Regional-based multi-module spatial–temporal networks predicting city-wide taxi pickup/dropoff demand from origin to destination

Taxi demand forecasting from origin to destination (OD) is an important component in managing public transportation needs on a city-wide scale. Accurate taxi demand forecasting may provide several benefits, including economic and traffic flow optimization. However, due to complicated spatial–temporal connections and irregular distant locations, predicting taxi demand becomes difficult. To address these issues, we proposed a novel architecture of multi-module spatial–temporal networks to collectively predict city-wide OD taxi demand. In our work to deal with adjacent areas in a city, we employed the 3D convolutional neural networks to extract the spatial–temporal dependencies and learn the OD taxi demand pattern. To handle remote areas, we created an attention-based auto encoder-decoder, in which the input of the set of convolutional layers creates a feature matrix. The feature matrix embeds the spatial–temporal correlation jointly and passing through the encoder layer. We encode the spatial–temporal features with the help of pooling layer, then flatten layer used the back-propagation method to decode the weight matrix. We apply the normalization function to determine the demand pattern influences. The influence vector we compute with the Euclidean distance formula to determine the similarity of all distant regions. Finally, we use the attention mechanism to calculate the attention weight score for each region that its neighbour impacted. Then we used long short-term memory, we captured the significant relationship of spatial–temporal dependencies with external factors. We train our model simultaneously to forecast city-wide taxi services OD. We have performed comprehensive experiments and large-scale dataset comparisons that reveal the taxi demand prediction problem.