Please use this identifier to cite or link to this item:
Title: Mobile sensor and sample-based algorithm for freeway incident detection
Authors: Cheu, R.L. 
Qi, H.
Lee, D.-H. 
Issue Date: 2002
Citation: Cheu, R.L.,Qi, H.,Lee, D.-H. (2002). Mobile sensor and sample-based algorithm for freeway incident detection. Transportation Research Record (1811) : 12-20. ScholarBank@NUS Repository.
Abstract: A mobile sensor and sample-based algorithm (MOSES) to detect incidents on freeways is described. The proposed algorithm is based on statistical difference in the mean section travel time from two sets of probe vehicle samples before and during an incident. Unlike other incident detection algorithms, which operate at fixed time intervals, this sample-based algorithm is applied to detect an incident whenever a fixed sample of new probe vehicles has traversed a freeway section. The incident detection performance of MOSES at various sampling rates and probe vehicle percentages in the traffic stream has been tested on a set of data generated by a calibrated microscopic traffic simulation model. The results are compared with those of two of the most promising neural network incident detection models, which use input from stationary sensors and operate on a fixed time interval. When more than 50% of the vehicles are sampled as probes, MOSES can achieve a detection rate and false alarm rate comparable to that of the two neural network models but with faster mean time to detection and lower misclassification.
Source Title: Transportation Research Record
ISSN: 03611981
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Dec 8, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.