Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/74052
Title: An arterial speed estimation model fusing data from stationary and mobile sensors
Authors: Cheu, R.L. 
Lee, D.-H. 
Xie, C.
Keywords: Arterial speed
Data fusion
Global Positioning Systems
Loop detectors
Neural networks
Probe vehicles
Issue Date: 2001
Source: Cheu, R.L.,Lee, D.-H.,Xie, C. (2001). An arterial speed estimation model fusing data from stationary and mobile sensors. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC : 573-578. ScholarBank@NUS Repository.
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) probe vehicle module which measure arterial speed using vehicles equipped with differential global positioning system receivers; (2) loop detector modules which estimate link speed using loop detector data, incorporating traffic signal timing parameters; and (3) 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.
Source Title: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
URI: http://scholarbank.nus.edu.sg/handle/10635/74052
Appears in Collections:Staff Publications

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