Impacts of experimental advisory exit speed sign on traffic speeds for freeway exit ramp

English version

Autoři: Yongfeng Ma ^aff001; Wenbo Zhang ^aff001; Xin Gu ^aff001; Jiguang Zhao ^aff004
Působiště autorů: School of Transportation, Southeast University, Nanjing, Jiangsu, China ^aff001; Jiangsu Key Laboratory of Urban ITS, Nanjing, Jiangsu, China ^aff002; Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing, Jiangsu, China ^aff003; HNTB Corporation, Tallahassee, FL, United States of America ^aff004
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0225203

Souhrn

Many crashes occur around freeway exit ramp areas in China due to excessive speeds and large speed variances. Traditionally, a single posted ramp speed limit sign is installed around the physical gore area to manage the speed. To address this issue, the study presented in this paper proposes the use of an advisory exit speed sign (AESS), which is an additional exit speed limit sign positioned along the deceleration lane to accommodate the speed changes ahead of the physical gore. The study selected three sites with similar exit ramp configurations and two scenarios (with AESS/without AESS) to quantify the influences of the AESS on the speed of exiting vehicles. The speed profiles of 480 vehicles were obtained based on 12 hours of data collection. A t-test was applied to verify the reduction in mean speed between the two scenarios. The results show that the AESS in this study was effective in reducing the mean speed and 85th percentile speed, especially in the taper and deceleration lane. It was clearly seen that drivers began to decelerate in advance when the AESS was installed, which led to a smooth deceleration process, especially on the segment between the theoretical gore and the physical gore. The AESS was also helpful in reducing speeding to some extent. Although the effects of the AESS on speed reduction at curved ramps were not ideal, the speed fluctuation range tended to be more contracted when the AESS was installed. This paper provides useful information for researchers, managers, and engineers when considering the implementation of AESS.

Klíčová slova:

Engineering and technology – Traffic safety – Rural areas – Deceleration – Police – Radar – Brakes

Zdroje

1. Fatema T, Ismail K, Hassan Y. Validation of probabilistic model for design of freeway entrance speed change lanes. Transportation Research Record, 2014, 2460(1): 97–106.

2. Gu X, Abdel-Aty M, Xiang Q, Cai Q, Yuan J. Utilizing UAV video data for in-depth analysis of drivers’ crash risk at interchange merging areas. Accident Analysis & Prevention, 2019, 123: 159–169.

3. Feng Z, Yang M, Kumfer W, Zhang W, Du Y, Bai H. Effect of longitudinal slope of urban underpass tunnels on drivers’ heart rate and speed: A study based on a real vehicle experiment. Tunnelling and underground space technology, 2018, 81: 525–533.

4. Ma C, Hao W, Xiang W, Yan W. The impact of aggressive driving behavior on driver-injury severity at highway-rail grade crossings accidents. Journal of Advanced Transportation, 2018, 2018.

5. Chen F, Chen S. Injury severities of truck drivers in single-and multi-vehicle accidents on rural highways. Accident Analysis & Prevention, 2011, 43(5): 1677–1688.

6. Chen F, Peng H, Ma X, Liang J, Hao W, Pan X. Examining the safety of trucks under crosswind at bridge-tunnel section: A driving simulator study. Tunnelling and Underground Space Technology, 2019, 92: 103034.

7. Chen F, Song M, Ma X. Investigation on the injury severity of drivers in rear-end collisions between cars using a random parameters bivariate ordered probit model. International journal of environmental research and public health, 2019, 16(14): 2632.

8. Chen F, Chen S, Ma X. Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. Journal of safety research, 2018, 65: 153–159. doi: 10.1016/j.jsr.2018.02.010 29776524

9. Xu C, Wang Y, Liu P, Wang W, Bao J. Quantitative risk assessment of freeway crash casualty using high-resolution traffic data. Reliability Engineering & System Safety, 2018, 169: 299–311.

10. McCartt A T, Northrup V S, Retting R A. Types and characteristics of ramp-related motor vehicle crashes on urban interstate roadways in Northern Virginia. Journal of Safety Research. 2004; 35(1): 107–114. doi: 10.1016/j.jsr.2003.09.019 14992851

11. Papadimitriou E, Theofilatos A. Meta-analysis of crash-risk factors in freeway entrance and exit areas. Journal of Transportation Engineering, Part A: Systems. 2017;143(10): 04017050. doi: 10.1061/JTEPBS.0000082

12. Guo Y, Li Z, Liu P, Wu Y. Modeling correlation and heterogeneity in crash rates by collision types using full Bayesian random parameters multivariate Tobit model. Accident Analysis & Prevention, 2019, 128: 164–174.

13. Guo Y, Li Z, Sayed T. Analysis of Crash Rates at Freeway Diverge Areas using Bayesian Tobit Modeling Framework. Transportation Research Record, 2019: 0361198119837219.

14. Guo Y, Essa M, Sayed T, Haque M. M., Washington S. A comparison between simulated and field-measured conflicts for safety assessment of signalized intersections in Australia. Transportation research part C: emerging technologies, 2019, 101: 96–110.

15. Li Y, Wang H, Wang W, Xing L., Liu S., Wei X. Evaluation of the impacts of cooperative adaptive cruise control on reducing rear-end collision risks on freeways. Accident Analysis & Prevention, 2017, 98: 87–95.

16. FHWA. Manual on uniform traffic control devices (MUTCD). US Department of Transportation, Washington D.C., 2009.

17. Voigt A.P.; Fenno D.W.; Borchardt D.W. Evaluation of vehicle speeds on freeway-to-freeway connector ramps in Houston. FHWA/TX-03/4318-1, 2003.

18. Fukutome I, Moskowitz K. Traffic behavior and off-ramp design. Highway Research Record. 1963; 21: 17–31.

19. Zhang L, Chen C, Zhang J, Fang S., You J., Guo J. Modeling lane-changing behavior in freeway off-ramp areas from the shanghai naturalistic driving study. Journal of Advanced Transportation. 2018. doi: 10.1007/s11116-016-9747-x

20. Brewer M A, Stibbe J. Investigation of design speed characteristics on freeway ramps using shrp2 naturalistic driving data. Transportation Research Record. 2019; 2673(3): 247–258.

21. Yang B, Liu P, Chan C Y, Xu C, Guo Y. Identifying the crash characteristics on freeway segments based on different ramp influence areas. Traffic injury prevention. 2019; 20(4): 386–391. doi: 10.1080/15389588.2019.1588965 31021664

22. Calvi A., Benedetto A., De Blasiis M.R. Driving simulator validation for deceleration lane design. Accident Analysis & Prevention. 2012; 45(1): 195–203.

23. El-Basha R, Hassan Y, Sayed T. Modeling freeway diverging behavior on deceleration lanes. Transportation Research Record: Journal of the Transportation Research Board. 2007;30–37.

24. Yan X, Abdel-Aty M, Radwan E, Wang X, Chilakapati P. Validating a driving simulator using surrogate safety measures. Accident Analysis & Prevention. 2008;40(1):274–288.

25. Guo T, Deng W, Lu J. Safety evaluation for freeway exit ramp based on speed consistency. Journal of Transportation Systems Engineering and Information Technology. 2010; 10(6):76–81.

26. Calvi A, Benedetto A, Blasiis M.R. de. A driving simulator study of driver performance on deceleration lanes. Accident Analysis & Prevention. 2012;45:195–203.

27. Retting R.A, McGee H.W, Farmer C.M. Influence of experimental pavement markings on urban freeway exit-ramp traffic speeds. Transportation Research Record: Journal of the Transportation Research Board. 2000;1705(1):116–121.

28. Freedman M, Olson P.L, Zador P.L. Speed actuated rollover advisory signs for trucks on highway exit ramps. Insurance Institute for Highway Safety, Arlington, Va., 1992.

29. Reddy V, Datta T, Savolainen P, Pinapaka S. Evaluation of innovative safety treatments. Volume 3: a study of the effectiveness of tyregrip high friction surface treatment. Florida Department of Transportation. 2008; FDOT-B-D500.

30. Hunter M, Boonsiripant S, Guin A, Rodgers M. O., Jared D. Evaluation of effectiveness of converging chevron pavement markings in reducing speed on freeway ramps. Transportation Research Record: Journal of the Transportation Research Board 2010, 50–58.

31. Zhu X, Dai Z, Chen F, Pan X, Xu M. Using the visual intervention influence of pavement marking for rutting mitigation–Part II: visual intervention timing based on the finite element simulation. International Journal of Pavement Engineering, 2019, 20(5): 573–584.

32. Wu G, Chen F, Pan X, Xu M, Zhu X. Using the visual intervention influence of pavement markings for rutting mitigation–part I: preliminary experiments and field tests. International Journal of Pavement Engineering, 2019, 20(6): 734–746.

33. Li L, Zhang J, Wang Y, Ran B. Missing value imputation for traffic-related time series data based on a multi-view learning method. IEEE Transactions on Intelligent Transportation Systems, 2018. doi: 10.1109/TITS.2018.2795381

34. Li Z, Liu P, Xu C, Duan H, Wang W. Reinforcement learning-based variable speed limit control strategy to reduce traffic congestion at freeway recurrent bottlenecks. IEEE transactions on intelligent transportation systems, 2017, 18(11): 3204–3217.

35. Kwon E, Brannan D, Shouman K, Isackson C, Arseneau B. Development and field evaluation of variable advisory speed limit system for work zones. Transportation Research Record. 2007; 2015:12–18.

36. Hou Y, Edara P, Sun C. Speed limit effectiveness in short-term rural interstate work zones. Transportation Letters: The International Journal of Transportation Research. 2013; 5(1): 8–14.

37. Voigt A.P, Stevens C.R, Borchardt D.W. Dual-advisory speed signing on freeway-to-freeway connectors in texas. Transportation Research Record: Journal of the Transportation Research Board. 2008; 2056:87–94.

38. Administration S. National standard for road traffic signs and markings. China, GB5768-2009.

39. Fitzpatrick K, Carlson P, Brewer M, Wooldridge M. Design speed, operating speed, and posted speed practices. In the 82nd annyal meeting of Transportation Research Board. Washington D.C., 2003.

40. Liu Z, Wang S, Zhou B, Cheng Q. Robust optimization of distance-based tolls in a network considering stochastic day to day dynamics. Transportation Research Part C: Emerging Technologies, 2017, 79: 58–72.

41. Bao J, Liu P, Qin X, Zhou H. Understanding the effects of trip patterns on spatially aggregated crashes with large-scale taxi GPS data. Accident Analysis & Prevention, 2018, 120: 281–294.

42. Wang L. Research on Relationship Between Speed Limits and Operating Speed for Freeway. Master's Thesis: China, 2011.

43. Lu J J, Zhang W, Sheng F. BPANN model for predicting speed standard deviation to evaluate safety performance on freeway exit ramps. In the 91st Annual Meeting of Transportation Research Board. Washington D.C., 2012.

Impacts of experimental advisory exit speed sign on traffic speeds for freeway exit ramp

Souhrn

Klíčová slova:

Zdroje

PLOS One

Aktuální možnosti diagnostiky a léčby litiáz