Exploring the non-linear associations between spatial attributes and walking distance to transit

When examining environmental correlates of walking distance to transit stops, few studies report the importance of spatial attributes relative to other factors. Furthermore, previous studies often assume that they have linear relationships with walking distance. Using the 2016 Transit On Board Survey in the Minneapolis and St. Paul Metropolitan Area, this study adopted the gradient boosting decision trees method to examine the relationships between walking distance and spatial attributes. Results showed that spatial attributes collectively have larger predictive power than other factors. Moreover, they tend to have non-linear associations with walking distance. We further identified the most effective ranges of spatial attributes to guide stop area planning and stop location choice in the region.     

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.     

Ride-sourcing compared to its public-transit alternative using big trip data

Ride-sourcing risks increasing GHG emissions by replacing public transit (PT) for some trips therefore, understanding the relation of ride-sourcing to PT in urban mobility is crucial. This study explores the competition between ride-sourcing and PT through the lens of big data analysis. This research uses 4.3 million ride-sourcing trip records collected from Chengdu, China over a month, dividing these into two categories, transit-competing (48.2%) and non-transit-competing (51.8%). Here, a ride-sourcing trip is labelled transit-competing if and only if it occurs during the day and there is a PT alternative such that the walking distance associated with it is less than 800 m for access and egress alike. We construct a glass-box model to characterise the two ride-sourcing trip categories based on trip attributes and the built environment from the enriched trip data. This study provides a good overview of not only the main factors affecting the relationship between ride-sourcing and PT, but also the interactions between those factors. The built environment, as characterised by points of interest (POIs) and transit-stop density, is the most important aspect followed by travel time, number of transfers, weather, and a series of interactions between them. Competition is more likely to arise if: (1) the travel time by ride-sourcing <15 min or the travel time by PT is disproportionately longer than ride-sourcing; (2) the PT alternative requires multiple transfers, especially for the trips happening within the transition area between the central city and the outskirts; (3) the weather is good; (4) land use is high-density and high-diversity; (5) transit access is good, especially for the areas featuring a large number of business and much real estate. Based on the main findings, we discuss a few recommendations for transport planning and policymaking.     

Multi-city exploration of built environment and transit mode use: Comparison of Melbourne,Amsterdam and Boston

The built environment is an important determinant of travel demand and mode choice. Establishing the relationship between the built environment and transit use using direct models can help planners predict the impact of neighborhood-level changes, that are otherwise overlooked. However, limited research has compared the impacts of the built environment for different networks and for individual transit modes.This paper addresses this gap by developing built environment and transit use models for three multimodal networks, Amsterdam, Boston and Melbourne, using a consistent methodology. A sample of train, tram and bus sites with similar station-area built environments are selected and tested to establish if impacts differ by mode. It is the first study that develops neighborhood-level indicators for multiple locations using a consistent approach.This study compares results for ordinary least squares regression and two-stage least squares (2SLS) regression to examine the impact of transit supply endogeneity on results. Instrumented values are derived for bus and tram frequency in Melbourne and bus frequency in Boston. For other mode and city combinations, the 2SLS approach is less effective at removing endogeneity.Results confirm that different associations exist between the built environment and transit modes, after accounting for mode location bias, and that this is true in multiple networks. Local access and pedestrian connectivity are more important for bus use than other modes. Tram is related to commercial density. This finding is consistent for all samples. Land use mix and bicycle connectivity also tend to be associated with higher tram use. Train use is highest where opportunities exist to transfer with bus. Population density is commonly linked to ridership, but its significance varies by mode and network.More research is needed to understand the behavioral factors driving modal differences to help planners target interventions that result in optimal integration of land use with transit modes.     

The spatial characteristics and influencing factors of modal accessibility gaps: A case study for Guangzhou,China

This paper presents an analysis of the spatial characteristic of the modal accessibility gap (MAG) in Guangzhou based on travel times for public transport and cars to public centers calculated by the Travel O-D point Intelligent Query System (TIQS). Four spatial regression models are used to investigate the effect on the MAG of six factors of the built environment (distance to city public centers, residential density, landuse mix, bus stop density, metro station density and road network density). The results show that travel time for a given origin to destination (OD) trip is likely to be larger by public transport than by car, especially for a trip of longer travel time. The MAG values of all the communities are larger than 0, indicating that when compared with public transport, travelling by car takes less travel time for individuals. Residential density, land-use mix, bus stop density and metro rail station density have significant negative direct impacts and indirect impacts (spatial spillover effects) on MAG, which indicates that appropriate increase in community residential density, providing diversified service facilities within a community, and improving public transport supply are beneficial to promote modal shift. Specifically, the role of metro rail is greater than that of buses. However, the road network density has significant direct positive impacts on MAG, and its spillover effect is also significantly positive, which indicates that building more roads is not an effective way to narrow the accessibility gap between public transport and cars but may facilitate more car travel instead.     

Walk-to-transit demand estimation methods applied at the parcel level to improve pedestrian infrastructure investment

Public transportation is a critical component of cities' transportation system that can be supported by a safe, complete, and connected pedestrian infrastructure. Agencies spend millions of dollars each year to improve transit ridership, yet many of the transit destinations do not have adequate pedestrian infrastructure to connect to the transit stops creating a substantial barrier to growing demand. This is particularly true in suburban areas. This paper presents a replicable methodology for estimating relative parcel-level transit demand such that analysts can conduct fine-grained evaluation and prioritization of the pedestrian network enhancements as they relate to public transit system. To this end, pedestrian infrastructure can boost transit ridership and enhance riders' safety. We rely on spatial data available in most cities coupled with land use and socioeconomic data to generate potential relative number of walk-to-transit trips for each parcel and weight the occupied road segments based on the results from mode choice and gravity models. Using this GIS-based tool, we identify road segments that have a higher potential in serving as a walking path to transit stops and prioritize gaps in existing sidewalk infrastructure. This result eliminates arbitrary sidewalk investment scoring programs and the reliance on transit walksheds to direct investment. We apply this method to a case study of the city of Knoxville and discuss the challenges and possible solutions. This approach can help city planners and engineers in data-oriented investment strategic management of sidewalk enhancement programs that support transit.     

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.     

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.     

Influence of the built environment on E-scooter sharing ridership: A tale of five cities

Electric scooter (e-scooter) sharing systems (ESSs) have been widely adopted by many cities around the world and have attracted a growing number of users. Although some studies have explored the usage characteristics and effects of the built environment on ESS ridership using one city as an example, few studies have considered multiple cities to obtain generalizable and robust results. To fill this research gap, we collect the ESS trip data of five cities in the U.S., namely Austin, Minneapolis, Kansas City, Louisville, and Portland, and explore the effects of the built environment on ESS ridership after controlling for socioeconomic factors. The temporal distributions of e-scooter ridership of different cities are similar, having a single peak period on weekdays and weekends between 11:30 and 17:30. In terms of spatial distribution, the ESS ridership is higher in universities and urban centers compared to other areas. Multilevel negative binomial model results show that ESS trips are positively correlated with population density, employment density, intersection density, land use mixed entropy, and bus stop density in the census block group. E-scooter ridership is negatively correlated with the median age of the population in the census block group and distance to the city center. The findings in this article can help operators understand the factors that affect the ridership of shared e-scooters, determine the changes in ridership when the built environment changes, and identify high-ridership areas when ESS is implemented in new cities.     

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.     

Taking another look at cycling demand modeling: A comparison between two cities in Canada and New Zealand

Cities around the world are investing in their cycle network as a way to reduce traffic congestions and improve population health. For effective infrastructure investment decision-making, it is critical to understand the factors affecting cycling demand. This study investigates the combined effects of spatial and temporal factors on the demand of cycling in Auckland, New Zealand and Kelowna, Canada. A latent segmentation-based negative binomial (LSNB) model is developed utilizing daily cycling counts over a year from Auckland and Kelowna. The LSNB model captures unobserved heterogeneity based on the temporal attributes, and land use and neighborhood characteristics of the cycling facilities. The models confirm the influence of weather, bicycle facility type, built environment, traffic, land use, and accessibility attributes. The model results suggest that higher temperature, lower rainfall, increased length of shared paths, lower AADT, and closer distance to bus stop are likely to increase cycling demand in Auckland. The model also confirms heterogeneity. For example, road-connectivity index and bike index vary across the urban and suburban areas of Auckland. For Kelowna, higher temperature, lower rainfall, lower snowfall, and closer distance to water bodies are likely to increase cycling demand. In the case of heterogeneity, the effects of road-connectivity index, bike index, and AADT varies over the urban and suburban areas of Kelowna. Analysis suggests that increasing bike index is one of the critical factors to increase cycling demand in the suburban areas of Auckland and Kelowna.     

Neighbourhood effects on station-level transit use: Evidence from the Taipei metro

While large high-density metropolitan areas with extensive transit networks experience greater use of rail transit than elsewhere, less is known about the neighbourhood effects that affect station use. This study applies the 5D model to analyse neighbourhood effects within 600 m of transit stations in the Taipei metropolitan area. The area is separated into three concentric zones, with separate functions for each zone. While population density, destination attractiveness, and distance to intermodal connections are important in all three zones, design features depend on their location vis-à-vis the centre. Intersection density is important in the downtown core, while bike share facilities affect station use in the intermediate ring. A geographically weighted regression (GWR) reveal that most 5D variables exhibit spatial serial dependence. The key GWR result is that population density has the greatest effect on station use in peripheral residential neighbourhoods.     

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.     

Accessibility and proximity effects of bus rapid transit on housing prices: Heterogeneity across price quantiles and space

Bus rapid transit (BRT) systems have mushroomed worldwide in the last few decades. An enriched understanding of BRT capitalization effects is essential. Although the BRT accessibility effect on housing prices has been extensively explored, the effect of proximity to the BRT corridor (which may be related to unattractive landscape and noise pollution) has been little scrutinized. More importantly, whether and how the two effects vary across price levels and space have yet to be sufficiently studied. To this end, we estimate the effect of BRT accessibility and proximity on housing prices by applying a battery of econometric methods (including hedonic pricing models, spatial regression models, quantile regression models, and a geographically weighted regression model) to 5185 observations in the housing market in Xiamen Island, China. The results of this study are: (1) BRT accessibility premiums and proximity penalties simultaneously exist in the housing market; (2) buyers of high-priced housing have a greater willingness to pay for avoiding the nuisances attributed to proximity to the BRT corridor; (3) the effect of BRT on housing prices is spatially heterogeneous; (4) the BRT accessibility effect is larger in suburban areas than in urban areas; and (5) housing prices are more predictable near the city centers than outside the area, which may be because a greater proportion of the price of a house near the city centers is derived from the location (rather than the building structure). Finally, policy implications (e.g., building acoustic barriers and planting vegetation along the BRT corridor and improving the transit service in suburban areas) are discussed.     

Accessibility evaluations for nighttime walking and bicycling for low-income shift workers

This paper addresses an important issue related to nighttime commuting of low-income shift workers who walk and/or bike to their workplace using public transit. A shift worker is anyone who follows a work schedule that is outside of the typical daytime working hours of a business day and commute after dark - by walking or bicycling to a transit stop. However, poor visibility conditions on sidewalks and bicycle lanes often thwart safety of their walking and bicycling activities. Therefore, this paper develops two simple scores - nighttime accessibility score for walking (NASW) and nighttime accessibility for bicycling (NASB) - for evaluating nighttime infrastructure for pedestrians and bicyclist. The scores consider the employment data, travel time and the physical distribution of streetlight poles along the sidewalks and bicycle lanes. Data from the city of El Paso in Texas is used to demonstrate the applicability of the two scores. Employment data from three prominent service industry sectors known to employ low-income shift workers – i) Retail Trade, ii) Accommodation and Food Services, and iii) Health Care and Social Assistance – are used for demonstration purposes. It is observed that amongst the three sectors analyzed, both NASW and NASB values are higher for regions in El Paso with low-income employment concentrations from the Health Care and Social Assistance sector. It is also observed that some prominent regions in north-east, south-east and west of El Paso need improvements in streetlight systems, sidewalks and bicycle network to facilitate walking and bicycling amongst low-income shift workers employed in Retail Trade, and Accommodation and Food Services industry sectors to access transit stops at night.     

Walking to transit: An unexpected source of physical activity

Using data from a regional survey carried out in Montreal, this paper shows empirical evidence that modal shift from car to transit contributes to the volume of daily physical activity. First, the paper presents a method to calculate the walking distance related to transit trips, and a totally disaggregate trip assignment model. The walking distance involved in every transit trip is then associated to the individuals' characteristics to estimate the number of steps made by the population in the whole transit system.Results show that, in average, a transit trip involves 1250 steps, required to access and egress the network as well as to transfer between routes or modes. Thus, a round trip represents 2500 steps, which account for 25% of the recommended volume of physical activity per day. Hence, analysis shows that the volume of walking varies according to attributes of the traveler (general decrease with age, higher for men) and of the trip (study and work trip involve more steps, the use of train generates more steps). A regression model confirms that these variables significantly affect the number of steps involved in transit trips. Such positive outcome, for the traveler himself, is an innovative argument to promote the use of transit that is also aligned with current sustainable transportation goals.     

Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago

As ride-hailing becomes more common in cities, public agencies increasingly seek transportation network company (TNC) service data to understand (and potentially regulate) demand and service response. Despite the increase in ride-hailing or TNC demand and subsequent research into its determinants, there remains little research on shared TNC trips and the spatial distribution of trip demand across demographic and land use variables. Using Chicago as a case study, shared TNC trip data from 2019 was used to estimate the count and ratio of shared ride services, based on built environment, demographic, location, time of day, and trip details. Findings reveal that trip length, day of week designation, density of pedestrian and multi-modal infrastructure, and underlying socioeconomic characteristics of the origin zones influence the proportion and count of shared ride-hail trips. Of concern is that those using transit or active modes may be taking more ride-hailing trips, but these Chicago-region results indicate that the provision of pedestrian infrastructure and remoteness to transit stops result in fewer shared trips.     

Suburbanization and transit-oriented development in China

The suburbanization of large Chinese cities has placed many residents in locations that are far less accessible than their prior residences, requiring motorized travel. This paper examines the impacts of relocation to outlying areas on job accessibility, commuting mode choice, and commuting durations based on a current-day and retrospective survey of recent movers to three suburban neighborhoods in Shanghai. Job accessibility levels were found to decline dramatically following the move, matched by increased motorized travel and longer commute durations. Relocating to a suburban area near a metrorail station, however, was found to moderate losses in job accessibility and for many, encourage switches from non-motorized to transit commuting. We conclude that transit-oriented development holds considerable promise for placing rapidly suburbanizing Chinese cities on a more sustainable pathway.     

Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou,China

Understanding the relationship between the rail transit ridership and the built environment is crucial to promoting transit-oriented development and sustainable urban growth. Geographically weighted regression (GWR) models have previously been employed to reveal the spatial differences in such relationships at the station level. However, few studies characterized the built environment at a fine scale and associated them with rail transit usage. Moreover, none of the existing studies attempted to categorize the stations for policy-making considering varying impacts of the built environment. In this study, taking Guangzhou as an example, we integrated multi-source spatial big data, such as high spatial resolution remote sensing images, points of interest (POIs), social media and building footprint data to precisely quantify the characteristics of the built environment. This was combined with a GWR model to understand how the impacts of the fine-scale built environment factors on the rail transit ridership vary across the study region. The k-means clustering method was employed to identify distinct station groups based on the coefficients of the GWR model at the local stations. Policy zoning was proposed based on the results and differentiated planning guidance was suggested for different zones. These recommendations are expected to help increase rail transit usage, inform rail transit planning (to relieve the traffic burden on currently crowed lines), and re-allocate industrial and living facilities to reduce the commute for the residents. The policy and planning implications are crucial for the coordinated development of the rail transit system and land use.     

The suburbanization of poverty and changes in access to public transportation in the Triangle Region,NC

During the last 50 years, a number of economic forces led to noteworthy changes in the geography of poverty in the US. Employment decentralization and lack of affordable housing have resulted in many low-income households migrating to suburban areas, and as of today, the majority of the low-income population of metropolitan regions resides in the suburbs. Focusing on the Triangle Region, NC, this study systematically explores the changes in the geography of poverty and transit access over time to better understand how accessibility to transit has changed for the low-income residents. Spatial panel data models are also estimated to investigate the relationship between poverty and transit access at the neighborhood level and how it varies spatially and temporally, while accounting for unobserved heterogeneity and spatial autocorrelation. Results indicate that between 1990 and 2015, the outer-ring suburbs experienced the highest increase in both poverty and transit access, although improvements in transit access were lower in the recently urbanized areas within the outer-ring suburbs. Inner-ring suburbs experienced a substantial rise in poverty rate and low-income population, and a comparable increase in transit access. Central cities remained the areas with the highest poverty rate and transit access in the Triangle Region but experienced smaller changes over time compared to suburban areas. The findings of the econometric analysis suggest that poverty rate and transit access are not significantly related at the neighborhood level when time-invariant unobservable characteristics are accounted for.