An Implementation of the AI-based Traffic Flow Prediction in The Resilience Control Scheme

Document Type : Research Paper

Authors

1 PhD candidate, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran

2 Associated Professor, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran

3 Postdoctoral fellows, Civil Engineering Department, McGill University, Montreal, Canada

4 Professor, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

Today, often a reliable and dynamic sensor system is found to be necessary to control intelligent transportation systems. While these dynamical sensor systems are often found to be useful for the ordinary situations, the resilience-control-related issues are not yet fully addressed in the literature. The traffic flow is an important resource, which if found to be disturbed by a malicious threat it may cause further insecurities, e.g. if the sensor data is not accessible due to a malicious sabotage of the on-the-road sensors. Furthermore, often centers for the data gathering and prediction are suffering from data-loss because of imperfections of the data gathering itself. To overcome the resulting difficulties, a prediction engine is required to estimate the traffic flow, with the ability to compensate for the lost sensors.
In this paper, a traffic flow prediction engine is proposed in which the artificial-intelligence-based methods are used to perform the optimization task. This method is implemented for the test in the real-world situation and its efficiency in traffic estimation is proved to be reliable. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained with the particle swarm optimization (PSO) algorithm and the Artificial Neural Network model (ANN) is used to predict the flow. In addition, The Principal Components Analysis (PCA) method is adopted to reduce the dimension of the features. The results show the method's efficiency in predicting the traffic flow. This prediction engine can be practically implemented and used as a replacement for the sensors to predict the traffic flow.

Keywords


-                  Abdi, J. and Moshiri, B. (2015) "Application of temporal difference learning rules in short-term traffic flow prediction", Expert Systems, Vol. 32, No. 1, pp. 49-64.
-                Abdulhai, B., Porwal, H. and Recker, W. (2002) "Short-term traffic flow prediction using neuro-genetic algorithms", ITS Journal-Intelligent Transportation Systems Journal, Vol.7, No. 1, pp. 3-41.
-                Barimani, N., Moshiri, B. and Teshnehlab, M. (2012) "State space modeling and short-term traffic speed prediction using kalman filter based on ANFIS", International Journal of Engineering and Technology, Vol.4, No. 2, pp. 116.
-                Biron, Z. A. (2017) "A resilient control approach to secure cyber physical systems (CPS) with an application on connected vehicles",Doctoral dissertation, Clemson University, pp. 1-97.
 
-                Chen, C. Hu, J., Meng, Qiang, and Zhang, Yi (2011) "Short-time traffic flow prediction with ARIMA-GARCH model", Intelligent vehicles symposium (IV), IEEE, pp. 607-612.
-         Daffertshofer, A., Lamoth, C. J., Meijer, O. G., and Beek, P. J. (2004) "PCA in studying coordination and variability: a tutorial", Clinical biomechanics, Vol. 19, No. 4, pp. 415-428.
-                Divsalar, M., Khatami Firouzabadi, A., Sadeghi, M., Behrooz, A. and Alavi, A. (2011) "Towards the prediction of business failure via computational intelligence techniques", Expert Systems, Vol. 28, No. 3, pp. 209-226.
 
-                Esbati, M., Ahmadieh Khanesar, M. and Shahzadi, A. (2018) "Modeling level change in Lake Urmia using hybrid artificial intelligence approaches", Theoretical and Applied Climatology, Vol. 133, No. 1, pp. 447-458.
 
-                Ferrara, A., Sacone, S., and Siri, S. (2018). "Emerging Freeway Traffic Control Strategies". Freeway Traffic Modelling and Control, pp. 293-311.
 
-                Ganin, A. A., Mersky, A. C., Jin, A. S., Kitsak, M., Keisler, J. M., & Linkov, I. (2019). "Resilience in intelligent transportation systems (ITS). Transportation", Research Part C: Emerging Technologies, 100, pp. 318-329.
 
-                Gong, X. Y. and Tang, S. M. (2003) "Integrated traffic flow forecasting and traffic incident detection algorithm based on non-parametric regression", Zhongguo Gonglu Xuebao, 2003, Vol. 16, No. 1, pp. 82-86.
 
-                Gupta, N., Ahuja, N., Malhotra, S., Bala, A. and Gurleen, K. (2017) "Intelligent heart disease prediction in cloud environment through ensembling", Expert Systems, Vol. 34, No. 3, pp.12-20.
 
-        Hadiuzzaman, M., Karim, A., Rahman, M., and Hasan, T. (2016) "Planning level regression models for prediction of the number of crashes on urban arterials in Bangladesh". International journal of transportation engineering, Vol. 3, No. 4, pp 267-275.
 
-                Hooshdar, S. and Adeli, H. (2004) "Toward intelligent variable message signs in freeway work zones: Neural network model", Journal of Transportation Engineering, Vol. 130, No. 1, pp. 83-93.
 
-                Hosseini, S. H., Moshiri, B., Rahimi-Kian, A. and Nadjar Araabi, B. (2014) "Traffic flow prediction using MI algorithm and considering noisy and data loss conditions: An application to Minnesota traffic flow prediction", Promet-Traffic & Transportation, Vol. 26, No. 5, pp. 393-403.
 
-                Lam, W. H., Tang, Y. F., and Tam, M. L. (2006) "Comparison of two non-parametric models for daily traffic forecasting in Hong Kong", Journal of Forecasting, Vol. 25, No. 3, pp. 173-192.
 
-                Li, C., Anavatti, S. G., and Ray, T (2011) "Short-term traffic flow prediction using different techniques", IECon 2011-37th Annual Conference on IEEE Industrial Electronics Society, IEEE, pp. 2423-2428.
 
-                Li, S., Wang, L. and Liu, B. (2013) "Prediction of short-term traffic flow based on PSO-optimized chaotic BP neural network", International Conference on Computer Sciences and Applications, IEEE, 2013, pp. 292-295.
 
-                López, A. A., de Quevedo, Á. D., Yuste, F. S., Dekamp, J. M., Mequiades, V. A., Cortés, V. M, Cobeña, D.G., Pulido, D. M., Urzaiz, F.I. and Menoyo, J.G. (2018) "Coherent Signal Processing for Traffic Flow Measuring Radar Sensor", IEEE Sensors Journal, Vol. 18, No. 12, pp. 4803-4813.
 
-                Ma, Z., Luo, G. and Huang, D. (2016) "Short term traffic flow prediction based on on-line sequential extreme learning machine", Eighth International Conference on Advanced Computational Intelligence (ICACI), IEEE,  2016, pp. 143-149.
 
-                Mamdoohi, A. R., Saffarzadeh, M. & Shojaat, S. (2015). "Capacity drop estimation based on stochastic approach applied to Tehran-Karaj freeway,. International Journal of Transportation Engineering, Vol. 2, No. 4, pp 279-288.
 
-                Poor Arab Moghadam, M., Pahlavani, P. and Naseralavi, S. (2016) "Prediction of car following behavior based on the instantaneous reaction time using an ANFIS-CART based model". International Journal of Transportation Engineering, Vol. 4, No. 2, pp 109-126.
 
-                Ramezani, M. and Geroliminis, N. (2012) "On the estimation of arterial route travel time distribution with Markov chains", Transportation Research Part B: Methodological, Vol. 46, No. 10, pp. 1576-1590.
 
-                Rojer Jang, J.S. (1993) "ANFIS: adaptive-network-based fuzzy inference system", IEEE transactions on systems, man, and cybernetics, Vol. 23, No. 3, pp. 665-685.
 
-  Shang, Q., Lin, C., Yang, Z., Bing, Qichun, and Zhou, Xiyang (2016) "A hybrid short-term traffic flow prediction model based on singular spectrum analysis and kernel extreme learning machine", PLoS one, Vol. 11, No. 8, e0161259.
 
-  Sun, S., Yu, G., and Zhang, C. (2004) "Short-term traffic flow forecasting using sampling Markov Chain method with incomplete data", IEEE Intelligent Vehicles Symposium, IEEE, pp. 437-441.
-  Torfehnejad, H., Jalali, A. (2018). "Traffic condition detection in freeway by using autocorrelation of density and flow". International Journal of Transportation Engineering, Vol. 6, No. 1, pp 85-98.
 
-  Trelea, I. C (2003). "The particle swarm optimization algorithm: convergence analysis and parameter selection", Information processing letters, Vol. 85, No.6, pp. 317-325.
 
-         Williams, B. M., and Hoel, L. A.  (1999) “Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process”, No. LTVA/29242/CE99/103.
 
-        Xia. (2020). Assessment of Freeway Link Performance Reduction due to Traffic Crashes Using Resilience Indices (Doctoral dissertation, pp. 30-60
 
-         Yu, F. and Song, Z. (2015) "The short-term traffic flow prediction method based on detectors PSO algorithm", Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), IEEE, pp. 890-893.
 
-         Zhao, L. and Wang, F. Y (2007) "Short-term fuzzy traffic flow prediction using self-organizing TSK-type fuzzy neural network", IEEE International Conference on Vehicular Electronics and Safety, IEEE, 2007, pp. 1-6.