Addressing transit mode location bias in built environment-transit mode use research

Many studies have identified links between the built environment (BE) and transit use. However, little is known about whether the BE predictors of bus, train, tram and other transit modes are different. Studies to date typically analyze modes in combination; or analyze one mode at a time. A major barrier to comparing BE impacts on modes is the difference in the types of locations that tend to be serviced by each mode. A method is needed to account for this ‘mode location bias’ in order to draw robust comparison of the predictors of each mode.This study addresses this gap using data from Melbourne, Australia where three types of public transport modes (train, tram, bus) operate in tandem. Two approaches are applied to mitigate mode location bias: a) Co-located sampling – estimating ridership of different modes that are located in the same place; and b) Stratified BE sampling – observations are sampled from subcategories with similar BE characteristics.Regression analyses using both methods show that the BE variables impacting ridership vary by mode. Results from both samples suggest there are two common BE factors between tram and train, and between tram and bus; and three common BE factors between train and bus. The remaining BE predictors – three for train and tram and one for bus - are unique to each mode. The study's design makes it possible to confirm this finding is valid irrespective of the type of locations serviced by modes. This suggests planning and forecasting should consider the specific associations of different modes to their surrounding land use to accurately predict and match transit supply and demand. The Stratified sampling approach is recommended for treating location bias in future mode comparison, because it explains more ridership variability and offers a transferrable approach to generating representative samples.     

Exploring the impact of public transport strikes on travel behavior and traffic congestion

Public transport (PT) disruption can occur due to various factors such as malfunctions and breakdowns of vehicles, power outages, and personnel strikes. This paper explores the network-wide impacts of PT strikes (train, tram, and bus strikes) on traffic congestion in Melbourne, Australia using a network modeling approach. A primary survey aimed to investigate the mode shift of users when each public transport mode ceases was conducted with 648 public transport users in May 2016. Findings show that train withdrawal was expected to result in 43% of users shifting to car. Smaller yet significant shifts to car was also expected with bus withdrawal (34%) and tram withdrawal (17%). Based on the survey results and the use of a four-step transport model, train withdrawal was expected to increase the number of severely congested road links by 130% and reduce the average travel speed from 48 km/h to 39 km/h (20% decrease). Bus and tram withdrawal was also found to increase congestion although the result was less severe. Future research should investigate the switching behavior in actual withdrawal events and explore the long-term effects of public transport withdrawal.     

Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal

We studied transit ridership from the perspective of the transit provider, with the objective of quantifying the influence of transit system operational attributes, transportation system infrastructure attributes and built environment attributes on the disaggregate stop level boardings and alightings by time of day for the bus transit system in the Montreal region. A Composite Marginal Likelihood (CML) based ordered response probit (ORP) model, that simultaneously allows us to incorporate the influence of exogenous variables and potential correlations between boardings and alightings across multiple time periods of the day is employed. Our results indicate that headway affects ridership negatively, while the presence of public transportation around the stop has a positive and significant effect. Moreover, parks, commercial enterprises, and residential area, amongst others, have various effects across the day on boardings and alightings at bus stops. An elasticity analysis provides useful insights. Specifically, we observe that the most effective way to increase ridership is to increase public transport service and accessibility, whereas enhancements to land use have a smaller effect on ridership. The framework from our analysis provides transit agencies a mechanism to study the influence of transit accessibility, transit connectivity, transit schedule alterations (to increase/reduce headway), and land-use pattern changes on ridership.     

Examining circuity of urban transit networks from an equity perspective

Circuity of transit networks, defined as the ratio of network to Euclidean distance traveled from origin to destination stop, has been known to influence travel behavior. In addition to the longer time spent in travel, for networks where fare is based on distance traveled, higher circuity also means higher fare for the same Euclidean distance. This makes circuity relevant from an equity perspective. Using a case study of the urban transit network of Amsterdam in the Netherlands, this study explores the role of transit circuity on the disparity in distance traveled by travelers' income profile and its implications on travel times and costs for networks with distance-based fares. The analysis is based on travel patterns from smart card data for bus, tram, and metro modes, combined with neighborhood level income data. Results reveal that in Amsterdam, the higher the share of high income people living in proximity to a transit stop, the lower the circuity of journeys from the stop, when controlled for the Euclidean distance covered and spatial auto-correlation. The uneven distribution of circuity exacerbates the disparity in distance traveled, and hence fare paid between the income groups. However, the travel time per Euclidean distance favors the low income group, possibly due to the circuitous routes serving these areas being compensated by higher travel speeds. This study highlights the role of transit network design in determining its equity outcomes and emphasizes the importance of considering equity during route and fare planning. The process followed can be adapted to examine equity for other urban networks.     

Built for active travel? Investigating the contextual effects of the built environment on transportation mode choice

The study investigates the role of the built environment attributes and their contextual effects on travel behaviour. The study utilized a dataset of 4739 respondents elicited from an online survey distributed in Hamilton City, Canada. A Nested Logit (NL) model and a quadratic polynomial trend surface are employed to spatially investigate the determinants influencing mode choice behaviour. The study contributes to our understanding of how geography moderates the impact of built environment attributes on mode choice behaviour. Socioeconomic demographics are found to play a pivotal role in explaining Hamiltonians' mode choice behaviour. For built environment attributes, sidewalk density is positively associated with walking and public transit use. Moreover, bike lane density is positively associated with biking and negatively associated with public transit use. Regarding land-use entropy (mix), the results show that high land-use entropy is negatively associated with choosing the car as a passenger travel mode. From a contextual perspective, the results affirmed that the influence of built environment attributes is not equally efficacious across the city. Improving the built environment attributes across the city reveals a substantial increase in walking and biking while decreasing the probability of choosing other modes. However, it is noteworthy to mention that the influence of improving the built environment is not homogeneous over geography.     

Spatiotemporal exploration of Melbourne pedestrian demand

Creating a cityscape conducive to a safe and efficient pedestrian experience requires a holistic understanding of the relationship between the built structures of a city and the movement of individuals within it. To empower policy makers to design, implement, and successfully deliver measures aimed at reducing footpath congestion and improving pedestrian safety the link between pedestrian volume and different features of the built environment needs to be investigated. Observed pedestrian counts at 50 intersections across the City of Melbourne are used as the input dependent variable of two regression models. A global ordinary least squares regression model and a local geographically weighted regression model are generated and evaluated for best fit of purpose. Spatiotemporal statistical handling is employed to clean the data of contextual anomalies. The output of the regression models identified eight key features as the most statistically significant predictors of pedestrian volume in Melbourne, Australia. These features include distance to schools and train stations and measures of footpath connectivity. This study reveals that due to significant spatial and temporal non-stationarity exhibited between pedestrian count sensors and built environment variables, the geographically weighted regression is the most appropriate modelling technique. This paper presents a methodology for the creation of a robust pedestrian prediction model.     

Study design impacts on built environment and transit use research

The different factors examined in studies linking the built environment and transit use explain about half of the variability in findings for travel behavior. Despite many differences in the research design of these studies, it is not known if choices about study design impact theoretical consistency in results and account for some of the unexplained variance between studies. This gap exists because multiple studies must be analyzed together to explore the topic. This study aims to fill this gap, using a sample of data points and statistical models from 146 studies identified through a comprehensive database search.This paper first synthesizes the study design adopted in empirical studies of the built environment and transit use. Meta-regression is then used to identify study design aspects causing significant differences. Selective reporting bias appears to slightly exaggerate estimates for built environment Density and Accessibility. Over 40% of variability in findings for Density and Diversity was explained by study design aspects. These include whether collinearity of variables is accounted for, the specificity of the sample population and transit mode, catchment size; and the number of explanatory variables specified.Overall the average correlations for built environment and transit use are weak (<0.2). Predictions of transit ridership based on built environment factors are likely to be imprecise, so models should be carefully specified. Given the impact of study design, adherence to best practice conventions could reduce variance within studies and dispersion between studies. For ambiguous specification issues, sensitivity testing could be used to generate prediction intervals. Further investigation of factors such as transit mode and catchment size would be useful to determine if there is a theoretically plausible reason to favor certain specifications.     

Catalysts and magnets: Built environment and bicycle commuting

What effects do bicycle infrastructure and the built environment have on people’s decisions to commute by bicycle? While many studies have considered this question, commonly employed methodologies fail to address the unique statistical challenge of modeling modes with small mode shares. Additionally, personal characteristics that are not adequately accounted for may lead to overestimation of built environment impacts.This study addresses these two key issues by using an ordered probit Heckman selection model to jointly estimate participation in and frequency of commuting by bicycle, controlling for demographics, residential preferences, and travel attitudes. The findings suggest a strong influence of attitudinal factors, with modest contributions of bicycle accessibility. Bicycle lanes act as “magnets” to attract bicyclists to a neighborhood, rather than being the “catalyst” that encourages non-bikers to shift modes. The results have implications for planners and policymakers attempting to increase bicycling mode share via the strategic infrastructure development.     

Built environment effects on leisure travel for children: Trip generation and travel mode

This study empirically analyzed the effects of built environment on leisure travel among children. Students of three elementary schools, namely Yangmingshan, Sanyu and Shilin, all located in the Shilin District of Taipei, were chosen to provide sample data. The negative binomial regression model and multinomial logit model were used to analyze trip generation and travel mode, respectively. This study reached the following empirical findings: (1) mixed land use, employment density, walkway quality, leisure facility supply and leisure travel distance encouraged generation of leisure trips for children; (2) intersection density, building density, employment density and walkway quality encouraged a child to use transit systems or non-motorized travel modes for leisure travel; and (3) vehicle density and leisure travel distance discouraged walking and biking but encouraged the use of transit systems for leisure travel involving children. Local government can use the empirical findings of this study to develop urban planning strategies to encourage children to perform leisure activities outside the home using transit systems or non-motorized travel modes.     

Predicting transit mode choice of New Jersey workers commuting to New York City from a stated preference survey

This study examines transit mode choice preferences of New Jersey commuters traveling to New York City (NYC) with the objective of assisting trans-Hudson ferry service planning on the basis of variables significantly affecting mode choice. A stated preference survey was conducted to collect data from a total of 2134 respondents who commute from northern New Jersey to NYC. A mixed multinomial logit model (MMLM) with random coefficients was used to analyze the data. The study considered the choice between four transit modes—ferry, PATH train, bus, and commuter rail—all of which can be used to cross the Hudson River to travel from New Jersey to NYC. Essential model results are presented for all four modes, but detailed results are discussed for only the ferry, PATH, and bus modes. Model results indicate that commuters' choices are affected not only by travel time and cost for the segment of the journey that crosses the Hudson River, but also reliability and comfort during that segment, number of access modes required by the journey, and travel time and cost of trips from home to stations/terminals before crossing the river. The results also show that commuters' attachment to the modes they currently use serves as a barrier to switching to other modes. The most important takeaway from this research for transit service planning is that there is a need to consider the attributes of both individual trip segments as well as entire journeys between commuters' home and work when exploring changes to transit service characteristics.     

Public bicycle as a feeder mode to rail transit in China: The role of gender,age, income,trip purpose,and bicycle theft experience

A marriage between public bicycle and rail transit presents new opportunities for sustainable transportation in Chinese cities. To examine determinants of public bicycle usage for rail transit access, an intercept survey of feeder mode choice among rail transit users was conducted near rail stations in Nanjing, China. Mode choice models were estimated with five feeder mode alternatives, including car, bus, walk, private bike, and public bike. By differentiating between public and private bicycle modes in the mode choice models, the study reveals the effects of personal demographics, trip characteristics, and station environments on public bicycle usage for rail transit access. Results show that female, older, and low-income rail commuters are less likely to use public bicycle to access rail transit. Rail commuters with bicycle theft experience and making school- or work-related trips are more likely to use public bicycle to access rail transit. Land use variables are largely insignificant in this study except that density shows a positive relationship with walking to rail transit. The results on demographic differences raise equity concerns when it comes to investing in public bicycle systems. Policy implications are discussed for Chinese cities to equitably boost public bicycle integration with rail transit.     

Measuring the inequality of accessible trams in Melbourne

In Melbourne, public policy has recently been enacted to improve access to the tram service with an emphasis upon people with a disability. Achieving that objective requires two elements. One is a low-floor tram and two is an elevated tram stop platform that facilitates boarding the tram. Currently the fleet of trams in Melbourne is made up of high-floor and low-floor designs, thus accessible services are unevenly distributed. In addition, construction of new tram stop platforms has been uneven. This suggests a form of inequality in accessible tram services for the disabled.Several studies of transport services have addressed this issue using the Gini coefficient and Lorenz curve calculations as seen in studies of disadvantaged groups such as the elderly. This research utilizes those approaches to estimate the current access of the disabled population to trams services in Melbourne. The approach compares the geography of the total and accessible tram services with the geography of the total and disabled population using the Gini coefficient and Lorenz curve.The results show that there is inequality in the accessible trams amongst people with a disability in Melbourne (Gini = 0.66) as 70% of the disabled population has access to only 22% of the accessible tram supply. In comparison, considering the total tram supply and the entire population, (Gini = 0.48) 70% of the population shares 40% of the tram supply. Hence, at this stage the provision of accessible tram services for people with a disability falls well below that of the general population. These results provide an insight into the current tram service inequality and can be used as a reference for future tram system investment. Further, the approach could be used to increase awareness of this matter and encourage an inclusive and sustainable public transport planning and development in both local and global contexts.     

A streetcar to be desired? The development of a new approach to measure perception of place quality in the context of tram network modernisation

Recent decades have seen a global resurgence in tram network development around the world. Despite a primary basis in transport, tram network development is increasingly framed as a spatial planning mechanism that is prioritised for its potential place-based outcomes. However, there has been limited academic research to investigate impacts of tram network development on community perception of place quality. This study contributes to the literature by presenting the results of 601 completed questionnaires investigating variation in perception of place quality between legacy and modernised tram streetscapes in Melbourne, Australia. Evidence demonstrates that modernised tram streetscapes were perceived to contain an enhanced physical design compared to their legacy counterparts. Additionally, modernised tram streetscapes were rated as higher quality locations overall that were more likely to facilitate a wide range of place-based activities and amenities. Overall findings provide evidence that tram modernisation can be framed as an opportunity for place quality enhancements, and appear to contradict some of the political complications that have played a role in stalling tram streetscape modernisation projects in Melbourne.     

Millennials,built form,and travel insights from a nationwide typology of U.S. neighborhoods

We examine the relationship between the built environment and the travel of Millennials in the United States. We develop a neighborhood typology to characterize the built environment and transportation networks in almost every U.S. census tract, allowing us to identify possible synergistic and/or threshold effects on travel. We measure travel behavior in two ways: (1) using a multi-faceted traveler typology created using latent class analysis, and (2) by measuring the vehicle miles of travel among people in each of these traveler types. This dual approach allows us to distinguish between the built environment changes needed to encourage travel by modes other than driving, and those needed to reduce vehicle miles traveled among drivers. Using a multinomial logistic regression, we find that travel patterns are relatively stable along much of the urban-rural continuum, everything else equal. Driving was substantially lower only in “Old Urban” neighborhoods, where densities, job access, and transit service are dramatically higher than in all other neighborhood types. This finding implies that dramatic changes in the built environment—doubling or even tripling development density or transit service—may do little to get young people out of their cars when initial densities or transit services are low, as they are in most of the U.S. Conversely, reducing vehicle miles traveled among drivers appears to require more modest built form changes, a finding that offers some room for optimism among those concerned with auto dependence.     

Advance transit oriented development typology: case study in Brisbane,Australia

Internationally, transit oriented development (TOD) is characterised by moderate to high density development with diverse land use patterns and well connected street networks centred around high frequency transit stops (bus and rail). Although different TOD typologies have been developed in different contexts, they are based on subjective evaluation criteria derived from the context in which they are built and typically lack a validation measure. Arguably there exist sets of TOD characteristics that perform better in certain contexts, and being able to optimise TOD effectiveness would facilitate planning and supporting policy development. This research utilises data from census collection districts (CCDs) in Brisbane with different sets of TOD attributes measured across six objectively quantified built environmental indicators: net employment density, net residential density, land use diversity, intersection density, cul-de-sac density, and public transport accessibility. Using these measures, a Two Step Cluster Analysis was conducted to identify natural groupings of the CCDs with similar profiles, resulting in four unique TOD clusters: (a) residential TODs, (b) activity centre TODs, (c) potential TODs, and (d) TOD non-suitability. The typologies are validated by estimating a multinomial logistic regression model in order to understand the mode choice behaviour of 10,013 individuals living in these areas. Results indicate that in comparison to people living in areas classified as residential TODs, people who reside in non-TOD clusters were significantly less likely to use public transport (PT) (1.4 times), and active transport (4 times) compared to the car. People living in areas classified as potential TODs were 1.3 times less likely to use PT, and 2.5 times less likely to use active transport compared to using the car. Only a little difference in mode choice behaviour was evident between people living in areas classified as residential TODs and activity centre TODs. The results suggest that: (a) two types of TODs may be suitable for classification and effect mode choice in Brisbane; (b) TOD typology should be developed based on their TOD profile and performance matrices; (c) both bus stop and train station based TODs are suitable for development in Brisbane.     

Uber service area expansion in three major American cities

Chiefly led by Uber, on-demand ride-hailing services have transformed the urban American transportation landscape in merely the past decade. Utilizing the proliferation of internet-enabled smartphones, this app-based company has provided city inhabitants with a convenient and reliable door-to-door mobility service, which has arguably improved car-based accessibility while also generating a host of negative environmental and societal externalities. While to date the utilization of Uber has largely been an urban phenomenon, the lasting success of this new mobility option likely rests within its ability to expand its services into suburban communities. Yet, given the competitive nature of the ride-hailing marketplace and genuine concerns over passenger and driver anonymity, transportation planners and urban policymakers have been stymied in their ability to access the disaggregate data sets needed to help assess whether these services are in fact extending beyond city centers and identify which factors may be contributing to any expansion into more peripheral suburban neighborhoods. By introducing a creative strategy using the privacy-related suppression processes of Uber Movement data, this study quantifies the continued expansion of Uber's ride-hailing service into outlying communities from 2016 to 2018 by employing a multilevel modeling approach to recognize the neighborhood-level socioeconomic and built environment factors most related to this service expansion in three major American cities: Boston, San Francisco, and Washington, DC.     

The influence of stratification by motor-vehicle ownership on the impact of built environment factors in Indian cities

Development of compact cities, many contend, aids in the promotion of sustainable modes (public transit, walking and cycling). But the studies done until now have inadequately represented the effect of socio-economic stratification on the relationship between built environment factors and non-motorized transport (NMT) mode choice, which is important in context of a developing country like India. The present study, done in the city of Bangalore, analyzes the influence of built environment factors –density and diversity - on the mode choice and trip distance for the two segments: respondents owning at least one personal vehicle and respondents not owning any personal vehicle. The built environment factors are analyzed for their marginal effects in the presence of various socio-demographic and alternate specific attributes. The results of the built environment factors for the vehicle non-owning group highlighted the requirement of a policy framework to reduce their trip distance by controlling their employment and housing location. The gender of a commuter had a significant effect on the choice of modes, and the results that females had a higher likelihood of using NMT compared with males contradicted the results in other cities. Also, the trip distance model determined that females preferred a shorter walking distance compared with males. Further, the study determined the need for a well-planned, inclusive and coordinated land-use and transport control strategies in the future.     

Autos,transit and bicycles: Comparing the costs in large Chinese cities

This study compares the full costs of seven passenger modes in the large Chinese cities facing the difficult yet crucial choice among alternative passenger transportation systems. The seven modes are evaluated at varied traffic volumes in hypothetical radial and circumferential commuting corridors. Using detailed estimates of private and social costs, the full cost of each mode is minimized by optimizing infrastructure investment and operation plans. On all corridors and across different scenarios, commuting by one or more forms of bus transit or bicycle costs less than automobile or rail. Nonetheless, in circumferential corridors, rail can be almost as cost-effective as bus under certain conditions, and bicycle can be less cost-effective than bus in some cases. Unlike results from similar studies conducted in the US, automobile commuting does not cost less than bus transportation at low traffic volumes.     

Heterogeneous effects of neighborhood type on commute mode choice: An exploration of residential dissonance in the Twin Cities

Many studies have measured residential and travel preferences to address residential self-selection and they often focused on the average or independent effect of the built environment on travel behavior. However, individuals' behavioral responses to built environment interventions may vary by their different tastes. Using the 2011 data from the Minneapolis–St. Paul metropolitan area, this study examines the influences of neighborhood type, travel attitudes, and their interaction terms on commute mode choice. The interactions between neighborhood type and travel attitudes have no significant impact on driving commute frequency whereas the effects of neighborhood type on the propensity for transit commute differ by transit preference. Specifically, urban consonants (including those in LRT neighborhoods) have the highest propensity for transit commute, followed by suburban dissonants, urban dissonants, and then suburban consonants. Therefore, individuals' heterogeneous responses to built environment elements should be taken into account in future research and in the design of land use and transportation policies aiming to shape urban travel.     

Shopping morphologies of urban transit station areas: A comparative study of central city station catchments in Toronto,San Francisco,and Melbourne

Synergies between shopping and public transit have long been noted, with main streets emerging along tram lines and shopping malls attached to train stations. The shopping-transit synergy is also at the core of transit-oriented development (TOD), a widespread planning approach to urban sustainability. However, there is a lack of morphological research investigating how shopping clusters around transit stations at a fine-geographic scale. This paper explores the shopping morphologies of three central-city subway station areas in Toronto, San Francisco, and Melbourne, mapping and measuring the extent of public and quasi-public shopfronts relative to the station. The morphological analysis of 200, 300, 400, and 500 m walking catchments shows that as the distance from the station increases, the proportion of shopfronts in the total catchment declines. Quasi-public shopping space can enhance the walkable catchment and permeability of a station area and may contribute to urbanity as long as it adds to diversity of access options. The findings highlight the role walkable shopping environments can play within urban transit station areas and show the importance of nuanced consideration being given to morphologies in the analysis and planning of TODs.