Discovery of temporal variations of public bike ridership using internet of things for building smart city

This paper analyzes the temporal variations of public bike riding over the hours, the days and the months. A whole year of usage data (34,098,787 trips) are collected using Internet of Things(IoT) for the bikesharing system in Ningbo, China. Data Mining shows significant seasonal variations of bike rental amounts. The seasonal reductions of bike riders are explained mainly by three factors: nonpermanent residents leaving the city during the Chinese Spring Festival, uncomfortable high/low outdoor temperature, and rainy/snowy weather. Weather impact analysis suggests that rainfall decreases rental amounts significantly, with 1mm of daily rainfall expected to reduce daily rental amount by about 10%. The results also show significant day-of-the-week variations, with rental amounts in weekends 20-30% lower than in weekdays, indicating a clear commuter pattern of usage. The commuter pattern is further proven by the time of day variation with the double rental peaks of morning and afternoon. It should be noted that the total usage in either morning or afternoon rush hours only accounts for 60% of the total bike supply, which indicates that some bikes in the system may have been positioned in the wrong place at peak times. It is further shown that peak hour usage accounts for only 10∼12% of AADT (annual average daily traffic), much lower than the peak hour percentages for other modes, indicating insufficient bike supply and improper positioning of infrequently used bikes. These temporal characteristics imply that the bike turnover rate could increase further if the efficiency of the bike supply is improved with proper location and relocation of bikes. Understanding these temporal characteristics of bikesharing ridership could help the service provider and policy maker to improve the bikesharing services for building Smart City.