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Sensors (Basel). 2017 Dec 6;17(12). pii: E2822. doi: 10.3390/s17122822.

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks.

Shi C1,2,3, Chen BY4,5,6, Lam WHK7, Li Q8,9,10.

Author information

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China. chaoyangshi@whu.edu.cn.
2
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China. chaoyangshi@whu.edu.cn.
3
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China. chaoyangshi@whu.edu.cn.
4
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China. chen.biyu@whu.edu.cn.
5
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China. chen.biyu@whu.edu.cn.
6
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China. chen.biyu@whu.edu.cn.
7
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China. william.lam@polyu.edu.hk.
8
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China. qqli@whu.edu.cn.
9
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China. qqli@whu.edu.cn.
10
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China. qqli@whu.edu.cn.

Abstract

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

KEYWORDS:

data fusion; evidence theory; spatial correlation; travel time distribution; uncertainty

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