Land use effects on bicycle ridership: a framework for state planning agencies

State and local agencies increasingly recognize the importance of bicycling activity and as the number of riders has grown over the past several years, the agencies are becoming more aware of the need to provide better bicycle infrastructure. This paper proposes a series of empirical models and applies them to the State of Maryland in the United States, using a spatial lag approach to explore land use, built environment, demographic, socio-economic, and traffic condition connections to bicycle ridership, defined as the number of bicycle trips generated by a given analysis zone per day. A set of models is proposed for three land-use typologies: urban, sub-urban and rural. The data that drives this analysis was obtained from a recently conducted Household Travel Survey (HTS) in the Baltimore–Washington region in Maryland. Results show that some land uses, socio-economic and demographic characteristics, and transit accessibility are positively correlated with bicycle ridership. Other types of land use, transport system characteristics and income level have an inverse relationship with bicycle ridership. The contributing factors to bicycle ridership vary with land-use typology. This proposed approach could be used to evaluate factors relevant to bicycle demand. State and local agencies are advised to build designated bicycle paths according to traffic conditions and increase bicycle-parking capacity in specific establishments.     

Land use and transport: research and policy challenges

In the debate on the impacts of land use on transport two questions are of particular importance: (1) does land use affect travel behaviour and (2) if land use affects travel behaviour, should land-use policies be partly based on (expected or assumed) transport impacts? There is enough evidence to conclude that land use can influence travel behaviour. However, this does not mean that policy-makers should choose land-use alternatives with the lowest level of car use. Possible future land-use and transport plans should be evaluated according to a broad range of factors. Several challenges for research and policy-making related to these aspects are presented here.     

Analysis on bike-share ridership for origin-destination pairs: Effects of public transit route characteristics and land-use patterns

Studies on bike-share programs have dramatically increased during the past decades. While numerous studies have examined various factors affecting bike-share demand at the station-level, few attempts have been made to understand bike-share ridership at the origin-destination (OD) level due to technical difficulties. The objective of this study is to examine whether existing public transit characteristics affect bike-share ridership at OD-level. We combined three datasets: (1) bike-share ridership data, (2) land-use and bike-transit infrastructure, and (3) bike-transit route characteristics between OD pairs of bike stations. Zero-inflated negative binomial (ZINB) regression models were used for the analysis. Our results showed that the travel distance between OD bike stations, land-use compositions, and the existence of bike-friendly infrastructures were significant factors determining bike-share ridership at the OD-level. In particular, a longer duration of public transit trips than bike-share, and more transit transfers, were associated with bike-share ridership. Further, this study showed that bike-share and public transit might compete with or promote each other, even within the city. The study's findings suggest that the relative efficiency of bike-share compared to public transit is highly associated with bike-share demand and help to increase the utility of bike-share system in response to several limitations of existing public transit networks.     

Residential dissonance and mode choice

Residential dissonance refers to the mismatch in land-use patterns between individuals’ preferred residential neighbourhood type and the type of neighbourhood in which they currently reside. Current knowledge regarding the impact of residential dissonance is limited to short-term travel behaviours in urban vs. suburban, and rural vs. urban areas. Although the prevailing view is that dissonants adjust their orientation and lifestyle around their surrounding land use over time, empirical evidence is lacking to support this proposition. This research identifies both short-term mode choice behaviour and medium-term mode shift behaviour of dissonants in transit oriented development (TODs) vs. non-TOD areas in Brisbane, Australia. Natural groupings of neighbourhood profiles (e.g. residential density, land use diversity, intersection density, cul-de-sac density, and public transport accessibility levels) of 3957 individuals were identified as living either in a TOD (510 individuals) or non-TOD (3447 individuals) areas in Brisbane using the TwoStep cluster analysis technique. Levels of dissonance were measured based on a factor analysis of 16 items representing the travel attitudes/preferences of individuals. Two multinomial logistic (MNL) regression models were estimated to understand mode choice behaviour of (1) TOD dissonants, and (2) non-TOD dissonants in 2009, controlling for socio-demographics and environmental characteristics. Two additional MNL regression models were estimated to investigate mode shift behaviour of (3) TOD dissonants, and (4) non-TOD dissonants between 2009 and 2011, also controlling for socio-demographic, changes in socio-demographic, and built environmental factors. The findings suggest that travel preference is relatively more influential in transport mode choice decisions compared with built environment features. Little behavioural evidence was found to support the adjustment of a dissonant orientation toward a particular land use feature and mode accessibility they represent (e.g. a modal shift to greater use of the car for non-TOD dissonants). TOD policies should focus on reducing the level of dissonance in TODs in order to enhance transit ridership.     

Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system

In recent years, there has been increasing attention on bicycle-sharing systems (BSS) as a viable and sustainable mode of transportation for short trips. However, due to the relatively recent adoption of BSS, there is very little research exploring how people consider these systems within existing transportation options. Given recent BSS growth around the world, there is substantial interest in identifying contributing factors that encourage individuals to use these systems. The current study contributes to this growing literature by examining BSS behavior at the trip level to analyze bicyclists’ destination preferences. Specifically, we study the decision process involved in identifying destination locations after picking up a bicycle at a BSS station, using a random utility maximization approach in the form of a multinomial logit model (MNL). The quantitative frameworks developed have been estimated using 2013 data from the Chicago’s Divvy system. In our modeling effort, we distinguish between BSS users with annual membership and short-term customers with daily passes. The developed model should allow bicycle-sharing system operators to plan services more effectively by examining the impact of travel distance, land use, built environment, and access to public transportation infrastructure on users’ destination preferences. Using the estimated model, we generated utility profiles as a function of distance and various other attributes, allowing us to represent visually the trade-offs that individuals make in the decision process. To illustrate further the applicability of the proposed framework for planning purposes, destination station-choice probability prediction is undertaken.     

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.     

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.     

Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning

Although many studies investigate the association between land use and station ridership, few examine their nonlinear and moderating relationships. Using metro smartcard data in Shenzhen, we develop a gradient boosting decision trees model to estimate the relative importance of land use variables and their threshold and moderating effects on ridership. We found that station betweenness centrality has the largest predictive power, followed by employment density and commercial floor area ratio (FAR). Results suggest that employment density, commercial FAR, and aggregate residential density should be set at 40,000 jobs/km², 2, and 77,000 persons/km², respectively, for maximizing ridership. The moderating effects show that population densification is more effective at terminal stations, whereas the policies intensifying nonresidential use work better at middle stations. These findings help planners prioritize land use strategies, identify effective ranges of land use metrics, and propose land use guidelines adaptive to the network position of stations.     

Integrating node-place and trip end models to explore drivers of rail ridership in Flanders,Belgium

The node-place model is an analytical framework that was devised to identify spatial development opportunities for railway stations and their surroundings at the regional scale. Today, the model is predominantly invoked and applied in the context of ‘transit-oriented development’ planning debates. As a corollary, these model applications share the pursuit of supporting a transition towards increased rail ridership (and walking and cycling), and therefore assumingly a transition to more sustainable travel behavior. Surprisingly, analyses of the importance of node and place interventions in explaining rail ridership remain thin on the ground. Against this backdrop, this paper aims to integrate the node-place model approach with current insights that derive from the trip end modeling literature. To this end, we apply a series of regression analyses in order to appraise the most important explanatory factors that impact rail ridership in Flanders, Belgium, today. This appraisal is based on both geographical and temporal data segmentations, in order to test for different types of railway stations and for different periods of the day. Additionally, we explore spatial nonstationarity by calibrating geographically weighted regression models, and this for different time windows. The models developed should allow policy and planning professionals to investigate the possible demand impacts of changes to existing stations and the walkable area surrounding them.     

The demand for reliable transit service: New evidence using stop level data from the Los Angeles Metro bus system

This study explores the role of service reliability in determining bus transit ridership. Using stop level service supply, demand, and performance data from the Los Angeles Metro bus system, I investigate whether reliability of a directional line serving a stop influences the number of passengers boarding the line at that stop, controlling for various other established factors affecting demand. This cross-sectional analysis of the variation in line boardings across about 1300 sample schedule time point bus stops served by about 300 directional bus lines over a six-month period uses a historical archive of real-time geo-referenced vehicle location data, and focuses on five different time periods, peaks and off-peaks, of a typical weekday. By evaluating two measures that capture different dimensions of bus service reliability, and by estimating a series of regression models, I find systematic evidence that higher average service punctuality (or schedule adherence) and lower variation in schedule deviation over time are associated with greater ridership, all else equal, particularly during the peak periods. This study also provides first empirical evidence that the effect of reliability on peak-period ridership is moderated by headway. The demand for reliability seems to be higher for lines with relatively longer headways. The findings indicate that service reliability influences transit mode choice and/or line/route selection, and suggest that system-wide ridership gains can be expected from reliability improvements. From an urban planning perspective, this study provides more evidence that good service quality can effectively compliment transformations in the urban fabric brought about by coordinated land use — transit plans to promote transit use.     

Transit ridership forecasting at station level: an approach based on distance-decay weighted regression

This article develops a rapid response ridership forecast model, based on the combined use of Geographic Information Systems (GIS), distance-decay functions and multiple regression models. The number of passengers boarding at each station in the Madrid Metro network is estimated as a function of the characteristics of the stations (type, number of lines, accessibility within the network, etc.) and of the areas they serve (population and employment characteristics, land-use mix, street density, presence of feeder modes, etc.). The paper considers the need to evaluate the distance threshold used (not the choice of a fixed distance threshold by assimilation from other studies), the distance calculation procedure (network distance versus straight-line distance) and, above all, the use of distance-decay weighted regression (so that the data from the bands nearer the stations have a greater weighting in the model than those farther away). Analyses carried out show that weighting the variables according to the distance-decay functions provides systematically better results. The choice of distance threshold also significantly improves outcomes. When an all-or-nothing function is used, the way the service area is calculated (straight-line or network distances) does not seem to have a decisive influence on the results. However, it seems to be more influential when distance-decay weighting is used.     

Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul

This study has dual research objectives: 1) to evaluate the land use characteristics of the pedestrian catchment areas (PCA) of subway stations in the Seoul metropolitan area (SMA) in terms of transit-oriented development (TOD) principles and 2) to investigate the influence of each PCA's land use characteristics on station-level ridership. The major findings can be summarized as follows. First, the built environments of subway PCAs in Seoul were found to be compatible with TOD principles in terms of density and diversity. They have declining density gradients of population and employment that extend outward from a station and have a high level of mixed-use land.Second, population and employment densities, land use mix diversity, and intermodal connectivity all have a positive impact on subway ridership, but differ in their spatial ranges. In particular, the influence spans of residential and commercial development patterns (population density and the proportion of commercial land) and mixed land use on ridership were limited to only the core and primary PCAs. Consequently, with respect to TOD in a compact city like Seoul, we recommend that a PCA be defined to encompass a radius of 600 m.     

Catchment if you can: The effect of station entrance and exit locations on accessibility

The success of passenger railway systems depends on their ridership and thus the population they serve. A mechanism to increase ridership is to expose the existing system to more people by reconfiguring the station itself, for instance by adding extra entrance and exit gates to shorten the walking distance from a trip's origin or its final destination. Gates are key nodes giving pedestrians access from street network to boarding/alighting facilities and vice versa. Stations and platforms are places not points, passengers may spend up to 6 min a trip walking between platforms and the end of the station nearest their origin or destination. This study systematically evaluates the accessibility of train stations and the effect of constructing an additional ‘far-side’ gate at stations with a single ‘near-side’ entrance. A three-step approach is defined to generate an isochrone as the catchment area for any transport node. Results indicate that stations with a single gate along their platforms (usually on one end of them) have the potential to increase the accessibility to jobs and population by around 10% on average. Due to the walking network and land use characteristics, some stations will benefit more significantly by retrofitting a new gate. Also, four linear regression models are developed to illustrate the effect of expanded accessibility on the number of entries and exits at each station for two peak periods. Then, stations are ranked based on their added ridership, which can help authorities to prioritize the development and allocating resources.     

Extreme weather,public transport ridership and moderating effect of bus stop shelters

This paper examines how extreme weather conditions influence urban public transport ridership with a particular focus on the role of bus stop shelters. Using bus ridership data from the Salt Lake City metropolitan area, we find that extreme weather such as very high and low temperatures, and heavy rainfall reduces public transport ridership, while bus stop shelters have a modest effect on mitigating ridership losses resulting from these adverse weather conditions. The moderating effect of shelters is more pronounced on weekdays, and for bus stops with lower service frequency and fewer transfers. Our research also shows that the installation of bus shelters correlates with a variety of factors including service frequency, land use types, and local socioeconomic and demographic characteristics. Overall, our findings suggest that public transport amenities with weather-proof attributes have the potential to retain and attract more ridership on extreme weather days.     

Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership

In this study, we employ spatial regression analysis to empirically investigate the impacts of land use, rail service coverage, and rail station accessibility on rail transit ridership in the city of Seoul and the surrounding metropolitan region. Our analyses suggest that a rail transit service coverage boundary of 500 m provides the best fit for estimating rail transit ridership levels. With regard to land use, our results confirm that density is positively related to rail transit ridership within a 750 m radius of each station. In contrast, land use diversity is not associated with rail transit ridership. We also found that station-level accessibility is as important as land use for explaining rail transit ridership levels. Finally, we conclude that development density and station-level accessibility measures such as the number of station entrances or exits and the number of bus routes at the station are the most important and consistent factors for promoting rail transit 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.     

Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach

Understanding the travel behaviors and activity patterns of vulnerable people is important for addressing social equity in urban and transportation planning. With the increasing availability of large-scale individual tracking data, new opportunities have emerged for studying people's travel behaviors and activity patterns. However, the data has not been fully exploited to examine the travel characteristics of vulnerable people and their implications for understanding transport-related disadvantage. This study proposes a methodological framework based on the concept of activity space that enables a comprehensive examination of vulnerable people's spatiotemporal travel characteristics and an investigation of the geographies of transport disadvantage. Using the proposed framework, a case study that investigates the bus activities of the vulnerable population using four-month smart card data is carried out in the city of Wuhu, China. The case study suggests that vulnerable people possess distinct travel behaviors that differ considerably from the mainstream population and that the implications of transport disadvantage, as revealed by the participation in bus activities, vary across different demographic groups and across different spatial contexts. Some of the empirical insights obtained from this study also differ from conclusions drawn from previous studies and will enrich our understandings of vulnerable people's activities. Overall, the paper makes two major contributions. Methodologically, the proposed framework can overcome some of the deficiencies of activity space-based approaches for understanding transport disadvantage and contribute broadly to the studies of travel behaviors and activities patterns using individual-level tracking data. Empirically, the study identifies varying spatial and temporal implications of transport disadvantage associated with different vulnerable groups, which could further shed light on public transit planning and service design.     

Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model

With the advance of intelligent transportation systems (ITSs) and data acquisition systems (DASs), it becomes possible in recent to explore the determinants of urban taxi ridership using multi-source heterogeneous data. This paper aims to use floating car data, points-of-interests (POIs) data and housing-price data to assess the influence of the built environment on taxi ridership. Within a scale of 0.5 km grid, critical indicators related to the economic aspect, intermodal connection, and land use factors were obtained using the multi-source data in Shanghai. To capture the spatial and temporal heterogeneity, Semi-parametric Geographically Weighted Poisson Regression (SGWPR) models are built over different time dimensions. It is found that SGWPR models result in higher goodness-of-fit than the generalized linear models. More importantly, the results show the impacts of built environment factors on taxi demand are highly heterogeneous, positive or negative in different city areas, reflected in the significant temporal variations of the effects. Overall, these findings suggest that the built environment factors have significant impacts on urban taxi demand, and the spatial context should not be ignored. Findings in this paper are expected to help better understand the relationship between urban taxi demand and built environment factors, improving the service level of the urban taxi system, and offering valuable insights into future urban and transportation planning.     

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

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

Activity participation as a mediating variable to analyze the effect of land use on travel behavior: A structural equation modeling approach

As activity based approach is receiving increased attention over the past decades, a variety of studies have explored the impacts of land use patterns not only on travel behavior but also on activity participation. Using data from the Tengzhou Household Travel Survey, this paper tests the relationships among land use patterns, activity participation and travel behavior, by comparing results from two structural equation models: one model without activity participation and a second model which adds activity participation as a mediating variable between land use patterns and travel behavior characteristics. The results suggest that the indirect effects through activity participation decision play important roles, which may reinforce the direct effects of land use patterns on travel behavior. Moreover, ignoring activity participation as a mediating variable may also lead to insignificant effects of land use patterns. We conclude that the intermediary nature of activity participation should be taken into consideration.