A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An arterial speed estimation model fusing data from stationary and mobile sensors
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.
An arterial speed estimation model fusing data from stationary and mobile sensors
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.
An arterial speed estimation model fusing data from stationary and mobile sensors
Ruey Long Cheu, (author) / Der-Horng Lee, (author) / Chi Xie, (author)
2001-01-01
481813 byte
Conference paper
Electronic Resource
English
An Arterial Speed Estimation Model Fusing Data from Stationary and Mobile Sensors
| British Library Conference Proceedings | 2001
Calibration-Free Arterial Link Speed Estimation Model Using Loop Data
| Online Contents | 2001
| British Library Online Contents | 2009