An analysis of patron arrival distributions of a bike sharing system

• Main Authors: Wan-Ting Shih, 施宛廷
• Other Authors: 周雍強
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9j32q5

碩士 === 國立臺灣大學 === 工業工程學研究所 === 106 === In recent years, in order to save energy and reduce carbon emissions and environmental pollution, all countries in the world are building bike sharing systems actively. The benefits of Public Bicycle-sharing System (PBS) are reducing energy consumption, pollution and helping to reduce traffic. When PBS linked to the latest business model of "sharing economy" by turning itself into a service system, it provides other users a chance to rent and return in another places, make the city''s transportation network more efficiency and complete, and the problems of "Last Mile" is also solved. For better operating the PBS, operators need to send staffs driving trucks or motorbikes to dispatch the bicycles from one location to another as to balance the demand and supply of bicycles on the site. Although the dispatching can make the problem of the lack of bicycles and the uncertainty of demand reduced, it is still necessary to optimize the scheduling mode and forecast the demand of enhance the bicycles utilization. In the construction of PBS demand forecasting model, we need to consider the distribution of customer arrivals, which is the frequency of demand. Based on current theories, they tried to set the frequency of patron arrival as the Poisson distribution. However, this may not be the case, in order to prove the Poisson distribution, this thesis choses the approach by looking at one site to collect actual user data, analyzing the patron arrival under the influence of different rainfalls, and using chi-square test to test the suitability of the frequency of demand. The result of chi-square test almost based on the assumptions of patron arrival as Poisson distribution. In conclusion, my study reflects that renting patron arrival under the different rainfalls can be assumpted as Poisson distribution, but the returning patron arrival except no rain situation can be assumpted as Poisson distribution.