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Safety and Delays at Level Crossings in the United States: Addressing the Need for Multi-Objective Resource Allocation

Part of the Lecture Notes in Mobility book series (LNMOB)

Abstract

The level crossings in the United States experience a significant number of accidents every year. The accidents can be reduced with the application of various countermeasures (e.g., traffic signal preemption, flashing lights, barrier cubs, gates). However, the application of countermeasures for all the level crossings in the United States is not feasible due to monetary limitations. Moreover, each countermeasure has a unique level of effectiveness and installation cost (e.g., the most effective countermeasures are typically more expensive than the least effective ones). Hence, selection of potent and cost-effective countermeasures at the riskiest level crossings is imperative to improve safety. While improving safety at level crossings with the application of countermeasures, there is a significant risk of waning highway vehicle flows, increasing delays, and negatively affecting the continuity of passenger and freight flows. In such a scenario, multi-objective resource allocation models could be instrumental, since such models can analyze the tradeoffs between conflicting objectives (e.g., minimizing the number of accidents vs. minimizing the total delay). Hence, this chapter presents a framework for multi-objective resource allocation to minimize the number of accidents and to minimize the total delay at level crossings. Furthermore, various methods for quantifying the number of accidents as well as delays due to the application of countermeasures at level crossings are reviewed. Solution methods for multi-objective resource allocation models, including exact and approximate optimization approaches, are also discussed. Finally, future research avenues for multi-objective resource allocation among level crossings are outlined.

Keywords

  • Level crossings
  • Accident prediction
  • Accident prevention
  • Delays
  • Traffic queuing
  • Resource allocation
  • Multi-objective optimization

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References

  • Abioye OF, Dulebenets MA, Pasha J, Kavoosi M, Moses R, Sobanjo J, Ozguven EE (2020) Accident and hazard prediction models for highway-rail grade crossings: a state-of-the-practice review for the USA. Railway Eng Sci 28(3):251–274

    Google Scholar 

  • AREMA (2004) Highway-rail grade crossing warning systems. In: Communications and Signal Manual, Section 3. Landover, MD

    Google Scholar 

  • Battiti R, Tecchiolli G (1992) Parallel biased search for combinatorial optimization: genetic Algorithms and Tabu Search. Microprocess Microsyst 16(7):351–367

    Google Scholar 

  • Beanland V, Grant E, Read GJ, Stevens N, Thomas M, Lenné MG, Stanton NA, Salmon PM (2018) Challenging conventional rural rail level crossing design: evaluating three new systems thinking-Based designs in a driving simulator. Saf Sci 110:100–114

    Google Scholar 

  • Beanland V, Salmon PM, Filtness AJ, Lenné MG, Stanton NA (2017) To stop or not to stop: contrasting compliant and non-Compliant driver behaviour at rural rail level crossings. Accid Anal Prev 108:209–219

    Google Scholar 

  • Berg V (1986) Critique of rail-highway grade crossing effectiveness ratios and resource allocation procedures. Transp Res Rec 1069:88–100

    Google Scholar 

  • Borsos A, Gabor M, Koren C (2016) Safety ranking of railway crossings in Hungary. Transp Res Procedia 14:2111–2120

    Google Scholar 

  • BTS (2020a) Seasonally adjusted transportation data. In: Variable: rail freight intermodal. https://www.transtats.bts.gov/osea/seasonaladjustment/?PageVar=RAIL_FRT_INTERMODAL. Accessed 13 July 2020

  • BTS (2020b) Seasonally adjusted transportation data. In: Variable: rail passenger miles. https://www.transtats.bts.gov/osea/seasonaladjustment/?PageVar=RAIL_PM. Accessed 13 July 2020

  • Cameron AC, Trivedi PK (2013) Regression analysis of count data, 2nd edn. Econometric Society Monographs. Cambridge University Press, Cambridge, UK

    MATH  Google Scholar 

  • Chadwick S, Zhou N, Saat MR (2014) Highway-rail grade crossing safety challenges for shared operations of high-speed passenger and heavy freight rail in the U.S. Saf Sci 68:128–137

    Google Scholar 

  • Cirovic G, Pamucar D (2013) Decision support model for prioritizing railway level crossings for safety improvements: application of the adaptive neuro-fuzzy system. Expert Syst Appl 40:2208–2223

    Google Scholar 

  • Dammeyer F, Voß S (1993) Dynamic tabu list management using the reverse elimination method. Ann Oper Res 41:29–46

    MATH  Google Scholar 

  • Dent M, Marinov M (2019) Introducing automated obstacle detection to British level crossings. Sustainable rail transport. Springer, Cham, pp 37–80

    Google Scholar 

  • Djordjević B, Krmac E, Mlinarić TJ (2018) Non-radial DEA model: a new approach to evaluation of safety at railway level crossings. Saf Sci 103:234–246

    Google Scholar 

  • Drexl A (1988) A Simulated Annealing approach to the multiconstraint zero-one knapsack problem. Computing 40(1):1–8

    MathSciNet  MATH  Google Scholar 

  • Dueck G, Scheuer T (1990) Threshold accepting: a general purpose optimization algorithm appearing superior to ssimulated annealing. J Comput Phys 90(1):161–175

    MathSciNet  MATH  Google Scholar 

  • Dulebenets MA (2018) A comprehensive multi-objective optimization model for the vessel scheduling problem in liner shipping. Int J Prod Econ 196:293–318

    Google Scholar 

  • Dulebenets MA, Moses R, Sobanjo J, Ozguven EE, Abioye OF, Kavoosi M, Pasha J, (2020a) Development of the Optimization Model for Improving Safety at Rail Crossings in Florida. A Technical Report Prepared for the Florida Department of Transportation. Florida Department of Transportation. Tallahassee, Florida, USA

    Google Scholar 

  • Dulebenets MA, Pasha J, Kavoosi M, Abioye OF, Ozguven EE, Moses R, Boot WR, Sando T (2020) Multiobjective optimization model for emergency evacuation planning in geographical locations with vulnerable population groups. J Manag Eng 36(2):04019043

    Google Scholar 

  • Eiben AE, Smith JE (2015) Introduction to Evolutionary Computing, 2nd edn. Springer-Verlag, Berlin Heidelberg, Berlin, Germany

    MATH  Google Scholar 

  • Elzohairy Y, Benekohal R (2000) Evaluation of Expected Accident Frequency Formulas for Rail-Highway Highway-Rail Grade Crossings. Urbana, Illinois, USA. http://www.idot.illinois.gov/Assets/uploads/files/Transportation-System/Research/Illinois-Transportation-Research-Center/2000.09.01%20-%20Evaluation%20of%20Expected%20Accident%20Frequency%20Formulas%20for%20Rail-Highway%20Crossings%20-%20VC-HR1%20FY98.pdf. Accessed 24 June 2020

  • Evans AW (2011) Fatal accidents at railway level crossings in Great Britain 1946–2009. Accid Anal Prev 43:1837–1845

    Google Scholar 

  • Faghri A, Demetsky M (1986) Evaluation of Methods for Predicting Rail-Highway Highway-Rail Grade Crossing Hazards. Charlottesville, Virginia, USA. http://www.virginiadot.org/vtrc/main/online_reports/pdf/86-r32.pdf. Accessed 24 June 2020

  • Farr EH (1981) Rail-Highway Crossing Resource Allocation Model. U.S. Department of Transportation. Research and Special Programs Administration. Transportation Systems Center. Cambridge Massachusetts, USA. https://rosap.ntl.bts.gov/view/dot/11160. Accessed 24 June 2020

  • FHWA (2002) Status of the Nation's Highways, Bridges, and Transit: 2002 Conditions and Performance Report. Chapter 26: Highway-Rail Grade Crossings. https://www.fhwa.dot.gov/policy/2002cpr/ch26.cfm. Accessed 24 June 2020

  • FHWA (2019) Status of the Nation's Highways, Bridges, and Transit: Conditions and Performance Report, 23rd edn. https://www.fhwa.dot.gov/policy/23cpr/pdfs/23cpr.pdf. Accessed 24 June 2020

  • Forgionne GA (2002) Selecting rail grade crossing investments with a decision support system. Inf Sci 144:75–90

    MATH  Google Scholar 

  • FRA (2020) Accident/Incident Data. https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx. Accessed 13 July 2020s

  • Freeman J, Rakotonirainy A (2015) Mistakes or deliberate violations? A study into the origins of rule breaking at pedestrian train crossings. Accid Anal Prev 77:45–50

    Google Scholar 

  • Greene WH (2018) Econometric analysis, 8th edn. Pearson Education Inc., Upper Saddle River, New Jersey, USA

    Google Scholar 

  • Hans Z, Albrecht C, Johnson P, Nlenanya I (2015) Development of Railroad Highway Grade Highway-Rail Grade Crossing Consolidation Rating Formula. A Technical Report Prepared for the Iowa Department of Transportation. Iowa Department of Transportation. Ames, Iowa, USA. https://lib.dr.iastate.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=1061&context=intrans_techtransfer. Accessed 24 June 2020

    Google Scholar 

  • Hu SR, Li CS, Lee CK (2010) Investigation of key factors for accident severity at railroad grade crossings by using a logit model. Saf Sci 48:186–194

    Google Scholar 

  • ITE (2006) Preemption of Traffic Signals Near Railroad Crossings: An ITE Recommended Practice. Institute of Transportation Engineers. https://www.ite.org/pub/?id=e1dca8bc%2D2354%2Dd714%2D51cd%2Dbd0091e7d820. Accessed 24 June 2020

  • Kavoosi M, Dulebenets MA, Abioye OF, Pasha J, Wang H, Chi H (2019) An augmented self-adaptive parameter control in evolutionary computation: a case study for the berth scheduling problem. Adv Eng Inf 42:100972

    Google Scholar 

  • Kavoosi M, Dulebenets MA, Pasha J, Abioye OF, Moses R, Sobanjo J, Ozguven EE (2020) Development of algorithms for effective resource allocation among highway-rail grade crossings: a case study for the State of Florida. Energies 13(6):1419

    Google Scholar 

  • Kavoosi M, Dulebenets MA, Abioye O, Pasha J, Theophilus O, Wang H, Kampmann R, Mikijeljević M (2020) Berth scheduling at marine container terminals: a universal island-based metaheuristic approach. Marit Bus Rev 5(1):30–66

    Google Scholar 

  • Keramati A, Lu P, Tolliver D, Wang X (2020) Geometric effect analysis of highway-rail grade crossing safety performance. Accid Anal Prev 138:105470

    Google Scholar 

  • Khan IU, Lee E, Khan MA (2018) Developing a highway rail grade crossing accident probability prediction model: a North Dakota case study. Safety 4:22

    Google Scholar 

  • Khattak A, Lee M (2018) Highway-Rail Crossing Safety Improvements by Diverting Motorist to Alternate Routes. https://www.utrgv.edu/railwaysafety/_files/documents/research/operations/utcrs_khattak_highway-rail-crossing-safety-improvement_final-report.pdf. Accessed 24 June 2020

  • Khattak A (2014) Investigation of train warning times and gate violations. Transp Res Rec 2458:104–109

    Google Scholar 

  • Khuri S, Bäck T, Heitkötter J (1994) The zero/one multiple knapsack problem and Genetic Algorithms. In: Proceedings of the 1994 ACM symposium on applied computing. Phoenix, Arizona, USA: ACM, pp. 188–193. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.2713&rep=rep1&type=pdf. Accessed 24 June 2020

  • Konur D, Golias MM, Darks B (2013) A mathematical modeling approach to resource allocation for railroad-highway crossing safety upgrades. Accid Anal Prev 51:192–201

    Google Scholar 

  • Laapotti S (2016) Comparison of fatal motor vehicle accidents at passive and active railway level crossings in Finland. IATSS Research 40:1–6

    Google Scholar 

  • Landry S, Jeon M, Lautala P, Nelson D (2019) Design and assessment of in-vehicle auditory alerts for highway-rail grade crossings. Transport Res f: Traffic Psychol Behav 62:228–245

    Google Scholar 

  • Larue GS, Wullems C (2015) Human factors evaluation of a novel Australian approach for activating railway level crossings. Procedia Manuf 3:3293–3300

    Google Scholar 

  • Larue GS, Naweed A, Rodwell D (2018) The road user, the pedestrian, and me: investigating the interactions, errors and escalating risks of users of fully protected level crossings. Saf Sci 110:80–88

    Google Scholar 

  • Lenné MG, Rudin-Brown CM, Navarro J, Edquist J, Trotter M, Tomasevic N (2011) Driver behaviour at rail level crossings: responses to flashing lights, traffic signals and stop signs in simulated rural driving. Appl Ergon 42:548–554

    Google Scholar 

  • Lord D, Washington SP, Ivan JN (2005) Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. Accid Anal Prev 37(1):35–46

    Google Scholar 

  • Lu P, Tolliver D (2016) Accident prediction model for public highway-rail grade crossings. Accid Anal Prev 90:73–81

    Google Scholar 

  • Oh J, Washington SP, Nam D (2006) Accident prediction model for railway-highway interfaces. Accid Anal Prev 38(2):346–356

    Google Scholar 

  • Ohlsson M, Peterson C, Söderberg B (1993) Neural networks for optimization problems with inequality constraints: the knapsack problem. Neural Comput 5(2):331–339

    Google Scholar 

  • Pasha J, Dulebenets MA, Abioye OF, Kavoosi M, Moses R, Sobanjo J, Ozguven EE (2020) A comprehensive assessment of the existing accident and hazard prediction models for the highway-rail grade crossings in the State of Florida. Sustainability 12(10):4291

    Google Scholar 

  • Rezvani AZ, Peach M, Thomas A, Cruz R, Kemmsies W (2015) Benefit-cost methodology for highway-railway grade crossing safety protocols as applied to transportation infrastructure project prioritization processes. Transp Res Procedia 8:89–102

    Google Scholar 

  • Rilett L, Appiah J (2008) Microsimulation Analysis of Highway-Rail Grade Crossings: A Case Study in Lincoln, Nebraska. In: IEEE/ASME/ASCE Joint Rail Conference, Wilmington, Delaware, USA, USA. DOI: https://doi.org/10.1115/JRC2008-63063

  • Rudin-Brown CM, Lenné MG, Edquist J, Navarro J (2012) Effectiveness of traffic light vs. boom barrier controls at road–rail level crossings: a simulator study. Accid Anal Prev 45:187–194

    Google Scholar 

  • Russo BJ, James E, Erdmann T, Smaglik EJ (2020) Pedestrian and bicyclist behavior at highway-rail grade crossings: an observational study of factors associated with violations, distraction, and crossing speeds during train crossing events. J Transp Saf Security 1–19. https://doi.org/10.1080/19439962.2020.1726545

  • Shmueli G, Minka TP, Kadane JB, Borle S, Boatwright P (2005) A useful distribution for fitting discrete data: revival of the Conway-Maxwell-Poisson distribution. Appl Stat 54(1):127–142

    MathSciNet  MATH  Google Scholar 

  • Sperry B, Naik B, Warner J (2017) Evaluation of grade crossing hazard ranking models. In: Ohio Transportation Engineering Conference, 2017. http://www.dot.state.oh.us/engineering/OTEC/2017Presentations/75/Sperry_75.pdf. Accessed 24 June 2020

  • Stefanova T, Oviedo-Trespalacios O, Freeman J, Wullems C, Rakotonirainy A, Burkhardt J, Delhomme P (2018) Contextual factors explaining risk-taking intentions at Australian level crossings. Saf Sci 110(2):145–161

    Google Scholar 

  • Tey L, Wallis G, Cloete S, Ferreira L (2013) Modelling driver behaviour towards innovative warning devices at railway level crossings. Accid Anal Prev 51:104–111

    Google Scholar 

  • US DOT (2019) Highway-Rail Crossing Handbook, 3rd Edn. https://safety.fhwa.dot.gov/hsip/xings/com_roaduser/fhwasa18040/fhwasa18040v2.pdf. Accessed 24 June 2020

  • Van Leeuwen J (1990) Algorithms and complexity. In: Handbook of theoretical computer science, 1st edn. Elsevier. Netherlands

    Google Scholar 

  • Washington S, Karlaftis MG, Mannering F, Anastasopoulos P (2020) Statistical and econometric methods for transportation data analysis, 3rd edn. Chapman Hall/CRC, Boca Raton, Florida

    MATH  Google Scholar 

  • Weissmann AJ, Weissmann J, Kunisetty JL, Warner J, Park E, Sunkari S, Protopapas A, Venglar S (2013) Integrated Prioritization Method for Active and Passive Highway-Rail Crossings. A Technical Report Prepared for the Texas Department of Transportation. Texas Department of Transportation. Austin, Texas, USA

    Google Scholar 

  • Yan X, Richards S, Su X (2010) Using hierarchical tree-based regression model to predict train–vehicle crashes at passive highway-rail grade crossings. Accid Anal Prev 42:64–74

    Google Scholar 

  • Zhang Z, Trivedi C, Liu X (2018) Automated detection of grade-crossing-trespassing near misses based on computer vision analysis of surveillance video data. Saf Sci 110:276–285

    Google Scholar 

  • Zhou X, Lu P, Zheng Z, Tolliver D, Keramati A (2020) Accident prediction accuracy assessment for highway-rail grade crossings using random forest algorithm compared with decision tree. Reliab Eng Syst Saf 200:106931

    Google Scholar 

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Acknowledgements

This study was supported by the Florida Department of Transportation (grant number BDV30-977-26). The opinions, findings, and conclusions expressed in this publication are those of the authors and not necessarily those of the Florida Department of Transportation or the U.S. Department of Transportation. The authors would like to thank Mr. Rickey Fitzgerald, Freight and Multimodal Operations Office Manager, for his involvement and valuable feedback throughout this study.

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Pasha, J., Dulebenets, M.A., Singh, P., Moses, R., Sobanjo, J., Ozguven, E.E. (2022). Safety and Delays at Level Crossings in the United States: Addressing the Need for Multi-Objective Resource Allocation. In: Marinov, M., Piip, J. (eds) Sustainable Rail Transport 4. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-030-82095-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-82095-4_4

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