2020 年 140 巻 2 号 p. 257-266
Bus transportation service is more influenced than other public transport by various factors such as traffic congestion, weather condition, number of passengers, traffic signals. These factors often cause delay and the users may feel inconvenience while waiting at the bus stop. In the case of snowfall event, a large delay occurs, which greatly reduces the convenience of the bus. This paper aims at highly accurate arrival time prediction for each bus stop section in snow event in urban area. We investigate vulnerability of bus operations to snowfall and incorporate into predictions using geographical characteristics. In each bus stop section, we estimate geographical characteristics (gradient angle and gradient direction) and snow accumulation amount with detailed spatial resolution as factors affecting bus delay. Then, we evaluate a prediction accuracy using the arrival time prediction model with multiple regression analysis and the Kalman filter. As a result of the multiple regression analysis, it was found that the geographical characteristics of each bus stop section were the explanatory variables that greatly affect the bus delay at snowfall event. Furthermore, we predicted the bus arrival time using actual bus operation data. Of the 29 routes, 18 routes showed improvement in the predicted arrival time.
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