Active Transport Modes Research Articles

Travel mode choice behavior analysis using multinomial logit models towards creating sustainable college campus: a case study of the University of Toledo, Ohio

By employing descriptive statistics and multinomial logistic regression models, the paper investigates factors that affect the travel mode choice behavior of a college community for both commuting to the campus from their residences and commuting on campus from one facility to another. Results show that car ownership, travel distance, age, gender, academic status, income, perception of safety, number of weekly trips, and weather conditions affect people’s mode choice decisions significantly. The study finds that increasing travel distance, age, and being a staff member positively impact car mode choice, faculty members are more likely to bike and walk compared to undergraduate students, and unlike the authors’ predictions, the higher-income people are more likely to walk on campus. Results also reveal that car users change travel modes more often than active transport mode users based on semesters and that although women are less likely to bike to the campus, they are more likely to walk and bike on campus. If the number of trips increases, people prefer cars to walk, bike, and ride a bus to commute to the campus while they prefer walking to driving a car on campus. The study contributes to help create pollution-free and healthy urban college campuses that, on larger scales, will further contribute to developing sustainable cities to achieve the Sustainable Development Goals as described by the United Nations, particularly goal number 11, “to make cities and human settlements inclusive, safe, resilient and sustainable,” and thereby contribute to improving urban living conditions.

A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater Melbourne

The field of spatial analysis in traffic crash studies can often enhance predictive performance by addressing the inherent spatial dependence and heterogeneity in crash data. This research introduces the Geographical Support Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assess spatial variations and evaluate the influence of a wide range of factors, including traffic, infrastructure, socio-demographic, travel demand, and land use, on the incidence of total and fatal-or-serious injury (FSI) crashes across Greater Melbourne’s zones. Utilizing data from the Melbourne Activity-Based Model (MABM), the study examines 50 indicators related to peak hour traffic and various commuting modes, offering a detailed analysis of the multifaceted factors affecting road safety. The study shows that active transportation modes such as walking and cycling emerge as significant indicators, reflecting a disparity in safety that heightens the vulnerability of these road users. In contrast, car commuting, while a consistent factor in crash risks, has a comparatively lower impact, pointing to an inherent imbalance in the road environment. This could be interpreted as an unequal distribution of risk and safety measures among different types of road users, where the infrastructure and policies may not adequately address the needs and vulnerabilities of pedestrians and cyclists compared to those of car drivers. Public transportation generally offers safer travel, yet associated risks near train stations and tram stops in city center areas cannot be overlooked. Tram stops profoundly affect total crashes in these areas, while intersection counts more significantly impact FSI crashes in the broader metropolitan area. The study also uncovers the contrasting roles of land use mix in influencing FSI versus total crashes. The proposed framework presents an approach for dynamically extracting distance matrices of varying sizes tailored to the specific dataset, providing a fresh method to incorporate spatial impacts into the development of machine learning models. Additionally, the framework extends a feature selection technique to enhance machine learning models that typically lack comprehensive feature selection capabilities.

Adopting smart technologies to encourage active transportation: a survey of pedestrians and cyclists in Perth, Western Australia

Cities are facing growing challenges and demand for urban transportation due to population growth and increased urbanization. Active transportation (AT), specifically walking and cycling are valuable modes of sustainable transportation that can mitigate the environmental impacts of motorized transportation and improve urban environment and people's health. Embracing technological solutions aimed at reducing obstacles to active transportation can create a more accommodating environment, that is not only ecologically sustainable, but also encourages individuals to opt for active modes of transportation. This paper investigates people's attitudes toward applying technology to increase AT. An online survey in Perth, Western Australia was conducted to explore pedestrians’ and cyclist’s attitudes toward using technology to facilitate AT. Findings suggest technological applications that improve safety and feasibility of active travel are the most impactful actors to encourage AT. These results can provide insight for designers, urban planners, and decision-makers when developing technologies or designing urban technological systems.

Decarbonizing Urban Mobility: A Methodology for Shifting Modal Shares to Achieve CO2 Reduction Targets

In most urban areas, mobility is predominantly reliant on automobiles, leading to significant negative environmental impacts, such as noise pollution, air pollution, and greenhouse gas emissions. To meet the objectives of the Paris Agreement, urgent action is required to decarbonize the mobility sector. This necessitates the development of assessment and planning tools to create effective decarbonization scenarios. Urban mobility must evolve to reduce dependency on fossil fuels by increasing public transport options and promoting active modes of transportation. This research presents a methodology to estimate the modal share required to shift car users to active modes and public transport, thereby achieving future CO2 emission reduction targets in the road transport sector. A case study in Braga, Portugal, demonstrates that to meet the 2040 target of 59,150 tons of CO2, 63% of trips must be made using active modes (e.g., walking and cycling) and 32% by public transport.

User intention to adopt public bicycle sharing system: a priori acceptance approach

ABSTRACT Bicycle-sharing services have received worldwide attention as a sustainable form of transportation. This study explores a priori acceptance of public bicycle-sharing system (PBSS) in India, which is still in the early phases of adopting PBSS. The effects of psychological factors, such as perceived usefulness (PU), perceived ease of use (PEoU), perceived fun (PF), health value (HV), and environment values (EV), on intention to use PBSS are studied, utilizing an extended technology acceptance model. 747 samples were collected from online questionnaires in Dehradun, India. Results of Structural Equation Modelling revealed that intention to use PBSS is strongly predicted by PU and PF together, while PF mediates the influence of PEoU. Furthermore, EV and HV generate positive behavior intention to use PBSS through PU, PF, and PEoU. The study suggests promoting PBSS as a green and active transportation mode offering high PU and enjoyment, with theoretical and practical implications discussed.

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).

Demographic Analysis of Active Transport Mode Users in Urban Context

Demographic Analysis of Active Transport Mode Users in Urban Context

Can self-reliant societies be potential adopters of electric bicycles? Examining the role of sociopsychological influences among the university employees in India

Electric bicycles (e-bikes) are gaining popularity globally as green and active modes of transport. Research on e-bike adoption to date has predominantly investigated various extrinsic motivations to use an e-bike, and little is known about the psychological influences. Examining the influence of psychological determinants is crucial to comprehend the unobserved individual and social factors affecting e-bike adoption. This study aims to determine the sociopsychological influences associated with e-bike use among the daily commuters (employees) of two university campuses in India which represent self-reliant societies. The study extends the classic Theory of Planned Behaviour (TPB) framework to account for influences like personal norms, knowledge of e-bikes, and parking anxiety, in addition to fundamental TPB constructs (attitudes, subjective norms, and perceived behavioural control). Results are constructed by a structural equation model (SEM) for a sample of 347 non-users of e-bikes. SEM model indicates that among all TPB constructs, subjective norms are the strongest predictors of intentions to use e-bikes. Both subjective norms and attitudes have a significant positive effect on adoption intentions, whereas perceived behavioural control has a significant negative effect. Personal norms had significant indirect positive effects on e-bike use intentions with fundamental TPB constructs as mediators. Interestingly, parking anxiety (anxiety from missing the car parking spaces) positively, and knowledge of e-bikes negatively influence the e-bike adoption intentions. Further, comparison by psychological influences reveals that a significant difference exists between the perceptions of the two university commuters towards e-bike use, possibly due to the distinct terrain and climate characteristics of the universities. The study findings have implications for designing effective e-bike interventions and awareness programs seeking to promote e-bike adoption, particularly in self-reliant communities.

Qualitative perspectives of bike share schemes in Melbourne, Australia

BackgroundBike share schemes are operational in many countries and aim to encourage modal shift from sedentary to active modes of transport. Understanding user experiences of bike share schemes can contribute to knowledge of motivations to use the schemes and barriers to use. Qualitative research into user experiences of bike share schemes in Australia has been conducted in Brisbane but not Melbourne, and most bike share scheme research is non-peer reviewed literature. This study describes experiences of bike share schemes in Melbourne. MethodsSemi-structured interviews were conducted with 21 bike riders in Melbourne, Australia some of whom used bike share schemes and some who did not. Interviewees were asked about their experiences using bike share schemes in Melbourne. ResultsBike share schemes provide a way for non-users to try bike riding without needing to purchase a bicycle. E-bike schemes are beneficial to people with health issues. User experiences of bike share schemes are not only related to the service itself, but also interactions with other road users and the overall bike riding environment. Mandatory helmet legislation was a barrier to using bike share schemes due to hygiene and convenience concerns. ConclusionsBike share schemes act as an entry to bike riding for people who do not own a bicycle. E-bike fleets are more accessible to people with health or mobility issues. Mandatory helmet legislation could be limiting bike riding's mode share in Melbourne. These findings are relevant to policy makers aiming to increase bike riding's mode share, introduce or expand bike share schemes or mandatory helmet legislation, or a combination of the two.

Widening the gap? Evidence from Germany for changes in the attitude behaviour gap for transportation during COVID-19

Transportation is a main contributor to global greenhouse gas emissions. For a sustainable transformation, consumers play a crucial role. While sustainability gains importance and is reflected in consumers' attitudes, consumers' behaviour does not always align with their sustainability attitudes - a discrepancy called the attitude behaviour gap. The COVID-19 pandemic severely disrupted people's daily lives and transport choices, potentially altering their attitude behaviour gap. Thus, the pandemic presents a unique opportunity to understand how crises and disruptions can lead to changes in the alignment between sustainable intentions and actions. Using local survey data from Germany, we identify socio-economic factors and transport infrastructure as key contributors to misalignments between attitudes and behaviour. Notably, during the pandemic, this gap widened, particularly among women and those with children, increasing inequalities among different socio-economic groups. This highlights the need for tailored policies to address the distinct mobility needs of these groups to allow them to act according to their attitudes and counteract increasing inequalities. Moreover, our findings underscore the importance of transitioning from individual motorized transport to public and more active modes of transport. This transition is particularly crucial in the German context, where levels of individual motorized transport are exceptionally high.

Travel behaviour changes among post-secondary students after COVID-19 pandemic – A case of Greater Toronto and Hamilton Area, Canada

Recent research has reported travel behaviour changes during the COVID-19 pandemic. Speculatively, these short-term disruptions in travel may lead to new habit formation and longer-term travel behaviour changes among young adults belonging to generations Y and Z. Focusing on post-secondary students within Greater Toronto and Hamilton Area, Canada and using longitudinal data collected in fall 2019 and spring 2022, our exploratory study examined the post-COVID-19 travel behaviour changes and analyzed whether these changes are associated with their socio-demographic characteristics and life events experienced over the course of pandemic. Results show that many public transit users and active travellers (pedestrians and cyclists) switched to cars for commuting post-pandemic. The post-pandemic retention of public transit use was lower compared to cars, while active transportation modes had the lowest post-pandemic retention rate. Some socio-demographic characteristics such as age, living situation, work hours and access to cars were significantly associated with these changes. In terms of life events, students who joined workforce after completion of education between 2019 and 2022 were more likely to shift their commute mode from public transit to cars, implying some influence of this life event on commute mode changes, in addition to pandemic-induced changes. Our findings suggest that the post-pandemic commute mode changes observed among young adults in the GTHA may not be a result of only COVID-19 pandemic and may also be partly associated with important life events that they experienced over the course of pandemic. Future transportation planning and policy implications, and directions for future research have been discussed.

The role of aesthetic quality in urban spaces to influence use of active transport modes

ABSTRACT This study investigates whether and how perceived aesthetic quality of urban spaces play a role in influencing the use of active transport modes. By focusing on urban spaces in terms of car-oriented or human-oriented types within the Reykjavik metropolitan region and interpretation of qualitative interviews, this investigation goes beyond previous studies that have identified particular characteristics of preselected urban spaces stimulating aesthetic experience. Analyses of routes to neighborhood grocery stores used by selected survey respondents show a relationship between choices of active modes and the two types of urban spaces. Morover, interpretation of qualitative interviews suggests explantory information about how perceived aesthetic quality of urban spaces has an important role in promoting the use of active transport mode. This depends on the options provided at different locations within the urban structure, shaped by the planning idelogies that have affected the aesthetic quality of urban spaces.

Understanding and including 'pink-collar' workers in employment-based travel demand models.

The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering-applied to 2017-20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type ('pink collar'), one male-dominated type ('blue collar') and one with both genders almost equally involved ('white collar'). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. 'Pink collar' workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters.

Urban Mobility and Road Safety: Approach to the Safety of Users of Soft Modes of Transport in Grand Lomé

Road insecurity is a major challenge for the international community. The aim of this article is to take stock of the safety of users of soft modes of transport in Grand Lomé. The methodology used in this research is based, on the one hand, on the literature review in order to examine the different theories that govern the analysis of road safety issues and particularly on the safety of vulnerable users of soft modes of transport, and on the other hand, on the analysis of the realities on the ground through surveys and interviews with stakeholders. The results reveal real difficulties in getting around users of active modes of transport on the roads of Grand Lomé. The main roads do not have sidewalks and pedestrian crossings. The sidewalks that exist on some of the new roads are, for the most part, narrow and illegally occupied by businesses, which prevent users of soft modes from benefiting from them. Bike lanes are almost non-existent on the developed lanes. Following this logic, the street belongs first and foremost to motorists. Pedestrians and cyclists are guests who must negotiate their passage on the roads of Grand Lomé.

Unlocking Urban Accessibility: Proximity Analysis in Bangkok, Thailand’s Mega City

Accessibility serves as the fundamental link for mode shifts, enabling access to activity areas and facilitating connections to other forms of travel. However, navigating the transportation network in urban areas of Bangkok, Thailand, reveals persistent inconveniences, discomfort, and safety concerns, thereby failing to adequately meet the needs of users. This study aims to examine urban accessibility, focusing on multi-travel connections to amenities and public transport. It focuses on understanding how the level of engagement of road users in social activities contributes to well-being by addressing proximity access through a multidisciplinary approach aimed at enhancing accessibility and integrating the built environment. The comprehensive and inclusive Accessibility by Proximity Index (API) is proposed as a tool to evaluate the level of access to essential services and activities for residents. Additionally, this study acknowledges the impact of the physical and utilization characteristics of urban space and active trajectories by considering various travel needs and daily route patterns. Consequently, the API can inform the development of planning and urban design strategies aimed at enhancing walkability and cycling as non-motorization infrastructures, thereby improving accessibility through active modes of transportation. It was observed that areas with a higher concentration of urban amenities exhibit greater accessibility by walking, cycling, and public transport, particularly in inner-city areas. Thus, envisioning a more sustainable and inclusive city with adequate development of public transportation access is essential in urban areas, prompting policy-level initiatives to enhance the environment and elevate the overall commuting experience.

Bikeability Disparities in Orange County, California: Intersection of Place and Demographics

Active transportation modes such as walking and biking are gaining popularity for their extensive health and environmental benefits, yet scholars know little about how place-based accessibility varies by area sociodemographic composition. This study is among the first to examine sociodemographic disparities (by both race and socioeconomic status) in bikeability while allowing for heterogeneity in disparities. Consideration of bikeability disparities is particularly critical within the framework of urban planning concepts that promote equitable accessibility and reduced dependency on automobiles, such as the 15-minute city. Geographically Weighted Regressions examined associations between census tract-level bikeability (using an index that combines five components), socioeconomic status, and percentage non-White residents (controlling for age of structures in tracts). Findings showed that the strength and directionality of associations between bikeability and race/socioeconomic status varied throughout the county, providing targeted information on where greater concentrations of low socioeconomic status and non-White residents were associated with lower bikeability.

Effects of public transportation use on non-exercise activity thermogenesis and health promotion: a mini-review.

Public transportation (PT) systems significantly shape urban mobility and have garnered attention owing to their potential impact on public health, particularly the promotion of physical activity. Beyond their transportation functions, PT systems also affect daily energy expenditure through non-exercise activity thermogenesis (NEAT). This mini-review surveys the existing literature to explore the effects of PT use on NEAT levels and subsequent health outcomes. A comprehensive literature search was conducted using the electronic databases PubMed, Google Scholar, and Web of Science. Keywords including "public transportation," "non-exercise activity thermogenesis," "physical activity," "health promotion," and related terms were used to identify relevant studies. This review highlights the multifaceted relationship between PT use and health promotion, emphasizing the potential benefits and challenges of increasing NEAT through public transit utilization. Overall, the findings suggest that PT use contributes positively to NEAT levels, and thus improves health outcomes. However, the extent of this impact may vary depending on individual and contextual factors. Interventions promoting active transportation modes, including public transit, hold promise for addressing sedentary behavior and fostering healthier lifestyles at the population level.

Exploring the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka

The COVID-19 outbreak created immense disruptions in our daily lives. Travel is one of the major areas severely impacted by the pandemic. Bangladesh experienced two waves of COVID-19 between March 2020 and July 2021. Although several studies focused on significant changes in the travel behavior of urban road users before and during the pandemic, hardly any research examined the differences between the two waves. Hence, it was important to study the differences in travel behavior between the two waves of the COVID-19 pandemic to understand how the effects of travel restrictions, health concerns, and the severity of the pandemic on people's travel decisions change over time. This study intends to investigate the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka and the factors that influence these changes. Data were collected through online questionnaire surveys using the Google form. Voluntary response and convenience sampling techniques were used to collect responses from 447 people in Dhaka. Descriptive statistics and non-parametric tests were conducted to analyze the data. In addition, two multinomial logistic models were developed to identify the factors behind the changes in travel behavior. This study reveals that work and shopping trips increased, whereas work-from-home and online shopping somewhat decreased during the second pandemic wave compared to the first pandemic wave. Most non-car owners who reduced their use of public transportation during the first pandemic increased their use during the second wave. There was no significant increase in the use of active transport modes for work and shopping trips in Dhaka. The perceived risk of COVID-19 infection was relatively lower during the second wave, influencing the respondents to travel more frequently for work and shopping purposes. Ensuring more adaptive public transportation, flexibility to work from home, and creating supportive infrastructures for active transport modes might help to provide a safe, affordable, and efficient transportation system for all during the future waves of the pandemic and other unprecedented events.

Association between local amenities, travel behaviours and urban planning: A spatial analysis of a nationwide UK household panel study.

IntroductionGlobally, there has been a recent resurgence in planning policies focused on local living (such as the 20-min neighbourhood) with proposed benefits of creating sustainable and healthy places. The policies aim to provide improved spatial access to local amenities within short walkable distances of home to encourage walking and cycling and discourage car use, however these pathways have not been examined. This study aimed to quantify the density and diversity of local amenities and their association with daily/weekly travel behaviours. MethodsWe used data from Understanding Society, a national panel survey of UK adults. Spatial data were used to quantify the number and diversity of amenities that have previously been associated with active travel and focused on in 20-min neighbourhood policies within small geographical areas across the UK. These were linked to individual-level data describing daily and weekly travel behaviour for: walking (at least 10-min), cycling, car and bus use. Logistic regression models measured the association between individual amenities, their diversity and daily/weekly travel, whilst controlling for individual factors such as age, sex and employment, residing in a retail centre, as well as area-level urbanicity and deprivation. ResultsOur analysis revealed variations in active travel and car usage patterns by sex, employment status, urbanicity, and area-level deprivation. Local amenities were associated with daily travel behaviours with more amenities generally associated with more frequent walking and less regular bus and car travel. Furthermore, increased amenity diversity was associated with reduced daily car use (OR: 0.77, p < 0.001). Similar patterns were apparent for weekly travel outcomes. ConclusionsBoth access to specific local amenities as well as their diversity are both important neighbourhood factors for achieving shifts from motorised to active transport modes. Policy makers and planners need to ensure the diversity of local amenities are included in local living policies.

Incorporating Land Use and Transport Interaction Models to Evaluate Active Mobility Measures and Interventions in Urban Areas: A case study in Southampton, UK

New transport strategies, which aspire to create sustainable and resilient cities, prioritize active and public transport modes. This paper investigates the differences in accessibility, mobility and activity patterns as a result of radical road space allocation changes. To do so, the TRANUS Land Use and Transport Interaction model is utilized. Also, the city of Southampton is selected as a case study and four scenarios inspired by the city's ongoing or planned active travel initiatives are the “Business as Usual” (BAU) scenario, the “Southampton Cycling Network” (SCN) scenario, the “Traffic Calming” (TC) scenario, and the “Combined” scenario, which involves the simultaneous deployment of both test scenarios. In the SCN scenario, the proportion of bicycle trips has noticeably risen, whereas in the TC scenario, public transport usage is maintained. Meanwhile, the Combined scenario significantly diminishes the appeal of private motorized transport over the long term. Regarding the impacts on activities, urban sprawl and the creation of new urban centres are observed in all scenarios. Interestingly, the SCN and combined scenarios have similarities in land use effects, but this happens because accessibility increases in peripheral zones in the former, whereas it reduces in central zones in the latter.