A combined Apriori algorithm and fuzzy controller for simultaneous ramp metering and variable speed limit determination in a freeway

Document Type : Original Article

Authors

1 Department of Civil Engineering, University of Calgary, Canada

2 Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

3 Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran

Abstract

This paper proposes an integrated system to control ramps and adjust variable speed limits. It includes three essential modules to predict the starting time of congestion and a fuzzy controller to determine the parameters and a model predictive control. An Apriori algorithm that is a powerful tool for frequent pattern mining is used in the first module. The proposed system is neither sensitive to the traffic distribution nor computationally intensive. Two traffic simulators of Aimsun and CTMSIM are applied to validate the results. Compared with the most recent algorithms, including Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), this system improves prediction accuracy up to 2.63%. The results of ramp metering and variable speed limit subsystems are also promising. The embedded controller shows 0.6% and 4% overall and rush hour improvement in the total travel time.

Keywords

References

[1] S. Abpeykar and M. Ghatee, A real-time decision support system for bridge management based on the rules generalized by cart decision tree and smo algorithms, AUT Journal of Mathematics and Computing, 1 (2020), pp. 95–100.
[2] M. S. Ahmed and A. R. Cook, Analysis of freeway traffic time-series data by using Box-Jenkins techniques, no. 722, 1979.
[3] B. Alsolami, R. Mehmood, and A. Albeshri, Hybrid statistical and machine learning methods for road traffic prediction: a review and tutorial, in Smart Infrastructure and Applications, Springer, 2020, pp. 115–133.
[4] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, arXiv preprint arXiv:1412.3555, (2014).
[5] H. R. Eftekhari and M. Ghatee, A similarity-based neuro-fuzzy modeling for driving behavior recognition applying fusion of smartphone sensors, Journal of Intelligent Transportation Systems, 23 (2019), pp. 72–83.
[6] J. R. D. Frejo, I. Papamichail, M. Papageorgiou, and B. De Schutter, Macroscopic modeling of variable speed limits on freeways, Transportation research part C: emerging technologies, 100 (2019), pp. 15–33.
[7] M. Ghatee and S. M. Hashemi, Traffic assignment model with fuzzy level of travel demand: An efficient algorithm based on quasi-logit formulas, European Journal of Operational Research, 194 (2009), pp. 432–451.
[8] A. H. Ghods, A. R. Kian, and M. Tabibi, Adaptive freeway ramp metering and variable speed limit control: a genetic-fuzzy approach, IEEE Intelligent Transportation Systems Magazine, 1 (2009), pp. 27–36.
[9] G. Gomes and R. Horowitz, Optimal freeway ramp metering using the asymmetric cell transmission model, Transportation Research Part C: Emerging Technologies, 14 (2006), pp. 244–262.
[10] M. Greguric, E. Ivanjko, and S. Mand ´ ˇzuka, The use of cooperative approach in ramp metering, Promet[1]Traffic&Transportation, 28 (2016), pp. 11–22.
[11] Y. Han, M. Ramezani, A. Hegyi, Y. Yuan, and S. Hoogendoorn, Hierarchical ramp metering in freeways: an aggregated modeling and control approach, Transportation research part C: emerging technologies, 110 (2020), pp. 1–19.
[12] A. Hegyi, S. Shladover, X. Lu, and D. Chen, A cooperative speed control algorithm to resolve jams, Delft University of Technology: Delft, The Netherlands, (2012).
[13] S. Heshami and L. Kattan, Ramp metering control under stochastic capacity in a connected environment: A dynamic bargaining game theory approach, Transportation Research Part C: Emerging Technologies, 130 (2021), p. 103282.
[14] S. Ishak and H. Al-Deek, Performance evaluation of short-term time-series traffic prediction model, Journal of transportation engineering, 128 (2002), pp. 490–498.
[15] X. Jin, Y. Zhang, F. Wang, L. Li, D. Yao, Y. Su, and Z. Wei, Departure headways at signalized intersections: A log-normal distribution model approach, Transportation research part C: emerging technologies, 17 (2009), pp. 318–327.
[16] D. Kang, Y. Lv, and Y.-y. Chen, Short-term traffic flow prediction with lstm recurrent neural network, in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2017, pp. 1–6.
[17] M. Kontorinaki, I. Karafyllis, and M. Papageorgiou, Local and coordinated ramp metering within the unifying framework of an adaptive control scheme, Transportation Research Part A: Policy and Practice, 128 (2019), pp. 89–113.
[18] A. Kotsialos, M. Papageorgiou, C. Diakaki, Y. Pavlis, and F. Middelham, Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool metanet, IEEE Transactions on intelligent transportation systems, 3 (2002), pp. 282–292.
[19] A. Kurzhanskiy and P. Varaiya, Ctmsim—an interactive macroscopic freeway traffic simulator, Department of Electrical Engineering and Computer Sciences, Berkeley, CA., USA, (2008).
[20] J. Li, L. Gao, W. Song, L. Wei, and Y. Shi, Short term traffic flow prediction based on lstm, in 2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP), IEEE, 2018, pp. 251–255.
[21] Z. Li, C. Xu, D. Li, P. Liu, and W. Wang, Comparing the effects of ramp metering and variable speed limit on reducing travel time and crash risk at bottlenecks, IET Intelligent Transport Systems, 12 (2017), pp. 120–126.
[22] L. Lin, Q. Wang, and A. W. Sadek, A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction, Transportation Research Part C: Emerging Technologies, 55 (2015), pp. 444–459.
[23] L. Lin, K. Yuan, and S. Ren, Analysis of urban freeway traffic flow characteristics based on frequent pattern tree, in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), IEEE, 2014, pp. 1719–1725.
[24] X. Ma, A. Karimpour, and Y.-J. Wu, Statistical evaluation of data requirement for ramp metering performance assessment, Transportation Research Part A: Policy and Practice, 141 (2020), pp. 248–26.
[25] X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, Long short-term memory neural network for traffic speed prediction using remote microwave sensor data, Transportation Research Part C: Emerging Technologies, 54 (2015), pp. 187–197.
[26] A. Meshkat, M. Zhi, J. L. Vrancken, A. Verbraeck, Y. Yuan, and Y. Wang, Coordinated ramp metering with priorities, IET Intelligent Transport Systems, 9 (2015), pp. 639–645.
[27] A. Miglani and N. Kumar, Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges, Vehicular Communications, 20 (2019), p. 100184.
[28] M. Moghaddam, M. Mesbah, and M. Hickman, Using big data sources for origin-destination matrix adjustment, in 39th Australasian Transport Research Forum (ATRF), Auckland, New Zealand, 2017.
[29] G. F. Paesani, System wide adaptive ramp metering in southern california, in ITS America 7th Annual Meet[1]ing and Exposition: Merging the Transportation and Communications RevolutionsIntelligent Transportation Society of America (ITS America), 1997.
[30] M. Papageorgiou, H. Hadj-Salem, J.-M. Blosseville, et al., Alinea: A local feedback control law for on-ramp metering, Transportation Research Record, 1320 (1991), pp. 58–67.
[31] I. Papamichail, M. Papageorgiou, V. Vong, and J. Gaffney, Heuristic ramp-metering coordination strategy implemented at monash freeway, australia, Transportation Research Record, 2178 (2010), pp. 10–20.
[32] S. Peeta and A. K. Ziliaskopoulos, Foundations of dynamic traffic assignment: The past, the present and the future, Networks and spatial economics, 1 (2001), pp. 233–265.
[33] L. Shuwen and X. Jiyi, An improved apriori algorithm based on matrix, in 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), IEEE, 2020, pp. 488–491.
[34] B. L. Smith and M. J. Demetsky, Traffic flow forecasting: comparison of modeling approaches, Journal of transportation engineering, 123 (1997), pp. 261–266.
[35] Y. J. Stephanedes, Performance indicators and policy evaluation in rural transit., (1981).
[36] C. Taylor, D. Meldrum, and L. Jacobson, Fuzzy ramp metering: Design overview and simulation results, Transportation Research Record, 1634 (1998), pp. 10–18.
[37] S. Tscharaktschiew, Why are highway speed limits really justified? an equilibrium speed choice analysis, Transportation research part B: methodological, 138 (2020), pp. 317–351.
[38] J. Xu, X. Zhao, and D. Srinivasan, On optimal freeway local ramp metering using fuzzy logic control with particle swarm optimisation, IET Intelligent Transport Systems, 7 (2013), pp. 95–104.
[39] H. Yang, M. G. Bell, and Q. Meng, Modeling the capacity and level of service of urban transportation networks, Transportation Research Part B: Methodological, 34 (2000), pp. 255–275.
[40] Y. Ye and C.-C. Chiang, A parallel apriori algorithm for frequent itemsets mining, in Fourth International Conference on Software Engineering Research, Management and Applications (SERA’06), IEEE, 2006, pp. 87– 94.
[41] X. Yuan, An improved apriori algorithm for mining association rules, in AIP conference proceedings, vol. 1820, AIP Publishing LLC, 2017, p. 080005.
[42] G. P. Zhang, Time series forecasting using a hybrid arima and neural network model, Neurocomputing, 50 (2003), pp. 159–175.
[43] L. Zhang, Q. Liu, W. Yang, N. Wei, and D. Dong, An improved k-nearest neighbor model for short-term traffic flow prediction, Procedia-Social and Behavioral Sciences, 96 (2013), pp. 653–662.
[44] Y. Zhang and P. A. Ioannou, Stability analysis and variable speed limit control of a traffic flow model, Transportation Research Part B: Methodological, 118 (2018), pp. 31–65.
[45] L. Zhao, Z. Li, Z. Ke, and M. Li, Fuzzy self-adaptive proportional–integral–derivative control strategy for ramp metering at distance downstream bottlenecks, IET Intelligent Transport Systems, 14 (2020), pp. 250–256.
AUT Journal of Mathematics and Computing
Volume 3, Issue 2
September 2022
Pages 237-251

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