Long-term impact of network access to bike facilities and public transit stations on housing sales prices in Portland,Oregon

Planners and economists generally accept that housing market values increase with proximity to transportation facilities through the provision of improved access to activity locations. While the market benefits of rail station access are well-documented, inconsistent and insufficient methods have led to limited agreement on the true value associated with this locational amenity. Far fewer hedonic price studies have assessed the influence of bike facility access on housing sales prices, and those that have generally analyze cross-sectional data. In this study, we estimated a spatial hedonic model using a bootstrapped pseudo panel to determine the joint impact of network proximity to bike lanes and off-street multi-use paths, as well as light rail and streetcar stations, on housing sales in Portland, Oregon, from 2002 to 2013. Our findings revealed housing sales prices increased as network distance to the nearest light rail transit and streetcar station decreased. Likewise, owner-occupied single-family and multifamily housing sales rose in conjunction with reduced street network access to regional multi-use bike paths; however, improved proximity to on-street bike lanes negatively affected housing values. In sum, we believe these findings may help to inform non-automotive transportation infrastructure financing mechanisms that rely on rising property values.     

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 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.     

Analysis of the impact of street-scale built environment design near metro stations on pedestrian and cyclist road segment choice: A stated choice experiment

The mismatch between the design of the micro-scale built environment around metro stations and pedestrian/cyclist preferences causes inconvenience and dissatisfaction. How to design streets near metro stations to provide a walking/biking friendly built environment is still a key question in promoting the use of metro systems. To identify which general attributes of the street-scale built environment are relevant for pedestrians/cyclists and increase walkability/cycle-ability, this paper reports the results of a stated choice experiment in which eight built environment attributes were systematically varied: street segment length, average number of building floors on both sides of the street, retail shops in frontage of streets, street crossing facilities for pedestrians/cyclists, width of sidewalks/bicycle paths, greenery, density of street lamps and crowdedness of pedestrian/cyclists to understand their influence on a road segment choice and preferences. A total of 803 respondents were recruited from Tianjin, China to complete the stated choice experiment through on-street face-to-face interviews. A multinomial logit model was estimated to unravel pedestrian/cyclist preferences using the stated choice data. The results indicate that pedestrians and cyclists have similar preferences for road segments with building lower than 6 floors, 50% retail shops in frontage, more greenery, lamps between 15 m and 30 m, more crossing facilities, wider sidewalk/bike lane and not crowded. These significant built environment attributes can be used in urban design projects with a walking/biking friendly built environment around a metro station.     

Exploring the effects of the rural built environment on household car ownership after controlling for preference and attitude: Evidence from Sichuan,China

With the rapid infrastructure development and economic growth in China, household car ownership in the country's rural areas has changed dramatically in the past 16 years. The total number of cars owned by households in rural areas is currently 12 times higher than it was 16 years ago. The exploration of the effects of the built environment on household car ownership in China's rural areas is worthwhile. However, few studies have investigated this topic. To fill in the research gap, this work collected 374 household data from rural areas in China to examine the effects of the built environment in Sichuan's rural areas on the number of cars in a household. It considered family structure, socioeconomic characteristics, and individual's perceptions of the built environment, preferences towards the built environment and attitudes towards car ownership (shortened to perceptions, preferences and attitudes from now on). Geographic information system (GIS) technology, combined with on-site measurement, was used for data collection. The multinomial logit model was applied for estimation. Household structure and the built environment (including the perceived built environment and the objective built environment) significantly influence the number of cars in a household. By contrast, preference and attitude attributes have less influence on car ownership. Most of the findings are in line with the literature in the context of Chinese cities. Nevertheless, new results are also found. For example, rural hukou, and building density have significant positive impacts on household car ownership in China's rural areas, which is in contrast with their effects on cities. As the first study on rural areas in China, this research provides some insights for rural planners and policymakers to understand better the relationship between built environment and household car ownership.     

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.     

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.     

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.     

The impact of topological properties of built environment on children independent mobility: A comparative study between discretionary vs. nondiscretionary trips in Dhaka

Researchers broadly represented the built environment (BE) using geographic and topological indicators. Despite studies have shown that the geographic BE affects children independent mobility (CIM), little is known about the effects of topological BE on CIM. Less so, how the effects vary between discretionary and nondiscretionary CIM trips. The study addresses these gaps using self-reported two-day mobility data of 151 children aged 10–14 years from Dhaka, Bangladesh. Geographic BE data (e.g. land uses, street width, building height) were collected through a virtual BE audit following each route. Topological BE data (e.g. step-depth, integration, choice) were derived in Depthmap X. CIM was measured in a binary scale by checking whether the reported trips were taken independently or not. Three binary logistic regression models (an overall model, a discretionary trip model, and a nondiscretionary trip model) were estimated to determine the effects of geographic and topological BE on CIM, controlling for other confounding effects. The findings demonstrate that both geographic and topological BE affect CIM. However, they affect discretionary and non-discretionary CIM differently – e.g. step-depth, angular connectivity and presence of institutional land use affect only non-discretionary CIM, whereas integration, recreational land use and traffic composition affect only discretionary CIM. The findings highlight that geographical features need to be considered in tandem with topological features of the BE, stratified by destination types, to maximise CIM.     

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).     

Exploring public transport equity between separate disadvantaged cohorts: a case study in Perth,Australia

This paper explores the equity distribution of public transport for three separate disadvantaged cohorts including elderly residents, low-income households and no-car households for Perth, Western Australia. It also undertakes a city-wide equity analysis of Perth and compares this with a published analysis for Melbourne. Overall the public transport distribution of the three socially disadvantaged groups was identified to be less equitable when compared to the population as a whole. The elderly had the most inequitable distribution of population relative to other cohorts. Perth’s population exhibits a 0.52 Gini coefficient suggesting a relatively unequal spatial distribution of services to the population. However, this is much better than Melbourne (at 0.68). Results imply that 70% of Perth’s population have only 33% of services supplied, whilst in Melbourne this figure was 19%. Policy implications and areas for future research in this field were identified.     

An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai,China

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R² = 0.774) than the OLS regression model (Adjusted R² = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.     

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.     

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.     

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.     

The factors in residents' mobility in rural towns of China: Car ownership,road infrastructure and public transport services

The improvement of rural people's mobility in developing countries has informed many policies. Still, debates remain on which policies are efficient, for instance, building more roads, providing public transport or promoting car ownership. The empirical evidence for these debates at the national level remains scarce. As a result, this paper aims to provide fresh evidence for discussions by examining residents' mobility in China using nationwide survey data with 12,524 respondents from 119 rural towns. The results of the analysis show car ownership is the most significant factor influencing rural people's mobility than other factors. Higher car ownership relates to a higher travel frequency to counties or cities. Other kinds of transport vehicles (i.e. electric cars, motorcycles and electric bikes) also have positive but relatively weaker impacts on rural mobility. For public transport, it is more accessible to access bus stops, which encourages travel to higher-order centres rather than increasing the frequency of county bus services. The accessibility of high-quality road systems tends to have a negative influence and has combined effects with levels of local services. People from towns with insufficient local services and poor access to highways travel the most frequently to higher-order centres. This study highlights the critical role of road investments and car ownership enhancement policies in improving mobility. Moreover, this study also underscores the supplemental role of public transport services given the current low car ownership rates in rural towns of China and the global consensus on sustainable green transport development. It highlights the importance of engaging eco-friendly and locally adaptive transport alternatives, such as electric cars and electric bikes. It also calls for a rational distribution of bus stops and more flexible, convenient, and physically accessible public transport and carshare modes in rural China.     

Varying influences of the built environment on daily and hourly pedestrian crossing volumes at signalized intersections estimated from traffic signal controller event data

Direct-demand models of pedestrian volumes (identifying relationships with built environment characteristics) require pedestrian data, typically from short-duration manual counts at a limited number of locations. We overcome these limitations using a novel source of pedestrian data: estimated pedestrian crossing volumes based on push-button event data recorded in traffic signal controller logs. These continuous data allow us to study more sites (1494 signalized intersections throughout Utah, US) over a much longer time period (one year) than in previous models, including the ability to detect variations across days-of-week and times-of-day. Specifically, we develop direct demand (log-linear regression) models that represent relationships between built environment variables (calculated at ¼- and ½-mile network buffers) and annual average daily and hourly pedestrian metrics. We control spatial autocorrelation through the use of spatial error models. All results confirm theorized relationships: There is more pedestrian activity at intersections with greater population and employment densities, a larger proportion of commercial and residential land uses, more connected street networks, more nearby services and amenities, and in lower-income neighborhoods with larger households. Notably, we also find relevant day-of-week and time-of-day differences. For example, schools attract pedestrian activity, but only on weekdays during daytime hours, and the coefficient for places of worship is higher in the weekend model. K-fold cross-validation results show the predictive power of our models. Results demonstrate the value of these novel pedestrian signal data for planning purposes and offer support for built environment interventions and land use policies to encourage walkable communities.     

Spatial impact analysis of trans-Yangtze highway fixed links: A case study of the Yangtze River Delta,China

As an important infrastructure connecting straits or rivers, the construction of fixed links has become an effective measure for improving traffic conditions and promoting socio-economic development in many countries and regions around the world. Thus, it has become a significant topic in the field of transport geography. Taking the Yangtze River Delta as its case area, this study proposes a spatial impact model of trans-Yangtze highway fixed links, consisting of three components: an accessibility model, modified gravity model, and traffic utilisation model, which are used to analyse, respectively, cross-Yangtze accessibility and changes to transport structure, cross-Yangtze urban interaction and changes to economic structure, and the utilisation relationship of fixed links. Making use of existing fixed links while planning and building new ones has become the basis for achieving regional integration and sub-regional cooperation in this area, the aim of which is to create the barrier-free circulation of elements. The results prove that this spatial impact model coincides with reality. The increase in the number of fixed links has significantly shortened cross-river travel time and facilitated a unification of north-south highway networks. It has also promoted the formation of a high-connection urban network along the river. Northern urban nodes have joined the southern economic circle and finally achieved north-south integration. There are complex relationships between fixed links and their hinterlands, showing obvious utilisation gaps between the links. The complete system of trans-Yangtze fixed links is comprised of several river crossing facilities and connected highways. The function of each link is related to its role in the regional highway network. From a small number of fixed links to the realisation of multiple trans-Yangtze bridges and tunnels with easy access to multiple arterial highways, a ‘many-to-many’ spatial pattern is created that ultimately leads to the evolution of regional transport and economic structures.     

Accessibility impact of the present and future high-speed rail network: A case study of Jiangsu Province,China

The large-scale implementation of a high-speed rail (HSR) network is often considered to have a significant effect on the spatial distribution of accessibility. In China, the development of HSR network has progressed rapidly since the first line commenced operation in 2008. As an important component of this network, Jiangsu province proposed an ambitious HSR construction program which planned to cover over 95% of its counties by 2030. Reduced travel time for passengers is one of the most important effects of HSR, and therefore this study aims to analyze the accessibility impact of the evolving HSR network in Jiangsu province from 2010–2030. A layered cost distance method, based on a door-to-door approach, is proposed to evaluate the present and future accessibility at a more detailed geographical level. The results demonstrate that, with the gradual development of the HSR network, accessibility levels across the province will be improved by about 9.6%, and the distribution of the gains will be uneven since the most significant improvements will occur in the more peripheral areas. The inequality in regional accessibility will decrease by an average of 25.7%, which will produce a more homogeneous accessibility landscape. In addition, several policy measures are suggested in order to further enhance the competitiveness of the HSR network in the transport market at a regional level. This extended period of exploratory and detailed analysis is expected to facilitate proactive public policy decisions related to improving the transport network.