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.     

Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity

Traffic state in the urban network is a direct reflection of the operational efficiency of the urban transportation system. As the busiest period of the day, traffic states during evening peak hours can effectively measure the capacity and efficiency of the transportation system. The primary objective of this study is to investigate how the potential factors affect traffic states during evening peak hours on weekdays. The geographically weighted regression (GWR) approach was proposed to model the spatial heterogeneity of traffic states and visualize the spatial distributions of parameter estimations. Four types of data including traffic state index (TSI) data, point of interests (POIs) data, road features data, and public transport facilities data were obtained from Shanghai in China to illustrate the procedure. According to the results, the GWR model outperformed the ordinary least square (OLS) model in the explanatory accuracy as well as the goodness of fit. The urban form was revealed to have a significant influence on traffic states and strong local variability for parameter estimations was observed. The number of public and commercial POIs, residential POIs, bus routes, bus stops, the average number of lanes, as well as average traffic volumes can significantly affect the traffic states spatially, and the estimated coefficients of each traffic analysis zone (TAZ) vary across regions. The conclusions of this study may contribute to making the planning and management strategies more efficient for alleviating traffic congestion.     

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.     

A GPS data-based analysis of built environment influences on bicyclist route preferences

This study examines the effects of built environment features, including factors of land use and road network, on bicyclists' route preferences using the data from the city of Seattle. The bicycle routes are identified using a GPS dataset collected from a smartphone application named “CycleTracks.” The route choice set is generated using the labeling route approach, and the cost functions of route alternatives are based on principal component analyses. Then, two mixed logit models, focusing on random parameters and alternative-specific coefficients, respectively, are estimated to examine bicyclists' route choice. The major findings of this study are as follows: (1) the bicycle route choice involves the joint consideration of convenience, safety, and leisure; (2) most bicyclists prefer to cycle on shorter, flat, and well-planned bicycle facilities with slow road traffic; (3) some bicyclists prefer routes surrounded by mixed land use; (4) some bicyclists favor routes which are planted with street trees or installed with street lights; and (5) some bicyclists prefer routes along with city features. This analysis provides valuable insights into how well-planned land use and road network can facilitate efficient, safe, and enjoyable bicycling.     

Operationalizing the neighborhood effects of the built environment on travel behavior

Evidence of a connection between the built environment and individual travel behavior is substantiated by multidisciplinary research. In general, compact development patterns exhibiting high concentrations of activity locations and a traditional street design support sustainable travel. However, uncertainty in the magnitude of this connection remains due to how the built environment has been operationalized, usually at a geographic boundary chosen out of convenience. This Portland, Oregon study uses household travel survey data to systematically examine variation in the magnitude of this association when measuring land development pattern, urban design, and transportation system features at various scales. Specifically, this study measures 57 built environment features describing an individual's trip origin and destination at 12 combinations of zonal systems and spatial extents, and assesses their effect on home-based mode choice. First, correlations between individual- and household-level walking behaviors and each combination of indicator and geographic boundary were measured to examine scaling and zoning effects associated with the modifiable areal unit problem (MAUP). These sensitivity test results informed the specification of home-based work and non-work multinomial logit models estimating the effect of sociodemographic, economic, and built environment features on mode choice. Our study findings offer new insight into the MAUP's scaling effect on measuring smart growth indicators and their connection to sustainable travel behavior.     

Causes and effects between attitudes,the built environment and car kilometres: A longitudinal analysis

Travel-related attitudes are believed to affect the connections between the built environment and travel behaviour. Previous studies found supporting evidence for the residential self-selection hypothesis which suggests that the impact of the built environment on travel behaviour could be overestimated when attitudes are not accounted for. However, this hypothesis is under scrutiny as the reverse causality hypothesis, which implies a reverse direction of influence from the built environment towards attitudes, is receiving increased attention in recent research. This study tests both directions of influence by means of cross-sectional and longitudinal structural equation models. GPS tracking is used to assess changes in travel behaviour in terms of car kilometres travelled. The outcomes show stronger reverse causality effects than residential self-selection effects and that land-use policies significantly reduce car kilometres travelled. Moreover, the longitudinal models show that the built environment characteristics provide a better explanation for changes in car kilometres travelled than the travel-related attitudes. This contradicts the cross-sectional analysis where associations between car kilometres travelled and travel-related attitudes were stronger. This highlights the need for more longitudinal studies in this field.     

Nonlinear effects of residential and workplace built environment on car dependence

The relationships between the built environment (BE) and car dependence have been thoroughly evaluated, with a primary focus on the residential BE; however, the effects of the BE at workplaces have remained largely unexplored. Little is known about the potential nonlinear effects of the BE at both locations. Using data from a household travel survey in Changchun, China, we aimed to reveal the nonlinear effects of the residential and workplace BE on car dependence by building a gradient boosting decision trees model. The results show that the BE at both locations has strong explanatory power for car ownership and car purchasing intention. With relative contributions values of 17.90% and 18.13%, respectively, the BE at workplaces contributes less to explaining the two dependent variables than the BE at residences. All BE attributes show nonlinear effects on car ownership, and car purchasing intention and the effects differ between residential and workplace locations.     

The impacts of the built environment on bicycle-metro transfer trips: A new method to delineate metro catchment area based on people's actual cycling space

Bicycle-metro integration is an efficient method of solving the “last mile” issue around metro stations. Built environment is believed to have a significant effect on cycling behavior. However, transfer cycling around metro stations, as a specific type of cycling behavior, has often been overlooked in transport research. In addition, static contextual units such as circular or street-network buffers are typically used to delineate metro catchment areas of transfer cycling trips. These methods are inaccurate to represent the actual geographic contexts of cycling trips, according to the uncertain geographic context problem (UGCoP). Thus, in this study, bicycle-metro catchment areas are delineated based on aggregating the end points of over three million transfer cycling trips. The impact of the built environment on transfer cycling behavior is also explored.First, we find that the aggregate-points buffer outperforms traditional static buffers in predicting transfer cycling trips. Second, we also identify a high level of spatial heterogeneity in catchment area and transfer cycling density between urban and suburban areas. Third, residential and working population density, bus stop density, and metro stations accessibility have a significant effect on bicycle-metro transfer cycling.     

Determinants of collective transport mode choice and its impacts on trip satisfaction in urban tourism

The continuous growth of tourism has important environmental impacts and transports account for a large proportion of the CO₂ emissions generated by tourists. Understanding the motivations and characteristics of collective transport users in contemporary cities may contribute to promote more sustainable forms of tourism. Based on an extensive questionnaire to international tourists in Barcelona, this study employs a multinomial logistic regression to explore the links among visitors' characteristics, motivations, and means of transportation, while an ordinal logistic regression is applied to investigate whether the preference for collective transport has an impact on the satisfaction with the trip. The novelty of our approach is testing the hypothesis that the choice of collective transports is more related to trip motivations (professional, leisure, or personal) than to socio-demographic or personal characteristics of tourists. The results show that professional travelers are more oriented to the use of private cars, but they prefer collective transports when the length of stay is higher and combined with other trip motivations. Also, using collective transports is linked to high satisfaction with the visit for the tourists using this form of transportation. This study puts forward policy implications and suggestions for future research directions, in particular regarding the utilization of non-motorized forms of transportation cities.     

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.     

Association between the built environment and children's independent mobility: A meta-analytic review

Children's independent mobility (CIM) is considered as a determining criterion of child-friendly built environment (BE). Researchers have made a substantial effort to identify the characteristics of the BE that affect CIM and thereby to inform city policy to promote CIM. Although the findings from these studies are useful to inform context specific CIM policy, together they provide inconclusive results. This study made a first attempt to draw a generalised conclusion through a meta-analysis of existing knowledge base. The analysis was conducted using primary studies reporting 13 BE-CIM links and published between 1980 and 2016. Overall effect size (ES), directions, and consistency of each link were calculated, also stratified by contexts, using the reported results from the primary studies and based on a random effect model. The results show that four BE factors (dead-end street, % of residential land, % of commercial land, and residential location type) have a positive association with CIM; traffic volume has a neutral association; and the remaining eight factors (vehicular street width, road density, intersection density, major road proportion, land use mix, availability of recreational facilities, residential density, and distance to destination) have a negative association. Living in a dead end street was found to have the strongest positive ES (0.352), with moderate level of consistency across the primary studies. In contrast, land use mix has the strongest negative ES (− 0.212) but with the highest level of inconsistency. Both ESs and consistencies, however, vary between developing and developed country contexts. Diversity in contexts, research design, and measurement instruments across the primary studies contributed to the heterogeneous results. The findings of this research serve as a guide for practitioners and researcher alike to make an informed decision about the BE factors that consistently foster or hinder CIM in different contexts.     

The indirect effect of the built environment on travel mode choice: A focus on recent movers

Many studies have demonstrated that the built environment has a strong impact on people's travel mode choice. However, the built environment also influences elements such as travel distance and car ownership, which might be the true predictors of which travel modes are chosen. In this study, we analyse the effects of changes in residential neighbourhood on changes in travel mode (for commute trips and leisure trips), both directly and indirectly through changes in car ownership, travel distances and travel attitudes. This study applies a structural equation modelling approach using quasi-longitudinal data from 1650 recently relocated residents in the city of Ghent, Belgium. Results indicate that the built environment has strong direct effects on active leisure trips and car use. However, distance (for car use) and attitudes (for active travel) were found to be important mediating variables. In sum, the effect of the built environment on travel mode choice might be more complex than commonly assumed as it partly seems mediated by travel distance and travel attitudes.     

堡子湾站两万吨列车组合能力研究

呼和浩特铁路局堡子湾站是我国第一个在既有客货混跑线路上开行两万吨重载列车的车站。研究堡子湾站两万吨列车组合能力,分析两万吨列车实际开行效果,以期达到组织好既有客货列车运行,提高区段输送能力和效率,确保完成大秦线运量任务目标的目的。     

A deeper investigation into the effect of the built environment on the use of ridehailing for non-work travel

Ridehailing has become a main-stream mobility option in many cities around the world. Many factors can influence an individual's decision to use ridehailing over other modes, and the growing need of policy makers to make built-environment and regulatory decisions related to ridehailing requires an increased understanding of these. This study develops a model that estimates how the built environment affects the decision to choose ridehailing for making non-work trips, while carefully accounting for a variety of confounding effects that could potentially bias the results (if ignored or improperly incorporated). These include: total number of trips, differences in supply between urban and non-urban areas, residential choice (e.g. urban versus non-urban areas), and household choice of whether to own a vehicle. We use individual-level data from a California travel survey that includes detailed attitude measurements to estimate an integrated choice and latent variable (ICLV) model to properly specify these effects. We include accessibility measures used elsewhere (e.g., Walkscore) plus measures developed for this study. Our analysis estimates the effect of these measures on ridehailing mode share, and how they differ between urban and non-urban areas. This analysis results in several major findings: we confirm that omission of latent preferences for residential location and vehicle ownership from the analysis can lead to biased results; previous studies may have overestimated the complementarity or substitution relationships between public transit and ridehailing by ignoring confounding effects; and even after accounting for other effects, individuals living in vibrant and walkable neighborhoods have a higher mode share for ridehailing (potentially using it instead of active modes).     

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.     

Exploring built environment correlates of walking distance of transit egress in the Twin Cities

Most studies on walking distance to transit stops either emphasize transit access or do not distinguish transit access and egress. Furthermore, environmental correlates of walking distance may differ by stop location. Using the 2010 Transit Onboard Survey in the Minneapolis and St. Paul Metropolitan Area, this study develops four models to compare the effects of the built environment around transit stops on walking distance of transit egress. Job density is negatively correlated with walking distance, consistent in all four models. Other built environment variables exhibit different impacts by stop location. Particularly, land use mix has positive impacts on walking distance for stops outside of downtown and suburban employment centers whereas job density is more important for suburban centers. Job accessibility and the number of intersections have significant effects on stops within downtown areas but have no significant impacts on stops outside of downtown areas. The number of transit stops has opposite impacts on walking distance for stops within and outside of downtown. Moreover, the built environment tends to have a larger impact on walking distance in downtown areas than non-downtown areas. We then discuss the implications for stop area land use planning and transit stop location choice.     

A new zone system to analyze the spatial relationships between the built environment and traffic safety

This study aims to investigate the impacts of the built environment on traffic safety at a zonal level using a newly developed crash-related zone system. Traffic analysis zones (TAZs) have been widely employed to analyze traffic safety at a macroscopic level. However, this zone system use may present problems. Unlike previous studies, in which new zoning systems were created from aggregating TAZs, in this study the new zone system, formed by traffic safety analysis zones (TSAZs), is created from the smallest available census units. Geographically Weighted Negative Binomial Regression (GWNBR) models are used and a comparative analysis between non-spatial global crash prediction models and spatial local GWPR (Geographically Weighted Poisson Regression) and GWNBR models using the two zonal systems is presented. We find that TSAZs based models performed better than TAZs based models, especially when combined to the GWNBR technique. Our results show that several features of the built environment are significant crash predictors, and that the relationships among these features and traffic safety vary across space. By combining a crash-related zonal system with spatial GWNBR models to understand the built environment effects on traffic safety, the results of the analysis can help urban planners to consider traffic safety proactively when planning or retrofitting urban areas.     

Multivariate analysis of the impacts of NAS investments: A case study of a capacity expansion at Dallas-Fort Worth Airport

Multivariate statistical analysis is used to estimate the impacts of a major capacity expansion at Dallas-Fort Worth International Airport. The model relates the Daily Flight Time Index (DFTI) to demand, weather, origin airport congestion, and the expansion itself. The effect of the capacity expansion on DFTI is found to depend strongly on visibility. On average, the index in the post-expansion period is 1.3 min less as a consequence of investment. This change includes a larger reduction in departure delay that is offset by an increase in taxi time. Moreover, the reduction caused by the expansion has been more than offset by increases in the DFTI resulting from other factors, notably increased demand and worse weather.