Stochastic Extension of Newell's Three-Detector Method-Reference-Cited by-同舟云学术

Stochastic Extension of Newell's Three-Detector Method

Published:2012-01 Issue:1 Volume:2315 Page:73-80
ISSN:0361-1981
Container-title:Transportation Research Record: Journal of the Transportation Research Board
language:en
Short-container-title:Transportation Research Record

Author:

Laval Jorge A.¹,He Zhengbing²,Castrillon Felipe¹

Affiliation:

1. School of Civil and Environmental Engineering, Georgia Institute of Technology, North Avenue, Atlanta, GA 30332.

2. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.

Abstract

A stochastic extension of Newell's three-detector method is presented. The method predicts the traffic states at an intermediate location given boundary data from downstream and upstream detectors. The method presented takes into account day-to-day variations in the arrivals, sensor detection errors, and variability in the fundamental diagram parameters. The output is the probabilistic distribution of predicted cumulative counts, which can be used to obtain confidence bounds on any traffic variable. The method is tested with empirical data.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Link

http://journals.sagepub.com/doi/pdf/10.3141/2315-08

Reference23 articles.

1. Real-time freeway traffic state estimation based on extended Kalman filter: a general approach

2. MihaylovaL., BoelR., and HegyiA. An Unscented Kalman Filter for Freeway Traffic Estimation. Proc., 11th IFAC Symposium on Control in Transportation Systems, August 2006, Delft, Netherlands, Vol. 11, Part 1, 2006.

3. A compositional stochastic model for real time freeway traffic simulation

Cited by 27 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Refining Time-Space Traffic Diagrams: A Simple Multiple Linear Regression Model;IEEE Transactions on Intelligent Transportation Systems;2024-02

2. Approximate Inference of Traffic Flow State at Signalized Intersections Using a Bayesian Learning Framework;IEEE Transactions on Intelligent Transportation Systems;2023-05

3. A stage pressure-based adaptive traffic signal control using reinforcement learning;International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021);2022-03-14

4. Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation;Transportation Research Part B: Methodological;2021-04

5. Bibliography;Introduction to Network Traffic Flow Theory;2021