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Article

EMPIRICAL STUDY OF ACCEPTED GAP AND LANE CHANGE DURATION WITHIN ARTERIAL TRAFFIC UNDER RECURRENT AND NON-RECURRENT CONGESTION

DOI: 10.7708/ijtte.2012.2(4).02


2 / 4 / 306-322 Pages

Author(s)

Saravanan Gurupackiam - Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, USA -

Steven Lee Jones Jr. - Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, USA -


Abstract

This paper investigated variations in accepted gaps and lane change duration on arterial under recurrent and non-recurrent congestion. Descriptive statistics and best-fit distributions were obtained for the two parameters for both traffic conditions. Hypothesis testing using Mann-Whitney U-Test showed that the means of accepted gaps and lane change durations were statistically different between the two types of traffic’ conditions. The study found that during non-recurrent congestion, drivers on an average accepted smaller gaps but took longer lane change durations. Based on the fact that the data were collected for the same flow-rate (70-90 vehicles/minute) in both traffic conditions and based on the literature, the reason for the above findings could be that, drivers get more frustrated during non-recurring congestion that they accept smaller gaps. Drivers visiting the study location for game day (non-recurrent) exhibit different driver behavioral characteristics when compared to regular commuters (recurrent) which could have also contributed to the statistical differences in the lane changing characteristics of two types of congestion. These findings have direct implications on the lane changing parameters used in microscopic traffic simulation and also help transportation planners and managers to understand driver behavior during recurrent and non-recurrent congestion and better manage the facilities.


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Acknowledgements:

The current study was funded by the University Transportation Center for Alabama. The authors express their appreciation for the support and encouragement of University Transportation Center for Alabama and Tuscaloosa Department of Transportation.


References:

Ahmed, K.I. 1999. Modeling drivers’ acceleration and lane changing behavior. Thesis (PhD). Massachusettžs Institute of Technology. 189 p.

 

Bonneson, J.; Zimmerman, K. 2004. Red-light-running handbook: an engineer’s guide to reducing red-light-related crashes. College Station, TX: Texas Transportation Institute, USA. 80 p.

 

Chen, X., Li, L.; Zhang, Y. 2010. A Markov model for headway/spacing distribution of road traffi’c, IEEE Transactions on Intelligent Transportation Systems. DOI: http://dx.doi.org/10.1109/TITS.2010.2050141, 11(4): 773-785.

 

Coifman, B.; Mishalani, R.; Wang, C.; Krishnamurthy, S. 2006. Impact of lane-change maneuvers on congested freeway segment delays – pilot study, Transportation Research Record. DOI: hžttp://dx.doi.org/10.3141/1965-16, 1965: 152-159.

 

Dorn, L. 2005. Driver behavior and training, volume 2 (human factors in road and rail transport). Aldershot, England: Ashgate. 501 p.

 

Goswami, V.; Bham, H.G. 2007. Gap acceptance behavior in mandatory lane changes under congested and uncongested traffi’c on a multi-lane freeway. In Compendium of Papers of the 86th Annual Transportation Research Board Annual Meeting, Paper # 07-2919.

 

Hadfield, J.C.; Estrela, L. 2004. Red-light-running in southeastern Massachusetts. Southeastern Massachusetts Metropolitan Planning Organization, Taunton, MA, USA. 13 p.

 

Hidas, P. 2002. Modeling lane changing and merging in microscopic traffi’c simulation, Transportation Research Part C. DOI: httžp://dx.doi.org/10.1016/S0968-090X(02)00026-8, 10(5): 351-371.

 

Hidas, P. 2005. Modelling vehicle interactions in microscopic simulation of merging and weaving, Transportation Research Part C. DOI: hžttp://dx.doi.org/10.1016/j.trc.2004.12.003, 13(1): 37-62.

 

Hwang, S.Y.; Park, C.H. 2005. Modeling of the gap acceptance behavior at a merging section of urban freeway. In Proceedings of the Eastern Asia Society for Transportation Studies 5: 1641-1656.

 

ITT„ Industries, Inc., Systems Division. 2005. CORSIM User’s guide, version 6.0.

 

Jones, S.L.; Sullivan, A.; Anderson, M.; Malave, D.; Cheekoti, N. 2004. Traffic simulation software comparison study. University Transportation Center for Alabama, Tuscaloosa, AL, USA. 58 p.

 

Kamyab, A.; McDonald, T.; Stribiak, J.J.; Storm, B. 2000. Red light running in Iowa, the scope, impact, and possible implications. Center for Transportation Research and Education, Ames, IA, USA. 50 p.

 

Kimley-Horn and Associates, Inc. 2010. NDOT Statewide Integrated Transportation Reliability Program, Technical Memorandum 5 – Short and Long Range Implementation Strategies. 59 p.

 

Lebanon County Metropolitan Organization with Gannež Fleming. 2008. Congestion management processes executive summary. 20 p.

 

Lee, G. 2006. Modeling gap acceptance at freeway merges. Thesis (MS). Massachusežtts Institute of Technology, USA. 105 p.

 

Lee, S.E.; Olsen, E.C.B.; Wierwille, W.W. 2003. A comprehensive examination of naturalistic lane changes. National Highway Tra’ffic Safety Administration, Washington D.C., USA. 213 p.

 

Navidi, W.C. 2007. Statistics for engineers and scientists, McGraw-Hill, New York, USA. 675 p.

 

Noyce, D.A.; Fambro, D.B.; Kacir, K.C. 2000. Traffic characteristics of protected/permitted left-turn signal displays, Transportation Research Record. DOI: httžp://dx.doi.org/10.3141/1708-04, 1708: 28-39.

 

Porter, B.E.; Berry, T.D. 2001. A nationwide survey of selfreported red light running: measuring prevalence, predictors, and perceived consequences, Accident Analysis and Prevention. DOI: httžp://dx.doi.org/10.1016/S0001-4575(00)00087-7, 33(6): 735-741.

 

Pulugurtha, S.S.; Pasupuleti, N. 2010. Assessment of link reliability as a function of congestion components, Journal of Transportation Engineering. DOI: httžp://dx.doi.org/10.1061/(ASCE)TE.1943-5436.0000156, 136(10): 903-913.

 

Ramanujam, V.; Choudhury, C.F.; Ben-Akiva, M.E. 2008. An extended lane changing model to capture lane-change duration. In Proceedings of 87th Transportation Research Board Annual Meeting, Paper #08-3041.

 

Raney, E.A.; Mokhtarian, P.L.; Salomon, I. 2000. Modeling individuals’ consideration of strategies to cope with congestion, Transportation Research Part F. DOI: httžp://dx.doi.org/10.1016/S1369-8478(00)00022-X, 3(3): 141-165.

 

Schrank, D.; Lomax, T. 2009. 2009 Urban mobility report. Texas Transportation Institute, College Station, TX, USA. 37 p.

 

Shabaan, S.E. 2005. Right turn split: A new design to alleviate weaving on arterial streets. Thesis (MS). University of Central Florida, USA. 163 p.

 

Stern, E. 1999. Reactions to congestion under time pressure, Transportation Research Part C. DOI: httžp://dx.doi.org/10.1016/S0968-090X(99)00013-3, 7(2-3): 75-90.

 

Thiemann, C.; Treiber, M.; Kesting, A. 2008. Estimating acceleration and lane-changing dynamics based on NGSIM trajectory data. In Proceedings of 87th Transportation Research Board Annual Meeting, Paper #08-2084.

 

Tijerina, L.; Garrož, W.R.; Stoltzfus, D.; Parmer, E. 2005. Eye Glance Behavior of Van and Passenger Car Drivers During Lane Change Decision Phase, Transportation Research Record. DOI: httžp://dx.doi.org/10.3141/1937-06, 1937: 37-43.

 

Toledo, T.; Zohar, D. 2007. Modeling the duration of lane changes, Transportation Research Record. DOI: httžp://dx.doi.org/10.3141/1999-08, 1999: 71-78.

 

Traffi’cware Corporation. 2003. SimTraffic 6, Traffic Simulation Software – User Guide.

 

Transport Research Center, European Conference of Ministers of Transport. 2007. Managing urban traffic congestion, summary document. 296 p.

 

Tsimhoni, O.; Kandt, A.S.; Flannagan, M.J. 2008. Driver perception of potential pedestrian conflict. Transportation Research Institute, The University of Michigan, Ann Arbor, MI, USA. 22 p.