Time-varying effects of influential factors on incident clearance time using a non-proportional hazard-based model

Incident clearance time is a major performance measure of the traffic emergency management. A clear understanding of the contributing factors and their effects on incident clearance time is essential for optimal incident management resource allocations. Most previous studies simply considered the average effects of the influential factors. Although the time-varying effects are also important for incident management agencies, they were not sufficiently investigated. To fill up the gap, this study develops a non-proportional hazard-based duration model for analyzing the time-varying effects of influential factors on incident clearance time. This study follows a systematic approach incorporating the following three procedures: proportionality test, model development/estimation, and effectiveness test. Applying the proposed model to the 2009 Washington State Incident Tracking System data, five factors were found to have significant but constant (or time independent) effects on the clearance time, which is similar to the findings from previous studies. However, our model also discovered thirteen variables that have significant time-varying impacts on clearance hazard. These factors cannot be identified through the conventional methods used in most previous studies. The influential factors are investigated from both macroscopic and microscopic perspectives. The population average effect evaluation provides the macroscopic insight and benefits long-term incident management, and the time-dependent pattern identification offers microscopic and time-sequential insight and benefits the specific incident clearance process.

[1]  P. Grambsch,et al.  Martingale-based residuals for survival models , 1990 .

[2]  Luke Keele,et al.  Proportionally Difficult: Testing for Nonproportional Hazards in Cox Models , 2010, Political Analysis.

[3]  J. Heckman,et al.  Does Unemployment Cause Future Unemployment? Definitions, Questions and Answers from a Continuous Time Model of Heterogeneity and State Dependence. , 1980 .

[4]  T F Golob,et al.  An analysis of the severity and incident duration of truck-involved freeway accidents. , 1987, Accident; analysis and prevention.

[5]  Asad J. Khattak,et al.  A Simple Time Sequential Procedure for Predicting Freeway Incident duration , 1995, J. Intell. Transp. Syst..

[6]  Jung-Taek Lee,et al.  Influential Factors in Freeway Crash Response and Clearance Times by Emergency Management Services in Peak Periods , 2005, Traffic injury prevention.

[7]  Janet M. Box-Steffensmeier,et al.  Duration models and proportional hazards in political science , 2001 .

[8]  D. Cox Regression Models and Life-Tables , 1972 .

[9]  D. Harrington,et al.  Counting Processes and Survival Analysis , 1991 .

[10]  Fred Mannering,et al.  Modeling Travelers' Postwork Activity Involvement: Toward a New Methodology , 1993, Transp. Sci..

[11]  J O'Quigley,et al.  Estimating average regression effect under non-proportional hazards. , 2000, Biostatistics.

[12]  Hyung Jin Kim,et al.  A COMPARATIVE ANALYSIS OF INCIDENT SERVICE TIME ON URBAN FREEWAYS , 2001 .

[13]  Kaan Ozbay,et al.  Estimation of incident clearance times using Bayesian Networks approach. , 2006, Accident; analysis and prevention.

[14]  Satish V. Ukkusuri,et al.  A random-parameter hazard-based model to understand household evacuation timing behavior , 2013 .

[15]  Fred L. Mannering,et al.  An exploratory hazard-based analysis of highway incident duration , 2000 .

[16]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data , 1980 .

[17]  Brian Lee Smith,et al.  FORECASTING THE CLEARANCE TIME OF FREEWAY ACCIDENTS , 2002 .

[18]  Kaan Ozbay,et al.  INCIDENT MANAGEMENT IN INTELLIGENT TRANSPORTATION SYSTEMS , 1999 .

[19]  G. Giuliano INCIDENT CHARACTERISTICS, FREQUENCY, AND DURATION ON A HIGH VOLUME URBAN FREEWAY , 1989 .

[20]  Debbie A. Niemeier,et al.  Analysis of activity duration using the Puget sound transportation panel , 2000 .

[21]  Woodrow Barfield,et al.  Statistical analysis of commuters' route, mode, and departure time flexibility , 1994 .

[22]  P. Grambsch,et al.  Modeling Survival Data: Extending the Cox Model , 2000 .

[23]  S. Travis Waller,et al.  Naive Bayesian Classifier for Incident Duration Prediction , 2007 .

[24]  Stefan Schönfelder,et al.  INTERSHOPPING DURATION: AN ANALYSIS USING MULTIWEEK DATA , 2002 .

[25]  F Mannering,et al.  Analysis of the frequency and duration of freeway accidents in Seattle. , 1991, Accident; analysis and prevention.

[26]  Ryuichi Kitamura,et al.  An analysis of household vehicle holding durations considering intended holding durations , 2000 .

[27]  Abolfazl Mohammadian,et al.  Modeling activity scheduling time horizon: Duration of time between planning and execution of pre-planned activities , 2006 .

[28]  Fred L. Mannering,et al.  HAZARD-BASED DURATION MODELS AND THEIR APPLICATION TO TRANSPORT ANALYSIS. , 1994 .

[29]  James J. Wang,et al.  Timing utility of daily activities and its impact on travel , 1996 .

[30]  Edward C. Sullivan,et al.  NEW MODEL FOR PREDICTING FREEWAY INCIDENTS AND INCIDENT DELAYS , 1997 .

[31]  Hyunho Chang,et al.  System architecture of a decision support system for freeway incident management in Republic of Korea , 2008 .

[32]  D. Stablein,et al.  Survival analysis of drug combinations using a hazards model with time-dependent covariates. , 1980, Biometrics.

[33]  R. Michael Alvarez,et al.  Event History Modeling: A Guide for Social Scientists , 2004 .

[34]  Jeffrey P. Kharoufeh,et al.  NONPARAMETRIC IDENTIFICATION OF DAILY ACTIVITY DURATIONS USING KERNEL DENSITY ESTIMATORS , 2002 .

[35]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[36]  Dušan Teodorović,et al.  FUZZY LOGIC SYSTEMS FOR TRANSPORTATION ENGINEERING: THE STATE OF THE ART , 1999 .

[37]  Harry Timmermans,et al.  Changing the duration of activities in resolving scheduling conflicts , 2008 .

[38]  L. Keele Covariate Functional Form in Cox Models , 2005 .

[39]  Mu-Han Wang,et al.  Modeling freeway incident clearance time , 1991 .

[40]  W. H. Carter,et al.  Analysis of survival data with nonproportional hazard functions. , 1981, Controlled clinical trials.

[41]  P. Grambsch,et al.  Proportional hazards tests and diagnostics based on weighted residuals , 1994 .

[42]  Markos Papageorgiou,et al.  An integrated control approach for traffic corridors , 1995 .

[43]  Ying Lee,et al.  Sequential forecast of incident duration using Artificial Neural Network models. , 2007, Accident; analysis and prevention.

[44]  Hsin-Li Chang,et al.  Exploratory analysis of motorcycle holding time heterogeneity using a split-population duration model , 2007 .

[45]  Haitham Al-Deek,et al.  Estimating Magnitude and Duration of Incident Delays , 1997 .

[46]  Younshik Chung,et al.  Development of an accident duration prediction model on the Korean Freeway Systems. , 2010, Accident; analysis and prevention.