Evaluation of Intersection Properties Using MARS Method for Improving Urban Traffic Performance: Case Study of Tekirdağ, Turkey

Year 2021, Volume: 32 Issue: 6, 11227 - 11256, 01.11.2021

Görkem Gülhan , Mustafa Özuysal , Hüseyin Ceylan

https://doi.org/10.18400/tekderg.551032

Cited By: 1

Abstract

Increasing urban traffic performance is a technical problem that has been investigated by many researchers these days. Traffic performance can be increased in many ways, as part of the transportation planning process, on smaller scales, or with different methods and techniques. The determination of traffic intervention areas in urban transportation planning is an intervention type that determines the rate at which the traffic performance will increase. Although transportation planning is an integrated issue, the type of traffic modification and prior intervention on intersections are often determined with partitive paradigms and strategies. It is a significant opportunity for decision makers to be informed in advance of the effects of intersection characteristics on the overall traffic performance. However, it is not an attempted or tested concept to perform a general assessment of the impact of the intersection characteristics on the overall performance of the intersections. In this study, a four-stage integrated analysis including the multivariate adaptive regression splines (MARS) method is proposed for the overall traffic performance evaluation. The traffic characteristics of intersections are first indexed and categorized. The intersection performance results are then obtained using the VISSIM traffic simulation software. Subsequently, the relationship between them is determined with the MARS method, and the effects of the intersection characteristics on the traffic performance are investigated. Tekirdağ, a metropolitan city located in northwestern Turkey, is selected for the case study. According to the obtained simulation results, the suggestions related to the development of intersection performances are made and tested.

Keywords

traffic performance, VISSIM, intersection, multivariate adaptive regression splines

References

• [1] Saka, A. A., Jeihani, M., James, P. A., Estimation of Traffic Recovery Time for Different Flow Regimes on Freeways. Morgan State University Department of Transportation and Urban Infrastructure Studies School of Engineering, Report No: MD-09-SP708B4L, 2008.
• [2] Ceylan, H., Bell, M. G. H., Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing. Transportation Research Part B: Methodological, 38 (4), 329–342, 2004.
• [3] Chiou, S. W., Joint optimization for area traffic control and network flow. Computers and Operations Research, 32 (11), 2821–2841, 2005.
• [4] Cantarella, G. E., Vitetta, A., The multi-criteria road network design problem in an urban area. Transportation, 33 (6), 567–588, 2006.
• [5] Chiou, S. W., A hybrid approach for optimal design of signalized road network. Applied Mathematical Modelling, 32 (2), 195–207, 2008.
• [6] Ceylan, H., Ceylan, H., A Hybrid Harmony Search and TRANSYT hill climbing algorithm for signalized stochastic equilibrium transportation networks. Transportation Research Part C: Emerging Technologies, 25, 152–167, 2012.
• [7] Szeto, W. Y., Jiang, Y., Wang, D. Z. W., Sumalee, A., A sustainable road network design problem with land use transportation interaction over time. Networks and Spatial Economics, 15 (3), 1–32, 2013.
• [8] Di, Z., Yang, L., Qi, J., Gao, Z., Transportation network design for maximizing flow-based accessibility. Transportation Research Part B: Methodological, 110, 209–238, 2018.
• [9] Sun, W., Wang, Y., Yu, G., Liu, H. X., Quasi-optimal feedback control for a system of oversaturated intersections. Transportation Research Part C: Emerging Technologies, 57, 224–240, 2015.
• [10] Chen, P., Sun, J., Qi, H., Estimation of delay variability at signalized intersections for urban arterial performance evaluation. Journal of Intelligent Transportation Systems, 21 (2), 94–110, 2017.
• [11] Liu, Y., Chang, G. L., An arterial signal optimization model for intersections experiencing queue spillback and lane blockage. Transportation Research Part C: Emerging Technologies, 19, 130–144, 2011.
• [12] Lim, K., Kim, J. H., Shin, E., Kim, D. G., A signal control model integrating arterial intersections and freeway off-ramps. KSCE Journal of Civil Engineering, 15 (2), 385–394, 2011.
• [13] Song, X., Tao, P., Chen, L., Wang, D., Offset optimization based on queue length constraint for saturated arterial intersections. Discrete Dynamics in Nature and Society, Volume 2012, 1–13, 2012.
• [14] Chen, F., Wang, L., Jiang, B., Wen, C., An Arterial Traffic Signal Control System Based on a Novel Intersections Model and Improved Hill Climbing Algorithm. Cognitive Computation, 7 (4), 464–476, 2015.
• [15] Xinwu, Y., Qiaohui, W., Huibin, X., Xiaoyan, X., A coordinated signal control method for arterial road of adjacent intersections based on the improved genetic algorithm. Optik, 127 (16), 6625–6640, 2016.
• [16] Stamatiadis, N., Kirk, A., Improving Intersection Design Practices Final Report - Phase I. Kentucky Transportation Center Research, Report No: KTC-10-09/SPR 380-09-1F, 2010.
• [17] Otković, I. I., Dadić, I., Comparison of delays at signal-controlled intersection and roundabout. Promet Traffic & Transportation, 21 (3), 157–165, 2009.
• [18] Persaud, B., Retting, R., Garder, P., Lord, D., Safety effects of roundabout conversions in the United States: empirical Bayes observational before-and-after study. Transportation Research Record, 1751, 1–8, 2001.
• [19] Rodegerdts, L., Bansen, J., Tiesler, C., Knudsen, J., Myers, E., Roundabouts: An Informational Guide. Report 672 - Second Edition, Transportation Research Board – National Cooperative Highway Research Program, Washington DC, USA, 2010.
• [20] Gross, F., Lyon, C., Persaud, B., Srinivasan, R., Safety effectiveness of converting signalized intersections to roundabouts. Accident Analysis and Prevention, 50, 234–241, 2013.
• [21] Kramer, R. P., New combinations of old techniques to rejuvenate jammed suburban arterials. In: Strategies to alleviate traffic congestion. Proceedings of ITE’s 1987 national conference, Washington, DC, Institute of Transportation Engineers, 139–148, 1987.
• [22] Hummer, J. E., Boone, J. L., The travel efficiency of unconventional arterial intersection designs. Transportation Research Record: Journal of the Transportation Research Board, 1500, 153–161, 1995.
• [23] Hummer, J. E., Reid, J. D., Unconventional left-turn alternatives for urban and suburban arterials: an update. In: Transportation research circular E-C019: Urban Street Symposium Conference Proceedings, 28–30 June, Dallas, TX, 1999.
• [24] Jagannathan, R., Bared, J., Design and operational performance of crossover displaced left-turn intersections. Transportation Research Record: Journal of the Transportation Research Board, 1881, 1–10, 2004.
• [25] Shahi, J., Choupani, A., Modelling the operational effects of unconventional U-turns at a highway intersection. Transportmetrica, 5 (3), 173–191, 2009.
• [26] El Esawey, M., Sayed, T., Unconventional USC intersection corridors: evaluation of potential implementation in Doha, Qatar. Journal of Advanced Transportation, 45 (1), 38–53, 2011.
• [27] El Esawey, M., Sayed, T., Analysis of unconventional arterial intersection designs (UAIDs): state-of-the-art methodologies and future research directions. Transportmetrica A: Transport Science, 9 (10), 860–895, 2013.
• [28] Autey, J., Sayed, T., El Esawey, M., Operational performance comparison of four unconventional intersection designs. Journal of Advanced Transportation, 47 (5), 536–552, 2012.
• [29] Naghawi, H., Idewu, W., Analysing delay and queue length using microscopic simulation for the unconventional intersection design superstreet. Journal of the South African Institution of Civil Engineering, 56 (1), 100–107, 2014.
• [30] Xiang, Y., Li, Z., Wang, W., Chen, J., Wang, H., Li, Y., Evaluating the operational features of an unconventional dual-bay U-turn design for intersections. Plos One, 11 (7), 1–18, 2016.
• [31] Lan, C-J., New optimal cycle length formulation for pretimed signals at isolated intersections. Journal of Transportation Engineering, 130 (5), 637–647, 2004.
• [32] Talmor, I., Mahalel, D., Signal design for an isolated intersection during congestion. Journal of the Operational Research Society, 58 (4), 454–466, 2007.
• [33] Yu, D., Tian, X., Xing, X., Gao, S., Signal timing optimization based on fuzzy compromise programming for isolated signalized intersection. Mathematical Problems in Engineering, Volume 2016, 1–12, 2016.
• [34] Chang, L., Analysis of bilateral air passenger flows: A non-parametric multivariate adaptive regression spline approach. Journal of Air Transport Management, 34, 123–130, 2014.
• [35] Ozuysal, M., Caliskanelli, S. P., Reliability estimation of public bus routes: applicability of MARS approach. Canadian Journal of Civil Engineering, 45 (10), 852-865, 2018.
• [36] Transportation Research Board (TRB), Highway Capacity Manual. National Research Council, Washington DC, 2010.
• [37] Gluck, J., Levinson, H. S., Stover, V., Impacts of access management techniques. National Cooperative Highway Research Program Report 420, Washington DC, 1999.
• [38] PTV AG, VISSIM 5.40-01-User Manual. PTV Planung Transport Verkehr AG, Karlsruhe, 2011.
• [39] Friedman, J.H., Multivariate adaptive regression splines. The Annals of Statistics, 19 (1), 1–67, 1991.
• [40] Friedman, J. H., Estimating functions of mixed ordinal and categorical variables using adaptive splines. California: Laboratory for Computational Statistics, Technical Report No. 108, Department of Statistics, Stanford University, USA, 1991.
• [41] Abdel-Aty, M., Haleem, K., Analyzing angle crashes at unsignalized intersections using machine learning techniques. Accident Analysis and Prevention, 43 (1), 461–470, 2011.
• [42] Haleem, K., Gan, A., Lu, J., Using multivariate adaptive regression crash splines (MARS) to develop modification factors for urban areas freeway interchange influence. Accident Analysis and Prevention, 55, 12–21, 2013.
• [43] Chang, L., Chu, H., Lin, D., Lui, P., Analysis of freeway accident frequency using multivariate adaptive regression splines. Procedia Engineering, 45, 824–829, 2012.
• [44] Xu, Y., Kong, Q. J., Liu, Y., A spatio-temporal multivariate adaptive regression splines approach for short-term freeway traffic volume prediction. Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, The Netherlands, October 6-9, 217-222 (in the proceedings book), 2013.
• [45] Xu, Y., Kong, Q. J., Klette, R., Liu, Y., Accurate and interpretable bayesian MARS for trafic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 15 (6), 2457–2469, 2014.
• [46] Salford Systems, SPM User Guide: Introducing MARS. https://www.salford-systems.com/support/spm-user-guide/help/mars, 2016.
• [47] TMM, Preperation of Traffic Regulation, Road and Intersection Preliminary Projects. Tekirdağ Metropolitian Municipality, Final Report (in Turkish), Tekirdağ, 2015.
• [48] Jekabsons, G., ARESLab: Adaptive regression splines - toolbox for Matlab/Octave. http://www.cs.rtu.lv/jekabsons, 2016.
• [49] Ozuysal, M., Caliskanelli, S. P., Tanyel, S., Baran, T., Capacity prediction for traffic circles: applicability of ANN. Proceedings of the Institution of Civil Engineers: Transport, 162 (4), 195–206, 2009.