Locating Station of One-Way Carsharing Based on Spatial Demand Characteristics

Abstract

This study aims to explore factors that affect carsharing demand characteristics in different time periods based on EVCARD transaction data, which is the largest station-based one-way carsharing program in Shanghai, China. Monthly usage intensity and degree of usage imbalance are used as proxies of demand. This study uses three groups of independent variables: carsharing station attributes, built environment (density, diversity, design, and destination accessibility), and transportation facilities. The adaptive elastic net regression is developed to identify factors that influence carsharing usage intensity and degree of usage imbalance after factor selection using extra-randomized-tree algorithm. Finally, a station layout is proposed according to both usage intensity and degree of imbalance. The main results of this study are presented as follows: (1) different effects of built environment and transportation factors cause dynamic demand across different time periods; (2) factors with positive and negative effect on carsharing demand are divided clearly for guidance of the carsharing station layout; (3) public parking space leads to more personal vehicle trip compared to a carsharing trip; and (4) as public transportation, the relationship of the metro and carsharing is complementary. However, the bus stop and carsharing have a competitive relationship. This study provides a carsharing layout method based on both usage intensity and degree of imbalance. Furthermore, several policies concerning carsharing are proposed.

Optimizing Location of Car-Sharing Stations Based on Potential Travel Demand and Present Operation Characteristics: The Case of Chengdu

“…The quantiles are 0.79 and 6.33, respectively, which means that the grids with average daily order numbers that are less than 0.79 and larger than 6.33 are chosen as the samples. [3,19]. Parking Lots has a positive impact on the probability of existence of demand, which implies that more space provide more opportunities to build car-sharing stations.…”

“…Parking Lots has a positive impact on the probability of existence of demand, which implies that more space provide more opportunities to build car-sharing stations. However, Chen's paper [3] concludes that more parking space leads to more private vehicle trips instead of car-sharing service.…”

“…One possible way is to do a full sample questionnaire but this requires high human and financial resources and the results might be biased since questionnaire contains much subjectivity. Another way is to use usage intensity as a proxy of demand [3] or use the number of bookings while this amount can only reflect the present demand that may be restricted by the number of car-sharing stations and vehicles. Therefore using a specific amount to represent the potential demand will cause deviation.…”

“…They usually pay for this after travelling according to the travelling distance or/and time [1,2]. This kind of new service allows people to avoid buying a car and spending time finding parking lots [3]. Car-sharing is becoming an increasingly important travelling mode in recent 5 years especially with rapid growth of electric vehicles since 2016.…”

“…Another paper introduces an intensity model that estimates the demand and an imbalance model that describes the difference between pick-up and dropoff to find the optimal location of stations of EVCARD in Shanghai, which involves usage intensity, usage imbalance, transportation information, and built environment. These two models utilize a combination of Elastic Net, the adaptive Least Absolute Shrinkage and LASSO [3].…”