Examination of the Factors Influencing the Clearance Time of Freeway Incidents

Abstract

Abstract:

Traffic incidents are the primary cause of delay in urban settings, reducing capacity and creating risks for both motorists and incident response personnel.As incident duration increases, the risk of secondary incidents or crashes also becomes a problem.In response to these issues, many communities have initiated incident management programs aimed at detecting, responding to, and clearing incidents in order to restore freeways to full capacity as quickly and safely as possible.This study involved the development of fully parametric hazard duration models to examine those factors impacting the time required by the Michigan Department of Transportation’s Freeway Courtesy Patrol to clear incidents that occurred on the freeway network in metropolitan Detroit.These models were developed using traffic flow data, roadway geometry information, and an extensive incident database.Four fully parametric hazard duration models are developed, each assuming a different underlying probability distribution for the hazard function.In general, each modeling framework provided similar results, though a log-logistic distribution is shown to provide a better fit for the incident clearance data in comparison to other distributions.Various factors were found to significantly affect incident clearance time, including the time of day and time of year at which the incident occurred, the geometric and traffic characteristics of the freeway segment, and the characteristics of the incident.

Key words: freeway, incident, parametric hazard duration model

摘要：

交通事故是导致高速公路延误的一个主要因素，同时也降低了道路通行能力、车辆以及人员的安全性，甚至有可能导致二次事故发生.对此，相关部门已开始制定一些事故管理方案以降低或者消除事故影响并最大限度地增加道路通行能力和车辆行驶安全性.应密歇根交通部要求对底特律市高速公路网事故清理时间影响因素进行分析，基于交通流数据、道路几何特征及大量的事故数据，建立了参数化危险持续时间模型.所建立的四个模型分别基于不同的概率分布.结果表明，四个模型的性能大体相似，但是相对于其他分布，loglogistic分布更适合于事故清理时间分析.对事故清理时间影响显著的因素有：事故发生的时间、路段交通特性和几何特性，以及事故特征.

关键词: 高速公路, 事故, 参数化危险持续时间模型

CLC Number:

U412.366

GHOSH Indrajit. Examination of the Factors Influencing the Clearance Time of Freeway Incidents[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(3): 75-89.

GHOSH Indrajit. 高速公路事故清理时间影响因素分析[J]. 交通运输系统工程与信息, 2012, 12(3): 75-89.

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URL: http://www.tseit.org.cn/EN/abstract/abstract18407.shtml

http://www.tseit.org.cn/EN/Y2012/V12/I3/75

References

[1] Neudorff L G, Randall J E, Reiss R, et al. Freeway management and operations handbook[R], Report FHWA-OP-04-003. Federal Highway Administration, Washington D.C., 2003.
[2] Giuliano G. Incident characteristics, frequency, and duration on a high volume urban freeway[R]. Report Number UCI-ITS-WP-88-7, Institute of Transportation Studies, University of California, Irvine, 1988.
[3] TRB, Highway Capacity Manual: Special Report 209[R]. Transportation Research Board, National Research Council, Washington, D.C., 1994.
[4] Carvell J D Jr, Balke K, Ullman J, et al. Freeway Management Handbook[R]. Report Number FHWA-SA-97-064, Texas Transportation Institute, Federal Highway Administration, U.S. Department of Transportation, 1997.
[5] Wang W, Chen H, Bell M C. A review of traffic incident duration analysis[J]. Journal of Transportation Systems Engineering and Information Technology, 2005, 5(3): 127–140.
[6] Khattak A, Rouphail N. Incident management assistance patrols: Assessment of investment benefits and costs[R]. NCDOT 2003-06, Center for Urban & Regional Studies, Department of City and Regional Planning, University of North Carolina at Chapel Hill, 2004.
[7] Dougald L E, Demetsky M J. Assessing return on investment of freeway safety service patrol programs[R]. Transportation Research Record 2047, Washington, D.C., 2008, 19–27.
[8] Garib A, Radwan A E, Al-Deek H. Estimating magnitude and duration of incident delays[J]. Journal of Transportation Engineering, 1997,123(6): 459–466.
[9] Smith K, Smith B L. Forecasting the Clearance Time of Freeway Accidents[R]. Research Report No. UVACTS-15-0-35, Center for Transportation Studies (University of Virginia), Charlottesville, VA, September 2001.
[10] Nam D, Mannering F L. An exploratory hazard-based analysis of highway incident duration[J]. Transportation Research Part A, 2000, 34(2): 85–102.
[11] Chung Y. Development of an accident duration prediction model on the korean freeway systems[J]. Accident Analysis and Prevention, 2010, 42(1): 282–289.
[12] Pearce V. incident management successful practices: A cross cutting study, improving mobility and saving lives[R]. Publication FHWA-JPO-99-018, U.S. Department of Transportation, Federal Transit Administration and Federal Highway Administration, Washington D.C., 2000.
[13] Dunn W M, Latoski S P. Safe and quick clearance of traffic incidents. a synthesis of highway practice[R]. National Cooperative Highway Research Program Synthesis 318, Transportation Research Board, Washington, D.C., 2003.
[14] Konduri S, Labi S, Sinha K C. Incident occurrence models for freeway incident management[C]. In: Transportation Research Record 1856, Washington, D.C., 2003: 125–134.
[15] Jones B, Janssen L, Mannering F L. Analysis of the frequency and duration of freeway accidents in Seattle[J]. Accident Analysis and Prevention, 1991, 23(4): 239–255.
[16] Madanat S, Feroze A. Prediction models for incident clearance time for Borman expressway[R]. Report Number FHWA/IN/JHRP-96/10, Joint Highway Research Project, March 1997, Purdue University.
[17] Golob T F, Recker W W, Leonard J D. An analysis of the severity and incident duration of truck-involved freeway accidents[J]. Accident Analysis and Prevention, 1987, 19(5): 375–395.
[18] Sethi V, Koppelman F S, Flannery C P. et al. Duration and travel time impacts of incidents[R]. ADVANCE Project Technical Report TRF-ID-202. Evanston, IL: Northwestern University, 1994.
[19] Khattak A, Schofer J, Wang M H. A simple procedure for predicting freeway incident duration[J]. IVHS Journal, 1995, 2(2): 113–138.
[20] Knibbe W J J, Alkim T P, Otten J F W, et al. Automatic estimation of incident duration on Dutch highways[C]. In: Proceedings of IEEE Intelligent Transportation Systems Conference, Toronto, Canada, September 2006, 870–874.
[21] Ozbay K, Noyan N. Estimation of incident clearance times using Bayesian Networks approach[J]. Accident Analysis and Prevention, 2006, 38(3): 542–555.
[22] Valenti G, Lelli M, Cucina D. A comparative study of models for the incident duration prediction[J]. European Transportation Research Review, 2010, 2(2): 103–111.
[23] Alkaabi A, Dissanayake D, Bird R. Analysing clearance time of urban traffic accidents in Abu Dhabi using hazard-based duration modeling method[C]. In: Proceeding of Transportation Research Board Annual Meeting 2011, Paper #11-3319, January 2011.
[24] Lee Y, Wei C. A computerized feature selection method using genetic algorithms to forecast freeway accident duration times[J]. Computer-Aided Civil and Infrastructure Engineering, 2010, 25(2): 132–148.
[25] Li R M, Zhao X Q, Yu X X, et al. Incident duration model on urban freeways based on discrete choice model[C]. International Conference on Electrical and Control Engineering (ICECE), 2010, 3826–3829.
[26] Li R M, Zhao X Q, Yu X X, et al. Incident duration model on urban freeways using three different algorithms of decision tree[C]. International Conference on Intelligent Computation Technology and Automation (ICICTA), 2010, 526–528.
[27] Chung Y, Walubita L F, Choi K. Modeling Accident Duration and Its Mitigation Strategies on South Korean Freeway Systems[J]. Transportation Research Record 2178, Washington, D. C., 2010, 49–57.
[28] Lee J, Chung J, Son B. Incident clearance time analysis for korean freeways using structural equation model[J]. Journal of the Eastern Asia Society for Transportation Studies, 2010, 8(0): 1878–1891.
[29] Zhan C, Gan A, Hadi M. Prediction of lane clearance time of freeway incidents using the m5p tree algorithm[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4): 1549–1557.
[30] Hensher D A, Mannering F L. Hazard-based duration models and their application to transport analysis[J]. Transport Reviews, 1994, 14(1): 63–82.
[31] WashingtonS P, Karlaftis M G, Mannering F L. Statistical and econometric methods for transportation data analysis[M]. 2nd Ed., Chapman and Hall/CRC, Boca Raton, Fl, 2010.
[32] Collett D. Modelling Survival Data in Medical Research[R]. Chapman & Hall/CRC, Boca Raton, FL, 2003.
[33] Jovanis P P, Chang H L. Disaggregate model of highway accident occurrence using survival theory[J]. Accident Analysis and Prevention, 1989, 21(5): 445–458.
[34] Chang H L, Jovanis P P. Formulating accident occurrence as a survival process[J]. Accident Analysis and Prevention, 1990, 22(5): 407–419.
[35] Mannering F L. Male/female driver characteristics and accident risk: Some new evidence[J]. Accident Analysis and Prevention, 1993, 25(1): 77–84.
[36] Mannering F L, Hamed M M. Occurrence, frequency, and duration of commuters’ work-to-home departure delay[J]. Transportation Research Part B, 1990, 24(2): 99–109.
[37] Hamed M M, Mannering F L. Modeling travelers' post-work activity involvement: toward a new methodology[J]. Transportation Science, 1993, 27(4): 381–394.
[38] Bhat C R. A hazard-based duration model of shopping activity with non-parametric baseline specification and non-parametric control for unobserved heterogeneity[J]. Transportation Research Part B, 1996, 30(3): 189–207.
[39] Bhat C R. A generalized multiple durations proportional hazard model with an application to activity behavior during the evening work-to-home commute[J]. Transportation Research Part B, 1996, 30(6): 465–480.
[40] Bhat C R, Frusti T, Zhao H, et al. Intershopping duration: An analysis using multiweek data[J]. Transportation Research Part B, 2004, 38(1): 39–60.
[41] Mannering F L, Winston C. Brand loyalty and the decline of american automobile firms[J]. Brookings Papers on Economic Activity. Microeconomics, 1991, 67–114.
[42] Gilbert C A. Duration model of automobile ownership[J]. Transportation Research Part B: Methodological, 1992, 26(2), 97–114.
[43] De Jong G A. Disaggregate model system of vehicle holding duration, type choice and use[J]. Transportation Research Part B, 1996, 30(4): 263–276.
[44] Yamamoto T, Kitamura R. An analysis of household vehicle holding durations considering intended holding durations[J]. Transportation Research Part A, 2000, 34(5): 339–351.
[45] Stathopoulos A, Karlaftis M G. Modeling duration of urban traffic congestion[J]. Journal of Transportation Engineering, 2002, 128(6): 587–590.
[46] Robinson M D, Nowak P M. An overview of freeway incident management in the United States[R], Michigan Department of Transportation, 1993.
[47] Savolainen P T, Ghosh I. Using detector data to identify and examine crashes and incidents on freeways[R], Final Report, NEXTRANS Project No. 051WY02, 2010, 40 pgs.
[48] Google, (2011). http://maps.google.com. Accessed on March 21, 2011.
[49] Skabardonis A, Petty K F, Bertini R L, et al. I-880 field experiment: analysis of incident data[J]. Transportation Research Record 1603, Washington, D.C., 1997, 72–79.
[50] Skabardonis A, Petty K F, Varaiya P P. Los Angeles I-10 field experiment: incident patterns[J]. Transportation Research Record 1683, 1999, Washington, D.C., 22–30.
[51] Lee J T, Fazio J. Influential factors in freeway crash response and clearance time by emergency management services in peak periods[J]. Traffic Injury Prevention, 2005, 6(4): 331–339.
[52] Ullmanand G L, Ogden M A. Analysis of major freeway incidents in Houston, Texas[J]. Transportation Research Record 1554, Washington, D.C., 1996, 221–227.