عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Providing accurate prediction for public transport (bus) travel time is valuable for both operators and passengers. It helps operators to implement their strategies like, different fleet assignment in different times of day. It also helps the passengers to experience less waiting time and it will increase their satisfaction. Public transport travel time is affected by several factors such as, passenger demand, weather condition, traffic flow and etc. which all have to be considered to have precise prediction. However, previous studies are mostly concentrated on historical a
data-based model which uses temporal variables (time of day, day of week) as an input. This paper develops artificial neural network (ANN) models to predict bus travel time by using range of input variables including traffic flow data and passenger demand collected from a bus route in Tehran, Iran. The paper examines the proposal model by comparing with two alternative models. A historical data-based model which uses temporal variables and a traffic flow data-based model which only uses traffic flow parameters as an input. The results show that the proposal model outperforms other model in RMSE and MAPE index.
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