Technical Papers
Sep 25, 2021

Modeling Lateral Acceleration on Ramp Curves of Service Interchanges in India: An Instrumented-Vehicle Study

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 12

Abstract

Ramps are a critical component of an interchange, which seek maximum driver attention to satisfy the deceleration and acceleration requirements. To improve safety and articulate significant safety schemes, it is important to analyze driver behavior on ramp interchanges at a microscopic level. Existing studies on highway geometry are limited to horizontal curves, and only a few studies were found to deal with ramp interchanges. In this study, a model based on support vector regression (SVR) was developed to estimate lateral acceleration (La) experienced by the drivers on diagonal, loop, and semidirect ramps of service interchanges. To establish these models, the continuous lateral acceleration profiles for 83 drivers were collected using an instrumented vehicle. The developed SVR models exhibited higher accuracy, measured by the values of coefficient of determination and the root-mean square error. Further, a sensitivity analysis was performed to measure the relative importance of input features. The results revealed that ramp curvature and ramp length are the two most significant variables that impact lateral acceleration on diagonal and semidirect ramps. However, for loop ramp connectors, operating speed (V85) (85th percentile speed) displayed the highest association with lateral acceleration. The models developed in this study can be used as a tool to estimate the lateral acceleration experienced by the drivers during the design process, thus enhancing the further understanding of driver safety on ramp interchanges.

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Data Availability Statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

References

AASHTO. 2011. A policy on geometric design of highways and streets. Washington, DC: AASHTO.
Adnan, M. A., N. Sulaiman, N. I. Zainuddin, and T. B. H. T. Besar. 2013. “Vehicle speed measurement technique using various speed detection instrumentation.” In Proc., 2013 IEEE Business Engineering and Industrial Applications Colloquium (BEIAC), 668–672. New York: IEEE.
Akande, K. O., T. O. Owolabi, and S. O. Olatunji. 2015. “Investigating the effect of correlation-based feature selection on the performance of support vector machines in reservoir characterization.” J. Nat. Gas Sci. Eng. 22 (2): 515–522. https://doi.org/10.1016/j.jngse.2015.01.007.
Akande, K. O., T. O. Owolabi, S. O. Olatunji, and A. Abdul Raheem. 2017. “A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir.” J. Petrol. Sci. Eng. 150 (Feb): 43–53. https://doi.org/10.1016/j.petrol.2016.11.033.
Alade, I. O., M. A. Abd Rahman, and T. A. Saleh. 2019a. “Modeling and prediction of the specific heat capacity of Al2 O3/water nanofluids using hybrid genetic algorithm/support vector regression model.” Nano-Structures Nano-Objects 17 (Feb): 103–111. https://doi.org/10.1016/j.nanoso.2018.12.001.
Alade, I. O., M. A. Abd Rahman, and T. A. Saleh. 2019b. “Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm.” Sol. Energy 183 (May): 74–82. https://doi.org/10.1016/j.solener.2019.02.060.
Axelsson, C., A. K. Skidmore, M. Schlerf, A. Fauzi, and W. Verhoef. 2013. “Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression.” Int. J. Remote Sens. 34 (5): 1724–1743. https://doi.org/10.1080/01431161.2012.725958.
Banerjee, A. K., N. Arora, and U. S. N. Murty. 2008. “Classification and regression tree (CART) analysis for deriving variable importance of parameters influencing average flexibility of CaMK kinase family.” Electron. J. Biol. 4 (1): 27–33.
Bared, J., A. Powell, E. Kaisar, and R. Jagannathan. 2005. “Crash comparison of single point and tight diamond interchanges.” J. Transp. Eng. 131 (5): 379–381. https://doi.org/10.1061/(ASCE)0733-947X(2005)131:5(379).
Basak, D., S. Pal, and D. C. Patranabis. 2007. Support vector regression 11, 203–224. Daejeon, Korea: KAIST Press.
Basu, C., Q. Yang, D. Hungerman, M. Sinahal, and A. D. Draqan. 2017. “Do you want your autonomous car to drive like you?” In Proc., 2017 12th ACM/IEEE International Conference on Human-Robot Interaction, 417–425. New York: IEEE.
Bauer, K. M., and D. W. Harwood. 1998. Statistical models of accidents on interchange ramps and speed-change lanes. Washington, DC: Federal Highway Administration.
Bhavsar, P., M. Chowdhury, A. Sadek, W. Sarasua, and J. Ogle. 2007. “Decision support system for predicting traffic diversion impact across transportation networks using support vector regression.” Transp. Res. Rec. 2024 (1): 100–106. https://doi.org/10.3141/2024-12.
Broeren, P. T. W., J. de Jong, G. Uittenbogerd, and J. Groot. 2015. “Ramps in interchanges: Relations between geometric design, crash rates and driving speeds.” In Proc., 5th Int. Symp. on Highway Geometric Design. Vancouver, BC, Canada: Univ. of British Columbia.
Channel Definitions. 2021. “Channel definitions.” Accessed July 10, 2021. https://en.racelogic.support/01VBOX_Automotive/01General_InformationKnowledge_Base/Channel_Definitions.
Chen, C., G. Zhang, Z. Qian, R. A. Tarefder, and Z. Tian. 2016. “Investigating driver injury severity patterns in rollover crashes using support vector machine models.” Accid. Anal. Prev. 90 (May): 128–139. https://doi.org/10.1016/j.aap.2016.02.011.
Cheu, R. L., D. Srinivasan, and E. T. Teh. 2003. “Support vector machine models for freeway incident detection.” In Proc., 2003 IEEE Int. Conf. on Intelligent Transportation Systems, 238–243. New York: IEEE.
Choudhari, T., and A. Maji. 2019. “Socio-demographic and experience factors affecting drivers’ run-off risk along horizontal curves of two-lane rural highway.” J. Saf. Res. 71 (Dec): 1–11. https://doi.org/10.1016/j.jsr.2019.09.013.
Choudhari, T., and A. Maji. 2021. “Risk assessment of horizontal curves based on lateral acceleration index: A driving simulator-based study.” Transp. Dev. Econ. 7 (1): 1–11. https://doi.org/10.1007/s40890-020-00111-2.
Cui, Y., J. S. Jin, S. Zhang, S. Luo, and Q. Tian. 2010. “Correlation-based feature selection and regression.” In Pacific-Rim conference on multimedia, 25–35. Berlin: Springer.
Dabbour, E., S. M. Easa, and A. O. Abd El Halim. 2004. “Radius requirements for reverse horizontal curves on three-dimensional alignments.” J. Transp. Eng. 130 (5): 610–620. https://doi.org/10.1061/(ASCE)0733-947X(2004)130:5(610).
Dadashova, B., K. Dixon, and R. Avelar. 2018. “Exploring the effects of important predictors of ramp speed choice.” Transp. Res. Rec. 2672 (38): 277–289. https://doi.org/10.1177/0361198118793497.
Delen, D., C. Kuzey, and A. Uyar. 2013. “Measuring firm performance using financial ratios: A decision tree approach.” Expert Syst. Appl. 40 (10): 3970–3983. https://doi.org/10.1016/j.eswa.2013.01.012.
Dhahir, B., and Y. Hassan. 2016. “Reliability-based design of horizontal curves on two-lane rural highways.” Transp. Res. Rec. 2588 (1): 22–31. https://doi.org/10.3141/2588-03.
Dhahir, B., and Y. Hassan. 2018. “Studying driving behaviour on horizontal curves using naturalistic driving study data.” Transp. Res. Rec. 2672 (17): 83–95. https://doi.org/10.1177/0361198118784384.
Dhahir, B., and Y. Hassan. 2019. “Modelling speed and comfort threshold on horizontal curves of rural two-lane highways using naturalistic driving data.” J. Transp. Eng., Part A: Syst. 145 (6): 04019025. https://doi.org/10.1061/JTEPBS.0000246.
Ding, A., X. Zhao, and L. Jiao. 2002. “Traffic flow time series prediction based on statistics learning theory.” In Proc., IEEE 5th Int. Conf. on Intelligent Transportation Systems, 727–730. New York: IEEE.
Du, W. J., G. D. Xia, F. Chen, and X. D. Pan. 2015. “Research on safety of freeway off-ramp lane changing behavior based on lateral force coefficient.” In Access management theories and practices, 201–210. Reston, VA: ASCE.
Durrant, A. J., and M. J. Hill. 2005. Technical assessment of the DL2s GPS data quality (including comparative data from VBOX3 and an optical speed sensor. Strelley, UK: Race Technology Limited.
Easa, S. M., and E. Dabbour. 2003. “Design radius requirements for simple horizontal curves on three-dimensional alignments.” Can. J. Civ. Eng. 30 (6): 1022–1033. https://doi.org/10.1139/l03-022.
Eboli, L., G. Mazzulla, and G. Pungillo. 2017. “How to define the accident risk level of car drivers by combining objective and subjective measures of driving style.” Transp. Res. Part F: Traffic Psychol. Behav. 49 (Sep): 29–38. https://doi.org/10.1016/j.trf.2017.06.004.
Fan, L., L. Lu, W. Deng, and J. J. Lu. 2015. “Role of vehicle trajectory and lateral acceleration in designing horizontal curve radius of off-ramp: A driving simulator based study.” Adv. Transp. Stud. 36 (Jul): 2015.
Farah, H., W. Daamen, and S. Hoogendoorn. 2019. “How do drivers negotiate horizontal ramp curves in system interchanges in the Netherlands?” Saf. Sci. 119 (2): 58–69. https://doi.org/10.1016/j.ssci.2018.09.016.
Farah, H., A. van Beinum, and W. Daamen. 2017. “Empirical speed behavior on horizontal ramp curves in interchanges in the Netherlands.” Transp. Res. Rec. 2618 (1): 38–47. https://doi.org/10.3141/2618-04.
Furtado, G. 2002. A vehicle stability on combined horizontal and vertical alignments (Doctoral dissertation. Ottawa: Carleton Univ.
Garnowski, M., and H. Manner. 2011. “On factors related to car accidents on German Autobahn connectors.” Accid. Anal. Prev. 43 (5): 1864–1871. https://doi.org/10.1016/j.aap.2011.04.026.
Gong, H., and N. Stamatiadis. 2008. “Operating speed prediction models for horizontal curves on rural four-lane highways.” Transp. Res. Rec. 2075 (1): 1–7. https://doi.org/10.3141/2075-01.
Hall, M. A. 1999. “Correlation-based feature selection for machine learning.” Ph.D. dissertation, Dept. of Computer Science, Univ. of Waikato.
Hanno, D. 2004. Effect of the combination of horizontal and vertical alignments on road safety (Doctoral dissertation. Vancouver, Canada: Univ. of British Columbia.
Hassan, Y., and S. M. Easa. 2003. “Effect of vertical alignment on driver perception of horizontal curves.” J. Transp. Eng. 129 (4): 399–407. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:4(399).
He, Y., X. Sun, and R. C. Coakley. 2008. “Safety analysis of freeway interchanges.” In Proc., 1st Int. Symp. on Transportation and Development Innovative Best Practices, 252–257. Reston, VA: ASCE.
Hong, W. C. 2011. “Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm.” Neurocomputing 74 (12–13): 2096–2107. https://doi.org/10.1016/j.neucom.2010.12.032.
Huang, B., Z. Wang, J. J. Lu, and L. Lu. 2012. “Modeling speed profile on freeway exit ramps using support vector regression.” In Proc., 12th COTA Int. Conf. of Transportation Professionals, 671–682. Reston, VA: ASCE.
Katz, B. J., S. O. Kuznicki, N. Kehoe, and J. Shurbutt. 2018. “Field study of driver exiting behavior at complex interchanges.” Transp. Res. Rec. 2672 (37): 19–30. https://doi.org/10.1177/0361198118792995.
Kecman, V. 2005. “Support vector machines–an introduction.” In Support vector machines: Theory and applications, 1–47. Berlin: Springer.
Kordani, A. A., and A. M. Molan. 2015. “The effect of combined horizontal curve and longitudinal grade on side friction factors.” KSCE J. Civ. Eng. 19 (1): 303–310. https://doi.org/10.1007/s12205-013-0453-3.
Kuzey, C., A. S. Karaman, and E. Akman. 2019. “Elucidating the impact of visa regimes: A decision tree analysis.” Tourism Manage. Perspect. 29 (Mar): 148–156. https://doi.org/10.1016/j.tmp.2018.11.008.
Lederer, P. R., L. F. Cohn, R. Guensler, and R. A. Harris. 2005. “Effect of on-ramp geometric and operational factors on vehicle activity.” J. Transp. Eng. 131 (1): 18–26. https://doi.org/10.1061/(ASCE)0733-947X(2005)131:1(18).
Levis, A. A., and L. G. Papageorgiou. 2005. “Customer demand forecasting via support vector regression analysis.” Chem. Eng. Res. Des. 83 (8): 1009–1018. https://doi.org/10.1205/cherd.04246.
Li, Z., P. Liu, W. Wang, and C. Xu. 2012. “Using support vector machine models for crash injury severity analysis.” Accid. Anal. Prev. 45 (Apr): 478–486. https://doi.org/10.1016/j.aap.2011.08.016.
Liapis, E. D., B. Psarianos, and E. Kasapi. 2001. “Speed behavior analysis at curved ramp sections of minor interchanges.” Transp. Res. Rec. 1751 (1): 35–43. https://doi.org/10.3141/1751-05.
Lin, W. C. 2004. A case study on support vector machines versus artificial neural networks. Pittsburgh: Univ. of Pittsburgh.
Lu, Z., W. Lv, Z. Xie, and T. Zhu. 2018. “Highway traffic volume prediction via stacking KNN, SVR, MLP, RNN.” In 2018 IEEE SmartWorld, ubiquitous intelligence & computing, advanced & trusted computing, scalable computing & communications, cloud & big data computing, internet of people and smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 1408–1413. New York: IEEE.
Lundy, R. A. 1965. The effect of ramp type and geometry on accidents. Sacramento, CA: California Department of Public Works.
Luo, X., D. Li, and S. Zhang. 2019. “Traffic flow prediction during the holidays based on DFT and SVR.” J. Sens. 2019 (Jan): 12.
Mahmud, M. S., M. Motz, T. Holpuch, J. Hankin, A. J. Ingle, T. J. Gates, and P. T. Savolainen. 2020. “Driver response to a dynamic speed feedback sign on freeway exit ramps based on sign location, interchange type, and time of day.” Transp. Res. Rec. 2020 (1): 03611981211015250.
Malaghan, V., D. S. Pawar, and H. Dia. 2020a. “Modeling operating speed using continuous speed profiles on two-lane rural highways in India.” J. Transp. Eng., Part A: Syst. 146 (11): 04020124. https://doi.org/10.1061/JTEPBS.0000447.
Malaghan, V., D. S. Pawar, and H. Dia. 2020b. “Speed prediction models for heavy passenger vehicles on rural highways based on an instrumented vehicle study.” Transp. Lett. 2020 (Aug): 1–10. https://doi.org/10.1080/19427867.2020.1811005.
McCartt, A. T., V. S. Northrup, and R. A. Retting. 2004. “Types and characteristics of ramp-related motor vehicle crashes on urban interstate roadways in Northern Virginia.” J. Saf. Res. 35 (1): 107–114. https://doi.org/10.1016/j.jsr.2003.09.019.
Melki, G., A. Cano, V. Kecman, and S. Ventura. 2017. “Multi-target support vector regression via correlation regressor chains.” Inf. Sci. 415 (Nov): 53–69. https://doi.org/10.1016/j.ins.2017.06.017.
Meng, J., Y. Gao, and Y. Shi. 2007. “Support vector regression model for measuring the permittivity of asphalt concrete.” IEEE Microwave Wireless Compon. Lett. 17 (12): 819–821. https://doi.org/10.1109/LMWC.2007.910462.
Montella, A., F. Galante, F. Mauriello, and M. Aria. 2015. “Continuous speed profiles to investigate drivers’ behavior on two-lane rural highways.” Transp. Res. Rec. 2521 (1): 3–11. https://doi.org/10.3141/2521-01.
Montgomery, D. C., G. C. Runger, and N. F. Hubele. 2009. Engineering statistics. Hoboken, NJ: Wiley.
Nourali, H., and M. Osanloo. 2019. “Mining capital cost estimation using support vector regression (SVR).” Resour. Policy 62 (Aug): 527–540. https://doi.org/10.1016/j.resourpol.2018.10.008.
Osborne, J. W., and E. Waters. 2002. “Four assumptions of multiple regression that researchers should always test.” Pract. Assess. Res. Eval. 8 (1): 2.
Portera, A., and M. Bassani. 2021. “Experimental investigation into driver behavior along curved and parallel diverging terminals of exit interchange ramps.” Transp. Res. Rec. 2021 (1): 0361198121997420. https://doi.org/10.1177/0361198121997420.
Said, D., Y. Hassan, and A. O. Abd El Halim. 2009. “Comfort thresholds for horizontal curve design.” Can. J. Civ. Eng. 36 (9): 1391–1402. https://doi.org/10.1139/L09-075.
Scherer, S., A. Dettmann, F. Hartwich, T. Pech, A. C. Bullinger, and G. Wanielik. 2015. “How the driver wants to be driven-modelling driving styles in highly automated driving.” In 7. Tagung Fahrer assistenz systeme. Munich, Germany: Technical Univ. of Munich.
Smola, A. J., and B. Schölkopf. 2004. “A tutorial on support vector regression.” Stat. Comput. 14 (3): 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88.
Tan, C. H. 2005. An investigation of comfortable lateral acceleration on horizontal curves. Pittsburgh: Pennsylvania State Univ.
Vanajakshi, L., and L. R. Rilett. 2004. “A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed.” In Proc., IEEE Intelligent Vehicles Symp. 2004, 194–199. New York: IEEE.
Vapnik, V. 2013. The nature of statistical learning theory. Berlin: Springer.
Wang, J., L. Li, D. Niu, and Z. Tan. 2012. “An annual load forecasting model based on support vector regression with differential evolution algorithm.” Appl. Energy 94 (4): 65–70. https://doi.org/10.1016/j.apenergy.2012.01.010.
Wang, L., and M. Abdel-Aty. 2016. “Microscopic safety evaluation and prediction for freeway-to-freeway interchange ramps.” Transp. Res. Rec. 2583 (1): 56–64. https://doi.org/10.3141/2583-08.
Wang, X., K. An, L. Tang, and X. Chen. 2015a. “Short term prediction of freeway exiting volume based on SVM and KNN.” Int. J. Transp. Sci. Technol. 4 (3): 337–352. https://doi.org/10.1260/2046-0430.4.3.337.
Wang, X., T. Wang, A. Tarko, and P. J. Tremont. 2015b. “The influence of combined alignments on lateral acceleration on mountainous freeways: A driving simulator study.” Accid. Anal. Prev. 76 (Apr): 110–117. https://doi.org/10.1016/j.aap.2015.01.003.
White, K., and R. Merala. 2017. Characterization of Janus V3 after market vehicle camera with global positioning and 3-axis accelerometer. Warrendale, PA: SAE International.
Wu, C. H., J. M. Ho, and D. T. Lee. 2004. “Travel-time prediction with support vector regression.” IEEE Trans. Intell. Transp. Syst. 5 (4): 276–281.
Xiao, J. 2019. “SVM and KNN ensemble learning for traffic incident detection.” Physica A 517: 29–35. https://doi.org/10.1016/j.physa.2018.10.060.
Xu, J., K. Yang, Y. Shao, and G. Lu. 2015. “An experimental study on lateral acceleration of cars in different environments in Sichuan, southwest China.” In Discrete dynamics in nature and society. Cairo, Egypt: Hindawi.
Yang, W. C., Z. X. Chen, M. Zhang, G. G. Guo, and W. B. Zhang. 2019. “The analysis of road condition causes of traffic accidents on mountainous highway and its safety countermeasures.” In Proc., 19th COTA Int. Conf. of Transportation Professionals, 3784–3796. Reston, VA: ASCE.
Zendehboudi, A., M. A. Baseer, and R. Saidur. 2018. “Application of support vector machine models for forecasting solar and wind energy resources: A review.” J. Cleaner Prod. 199 (Oct): 272–285. https://doi.org/10.1016/j.jclepro.2018.07.164.
Zhu, J., and I. Tasic. 2021. “Safety analysis of freeway on-ramp merging with the presence of autonomous vehicles.” Accid. Anal. Prev. 152 (Mar): 105966. https://doi.org/10.1016/j.aap.2020.105966.

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Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 147Issue 12December 2021

History

Received: Mar 10, 2021
Accepted: Aug 17, 2021
Published online: Sep 25, 2021
Published in print: Dec 1, 2021
Discussion open until: Feb 25, 2022

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Sarika Pothukuchi [email protected]
Research Scholar, Transportation Systems Engineering, Dept. of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Telangana 502285, India. Email: [email protected]
Assistant Professor, Transportation Systems Engineering, Dept. of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Telangana 502285, India (corresponding author). ORCID: https://orcid.org/0000-0003-4228-3283. Email: [email protected]

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