Name
Abstract | ||
|---|---|---|
Predicting the travel time in real time is challenging due to dynamic changes of the traffic. With the help of GPS, wireless mobile communication, and big data technologies, several link travel time distribution capturing algorithms have emerged to generate the prediction of the route travel time distribution in short term. In this paper, we propose a data fusion model which can combine the historical and real time distribution to predict the link level travel time distribution. In the model, the route is represented with Markov chain, where the Markov state is identified by the travel time of probe vehicles. Experimental results prove that the proposed method has high accuracy in predicting route travel time distribution, and it is robust in spite of the fluctuation of real-time data. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISC2.2015.7366161 | 2015 IEEE First International Smart Cities Conference (ISC2) |
Keywords | Field | DocType |
Data fusion,Travel time,Markov chain | Time distribution,Computer science,Markov chain,Link level,Decision support system,Real-time computing,Sensor fusion,Global Positioning System,Travel time,Big data | Conference |
Citations | PageRank | References |
1 | 0.38 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daxin Tian | 1 | 204 | 32.49 |
| Yong Yuan | 2 | 3 | 0.78 |
| haiying xia | 3 | 20 | 1.22 |
| fengtian cai | 4 | 1 | 0.38 |
| Yunpeng Wang | 5 | 194 | 25.34 |
| jiajie wang | 6 | 23 | 6.83 |