Lijun Zhu
ABSTRACT
Advocating public transport travel can reduce carbon emissions and pollutant emissions, alleviating the problem of global warming. Accurate bus travel time prediction can improve the level of public transportation services and contribute to the improvement of intelligent transportation systems. Unfortunately, there are many complex factors that affect the accuracy of bus travel time prediction, which greatly affects the accuracy of the prediction. In addition, weather and travel dates have a greater impact on road congestion. Therefore, this paper proposes a bus travel time prediction method based on multi-source data fusion and captures external influencing factors such as weather and travel date. Learning spatial-temporal features between trajectory data using CNN and LSTM. The experimental results show that the model based on multi-source data fusion proposed in this paper can provide higher prediction accuracy compared with the model based on single-source data.
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