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QA-Share
ACM Transactions on Sensor Networks ( IF 4.1 ) Pub Date : 2020-01-30 , DOI: 10.1145/3375406 Qiang Ma 1 , Zhichao Cao 1 , Kebin Liu 1 , Xin Miao 1
ACM Transactions on Sensor Networks ( IF 4.1 ) Pub Date : 2020-01-30 , DOI: 10.1145/3375406 Qiang Ma 1 , Zhichao Cao 1 , Kebin Liu 1 , Xin Miao 1
Affiliation
Taxi-sharing allows occupied taxis to pick up new passengers on the fly, promising to reduce waiting time for taxi riders and increase productivity for drivers. However, it becomes more difficult to strike the balance between a driver’s profit and a passenger’s quality of service (QoS). In this article, we propose QA-Share, a QoS-aware taxi-sharing system, by addressing two important challenges. First, QA-Share maximizes driver profit and user experience at the same time. Second, QA-Share optimizes these two metrics by dynamically adapting its schedule as new requests arrive. To address these two challenges, we formulated the optimization problem using integer linear programming and derived the optimal solution under a small system scale. Moreover, we also designed a heuristic algorithm to deal with the situation where more passenger requests for taxi service come at the same time. We evaluate our approach with a real-world dataset in a Chinese city—Zhenjiang—that contains the GPS traces recorded by more than 3,000 taxis during a period of 3 months. The results show that both QoS and profit increase by 38% compared to the current schemes. Moreover, as the first study that has conducted simulations with real traces with a population of 3 million and 3,000 taxis, we prove that taxi-sharing is a viable approach in a medium-size city.
更新日期：2020-01-30
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