Name
Abstract | ||
|---|---|---|
Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories. We use rigorous A/B tests to compare our new taxi dispatch method with state-of-the-art models using data collected in Beijing. Experimental results show that the proposed method is significantly better than other state-of-the art models in terms of global success rate (increased from 80% to 84%). Moreover, we have also achieved significant improvement on other metrics such as user's waiting-time and pick-up distance. For our destination prediction algorithm, we show that our proposed model is superior to the baseline model by improving the top-3 accuracy from 89% to 93%. The proposed taxi dispatch and destination prediction algorithms are both deployed in our online systems and serve tens of millions of users everyday. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3097983.3098138 | KDD |
Keywords | Field | DocType |
Taxi dispatch,destination prediction,combinatorial optimization,circular distribution | Data mining,User experience design,Computer science,Taxis,Response time,Combinatorial optimization,Prediction algorithms,Acceptance rate,Artificial intelligence,Beijing,Machine learning,Bayesian probability | Conference |
ISBN | Citations | PageRank |
978-1-4503-4887-4 | 26 | 0.95 |
References | Authors | |
12 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingyu Zhang | 1 | 34 | 4.43 |
| Tao Hu D D S | 2 | 69 | 24.10 |
| Yue Min | 3 | 26 | 0.95 |
| Guobin Wu | 4 | 51 | 3.43 |
| Junying Zhang | 5 | 153 | 21.12 |
| Pengcheng Feng | 6 | 26 | 0.95 |
| Pinghua Gong | 7 | 349 | 15.61 |
| Jieping Ye | 8 | 6943 | 351.37 |