Title :
An arterial speed estimation model fusing data from stationary and mobile sensors
Author :
Cheu, Ruey Long ; Lee, Der-Homg ; Xie, Chi
Author_Institution :
Dept. of Civil Eng., Nat. Univ. of Singapore, Singapore
Abstract :
This paper presents an arterial speed estimation model using data from two distinct sources: mobile probe vehicles and inductive loop detectors. The model consists of three modules: (1) the probe vehicle module which measures arterial speed using vehicles equipped with differential global positioning system receivers; (2) the loop detector module which estimates the link speed using loop detector data, incorporating traffic signal timing parameters; and (3) the data fusion module which uses a neural network to combine outputs from the above two modules to improve the speed estimation accuracy. The computational procedures of the three modules are presented. This paper presents a validation test of the model using a set of data generated from a calibrated simulation model. Our test results show that, the probe vehicle and loop detector modules are capable of making speed estimation with 2-RMSE of less than 3.20 km/h. Using a neural network to fuse the estimates from the two sources reduces the 2-RMSE to less than 1.32 km/h
Keywords :
Global Positioning System; least mean squares methods; neural nets; road traffic; sensor fusion; traffic control; traffic engineering computing; Global Positioning Systems; data fusion; loop detectors; neural networks; probe vehicles; road traffic; root-mean-square error; terms-arterial speed; Detectors; Neural networks; Position measurement; Probes; Telecommunication traffic; Testing; Traffic control; Vehicle detection; Vehicles; Velocity measurement;
Conference_Titel :
Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE
Conference_Location :
Oakland, CA
Print_ISBN :
0-7803-7194-1
DOI :
10.1109/ITSC.2001.948723