01591800

Component

https://doi.org/10.3141/2587-14

Bikesharing systems have been expanding rapidly. They are recognized as a strategic tool to integrate transit and to foster sustainable transportation in urban areas. A critical problem that arises in the planning and deployment of a bikesharing system lies in demand–supply imbalance as the result of the spatiotemporal dynamics of demand patterns. The problem manifests itself in situations in which no bikes are available to rent and no docks are available for bike return at certain popular stations. This study developed a framework for modeling user behavior in response to such situations. Particular emphasis was placed on user on-site choice behavior and the behavioral aspect related to wait time if a decision to wait for the next bike or dock was made. These behaviors were approached with the use of a discrete choice model and a duration model, respectively. Further, a discrete-continuous process was proposed to address the potential interrelation that existed between the choice to wait and the length of waiting time to be endured. Within the developed framework, the models were estimated on the basis of the data from a revealed preference–stated preference survey conducted on the YouBike system in Taipei, Taiwan. The estimation results provided insights into user familiarity and uncertainty toward the system, travel situation, or environment and the associated effects on user behavior of a bikesharing system under a demand–supply imbalance.

01586671

16-6903

English

pp 117–124

2016

Transportation Research Record: Journal of the Transportation Research Board

9780309441391

Print

References (20) ; Tables (8)

Behavior; Bicycling; Demand; Public transit; Supply; Transportation planning; Urban areas; Vehicle sharing

Pedestrians and Bicyclists; Planning and Forecasting; Public Transportation; Vehicles and Equipment

PRP, TRIS, TRB, ATRI

Jan 12 2016 7:00PM

• Bicycle Education for Children: Evaluation of a Program in Montreal, Quebec, Canada
• Bicycle Headway Modeling and Its Applications
• Bicycling to Rail Stations in New Jersey
• Estimating Current and Potential Bicycle Use for Statewide Planning
• How Representative of Bicycling Populations Are Smartphone Application Surveys of Travel Behavior?
• Is Bikesharing Contagious? Modeling Its Effects on System Membership and General Population Cycling
• Learning from Insurance Data: Injuries to Other Road Users in Motorcyclist At-Fault Crashes
• Modeling Bicycling to Elementary and Junior High Schools with Bike Rack Counts
• Modeling Route Choice of Utilitarian Bikeshare Users with GPS Data
• Modeling the Impacts of Facility Type, Trip Characteristics, and Trip Stressors on Cyclists’ Comfort Levels Utilizing Crowdsourced Data
• Modeling the Speed, Acceleration, and Deceleration of Bicyclists for Microscopic Traffic Simulation
• Network Connectivity for Low-Stress Bicycling
• Revealed Bicyclist Route Preferences and Street Conditions
• Revisiting the Four Types of Cyclists: Findings from a National Survey
• Visibility of Two-Wheelers Approaching Left-Turning Vehicles Compared with Other Hazards Under Nighttime Conditions at Urban Signalized Intersections
• When a Diet Prompts a Gain: Impact of a Road Diet on Bicycling in Davis, California