Journal
IET INTELLIGENT TRANSPORT SYSTEMS
Volume 8, Issue 1, Pages 51-57Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-its.2013.0052
Keywords
interpolation; principal component analysis; probability; traffic engineering computing; road traffic control; traffic management applications; traffic control applications; traffic flow data prediction; sensor failure; transmission error; missing traffic data estimation; data imputation methods; prediction methods; interpolation methods; statistical learning methods; reconstruction errors; statistical behaviours; running speeds; probabilistic principal component analysis; PPCA; numerical tests
Funding
- National Basic Research Programme of China (973 Project) [2012CB725405]
- National Natural Science Foundation of China [51138003, 51278280]
- Hi-Tech Research and Development Program of China (863 Project) [2011AA110301]
Ask authors/readers for more resources
Many traffic management and control applications require highly complete and accurate data of traffic flow. However, because of various reasons such as sensor failure or transmission error, it is common that some traffic flow data are lost. As a result, various methods were proposed by using a wide spectrum of techniques to estimate missing traffic data in the last two decades. Generally, these missing data imputation methods can be categorised into three kinds: prediction methods, interpolation methods and statistical learning methods. To assess their performance, these methods are compared from different aspects in this paper, including reconstruction errors, statistical behaviours and running speeds. Results show that statistical learning methods are more effective than the other two kinds of imputation methods when data of a single detector is utilised. Among various methods, the probabilistic principal component analysis (PPCA) yields best performance in all aspects. Numerical tests demonstrate that PPCA can be used to impute data online before making further analysis (e.g. make traffic prediction) and is robust to weather changes.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available