Technical Papers
May 28, 2018

Surrogate Safety Assessment of Work Zone Rear-End Collisions in a Connected Vehicle Environment: Agent-Based Modeling Framework

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 144, Issue 8

Abstract

This paper presents an agent-based modeling and simulation (ABMS) framework to evaluate the safety performance impacts of connected vehicle (CV) technologies in a work zone setting. This research is primarily motivated by the lack of a systematic evaluation platform to verify the safety benefits of CV technology in diverse settings, such as a work zone, caused by either a lane drop, speed reduction, or both. The guiding research question is how and when the safety benefits will start to appear as the market penetration (MP) level of CV increases and how the safety performance is affected with regard to free-flow, medium, and highly congested traffic conditions. Results show that the safety improvements are dependent on MP level and traffic flow rates. The higher the traffic flow rate, the higher MP level is needed to show improvement in the safety performance of the work zone section. In addition, a potential path of Vision Zero is likely to be achieved by an MP level of 100%. These results will inform the federal and state agencies’ decision makers to strategize the implementation of CV technologies and quantify the delicate balance between costs and benefits.

Get full access to this article

View all available purchase options and get full access to this article.

References

Andrews, S., and M. Cops. 2009. Vehicle infrastructure integration proof of concept executive summary—Vehicle. Washington, DC: USDOT.
Bertini, R., H. Wang, M. Wahid, H. M. Abdulsattar, and K. Carstens. 2016. Inventory of connected vehicle applications as part of preparing a possible oregon road map for connected vehicle/cooperative systems deployment scenario. Washington, DC: National Academies of Sciences, Engineering, and Medicine.
Bonabeau, E. 2002. “Agent-based methods and techniques for simulating human systems.” Supplement, Proc. Natl. Acad. Sci. U.S.A. 99 (S1): 7280–7287. https://doi.org/10.1073/pnas.082080899.
Bonsall, P., R. Liu, and W. Young. 2005. “Modelling safety-related driving behavior: Impact of parameter values.” Transp. Res. Part A: Policy Pract. 39 (5): 425–444. https://doi.org/10.1016/j.tra.2005.02.002.
Brilon, W., J. Geistefeldt, and M. Regler. 2005. “Reliability of freeway traffic flow: A stochastic concept of capacity.” In Proc., 16th Int. Symp. on Transportation and Traffic Theory, 125–144. College Park, MD.
Bu, F., H.-S. Tan, and J. Huang. 2010. “Design and field testing of a cooperative adaptive cruise control system.” In Proc., 2010 American Control Conf., 4616–4621. Baltimore, MD.
Dia, H. 2002. “An agent-based approach to modelling driver route choice behaviour under the influence of real-time information.” Transp. Res. Part C: Emerging Technol. 10 (5–6): 331–349. https://doi.org/10.1016/S0968-090X(02)00025-6.
FHWA (Federal Highway Administration). 2009. Guidelines for temporary traffic control in work zones. Washington, DC: FHWA.
Gazis, D. C., R. Herman, and R. W. Rothery. 1961. “Nonlinear follow-the-leader models of traffic flow.” Oper. Res. 9 (4): 545–567. https://doi.org/10.1287/opre.9.4.545.
Genders, W., and S. Razavi. 2015. “Impact of connected vehicle on work zone network safety through dynamic route guidance.” J. Comput. Civ. Eng. 30 (2): 4015020. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000490.
Gettman, D., and L. Head. 2003. “Surrogate safety measures from traffic simulation models.” Transp. Res. Rec. 1840: 104–115. https://doi.org/10.3141/1840-12.
Grimm, V. 2002. “Visual debugging: A way of analyzing, understanding and communication bottom-up simulation models in ecology.” Nat. Resour. Model. 15 (1): 23–38. https://doi.org/10.1111/j.1939-7445.2002.tb00078.x.
Hayward, J. C. 1972. Near miss determination through use of a scale of danger. 24–34. State College, PA: Pennsylvania State Univ.
Hirst, S., and R. Graham. 1997. “The format and presentation of collision warnings.” In Ergonomics and safety of intelligent driver interfaces, edited by N. Y. Ian, 203–219. Mahwah, NJ: Lawrence Erlbaum Associates.
Hogema, J. H., and W. H. Janssen. 1996. “Effect of intelligent cruise control on driving behavior: A simulator study.” In Proc., Intelligent Transportation: Realizing the Future. Abstracts of the Third World Congress on Intelligent Transport Systems. Washington, DC: Transportation Research Board.
Hyden, C. 1996. “Traffic conflicts technique: State of the art.” In Vol. 37 of Traffic safety work with video processing, edited by H. H. Topp, 3–14. Germany: Transportation Dept., Univ. Kaiserslautern.
Jones, S., and B. Philips. 2013. “Cooperative adaptive cruise control: Critical human factors issues and research questions.” In Proc., 7th Int. Driving Symp. on Human Factors in Driver Assessment, Training, and Vehicle Design CRI, 121–127. Washington, DC: Federal Highway Administration.
Kattan, L., M. Moussavi, and B. Far. 2010. “Evaluating the potential benefits of vehicle to vehicle communication (V2V) under incident conditions in the PARAMICS model.” In Proc., 13th Int. IEEE Conf. on Intelligent Transportation, 9. New York: IEEE.
Lertworawanich, P. 2006. “Safe-following distances based on the car-following model.” In PIARC Int. Seminar on Intelligant Transport SYstem (ITS) In Road Network Operations. Paris, France: World Road Association-PIARC.
Lin, P.-W., K.-P. Kang, and G.-L. Chang. 2004. “Exploring the effectiveness of variable speed limit controls on highway work-zone operations.” J. Intell. Transp. Syst. 8 (3): 155–168. https://doi.org/10.1080/15472450490492851.
Maitipe, B., and M. I. Hayee. 2011. Development and field demonstration of DSRC-based traffic information system for the work zone. Minneapolis, MN: Univ. of Minnesota.
Mannering, F. L., and S. S. Washburn. 2007. Principles of highway engineering and traffic analysis. Hoboken, NJ: Wiley.
Minderhoud, M. M., and P. H. L. Bovy. 2001. “Extended time-to-collision measures for road traffic safety assessment.” Accid. Anal. Prev. 33 (1): 89–97. https://doi.org/10.1016/S0001-4575(00)00019-1.
Naus, G. J. L., R. P. A. Vugts, J. Ploeg, M. J. G. De Molengraft, and M. Steinbuch. 2010. “String-stable CACC design and experimental validation.” IEEE Trans. Veh. Technol. 59 (9): 4268–4279. https://doi.org/10.1109/TVT.2010.2076320.
NHTSA (National Highway Traffic Safety Administration). 2014. Traffic safety facts 2014: A compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system. Washington, DC: NHTSA.
Olia, A., W. Genders, and S. N. Razavi. 2013. “Microsimulation based impact assessment of the vehicle to vehicle (V2V) system for work zone safety.” In Proc., CSCE 2013 General Conf. Montreal, QC: Canadian Society of Civil Engineers.
Ozbay, K., H. Yang, B. Bartin, and S. Mudigonda. 2008. “Derivation and validation of a new simulation-based surrogate safety measure.” Transp. Res. Rec. 2083: 105–113. https://doi.org/10.3141/2083-12.
Paikari, E., L. Kattan, S. Tahmasseby, and B. H. Far. 2013. “Modeling and simulation of advisory speed and re-routing strategies in connected vehicles systems for crash risk and travel time reduction.” In Proc., 26th IEEE Canadian Conf. on Electrical and Computer Engineering, 1–4. New York: IEEE.
Roodell, B., and M. I. Hayee. 2010. Development of a low-cost interface between cell phone and DSRC-based vehicle unit for efficient use of intellidrive infrastrcuture. Duluth, MN: Dept. of Electrical and Computer Engineering, Univ. of Minnesota.
Sanchez, S. M., and T. Lucas. 2002. “Exploring the world of agent-based simulations: Simple models, complex analyses.” In Proc., 2002 Winter Simulation Conf., 116–126. Quantico, VA: Marine Corps Combat Development Command.
Talebpour, A., and H. S. Mahmassani. 2014. “Modeling acceleration behavior in a connected environment.” In Proc., Symp. on Celebrating 50 Year of Traffic Flow Theory. Portland, OR.
Talebpour, A., H. S. Mahmassani, and S. H. Hamdar. 2015. “Modeling lane-changing behavior in a connected environment: A game theory approach.” Transp. Res. Procedia 7 (59): 420–440. https://doi.org/10.1016/j.trpro.2015.06.022.
Thiele, J. C. 2014. “R Marries NetLogo: Introduction to the RNetLogo Package.” J. Stat. Software 58 (2): 1–41. https://doi.org/10.18637/jss.v058.i02.
Toledo, T., C. Choudhury, and M. Ben-Akiva. 2005. “Lane-changing model with explicit target lane choice.” Transp. Res. Rec. 1934: 157–165. https://doi.org/10.3141/1934-17.
Ullman, G. L., M. D. Finley, J. E. Bryden, R. Srinivasan, and F. M. Council. 2008. Traffic safety evaluation of nighttime and daytime work zones. Washington, DC: Transportation Research Board.
Van Arem, B., C. J. Van Driel, and R. Visser. 2006. “The impact of cooperative adaptive cruise control on traffic flow characterisics.” IEEE Trans. Intell. Transp. Syst. 7 (4): 429–436. https://doi.org/10.1109/TITS.2006.884615.
Van der Horst, R. 1991. “Time-to-collision as a cue for decision-making in braking.” In Vision in vehicles III, edited by A. G. Gale, I. D. Brown, C. M. Haslegrave, I. Moorhead, and S. Taylor, 19–26. Amsterdam, Netherlands: Elsevier.
Vogel, K. 2003. “A comparison of headway and time to collision as safety indicators.” Accid. Anal. Prev. 35 (3): 427–433. https://doi.org/10.1016/S0001-4575(02)00022-2.
Wang, H., K. Rudy, J. Li, and D. Ni. 2010. “Calculation of traffic flow breakdown probability to optimize link throughput.” Appl. Math. Modell. 34 (11): 3376–3389. https://doi.org/10.1016/j.apm.2010.02.027.
Wilensky, U. 1999. Netlogo 5.2.1. Evanston, IL: Northwestern Univ.
Xiang, X., R. Kennedy, and G. Madey. 2005. “Verification and validation of agent-based scientific simulation models.” In Proc., Agent-Directed Simulation Conf., 47–55. Richmond, CA: California PATH.
Xu, Q., K. Hedrick, R. Sengupta, and J. Vanderwerf. 2002. “Effects of vehicle-vehicle/ roadside-vehicle communication on adaptive cruise controlled highway systems.” In Vol. 2 of Proc., 56th Vehicular Technology Conf., 1249–1253. New York: IEEE.
Zeng, X., K. Balke, and P. Songchitruska. 2012. Potential connected vehicle applications to enhance mobilit, safety, and environmental security. Alexandria, VA: National Technical Information Service.

Information & Authors

Information

Published In

Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 144Issue 8August 2018

History

Received: Mar 23, 2017
Accepted: Feb 16, 2018
Published online: May 28, 2018
Published in print: Aug 1, 2018
Discussion open until: Oct 28, 2018

Permissions

Request permissions for this article.

Authors

Affiliations

Graduate Research Assistant, School of Civil and Construction Engineering, Oregon State Univ., 101 Kearny Hall, Corvallis, OR 97331. ORCID: https://orcid.org/0000-0001-6369-3869. Email: [email protected]
Alireza Mostafizi [email protected]
Graduate Research Assistant, School of Civil and Construction Engineering, Oregon State Univ., 101 Kearny Hall, Corvallis, OR 97331. Email: [email protected]
Haizhong Wang, M.ASCE [email protected]
Assistant Professor, School of Civil and Construction Engineering, Oregon State Univ., 101 Kearny Hall, Corvallis, OR 97331 (corresponding author). Email: [email protected]

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.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share