Cheng Cheng

Vehicle evaluation parameters, which are increasingly of concern for governments and consumers, quantify performance indicators, such as vehicle performance, emissions, and driving experience to help guide consumers in purchasing cars. While past approaches for driving cycle prediction have been proven effective and used in many countries, these algorithms are difficult to use in China with its complex traffic environment and increasingly high frequency of traffic jams. Meanwhile, we found that the vehicle dataset used by the driving cycle prediction problem is usually unbalanced in real cases, which means that there are more medium and high speed samples and very few samples at low and ultra-high speeds. If the ordinary clustering algorithm is directly applied to the unbalanced data, it will have a huge impact on the performance to build driving cycle maps, and the parameters of the map will deviate considerable from actual ones. In order to address these issues, this paper propose a novel driving cycle map algorithm framework based on an ensemble learning method named multi-clustering algorithm, to improve the performance of traditional clustering algorithms on unbalanced data sets. It is noteworthy that our model framework can be easily extended to other complicated structure areas due to its flexible modular design and parameter configuration. Finally, we tested our method based on actual traffic data generated in Fujian Province in China. The results prove the multi-clustering algorithm has excellent performance on our dataset.

The GPS trajectories are rich with potential information that could be used to explore the regulation of traffic to serve the public. While that past approaches for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. In this paper, we propose a novel Neural Network with spatial-temporal correlations, named L-CNN based on CNN and LSTM, and develop an effective real-time prediction model to forecast the most likely potential passenger for taxi drivers. It is noteworthy that our model can be easily extended to other real-time traffic prediction problem such as road traffic and flow prediction. Finally, we test our method based on GPS trajectories generated by Cheng Du taxi. The method presented provides passenger prediction over 15-min intervals for up to 1h in advance and the results prove the efficiency of our proposed prediction system.