期刊论文、会议论文

Niu, Xiaoguang

英文

Communications in Computer and Information Science

1865-0929

2015

501

548-557

10.1007/978-3-662-46981-1_52

This work was partially supported by National Key Basic Research Program of China “973 Project” (Grant No. 2011CB707106), Development Program of China “863 Project” (Grant No. 2013AA122301), National Natural Science Foundation of China “NSFC” (Grant No. 61103220, 61303212) and the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT1278).

本校为其他机构

Traffic flow condition prediction is a basic problem in the transportation field. It is challenging to play out full potential of temporally-related information and overcome the problem of data sparsity existed in the traffic flow prediction. In this paper, we propose a novel urban traffic prediction mechanism namely C-Sense consisting of two parts: CRF-based temporal feature learning and sequence segments matching. CRF-based temporal feature learning exploits a linear-chain condition random field (CRF) to explore the temporal transformation rule in the traffic flow state sequence with supplem...