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

Car-sharing is becoming an increasingly popular travel mode in China and many companies invest plenty of money on that including vehicle enterprises and Internet companies. But most of them build car-sharing stations by their experience or randomly as long as there is parking space in the early development of their business. This results in many stations with low operational efficiency and causes capital loss. This study aims to use different data source with statistical models and machine learning algorithm to help car-sharing operator to choose the optimal location of new stations and adjust the location of existing stations. We select Chengdu where there are huge amounts of car-sharing travel demand and several large car-sharing operators as the research area and two main operators as the research objects. Chengdu is divided into 58724 squared grids each of which is 0.5km?0.5km instead of focusing on the buffers generated by stations. We try to find a model to estimate a potential travel demand value for each small grid with three data sources: order data, population data, and Point of Interest (POI) data. This problem is transformed into a binary form and five different methods, Logistic Regression, Logistic Regression with LASSO, Naive Bayes, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, are implemented. The optimal model, Logistic Regression with LASSO, is chosen to estimate the probability of existence of demand in all grids. With car-sharing order data from different operators, an existing order heat value is also computed for each grid. Then we analyze and classify all the grids into four groups. For different groups of grids, we give different suggestions on the optimal location of stations. This study focuses on a more competitive market and finds the influential factors on order number. Suggestions on the optimal location of stations are given in consideration of competitors. We hope that our research can help operators improve their business and make rational plans.