Traffic Incident Clearance Time and Arrival Time Prediction Based on Hazard Models

Abstract

Accurate prediction of incident duration is not only important information of Traffic Incident Management System, but also an effective input for travel time prediction. In this paper, the hazard based prediction models 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.

Severity analysis of crashes involving in-state and out-of-state large truck drivers in Alabama: A random parameter multinomial logit model with heterogeneity in means and variances

“…As such, much effort has been devoted to understanding the factors that contribute to truck-involved crashes and crash outcomes. Considering that crash incident clearance time is significantly impacted by the crash severity ( Ding et al., 2015 ; Islam et al., 2021 ; Ji et al., 2014 ), this study contributes to this effort by exploring differences and similarities in crashes involving in-state and out-of-state truck drivers. It is worth noting that a preliminary analysis of the data used in the study showed similar proportions of crashes involving in-state and out-of-state drivers across the years.…”

Evaluating the Impact of Freeway Service Patrol on Incident Clearance Times: A Spatial Transferability Test

“…Previous studies have found various statistical modeling methods to be appropriate for examining incident clearance times. ese models include simple regression models [29], switching regression models [30], quantile regression models [2,31], hazard-based duration models [32][33][34][35][36][37][38], accelerated failure time (AFT) models [33,39,40], finite mixture models [41], generalized F distribution models [42], artificial neural network models [43], and Bayesian network models [44]. Hazard-based duration models have been found to be more appropriate in examining duration data [32,45,46]; therefore, in this paper, random parameters hazard-based duration models were employed to identify contributing factors of incident clearance time.…”

Bus arrival time prediction and measure of uncertainties using survival models

“…Recently, survival models are being used for various transport applications such as incident detection [27][28][29], arrival time modelling [19], incident and arrival time [30], driver distraction [31][32][33], service time estimation [34], urban traffic congestion duration modelling [35], accident duration modelling [36], urban arrival times analysis [37], choice of car-share vehicle [38] and so on. Hazard-based methods have been applied to many transportation applications (including arrival times and travel time) [37].…”