Learning to Estimate the Travel Time
Zheng Wang (Didi Chuxing); Kun Fu (Didi Chuxing); Jieping Ye (Didi Chuxing)
Vehicle travel time estimation or estimated time of arrival (ETA) is one of the most important location-based services (LBS). It is becoming increasingly important and has been widely used as a basic service in navigation systems and intelligent transportation systems. This paper presents a novel machine learning solution to predict the vehicle travel time based on floating-car data. First, we formulate ETA as a pure spatial-temporal regression problem based on a large set of effective features. Second, we adapt different existing machine learning models to solve the regression problem. Furthermore, we propose a Wide-Deep-Recurrent (WDR) learning model to accurately predict the travel time along a given route at a given departure time. We then jointly train wide linear models, deep neural networks and recurrent neural networks together to take full advantages of all three models. We evaluate our solution offline with millions of historical vehicle travel data. We also deploy the proposed solution on Didi Chuxing’s platform, which services billions of ETA requests and benefits millions of customers per day. Our extensive evaluations show that our proposed deep learning algorithm significantly outperforms the state-of-the-art learning algorithms, as well as the solutions provided by leading industry LBS providers.