A1 A. Ramezani

A1 B. Moshiri

A1 A.R. Kian

A1 B.N. Aarabi

A1 B. Abdulhai

PB iet

T1 Distributed maximum likelihood estimation for flow and speed density prediction in distributed traffic detectors with Gaussian mixture model assumption

JN IET Intelligent Transport Systems

VO 6

IS 2

SP 215

OP 222

AB In this study a distributed maximum likelihood estimator (MLE) has been presented to estimate ML function of traffic flow and mean traffic speed in a freeway. This algorithm uses traffic measurements including volume, occupancy and mean speed which gathered by some inductive loop detectors. These traffic detectors (traffic sensors) located in certain distances in the freeway network such that they establish a distributed sensor network (DSN). The presented distributed estimator has employed a distributed expectation maximisation algorithm to calculate MLE. In the E-step of this algorithm, each sensor node independently calculates local sufficient statistics by using local observations. A consensus filter is used to diffuse local sufficient statistics to neighbours and estimate global sufficient statistics in each node. In the M-step of this algorithm, each sensor node uses the estimated global sufficient statistics to update model parameters of the Gaussian mixtures, which can maximise the log-likelihood in the same way as in the standard EM algorithm. As the consensus filter only requires each node to communicate with its neighbours, the distributed algorithm is scalable and robust. A set of field traffic data from Minnesota freeway network has been used to simulate and verify the proposed distributed estimator performance.

K1 distributed expectation maximisation algorithm

K1 distributed sensor network

K1 M-step

K1 global sufficient statistics

K1 MLE

K1 speed density prediction

K1 E-step

K1 traffic sensors

K1 mean traffic speed

K1 consensus filter

K1 local sufficient statistics

K1 traffic flow prediction

K1 Gaussian mixture model assumption

K1 Minnesota freeway network

K1 distributed traffic detector

K1 ML function estimation

K1 log likelihood

K1 distributed maximum likelihood estimation

K1 inductive loop detectors

K1 traffic measurements

DO https://doi.org/10.1049/iet-its.2010.0189

UL https://digital-library.theiet.org/;jsessionid=mbkjnviwp7nh.x-iet-live-01content/journals/10.1049/iet-its.2010.0189

LA English

SN 1751-956X

YR 2012

OL EN