A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Map-reduce for calibrating massive bus trajectory data
Accurate bus trajectory data is the basis of many public transportation applications. However, trajectory data sampled by GPS devices contains notable direction errors. We cannot determine the travelling direction of the bus through trajectory data. To address this problem, we utilize k-nearest neighbor algorithm (K-NN) to determine the direction of the bus trajectory. Meanwhile, the voluminous bus trajectory data accumulated daily need to be process efficiently for further data mining. To meet the scalability and performance requirements, in this paper, we use Map-Reduce programming model for trajectory data direction correcting and projecting the bus GPS point to the road link. Particularly, we compare execution time through setting different amount of reduce to express the extent of running time can be affected. Experimental results indicate that the K-NN algorithm improves the accuracy of the direction field in raw bus trajectory data significantly. By comparing the efficiency under different reduce quantities. The result shows that parallel processing framework improves the computational efficiency by a factor of 2 at least, obtaining.
Map-reduce for calibrating massive bus trajectory data
Accurate bus trajectory data is the basis of many public transportation applications. However, trajectory data sampled by GPS devices contains notable direction errors. We cannot determine the travelling direction of the bus through trajectory data. To address this problem, we utilize k-nearest neighbor algorithm (K-NN) to determine the direction of the bus trajectory. Meanwhile, the voluminous bus trajectory data accumulated daily need to be process efficiently for further data mining. To meet the scalability and performance requirements, in this paper, we use Map-Reduce programming model for trajectory data direction correcting and projecting the bus GPS point to the road link. Particularly, we compare execution time through setting different amount of reduce to express the extent of running time can be affected. Experimental results indicate that the K-NN algorithm improves the accuracy of the direction field in raw bus trajectory data significantly. By comparing the efficiency under different reduce quantities. The result shows that parallel processing framework improves the computational efficiency by a factor of 2 at least, obtaining.
Map-reduce for calibrating massive bus trajectory data
Dapeng Li, (author) / Haitao Yu, (author) / Xiaohua Zhou, (author) / Mengdan Gao, (author)
2013-11-01
301330 byte
Conference paper
Electronic Resource
English
Calibrating Car-Following Models by Using Trajectory Data: Methodological Study
| British Library Online Contents | 2008
An efficient data processing framework for mining the massive trajectory of moving objects
| Online Contents | 2015
An efficient data processing framework for mining the massive trajectory of moving objects
| Online Contents | 2017