摘要: |
Weather is an important contributing factor for bus operations. It is essential to develop a weather-responsive bus scheduling system to improve the level of bus service. Bus travel time is a significant factor to be considered while planning bus schedules. For example, if the next day's bus travel time can be predicted according to the weather forecast and historical bus global positioning system (GPS) data, then it will be significant for adjusting bus scheduling in a timely manner. Harbin is the northernmost provincial capital of China with a population of about 4.5 million people. The modal share of bus transport is 40%, and the bus fleet includes 5,000 GPS-equipped buses. The minimum temperature in winter can reach -38°C. Harbin is considered as the study area for our research because of the relative dominance of public transit usage, long winters, and cold weather. In this study, the bus operation data of Harbin in the winter of 2011 and 2012 were analyzed from a statistical perspective. The travel time series of successive buses was found to be autocorrelated, i.e., the travel times of previous two buses affect the travel time of a third bus. Thus, a fitting model for the next day's bus travel time was proposed based on the historical GPS data and cumulative snowfall level in the weather forecast. The model was evaluated by taking Bus Lines 18, 68, and 69 in Harbin as examples. The mean absolute percentage error of each bus line was less than 9%. Taking Bus Line 18 as an example, it was found that the bus travel time increased by 0.483 min if the cumulative snowfall level increased by 1. |