Is the built-environment at origin,on route,and at destination associated with bicycle commuting? A gender-informed approach

There is limited evidence on the gender differences and location-specific built-environment factors associated with bicycling in Latin American cities. This study aimed to assess commuting in Bogotá by (1) analyzing the gender-specific trend of the standardized number of bicycle commuters during 2005–2017; and (2) assessing the socio-demographic, community, built-environment and natural factors associated with bicycle commuting stratified by gender. This secondary-data analysis included data from the Household Travel Surveys and Multipurpose Surveys to calculate the number of bicycle commuters per habitant from 2005 to 2017 by gender. We assessed the socio-demographic and built-environment factors fitting generalized additive models stratified by gender using the 2015 Household Travel Survey. Although both women and men increased the standardized number of bicycle commuters, male commuters show a steeper trend than women, evidencing the widening gender gap in bicycle commuting over time. Bicycle commuting was negatively associated with household motor vehicle ownership, steeper terrain slope, longer commute distance, and scarce low-stress roads at trip origin and route. Among women, the availability of bike paths at the trip destination was positively associated with bicycling, while age and being a student were negatively associated with bicycling. Among men, living in areas with the lowest socio-economic status was positively associated with bicycling, while having a driver's license and living close to bus rapid transit stations were negatively associated with bicycling. In conclusion, bicycle and transport infrastructure play different roles in commuting by bicycle by gender and trip stages (origin – route – destination).     

Does bicycle network level of traffic stress (LTS) explain bicycle travel behavior? Mixed results from an Oregon case study

This paper draws on census data, mode choice, and regional household travel survey data to investigate the relationship between the varying levels of traffic stress (LTS) routes and bicycle travel behavior. Specifically, does bicycle level of traffic stress explain bicycle behavior enough to warrant its use given the limitations? Bicycle level of traffic stress is a new system of bicycle infrastructure classification for cities that cannot afford existing alternatives such as bicycle level of service which is more data-intensive. The LTS criteria lacks many of the input variables found significant in predicting both route choice and mode choice, including several that are required for its more expensive alternatives. The authors select the Salem-Keizer metropolitan area, a case study representative of the kinds of small and medium-sized communities that may prefer to use the LTS to evaluate cycling infrastructure given its lower input costs. The results validate LTS on travel behavior data from the Oregon Household Activity Survey (OHAS), but not mode choice data from the American Community Survey (ACS). These results suggest the LTS criteria may not be useful for cities looking to prioritize infrastructure improvements for specifically increasing commuter cycling. That said, results suggest the system provides a valid measure of a household's propensity to cycle. Further research on a broader cross-section of communities can clarify these mixed results.     

Mapping bicycle use and the risk of accidents for commuters who cycle to work in Belgium

This paper explores the spatial patterns of bicycle use for commuting and the risk cyclists run being injured in a road accident when commuting to work in Belgium. Exploratory data analyses suggest that the observed differences in the use of the bicycle to get to work are strongly linked to the urban hierarchy: commuters are more inclined to cycle in cities and specifically in regional towns (with 25 000 to 120 000 inhabitants). In large cities (more than 200 000 inhabitants), less commuting by bicycle takes place. The relationship between bicycle use and the risk of being seriously injured or killed in a road accident is also studied. A cluster analysis confirms that high proportions of commuter cyclists are correlated with low risks of becoming a casualty. It also shows that there are strong spatial differences (regional and between different types of towns) in bicycle use and the risk of an accident. This suggests that cycling policies should be spatially differentiated.     

Attitudes,norms and difficulties underlying road sharing intentions as drivers and cyclists: Evidence from the Czech Republic

Cycling-inclusive urban planning is attracting attention worldwide because of the environmental, health, economic, and transport benefits inherent to cycling from the individual and the societal perspectives. The Czech Republic is one of the emerging cycling countries that are trying to promote and support cycling, but cycling mode shares are low because of the poor quality of the scarce infrastructure and the psychological barrier of the perceived lack of safety when cycling in mixed traffic. This study takes a multimodal approach based on the Theory of Planned Behaviour and focuses on the factors underlying the willingness to share the road from the perspective of cyclists and drivers: positive attitudes toward cycling, positive attitudes toward cyclists’ behavior, social norms toward cycling and anxiety to share the road. A web-based questionnaire was tailor-designed and administered in the Czech Republic via social networks, web forums, and the Civinet network, and a hybrid bivariate-ordered model tested the behavioral framework. Results show that the willingness to share the road as a cyclist or as a driver relates positively to positive attitudes towards cycling and cyclists’ behavior, and negatively to the anxiety of sharing the road. Moreover, mediator effects are observed and a clear relation emerges between the experience on the road as both a cyclist and a driver on the willingness to share the road as a cyclist. Lastly, results show that the factors underlying road sharing intentions are related significantly to gender, travel habits, and perceived personal and infrastructure-related barriers to cycling.     

Environmental correlates of dock-less shared bicycle trip origins and destinations

It remains unclear whether environmental conditions, including bicycle lane provision, are factors in dock-less bicycle uptake at the decision point. In conditions of generous bicycle supply and the ability to park a bicycle almost anywhere, it is important to know what local conditions might be influential in its use. The present study considered the role of 13 environmental factors in uptake and deposit rates by street segment, in a sample from a database of more than 3 m records over a ten-day period in May 2017, in Beijing, China. Street segments, defined by intersections, were coded for environmental factors using satellite and street view imagery. Uptake and especially deposit rates vary greatly by street segment and by local area. Linear regression reveals that 6 factors are highly significant in higher uptake and deposit – the presence of a dedicated bicycle pathway, street lighting, sidewalks, bicycle parking lots, POI density, and street segment length – while 3 were highly significant in lower uptake rates – interruptions in bicycle lane continuity, distance to the metro station, and elaborate environmental design. Altogether, environmental factors account for 42.4% of the variance in local bicycle trip origins and 38.7% of variance in destinations on street segments. This research contributes new understanding of the important role of local environmental factors in higher bicycle mode share in the context of a dock-less rental bicycle system.     

Modeling commuter modal shift from car trips to cycling: Scenario construction and outcomes for Stockholm,Sweden

This article presents the construction and outcomes of scenarios modeling commuter modal shift from car trips to cycling in the metropolitan region of Stockholm, the capital of Sweden. Building and improving upon previous studies in terms of both methodological approach and degree of spatial resolution of the modeling output, we examine scenarios where car commuters able to reach their workplace within 30 and 50 minutes of cycling shift commuting mode. Overall, car–bicycle modal shift figures were 31.6% and 48.7%, respectively. However, there were considerable geographical differences. While a substantial number of new bicycle commuters appeared in all five macro-level subdivisions of the study area, relative modal shift was by far the highest among car commuters living in the Inner City and its immediate surroundings.     

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.     

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

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

Catalysts and magnets: Built environment and bicycle commuting

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

Do your neighbors affect your bicycling choice? A spatial probit model for bicycling to The Ohio State University

Neighborhood social effects have recently become a focus of interest in transportation research, whereby transportation mode choice is not only affected by an individual’s characteristics and transportation system conditions, but also by the mode choices of that individual’s social neighbors. This study supports the neighborhood social effects argument, using a spatial econometrics approach and data from The Ohio State University (OSU) 2012 Campus Transportation Survey. A spatial probit model of commuters’ mode choices (bicycling versus non-bicycling) is estimated, accounting for spatial autocorrelation. The results show that the more OSU-affiliated bicycle riders are residing around an individual OSU commuter, the more attractive bicycling becomes, controlling for other factors such as gender, status, proximity to campus, bicycle infrastructure and attitudes. The results indicate that students and males are more likely to commute by bicycles. The probability of choosing bicycles decreases with distance from campus. In addition, proximity to bicycle infrastructure and physical environment both encourage respondents to bicycle. Feeling of safety, travel cost and concern for the environment also affect bicycling choice.     

An international comparison of the self-reported causes of cyclist stress using quasi-naturalistic cycling

This study explores the influences of attitudes and setting on cyclists' stated causes of stress using survey techniques and quasi-naturalistic cycling in both Delft, The Netherlands and Atlanta, Georgia, USA. The study recruited 28 participants in Delft and 41 in Atlanta. Participants cycled approximately 30 min on specified routes in both cities on an instrumented bicycle. Prior to cycling, the participants filled in a written survey about their cycling habits, attitudes, and demographics. At specified points during and after the ride, participants were interviewed about their stress levels throughout the ride and the causes of those stress levels. Thematic analysis and statistical methods are used to understand the interactions of setting (country), attitudes, stated stress, and sensor data. The top three stressors were motor vehicles, pavement, and poor infrastructure; 83% of participants mentioned a motor vehicle causing stress, 64% mentioned road surface, and 58% mentioned infrastructure. The results confirm the importance of motor vehicle interaction to cyclist stress, but also highlight some new insights on stress such as the importance of pavement condition. Speed differentials between cyclists and vehicles were also shown to be important and suggested cyclists in Delft felt comfortable to travel their ideal speed. This speed preference was supported by GPS data that showed the cyclists in Delft were cycling at speeds about half (12 kph) that of the cyclists in Atlanta (24 kph). Review of close-pass events demonstrated that cyclists in Delft were more comfortable with closer passes which could be associated with their belief that motorists notice them and/or speed differences between the vehicle and bicycle. The results also suggest that number of vehicle travel lanes can have mixed impacts on cyclist stress. These findings can be taken into consideration when designing bicycle facilities to create low-stress cycling networks.     

Circuity in urban transit networks

This paper investigates the circuity of transit networks and examines auto mode share as a function of circuity and accessibility to better understand the performance of urban transit systems. We first survey transit circuity in the Minneapolis–St. Paul, Minnesota, region in detail, comparing auto and transit trips. This paper finds that circuity can help to explain mode choices of commuters. We then investigate thirty-five additional metropolitan areas in the United States. The results from these areas show that transit circuity exponentially declines as travel time increases. Moreover, we find that the circuity of transit networks is higher than that of road networks, illustrating how transit systems choose to expand their spatial coverage at the expense of directness and efficiency in public transportation networks. This paper performs a regression analysis that suggests the circuity of transportation networks can estimate transit accessibility, which helps to explain mode share.     

Can the built environment explain gender gap in cycling? An exploration of university students' travel behavior in Toronto,Canada

The benefits of cycling as a healthier and more sustainable transportation alternative to private automobile is emphasized in both literature and policy. One key policy challenge in improving cycling rates is the significant gender gap in cycling that exists across urban regions in North America. In this study, travel behavior of >10,000 students attending four universities in Toronto, Canada, was analyzed to explore gender-based differences in cycling uptake. The mode share for cycling was higher for non-commute trips (9%) when compared to commuting trips to universities (7.6%). In addition, men had higher cycling rates than women, for both commute and non-commute trips. Results from binomial logistic regression models indicate that the built environment-related correlates were different between male and female students, and between commute and non-commute trips. Access to bicycle lanes or cycle tracks was found to increase the odds of female commuter cycling. This effect, however, was moderate in the neighborhoods with higher land use mix. Further, high-speed traffic was a significant barrier to cycling among female commuters. Noticeably, our analysis did not find major gender-based differences in the coefficients relating to travel attitudes and preferences. The findings provide a Canadian comparison to the limited international research on this topic, as well as offer new insights particularly relating to cycling for non-commute trips. The results identify potential avenues for policy intervention regarding the promotion of healthy and sustainable travel behavior among post-secondary students, and more broadly, the millennial generation.     

Why do bicyclists take detours? A multilevel regression model using smartphone GPS data

Bicyclists often deviate from the shortest possible routes and take detours in search of more pleasant riding conditions. The extent of detours and the factors affecting bicyclists to ride excess distances have not yet been fully explored. This study aims to measure and analyze the detour extent of utilitarian bicycle trips and their relationships with the route-level environmental components using data collected from individual bicyclists' smartphone GPS in Columbus, Ohio. Comparing the chosen routes with their shortest counterparts, we calculate two detour indices (a distanced-based index and an area-based index) and provide a comparative analysis of built environment attributes for low, moderate, and high levels of detours. We then estimate multilevel mixed-effect generalized linear regression models to identify the contribution of built-environment characteristics to such detours while accounting for individual heterogeneity. We find that most bicycle trips (91.1%) include a detour and are 13.5% longer on average than their shortest alternatives with large variations. Detour degrees are higher for long-distance trips and for peak-period trips. We find that bicyclists choose routes with smaller shares of commercial and single-family land-uses and low levels of land-use diversity. Longer detours are positively associated with street greenery. We find that sparse bicycle facilities and high-speed limits are strong contributors to bicyclists' detour decisions, while multilevel mixed-effect linear regression models further present significant heterogeneity in bicyclists' responses to some environmental attributes. The area-based detour index performs better in explaining the relationships between land-use features and detour degrees.     

Optimizing EV-based P&R subsidy policies for commuting corridor based on cross-nested logit model

The promotion of electric vehicles (EVs) is restricted by cruising range limitation and charging station deficiency. Given the mature development of Park and Ride (P&R) mode, which is used in many cities worldwide to attract more travelers to use public transit, a new travel mode, i.e., EV-based P&R is introduced as an alternative for commuters’ daily travel. This seems quite attractive to expand the use of EVs and further increase their market share. This paper aims to investigate the impact of EV-based P&R introduction on travel mode choice along commuting corridor, and further aid in the optimal subsidy policies decision for the government. A bi-level model is proposed to model the presented problem. The lower level describes commuters’ joint mode and transfer choice behavior through a cross-nested logit (CNL) model, while the upper level minimizes the system cost. A genetic algorithm is developed to solve the formulated model with a partial linearization algorithm for solving the lower level model. And a numerical example is then used to demonstrate the effectiveness of the methodology and illustrate how the network flow pattern reshapes due to the introduction of EVs into the P&R mode and the change of corresponding subsidy policies. As the results show, improving the EV hardware, applying the intelligent supporting service system, developing new technologies for EV fast charging, appropriately improving the parking space capacity, and increasing the parking fee of transfer stations near the central business district (CBD) are all helpful to save the social cost and promote the usage of EVs.     

Exploring the effectiveness of bus rapid transit a prototype agent-based model of commuting behavior

The introduction of Bus Rapid Transit (BRT), typically involving the use of exclusive bus lanes and related bus priority measures, is increasingly advocated as a flexible and cost-effective way of improving the attractiveness of public transit in congested urban areas by reducing travel times and variability. These schemes typically involve the reallocation of road space for exclusive use by buses, presenting commuters with potentially competing incentives: buses on BRT routes can run faster and more efficiently than buses running in general traffic, potentially attracting commuters to public transit and reducing congestion through modal shift from cars. However, a secondary impact may also exist; remaining car users may be presented with less congested road space, improving their journey times and simultaneously acting as an incentive for some bus-users to revert to the car. To investigate the potential for these primary and secondary impacts, we develop a prototype agent-based model to investigate the nature of these interactions and how they play out into system-wide patterns of modal share and travel times. The model allows us to test the effects of multiple assumptions about the behaviors of individual agents as they respond to different incentives introduced by BRT policy changes, such as the implementation of exclusive bus lanes, increased bus frequency, pre-boarding ticket machines and express stops, separately and together. We find that, under our assumptions, these policies can result in significant improvements in terms of individual journey times, modal shift, and length of rush hour. We see that the addition of an exclusive bus lane results in significant improvements for both car users and bus riders. Informed with appropriate empirical data relating to the behavior of individual agents, the geography and the specific policy interventions, the model has the potential to aid policymakers in examining the effectiveness of different BRT schemes, applied to broader environments.     

Investigating the associations between road network structure and non-motorist accidents

Road networks channel traffic flow and can impact the volume and proximity of walking and bicycling. Therefore, the structure of road networks—the pattern by which roads are connected—can affect the safety of non-motorized road users. To understand the impact of roads’ structural features on pedestrian and bicyclist safety, this study analyzes the associations between road network structure and non-motorist-involved crashes using data from 321 census tracts in Alameda County, California. Average geodesic distance, network betweenness centrality, and an overall clustering coefficient were calculated to quantify the structure of road networks. Three statistical models were developed using the geographically weighted regression (GWR) technique for the three structural factors, in addition to other zonal factors including traffic behavior, land use, transportation facility, and demographic features. The results indicate that longer average geodesic distance, higher network betweenness centrality, and a larger overall clustering coefficient were related to fewer non-motorist-involved accidents. Thus, results suggest that: (1) if a network is more highly centered on major roads, there will be fewer non-motorist-involved crashes; (2) a network with a greater average number of intersections on the shortest path connecting each pair of roads tends to experience fewer crashes involving pedestrians and bicyclists; and (3) the more clustered road networks are into several sub-core networks, the lower the non-motorist crash count. The three structural measurements can reflect the configuration of a network so that it can be used in other network analyses. More information about the types of road network structures that are conducive to non-motorist traffic safety can help to guide the design of new networks and the retrofitting of existing networks. The estimation results of GWR models explain the spatial heterogeneity of correlations between explanatory factors and non-motorist crashes, which can support regional agencies in establishing local safety policies.     

Prioritizing low-stress and equitable bicycle networks using neighborhood-based accessibility measures

As cities across the United States strive to create comfortable bicycle networks for mainstream users, three topics have garnered attention: project prioritization criteria, accessibility to everyday destinations, and social equity. However, these topics have not often been integrated in research or practice. This paper introduces a method to assess the extent to which reductions in “Bicycle Level of Traffic Stress” (LTS) on segments of a citywide bicycle network increase accessibility to supermarkets, pharmacies, banks, and public libraries. Six accessibility performance measures are developed and evaluated for 278 neighborhoods in Baltimore, Maryland using a GIS-based approach. The demographic distributions of accessibility results are further analyzed, focusing on disadvantaged populations. Using a set of 106 proposed bicycle projects, the marginal accessibility gains and cumulative demographic impact across affected neighborhoods are assessed for each project. These results are ranked and crosslisted to identify a set of projects that balance accessibility gains with equity objectives. The prioritization results demonstrate some overlap with the short-term priorities embodied in City of Baltimore's 2015 Bike Master Plan, but they also highlight projects in other areas, specifically those that would serve neighborhoods most disadvantaged in terms of racial segregation, high poverty rates, and low rates of vehicle ownership.     

Continuum modeling approach to the spatial analysis of air quality and housing location choice

Today, air pollution is a great issue, and the transport sector is an important emission source. In this study, we present an integrated land use, transport, and environment model in which transport-related pollutants are assumed to influence people's housing location choices, and a continuum modeling approach is applied. The pollutants generated by the transport sector are dispersed by the wind and they affect air quality. The air quality changes people's housing choices, which in turn changes their travel behavior. We assume that the road users are continuously distributed over the city, that the road network is relatively dense, and that this network can be approximated as a continuum. The total demand is categorized into several classes, and the modeled region contains several subdistricts. People who live in different subdistricts or who belong to different classes of commuters are assumed to have different perceptions of travel time, air quality, and the housing provision–demand relationship. The finite element method and the Newton–Raphson algorithm are adopted to solve this problem, and a numerical valuation is given to illustrate the effectiveness and efficiency of the proposed model.