Abstract
This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell's method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark's approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 132-157 |
| Number of pages | 26 |
| Journal | Transportation Research Part B: Methodological |
| Volume | 57 |
| DOIs | |
| State | Published - Nov 2013 |
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Keywords
- Clark's approximation
- Kinematic wave method
- Probit model
- Three-detector problem
- Traffic state estimation
ASJC Scopus subject areas
- Management Science and Operations Research
- Transportation
Cite this
Traffic state estimation and uncertainty quantification based on heterogeneous data sources : A three detector approach. / Deng, Wen; Lei, Hao; Zhou, Xuesong.
In: Transportation Research Part B: Methodological, Vol. 57, 11.2013, p. 132-157.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Traffic state estimation and uncertainty quantification based on heterogeneous data sources
T2 - A three detector approach
AU - Deng, Wen
AU - Lei, Hao
AU - Zhou, Xuesong
PY - 2013/11
Y1 - 2013/11
N2 - This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell's method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark's approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment.
AB - This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell's method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark's approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment.
KW - Clark's approximation
KW - Kinematic wave method
KW - Probit model
KW - Three-detector problem
KW - Traffic state estimation
UR - http://www.scopus.com/inward/record.url?scp=84885404726&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885404726&partnerID=8YFLogxK
U2 - 10.1016/j.trb.2013.08.015
DO - 10.1016/j.trb.2013.08.015
M3 - Article
AN - SCOPUS:84885404726
VL - 57
SP - 132
EP - 157
JO - Transportation Research, Series B: Methodological
JF - Transportation Research, Series B: Methodological
SN - 0191-2615
ER -