01711089

Component

With the growing usage of the internet, the possibility for shared mobility has risen just as much. Beside ride-sharing, bike-sharing, and shared parking, this applies, especially to car-sharing. Past research activities have often been limited to the economic, ecological, and urban benefits of car-sharing, such as the number of privately owned cars that could be replaced by car-sharing vehicles or the potential to save parking space. These analyses disregard the user’s behavior and patterns of usage. However, to analyze, e.g., future market shares of car-sharing, we first have to evaluate how car-sharing members use car-sharing and what purposes the trips might serve. One such study has been conducted in Germany, however, using free-floating car-sharing data. The focus of research is put on data from a station-based car-sharing provider and what kind of user or usage profiles can be identified. The authors investigated this by performing a cluster analysis using the k-means algorithm. The results indicate that there are five types of station-based car-sharing users and usage respectively. There are commercial users, users who use car-sharing for regular and users who use it for irregular activities. Furthermore, car-sharing vehicles are used to replace a second car and also for long distance travels. These findings are in part consistent with the study on free-floating car-sharing but also show some dissimilarities, as to be expected since the two systems generally serve different purposes.

19-02785

English

23p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Shared mobility; Special user groups; Travel behavior; Travel patterns; Vehicle sharing

Highways; Operations and Traffic Management; Planning and Forecasting

Transportation Research Board Annual Meeting 2019 Paper #19-02785

TRIS, TRB, ATRI

Jul 16 2019 10:31AM

• A Deep Learning Approach for Lane-Based Short-Term Traffic Volume Prediction at Signalized Intersections
• A New Multi-Source Traffic Data Integration Framework for Traffic Analysis and Performance Measurement
• Analysis of the Effects of an HOV Lane on a German Freeway - A Simulation Study with PTV Vissim
• Combining Attitudes and Travel Behavior - A Comparison of Urban Mobility Types Identified in Shanghai, Berlin and San Francisco
• Comparison of Superpave Gyratory Compactor and Impact Hammer for Compaction of Unbound Granular Materials
• Containership Routing and Scheduling Model with Multiple Time Windows, Split Loads and Berth Constraints
• Feasibility Investigation of Upgrading Gravel Road to Otta Seal Surface: an Economic Analysis Approach
• Machine Learning and Daily Activity Patterns
• Multiobjective Capacitated Location-Allocation Model for Urban Logistics Delivery Facilities
• Regulating Airborne Shipping Emissions: Current Status, Future Outlook and Insights on Industry Responses
• Simulation of Autonomous Vehicles Based on Wiedemann's Car Following Model in PTV Vissim
• The impact of movement speed on pedestrians’ obstacle avoidance
• Traffic Queue Monitoring with Mask Region-Based Convolutional Neural Network