- freely available
Remote Sens. 2019, 11(11), 1265; https://doi.org/10.3390/rs11111265
- First of all, traffic data are affected by complex spatial–temporal correlation, and there is an apparent periodicity in traffic data. Traffic states between different regions will affect each other, and there may be interactions between regions that are far away. Although the models combining CNN, RNN and their variants have achieved good results, this kind of method separates the interaction between temporal correlation and spatial correlation.
- Secondly, there is little work in predicting taxi drop-off demand. If there is a high taxi demand for drop-off in a particular region at a certain time, the vacancy rate of taxis in this region will be high, and it will be required for reasonable evacuation. At the same time, the high traffic volume will be a test for the infrastructure and road conditions of the corresponding region. Therefore, predicting taxi demand for drop-off can provide advice to taxi drivers and city managers. Besides, taxi demand for pick-up and drop-off may affect each other, since empty cars may stimulate people’s desire to take a taxi and increase the number of taxi pick-ups in surrounding areas.
- We propose to consider the prediction of taxi demand for pick-up and drop-off as related tasks, and we constructed a feature extraction component based on multi-task learning and 3D CNN to extract spatiotemporal features concurrently.
- We propose to deem the demand situation of the urban taxi as a video and use 3D CNN to capture the spatiotemporal correlation and the complex correlation between taxi demand for pick-up and drop-off.
- We combined external factors, such as weather, day of the week and public transport conditions, to simultaneously predict taxi demand for pick-up and drop-off.
- We conducted extensive theoretical analysis and experiments on a real-world dataset in Chengdu and achieved better performance and efficiency than other baselines.
2. Related Work
2.1. Traditional Approach
2.2. Deep Learning Approach
- is the category of data, that is, for taxi pick-up data and taxi drop-off data.
- is the depth of data.
- is the number of grid columns.
- is the number of grid rows.
4.1. Partition of Historical Data
4.2. Multi-Task Spatiotemporal Feature Extraction Component
4.3. Feature Embedding Component
4.4. External Factors Component
4.5. Prediction Component
5. Experiment and Discussion
5.2. Experimental Settings
- Historical average (HA): By simply averaging the values of previous taxi pick-up and taxi drop-off demands at the same location and the same time interval, we can get the predicted value.
- Autoregressive integrated moving average (ARIMA): ARIMA is a well-known model for predicting times series.
- XGBoost: XGBoost is a well-known powerful model and is widely used by data scientists to achieve state-of-the-art results on many machine learning challenges .
- Multiple layer perceptron (MLP): We compared our model with an MLP which consisted of 4 hidden layers. Each layer had 128, 128, 128, and 64 hidden units, respectively.
- Long Short-Term Memory: LSTM is a special kind of RNN, capable of learning long-term dependencies, and it is widely used in time series processing.
- ST-ResNet : ST-ResNet is an end-to-end traffic prediction approach based on deep learning, which uses the residual network to capture the spatial and temporal characteristics of crowd traffic, and also combines with external factors.
- Taxi3D-single: To verify the effectiveness of MTL, in this variant, we designed two independent networks which extracted spatiotemporal features from taxi pick-up data and drop-off data respectively. We then stacked the outputs and fed them into the feature embedding component as Taxi3D does.
- Taxi3D-lstm: In this variant, we fed the output of the multi-task spatiotemporal feature extraction component into the LSTM for predicting.
- Taxi3D-na: Taxi3D without attention-based LSTM; we simply reshaped the output of Figure 2a and stacked them into tensor, which was taken as the input of 3D ResNet.
- Taxi3D-nr: We used a 4-layers 3D CNN instead of 3D ResNet in feature embedding component.
- Taxi3D-ne: This variant removes the external factors component.
5.4. Evaluation Metric
5.5. Tuning Hyperparameters
5.6. Model Comparsion
5.7. Variants Comparsion
6. Conclusions and Future Work
Conflicts of Interest
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