Nei-TTE: Intelligent Traffic Time Estimation Based on Fine-Grained Time Derivation of Road Segments for Smart City

Abstract

With the development of the Internet of Things and big data technology, the intelligent transportation system is becoming the main development direction of future transportation systems. The time required for a given trajectory in a transportation system can be accurately estimated using the trajectory data of the taxis in a city. This is a very challenging task. Although historical data have been used in existing research, excessive use of trajectory information in historical data or inaccurate neighbor trajectory information does not allow for a better prediction accuracy of the query trajectory. In this article, we propose a deep learning method based on neighbors for travel time estimation (TTE), called the Nei-TTE method. We divide the entire trajectory into multiple disjoint segments and use the historical trajectory data approximated at the time level. Our model captures the characteristics of each segment and utilizes the trajectory characteristics of adjacent segments as the road network topology and speed interact. We use velocity features to effectively represent adjacent segment structures. The experiments on the Porto dataset show that the experimental results of our model are significantly better than those of the existing models.