期刊
OPTICAL MEMORY AND NEURAL NETWORKS
卷 28, 期 3, 页码 222-230出版社
SPRINGERNATURE
DOI: 10.3103/S1060992X19030081
关键词
arrival time prediction; artificial neural network; long short-term memory; intelligent transportation system
类别
资金
- RFBR [18-07-00605 A, 18-29-03135-mk]
Arrival time of public vehicles to transport stops is a key point of information systems for passengers. Accurate information on the arrival time is important for travel arrangements since it helps to decrease the wait time at a stop and to choose an optimal alternate route. Recently, such information has been included to mobile navigation applications too. In the present paper, we analyze the abilities of the LSTM neural network to predict the arrival time of public vehicles. This model accounts for heterogeneous information about transport situation that directly or indirectly has an impact on the travel time prediction and includes statistical and real-time data of traffic flow. We examined the model experimentally using traffic data on bus routes in the city of Samara, Russia. The obtained results confirm that the predictions provided by our model are of a high quality and it can be used for real-time arrival time prediction of public transport in the case of a large-scale transportation network.
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