Incident-Induced Diversion Behavior: Existence, Magnitude, and Contributing Factors
Publication: Journal of Transportation Engineering
Volume 138, Issue 10
Abstract
The need to understand the effects of diverting traffic is emphasized by growing congestion and delays. This paper examines incident-induced diversion behavior by using loop-detector data and incident records on a freeway in Virginia. This work diverges from previous studies by (1) addressing both existence of diversion and its magnitude, (2) relying on field data rather than surveys, and (3) statistically relating diversion behavior and magnitude to quantifiable incident characteristics and traffic conditions. A dynamic programming-based procedure is used to identify diversions by isolating transient level shifts, and the diversions are associated with incident and traffic characteristics and variable message sign (VMS) displays through a binary logit model. The magnitude of the diversion is statistically related to traffic conditions via a linear regression model. The models indicate that the probability of triggering a diversion increases when an incident lasts longer, more general-purpose lanes are blocked, and speeds are lower. The results on the effects of trip purpose/time and information availability are consistent with previous studies. The magnitude of the diversion, measured by diversion rate, is related to instant traffic flow characteristics, general traffic demand considerations, and the incident characteristics.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors thank MAUTC for funding the larger project of which this study is a part. The authors remain solely responsible for the material contained in this manuscript.
References
Abdel-Aty, M., and Abdalla, M. F. (2004). “Modeling drivers’ diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function.” Transp., 31(3), 327–348.
Abdel-Aty, M., and Abdalla, M. F. (2006). “Examination of multiple mode/route-choice paradigms under ATIS.” IEEE Trans. ITS, 7(3), 332–348.
Abdel-Aty, M., Kitamura, R., and Jovanis, P. (1997). “Using stated preference data for studying the effect of advanced traffic information on drivers’ route choice.” Transp. Res. C: Emerging Technol., 5(1), 39–50.
Al-Deek, H., Venkata, S., and Flick, J. (2009). “Impact of dynamic message signs on satisfaction and diversion of toll road travelers.” Transp. Res. Rec., 2099, 1–13.
Bai, J., and Perron, P. (1998). “Estimating and testing linear models with multiple structural changes.” Econometrica, 66(1), 47–78.
Balke, N. S. (1993). “Detecting level shifts in time series.” J. Bus. Econ. Stat., 11(1), 81–92.
Benson, B. G. (1996). “Motorist attitudes about content of variable-message signs.” Transp. Res. Rec., 1550, 48–57.
Bonsall, P. W., and Merrall, A. (1996). “Analysing and modelling the influence of roadside variable message displays on drivers’ route choice.” Proc., 7th World Conf. Transportation Research, Sydney, Australia, Pergamon Press, Oxford, UK, 11–26
Bradley, M. (1988). “Realism and adaption in designing hypothetical travel choice concepts.” J. Transp. Econ. Policy, 22(1), 121–137.
Calit2. (2010). “California traffic report.” 〈http://traffic.calit2.net/〉 (Jun. 3, 2011).
Cameron, C., and Trivedi, P. (2009). Microeconometrics using stata, Stata Press, College Station, TX.
Caplice, C., and Mahmassani, H. S. (1992). “Aspects of commuting behavior: preferred arrival time, use of information and switching propensity.” Transp. Res. A, 26, 409–418.
Chen, P. S.-T., and Mahmassani, H. S. (1999). Dynamics of urban commuter behavior under real-time traffic information, Univ. of Texas, Austin, TX.
Choocharukul, K. (2008). “Effects of attitudes and socioeconomic and travel characteristics on stated route diversion: structural equation modeling approach of road users in Bangkok, Thailand.” Transp. Res. Rec., 2048(–1), 35–42.
Federal Highway Administration (FHWA). (2005). Traffic congestion and reliability: Trends and advanced strategies for congestion mitigation, FHWA, Washington DC.
Foo, S., Abdulhai, B., and Hall, F. L. (2008). “Impacts on traffic diversion rates of changed message on changeable message sign.” Transp. Res. Rec., 2047, 11–18.
Hato, E., Taniguchi, M., Sugie, Y., Kuwahara, M., and Morita, H. (1999). “Incorporating an information acquisition process into a route choice model with multiple information sources.” Transp. Res. C: Emerging Technol., 7(2–3), 109–129.
Hosmer, D. W. (2000). Applied logistic regression, Wiley, Hoboken, NJ.
Huo, H., and David, L. (2006). Effectiveness of VMS using empirical loop detector data, Univ. of California, Berkeley, Berkeley, CA.
INRIX. (2011). “INRIX traffic.” 〈http://www.inrixtraffic.com/〉 (Jun. 3, 2011).
Kattan, L., Habib, K., Nadeem, S., and Islam, T. (2010). “Modeling travelers’ responses to incident information provided by variable message signs in Calgary, Canada.” Transp. Res. Rec., 2185, 71–80.
Khattak, A., Koppelman, F., and Schofer, J. (1993). “Stated preferences for investigating commuters’ diversion propensity.” Transp., 20, 107–127.
Khattak, A., Polydoropoulou, A., and Ben-Akiva, M. (1996). “Modeling revealed and stated pretrip travel response to advanced traveler information systems.” Transp. Res. Rec., 1537(1), 46–54.
Mahmassani, H., Chen, P. S., and Srinivasan, K. (1998). Effect of information quality on the response of commuters to real-time traffic information: Laboratory experiment # 5 results, WESTAT Corporation, Silver Springs, MD.
Mahmassani, H. S., and Liu, Y.-H. (1999). “Dynamics of commuting decision behaviour under advanced traveller information systems.” Transp. Res. C: Emerging Technol., 7(2–3), 91–107.
Peeta, S., and Ramos, J. (2006). “Driver response to variable message signs-based traffic information.” IEEE Proc. Intell. Transp. Sys., 153(1), 2–10.
Peeta, S., Ramos, J., and Pasupathy, R. (2000). “Content of variable message signs and on-line driver behavior.” Transp. Res. Rec., 1725, 102–108.
Peeta, S., and Yu, J. (2004). “Adaptability of a hybrid route choice model to incorporating driver behavior dynamics under information provision.” IEEE Trans. Syst. Man Cybern. A, 34(2), 243–256.
Peeta, S., and Yu, J. (2005). “A hybrid model for driver route choice incorporating en-route attributes and real-time information effects.” Network and Spat. Econ., 5(1), 21–40.
Polydoropoulou, A., Ben-Akiva, M., and Kaysi, I. (1994). “Influence of traffic information on drivers route choice behavior.” Transp. Res. Rec., 1453, 56–65.
Polydoropoulou, A., Ben-Akiva, M., Khattak, A., and Lauprete, G. (1996). “Modeling revealed and stated en-route travel response to advanced traveler information systems.” Transp. Res. Rec., 1537, 38–45.
Royston, P., and Altman, D. (1994). “Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling.” J. Roy. Stat. Soc. C, 43(3), 429–467.
Sauerbrei, W., Meier-Hirmer, C., Benner, A., and Royston, P. (2006). “Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs.” Comput. Stat. Data Anal., 50(12), 3464–3485.
Schroeder, J., and Demetsky, M. (2011). “Data analysis of diversion and dynamic message signs.” J. Transp. Eng., 137(9), 608–614.
Srinivasan, K., and Mahmassani, H. (2002). Dynamic decision and adjustment processes in commuter behavior under real-time information, Texas Transportation Institute, College Station, TX.
Tsay, R. (1988). “Outliers, level shifts, and variance changes in time series.” J. Forecast., 7, 1–20.
Tsirimpa, A., Polydoropoulou, A., and Antoniou, C. (2007). “Development of a mixed multi-nomial logit model to capture the impact of information systems on travelers’ switching behavior.” J. Intell. Transp. Syst.: Technol. Plann. Oper., 11(2), 79–89.
Van Vuren, T., and Leonard, D. (1994a). “Urban congestion caused by incidents.” Traffic Eng. Control, 35, 422–429.
Van Vuren, T., and Leonard, D. (1994b). “Advanced and conventional incident management in congested urban networks.” Proc., 4th Int. Conf. Advanced Technologies in Transportation and Traffic Management, Centre for Transportation Studies, Nanyang Technological Univ., Singapore, 269–276.
Wardman, M. (1988). “A comparison of revealed preference and stated preference models of travel behavior.” J. Transp. Econ. Policy, 22(1), 71–91.
Yim, Y., and Ygnace, J.-L. (1996). “Link flow evaluation using loop detector data: traveler response to variable-message signs.” Transp. Res. Rec., 1550, 58–64.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 138 • Issue 10 • October 2012
Pages: 1239 - 1249
Copyright
© 2012 American Society of Civil Engineers.
History
Received: Jul 27, 2011
Accepted: Mar 30, 2012
Published online: Apr 2, 2012
Published in print: Oct 1, 2012
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.