Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto
TL;DR: In this article, a comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership.
Abstract: Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.
Citations
More filters
TL;DR: Using big data techniques, this study estimates the impacts of bike sharing on energy use and carbon dioxide and nitrogen oxide emissions in Shanghai from a spatiotemporal perspective and finds morning and evening peaks of the environmental benefits are higher than morning peaks.
317 citations
TL;DR: A dynamic demand forecasting model for station-free bike sharing using the deep learning approach and the developed long short-term memory neural networks (LSTM NNs) provide better prediction accuracy than both conventional statistical models and advanced machine learning methods for different time intervals.
Abstract: The station-free sharing bike is a new sharing traffic mode that has been deployed in a large scale in China in the early 2017. Without docking stations, this system allows the sharing bike to be parked in any proper places. This study aimed to develop a dynamic demand forecasting model for station-free bike sharing using the deep learning approach. The spatial and temporal analyses were first conducted to investigate the mobility pattern of the station-free bike sharing. The result indicates the imbalanced spatial and temporal demand of bike sharing trips. The long short-term memory neural networks (LSTM NNs) were then developed to predict the bike sharing trip production and attraction at TAZ for different time intervals, including the 10-min, 15-min, 20-min and 30-min intervals. The validation results suggested that the developed LSTM NNs have reasonable good prediction accuracy in trip productions and attractions for different time intervals. The statistical models and recently developed machine learning methods were also developed to benchmark the LSTM NN. The comparison results suggested that the LSTM NNs provide better prediction accuracy than both conventional statistical models and advanced machine learning methods for different time intervals. The developed LSTM NNs can be used to predict the gap between the inflow and outflow of the sharing bike trips at a TAZ, which provide useful information for rebalancing the sharing bike in the system.
205 citations
TL;DR: In this paper, a comprehensive review on the factors affecting bike-sharing demand is presented to bridge the gaps by deepening the knowledge on weather, built environment and land use, public transportation, station level, socio-demographic effects, temporal factors, and safety.
196 citations
TL;DR: In this article, the authors employed a multiple linear regression model to examine the influence of built environment variables on trip demand as well as on the ratio of demand to supply (D/S) at bike stations.
160 citations
TL;DR: It is shown that people use e-scooter sharing almost exclusively in central Austin, and e- scooters may be a substitute for some short non-work trips, reducing car usage, and benefiting the environment.
Abstract: In this study, we explore the usage of e-scooter sharing services in Austin, Texas over about a six-month period The study is based on trip records of all the shared e-scooter operators in Austin and includes trip start and end locations We use both analysis of trip patterns and spatial regression techniques to examine how the built environment, land use, and demographics affect e-scooter trip generation Our findings show that people use e-scooters almost exclusively in central Austin Commuting does not seem to be the main trip purpose, and usage of e-scooters is associated with areas with high employment rates, and in areas with bicycle infrastructure People use e-scooter sharing regardless of the affluence of the neighborhood, although less affluent areas with high usage rates have large student populations, suggesting that students use this mode of travel Implications for planners suggest that better bicycle infrastructure will facilitate e-scooter usage, college towns are a ready market for e-scooter sharing services, and e-scooters may be a substitute for some short non-work trips, reducing car usage, and benefiting the environment
143 citations
References
More filters
Book•
01 Jan 2006TL;DR: Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models.
Abstract: Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.
9,098 citations
06 Oct 2015
TL;DR: The core computational algorithms are implemented using the Eigen C++ library for numerical linear algebra and RcppEigen``glue''.
8,543 citations
TL;DR: In this article, the authors examined the effect of the variance inflation factor (VIF) on the results of regression analyses, and found that threshold values of the VIF need to be evaluated in the context of several other factors that influence the variance of regression coefficients.
Abstract: The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index.
7,165 citations
TL;DR: In this article, neighborhood environment characteristics proposed to be relevant to walking/cycling for transport are defined, including population density, connectivity, and land use mix, with evidence suggesting that residents from communities with higher density, greater connectivity and more land-use mix report higher rates of walking and cycling for utilitarian purposes than low-density, poorly connected, and single land use neighborhoods.
Abstract: Research in transportation, urban design, and planning has examined associations between physical environment variables and individuals' walking and cycling for transport. Constructs, methods, and findings from these fields can be applied by physical activity and health researchers to improve understanding of environmental influences on physical activity. In this review, neighborhood environment characteristics proposed to be relevant to walking/cycling for transport are defined, including population density, connectivity, and land use mix. Neighborhood comparison and correlational studies with nonmotorized transport outcomes are considered, with evidence suggesting that residents from communities with higher density, greater connectivity, and more land use mix report higher rates of walking/cycling for utilitarian purposes than low-density, poorly connected, and single land use neighborhoods. Environmental variables appear to add to variance accounted for beyond sociodemographic predictors of walking/cycling for transport. Implications of the transportation literature for physical activity and related research are outlined. Future research directions are detailed for physical activity research to further examine the impact of neighborhood and other physical environment factors on physical activity and the potential interactive effects of psychosocial and environmental variables. The transportation, urban design, and planning literatures provide a valuable starting point for multidisciplinary research on environmental contributions to physical activity levels in the population.
2,218 citations
TL;DR: Elasticities of travel demand with respect to density, diversity, design, and regional accessibility are derived from selected studies and may be useful in travel forecasting and sketch planning and have already been incorporated into one sketch planning tool, the Environmental Protection Agency’s Smart Growth Index model.
Abstract: The potential to moderate travel demand through changes in the built environment is the subject of more than 50 recent empirical studies. The majority of recent studies are summarized. Elasticities of travel demand with respect to density, diversity, design, and regional accessibility are then derived from selected studies. These elasticity values may be useful in travel forecasting and sketch planning and have already been incorporated into one sketch planning tool, the Environmental Protection Agency's Smart Growth Index model. In weighing the evidence, what can be said, with a degree of certainty, about the effects of built environments on key transportation "outcome" variables: trip frequency, trip length, mode choice, and composite measures of travel demand, vehicle miles traveled (VMT) and vehicle hours traveled (VHT)? Trip frequencies have attracted considerable academic interest of late. They appear to be primarily a function of socioeconomic characteristics of travelers and secondarily a function...
1,706 citations