Context-aware Route Recommendation with Weight Learning through Deep Neural Networks

We consider real-time origin-destination transportation requests in, e.g., ride-hailing, and for each request, provide a context-aware route recommendation. To overcome the difficulty of estimating uncertain user preferences toward multiple route features, we design and implement an approach via a combination of the weighted shortest path problem and deep learning, and evaluate it using real-world transportation data. Specifically, the proposed approach learns weights from historical choices of drivers through a deep neural network by minimizing the total weighted costs of historical routes and maximizing those of the non-chosen routes. We evaluate our method and two benchmarks with 4 million requests received by Didi Chuxing in Beijing. Based on the results, we demonstrate that distinguishing request scenarios helps provide preferable context-aware route recommendations.