimizing the public transport system in the aging society.

Residential Electricity Consumption Patterns and their Relationship to Commute Times by Mode

In this study, the correlation between emerging electricity consumption patterns in the Chicago area and commute times to work by mode are explored. We found that when aggregating similar daily electricity consumption curves, areas sharing similar electricity patterns are geographically closer to each other and located in regions that are strictly urban or suburban. Furthermore, we modelled these clusters in terms of commute times to work by mode. The findings suggest that morning electricity consumption differs significantly among clusters and that it is caused primarily by long duration public transport trips.

Investigating the role of micromobility for first- and last-mile connections to public transport

The integration of private and shared micromobility with existing public transport services is often presented as a valuable step to improve urban mobility systems. In this study, the combination of micromobility and active modes with public transport was examined through a survey that included a stated preference experiment. Normal error component mixed logit models were estimated to determine how commuters in County Dublin, Ireland, travel on the first- and last-mile of public transport trips. The survey results shed light on the influence of user characteristics and mobility patterns on mode choice for multimodal public transport trips including private and shared micromobility, allowing to analyse trade-offs between the different micromobility modes and active modes. The mixed logit model analysis shows that young (<35 years old) and male respondents present a strong preference for micromobility, while older and female respondents prefer walking. Overall, walking is preferred by most respondents both for the first- and the last-mile, even if the other options provide significant travel time reductions. The importance of secure parking availability at stations is also reflected in the results and shows that a seamless transition between the modes and an adequate provision of infrastructure are crucial to facilitate micromobility adoption. Age, gender, previous experience with micromobility, and interest in micromobility are found to be significant factors that influence mode choice. These results confirm a growing interest for micromobility in younger generations, while also showing that regulations and adequate infrastructure are needed to promote equitable access to these modes, with a particular focus on attracting users from more carbon-intensive modes such as private car and ride-hailing trips.

Investigating occasional travel patterns based on smartcard transactions

Public transportation (PT) studies often neglect non-routine trips focusing predominantly on commuting. However, recent research revealed that occasional trips make up a substantial portion of public transport journeys, and traveler preferences for non-routine trips diverge from their preferences for regular commuting. We study non-routine trips based on a database of 63 million smartcard (SC) records of PT boardings made in Israel during June 2019. The characteristics of these trips are revealed by clustering PT users’ boarding records based on the location of the boarding stops and time of day, applying an extended DBSCAN algorithm. Our major findings are that (1) conventional home-work-home commuters are a minority in Israel and constitute less than 15% of the riders; (2) at least 30% of the PT trips do not belong to any cluster and can be classified as occasional; (3) The vast majority of users make both recurrent and occasional trips. A linear regression model provides a good estimate (R2 = 0.85) of the number of occasional boardings at a stop as a function of the total number of boardings, time of day, and land use composition around the location of trip origin. We discuss the potential applications of our approach in the landscape of diverse flexible PT.

Measuring Effectiveness of Non-Motorized Vehicles on Spatial Extent for TOD Development: A Case Study for MRT 6 in Dhaka

To improve the effectiveness of conventional walking-based Transit-Oriented Development (TOD), introducing Non-Motorized Vehicles (NMV)-based TOD will be a more efficient alternative since it will increase the spatial extent of the TOD node’s buffer area. This paper discusses the available Bicycle-based TOD (BTOD) concept. Based on this concept, the spatial extent of NMV-based TOD (NTOD) has been calculated. A trip-based survey has been conducted on the selected regions of Dhaka along the line of MRT 6 regarding who uses NMV (Rickshaw, Bicycle) and public transport (PT) for his daily commuting. A trip chain analysis was performed first to capture the trip pattern of daily commuters. From the trip chain analysis, it has been found that PT-based trip chains dominated the city’s entire trip chain. Consequently, it has also been found that walking-only and walking-NVM accessible trips dominate 32% and 23% of the entire PT trip chains. However, based on the findings of trip chain analysis to calculate the spatial extent by developing regression models, it has been observed that the access trip length for walking ranges from 1.2-1.4 km, whereas the access trip length for NVM ranges from 4-4.7 km. In Dhaka, the catchment area of NTOD can increase the accessibility potentially by spatial extent expansion of about 70% fro m the conventional TOD. Therefore, it can be inferred that NVM will give more spatial accessibility than walking. As a result, a new window of planning and design strategies will be opened for the planners and policymakers around TOD station area planning considering NMV. Along with strategic recommendations, limitations, and future research agendas have also been discussed in this paper.