Traffic incident clearance time and arrival time prediction based on hazard models


Autoria(s): Ji, Yang beibei; Jiang, Rui; Qu, Ming; Chung, Edward
Data(s)

31/03/2014

Resumo

Accurate prediction of incident duration is not only important information of Traffic Incident Management System, but also an ffective input for travel time prediction. In this paper, the hazard based prediction odels are developed for both incident clearance time and arrival time. The data are obtained from the Queensland Department of Transport and Main Roads’ STREAMS Incident Management System (SIMS) for one year ending in November 2010. The best fitting distributions are drawn for both clearance and arrival time for 3 types of incident: crash, stationary vehicle, and hazard. The results show that Gamma, Log-logistic, and Weibull are the best fit for crash, stationary vehicle, and hazard incident, respectively. The obvious impact factors are given for crash clearance time and arrival time. The quantitative influences for crash and hazard incident are presented for both clearance and arrival. The model accuracy is analyzed at the end.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/71698/

Publicador

Hindawi Publishing Corporation

Relação

http://eprints.qut.edu.au/71698/1/Traffic_Incident_Clearance_Time_and_Arrival_Time_Prediction_Based_on_Hazard_Models.pdf

DOI:10.1155/2014/508039

Ji, Yang beibei, Jiang, Rui, Qu, Ming, & Chung, Edward (2014) Traffic incident clearance time and arrival time prediction based on hazard models. Mathematical Problems in Engineering, 2014.

Direitos

Copyright 2014 please consult author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Fonte

School of Civil Engineering & Built Environment; Science & Engineering Faculty; Smart Transport Research Centre

Palavras-Chave #090507 Transport Engineering #Traffic Incident #Hazard Models #Traffic Incident Management System
Tipo

Journal Article