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Impacts of Winter Weather on Bus Travel Time in Cold Regions: Case Study of Harbin, China

机译:冬季天气对寒冷地区公交车出行时间的影响:以中国哈尔滨市为例

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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 degrees 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. (C) 2018 American Society of Civil Engineers.
机译:天气是公交运营的重要因素。开发天气响应公交计划系统以提高公交服务水平至关重要。在计划公交时刻表时,公交旅行时间是要考虑的重要因素。例如,如果可以根据天气预报和历史公交全球定位系统(GPS)数据预测第二天的公交时间,那么对于及时调整公交时间表将具有重要意义。哈尔滨是中国最北端的省会,人口约450万人。公交运输的方式份额为40%,公交车队包括5,000辆配备GPS的公交车。冬季的最低温度可以达到-38摄氏度。哈尔滨被认为是我们研究的研究区域,因为公共交通的使用,较长的冬季和寒冷的天气相对占主导地位。本研究从统计的角度分析了2011年和2012年冬季哈尔滨市公交车运行数据。发现连续公共汽车的旅行时间序列是自相关的,即,前两辆公共汽车的旅行时间影响第三辆公共汽车的旅行时间。因此,基于历史GPS数据和天气预报中的累计降雪水平,提出了第二天公交旅行时间的拟合模型。以哈尔滨的18、68和69号公交车为例对模型进行了评估。每条总线的平均绝对百分比误差小于9%。以18号线为例,发现如果累计降雪量增加1,则公交车旅行时间增加0.483分钟。(C)2018年美国土木工程师学会。

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