Abstract
Predicting traffic disturbances is a challenging problem in urban cities. Emergency vehicles (EV) is one of the biggest disturbances that affect traffic fluidity. The goal of this paper is to provide a machine learning application to deal with emergency cases in traffic networks. Particularly, we investigate the use of deep learning techniques coupled with Artificial Immune System to tackle the issue of EV guidance at signalized intersections. To accomplish this goal, we develop a traffic signal control system capable to estimate traffic status, guide EV to reach their destinations while assuming better traffic condition, control traffic signals, and adapt to new disturbances. For traffic forecasting, the suggested system inherits the advantages of convolutional neural networks, classification, and long short term memory. To control traffic signals, the suggested system uses the immune memory algorithm. To enhance and adapt control decisions to traffic disturbances, the suggested system uses a continuous learning approach assumed by an adapted reinforcement learning algorithm. Assessments using well-known algorithms from the literature are detailed in this work. The benchmarking algorithms are the preemptive longest queue first matching weight matrix system, the pre-emptive immune memory algorithm inspired case-based reasoning, and the preemptive optimized stage based fixed time algorithm. Experiments show a competitive performance of the suggested system compared to benchmarking algorithms.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bouhana A, Fekih A, Abed M, Chabchoub H (2013) An integrated case-based reasoning approach for personalized itinerary search in multimodal transportation systems. Transp Res Part C Emerg Technol 31:30–50. https://doi.org/10.1016/j.trc.2013.02.014
Darmoul S, Elkosantini S, Louati A, Ben Said L (2017) Multi-agent immune networks to control interrupted flow at signalized intersections. Transportation Research Part C: Emerging Technologies 82:290–313. https://doi.org/10.1016/j.trc.2017.07.003
De Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, London
de Gier J, Garoni TM, Rojas O (2010) Traffic flow on realistic road networks with adaptive traffic lights. Theory Exp. https://doi.org/10.1088/1742-5468/2011/04/P04008
Diala D, Sid-Ali A, Abderrahman EM, Habib C (2012) A Dynamic multi-criteria aid for process driving using case-based reasoning. J Decis Syst 18(4):459–484
Dogan E, Akgungor AP, Arslan T (2016) Estimation of delay and vehicle stops at signalized intersections using artificial neural network. Eng Rev 36(2):157–165
Eichler M, Daganzo CF (2006) Bus lanes with intermittent priority: strategy formulae and an evaluation. Transp Res Part B Methodol 40(9):731–744. https://doi.org/10.1016/j.trb.2005.10.001
El-Tantawy S, Abdulhai B, Abdelgawad H (2014) Design of reinforcement learning parameters for seamless application of adaptive traffic signal control. J Intell Transp Syst 18(3):227–245. https://doi.org/10.1080/15472450.2013.810991
Genders W, Razavi S (2016) Using a deep reinforcement learning agent for traffic signal control. Undefined. Retrieved from https://www.semanticscholar.org/paper/Using-a-Deep-Reinforcement-Learning-Agent-for-Genders-Razavi/5f5242d6e3e9fceb9568f9d2658a2d1aacb475c0
Glick J (2015) Reinforcement slearning for adaptive traffic signal control. Stanford University, Stanford
Guler SI, Gayah VV, Menendez M (2016) Bus priority at signalized intersections with single-lane approaches: a novel pre-signal strategy. Transp Res Part C Emerg Technol 63:51–70. https://doi.org/10.1016/j.trc.2015.12.005
Hammami M, Bechikh S, Louati A, Makhlouf M, Said LB (2020) Feature construction as a bi-level optimization problem. Neural Comput Appl 1–22. https://doi.org/10.1007/s00521-020-04784-z
Houli D, Zhiheng L, Yi Z, Pappis C, Mamdani E, Trabia M, Daganzo C (2010) Multiobjective reinforcement learning for traffic signal control using vehicular ad hoc network. EURASIP J Adv Signal Process 2010(1):724035. https://doi.org/10.1155/2010/724035
Huang Y-S, Shiue J-Y, Luo J (2015) A traffic signal control policy for emergency vehicles preemption using timed petri nets. IFAC-PapersOnLine 48(3):2183–2188. https://doi.org/10.1016/j.ifacol.2015.06.412
Huisken G, van Berkum EC (2003) A comparative analysis of short-range travel time prediction methods. Retrieved from https://research.utwente.nl/en/publications/a-comparative-analysis-of-short-range-travel-time-prediction-meth
Jain A, Tompson J, Andriluka M, Taylor GW, Bregler C (2013) Learning human pose estimation features with convolutional networks. Retrieved from https://arxiv.org/abs/1312.7302
Kim T-Y, Cho S-B (2018) Web traffic anomaly detection using C-LSTM neural networks. Expert Syst Appl 106:66–76. https://doi.org/10.1016/J.ESWA.2018.04.004
Krizhevsky A, Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.299.205
Kumar SV, Vanajakshi L (2015) Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur Transp Res Rev 7(3):21. https://doi.org/10.1007/s12544-015-0170-8
Labreuche C (2003) The Choquet integral for the aggregation of interval scales in multicriteria decision making. Fuzzy Sets Syst 137(1):11–26. https://doi.org/10.1016/S0165-0114(02)00429-3
Lampert CH, Blaschko MB, Hofmann T (2008) Beyond sliding windows: Object localization by efficient subwindow search. IEEE Conf Comput Vis Pattern Recognit 2008:1–8. https://doi.org/10.1109/CVPR.2008.4587586
Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Networks 8(1):98–113. https://doi.org/10.1109/72.554195
Lecun Y, Eon Bottou L, Bengio Y, Patrick H (1998) Gradient-based learning applied to document recognition. Proc IEEE. Retrieved from https://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf
Li Y, Yu R, Shahabi C, Liu Y (2017). Graph convolutional recurrent neural network: data-driven traffic forecasting. Undefined. Retrieved from https://www.semanticscholar.org/paper/Graph-Convolutional-Recurrent-Neural-Network%3A-Li-Yu/5618bb2aff7ecdb0a2ae7c57838d156f731008ff
Louati A, Darmoul S, Elkosantini S, Ben Said L (2018a) An artificial immune network to control interrupted flow at a signalized intersection. Inf Sci 433–434:70–95. https://doi.org/10.1016/j.ins.2017.12.033
Louati A, Elkosantini S, Darmoul S, Ben Said L (2016) A case-based reasoning system to control traffic at signalized intersections. IFAC-PapersOnLine 49(5):149–154. https://doi.org/10.1016/j.ifacol.2016.07.105
Louati A, Elkosantini S, Darmoul S, Ben Said L (2019) An immune memory inspired case-based reasoning system to control interrupted flow at a signalized intersection. Artif Intell Rev 52(3):2099–2129. https://doi.org/10.1007/s10462-017-9604-0
Louati A, Elkosantini S, Darmoul S, Louati H (2018b) Multi-agent preemptive longest queue first system to manage the crossing of emergency vehicles at interrupted intersections. Eur Transp Res Rev 10(2):52. https://doi.org/10.1186/s12544-018-0317-5
Louati A, Louati H, Nusir M, Hardjono B (2020) Multi-agent deep neural networks coupled with LQF-MWM algorithm for traffic control and emergency vehicules guidance. J Ambi Intell Humanized Comput
Mannion P, Duggan J, Howley E(2016) An experimental review of reinforcement learning algorithms for adaptive traffic signal control. In: Autonomic road transport support systems, pp 47–66. https://doi.org/10.1007/978-3-319-25808-9_4
Marcianò FA, Musolino G, Vitetta A (2014) Signal setting optimization on urban road transport networks: the case of emergency evacuation. Saf Sci 72:209–220. https://doi.org/10.1016/J.SSCI.2014.08.005
Marichal J-L (2004) Tolerant or intolerant character of interacting criteria in aggregation by the Choquet integral. Eur J Oper Res 155(3):771–791. https://doi.org/10.1016/S0377-2217(02)00885-8
Marsetič R, Šemrov D, Žura M (2014) Road artery traffic light optimization with use of the reinforcement learning. PROMET Traffic Transp 26(2):101–108. https://doi.org/10.7307/ptt.v26i2.1318
Moranduzzo T, Melgani F (2014a) Automatic car counting method for unmanned aerial vehicle images. IEEE Trans Geosci Remote Sens 52(3):1635–1647. https://doi.org/10.1109/TGRS.2013.2253108
Moranduzzo T, Melgani F (2014b) Detecting cars in UAV images with a catalog-based approach. IEEE Trans Geosci Remote Sens 52(10):6356–6367. https://doi.org/10.1109/TGRS.2013.2296351
Murty MN, Devi VS (2011) Nearest neighbour based classifiers. In: Undergraduate topics in computer science. Pattern recognition, pp 48–85. https://doi.org/10.1007/978-0-85729-495-1
Nagy AM, Simon V (2018) Survey on traffic prediction in smart cities. Pervasive Mobile Comput 50:148–163. https://doi.org/10.1016/J.PMCJ.2018.07.004
Ordóñez F, Roggen D, Ordóñez FJ, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1):115. https://doi.org/10.3390/s16010115
Pinheiro PHO, Collobert R (2014) Recurrent convolutional neural networks for scene labeling. Undefined. Retrieved from https://www.semanticscholar.org/paper/Recurrent-Convolutional-Neural-Networks-for-Scene-Pinheiro-Collobert/1550caab8d12c3f0ea19faaaa6bab3bdd092bafd
Python Software Foundation (2017) SPADE 2.3 : python package index
Qin X, Khan AM (2012) Control strategies of traffic signal timing transition for emergency vehicle preemption. Transp Res Part C Emerg Technol 25:1–17. https://doi.org/10.1016/j.trc.2012.04.004
Raj J, Bahuleyan H, Vanajakshi LD (2016) Application of data mining techniques for traffic density estimation and prediction. Transp Res Proced 17:321–330. https://doi.org/10.1016/J.TRPRO.2016.11.102
Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031
Shao W, Yang W, Liu G, Liu J (2012) Car detection from high-resolution aerial imagery using multiple features. IEEE Int Geosci Remote Sens Symp 2012:4379–4382. https://doi.org/10.1109/IGARSS.2012.6350403
Simard PY, Steinkraus D, Platt JC (2003) Best practices for convolutional neural networks applied to visual document analysis. Retrieved from https://cognitivemedium.com/assets/rmnist/Simard.pdf
Sun S, Zhang C, Zhang Y (2017) Traffic flow forecasting using a spatio-temporal bayesian network predictor. Retrieved from https://arxiv.org/abs/1712.08883
Zhao T, Nevatia R (2001) Car detection in low resolution aerial image. Proc Eighth IEEE Int Conf Comput Vis ICCV 1:710–717. https://doi.org/10.1109/ICCV.2001.937593
Vedaldi A, Gulshan V, Varma M, Zisserman A (2009) Multiple kernels for object detection. In: 2009 IEEE 12th international conference on computer vision, pp 606–613. https://doi.org/10.1109/ICCV.2009.5459183
Wang P, Cao Y, Shen C, Liu L, Shen HT (2015) Temporal pyramid pooling based convolutional neural networks for action recognition. Retrieved from https://arxiv.org/abs/1503.01224
Wang X, Yang M, Zhu S, Lin Y (2015) Regionlets for generic object detection. IEEE Trans Pattern Anal Mach Intell 37(10):2071–2084. https://doi.org/10.1109/TPAMI.2015.2389830
Westgate BS, Woodard DB, Matteson DS, Henderson SG (2013) Travel time estimation for ambulances using Bayesian data augmentation 1. Ann Appl Stat 7(2):1139–1161. https://doi.org/10.1214/13-AOAS626
Chen X, Xiang S, Liu C-L, Pan C-H (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci Remote Sens Lett 11(10):1797–1801. https://doi.org/10.1109/LGRS.2014.2309695
Yu H, Wu Z, Wang S, Wang Y, Ma X (2017). Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Retrieved from https://arxiv.org/abs/1705.02699
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author wishes to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations'
Rights and permissions
About this article
Cite this article
Louati, A. A hybridization of deep learning techniques to predict and control traffic disturbances. Artif Intell Rev 53, 5675–5704 (2020). https://doi.org/10.1007/s10462-020-09831-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-020-09831-8