A Multi-criteria System for Recommending Taxi Routes with an Advance Reservation

Jie Yu Fang, Fandel Lin, Hsun Ping Hsieh

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)


As the demand of taxi reservation services has increased, the strategies of how to increase the income of taxi drivers with advanced service have attracted attention. However, the demand is usually unmet due to the imbalance of profit. In this paper, we propose a multi-criteria route recommendation framework that considers real-time spatial-temporal predictions and traffic network information, aiming to optimize a taxi driver’s profit when the driver has an advance reservation. Our framework consists of four components. First, we build a grid-based road network graph for modeling traffic network information during the search routes process. Next, we conduct two prediction modules that adopt advanced deep learning techniques to guide a proper search direction in the final planning stage. One module, taxi demand prediction, is used to estimate the pick-up probabilities of passengers in the city. Another one is destination prediction, which can predict the distribution of drop-off probabilities and capture the flow of potential passengers. Finally, we propose our J* (J-star) algorithm, which jointly considers pick-up probabilities, drop-off distribution, road network, distance, and time factors based on the attentive heuristic function. Compared with existing route planning methods, the experimental results on a real-world dataset (NYC taxi datasets) have shown our proposed approach is more effective and robust. Moreover, our designed search scheme in J* can decrease the computing time and make the search process more efficient. To the best of our knowledge, this is the first work that focuses on designing a guiding route, which can increase the income of taxi drivers when they have an advance reservation.

主出版物標題Machine Learning and Knowledge Discovery in Databases
主出版物子標題Applied Data Science Track - European Conference, ECML PKDD 2020, Proceedings
編輯Yuxiao Dong, Dunja Mladenic, Craig Saunders
發行者Springer Science and Business Media Deutschland GmbH
出版狀態Published - 2021
事件European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
持續時間: 2020 9月 142020 9月 18


名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12460 LNAI


ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
城市Virtual, Online

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 電腦科學(全部)


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