Detection of road traffic anomalies based on computational data science

Ancans G, Bobrovsa V, Ancansb A, Kalibatiene D. Spectrum. Considerations for 5G mobile communication systems. Procedia Comput Sci. 2017;104:509–16.

Aqib M, Mehmood R, Alzahrani A, Katib I, Albeshri A. A deep learning model to predict vehicles occupancy on freeways for traffic management. Int J Comput Sci Netw Secu. 2018;18(12):1–8.

Alrajhi M, Kamel M. A deep-learning model for predicting and visualizing the risk of road traffic accidents in Saudi Arabia: a tutorial approach. IJACSA. 2019;10(11):475–83.

Dey KC, et al. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in a heterogeneous wireless network—Performance evaluation. Transp Res Part C. 2016;68:168–84.

Berman SD, Buczak LA, Chavis SJ, Corbett LC. A survey of deep learning methods for cyber security. Information. 2019;10:122.

Qin H, Yan M, Ji H. Application of Controller Area Network (CAN) bus anomaly detection based on time series prediction. Veh Commun. 2021;27:100291.

Brandl O. V2X traffic management. Elektrotech Inftech. 2016;133(7):353–5.

Borzacchielo TM. The use of data from mobile phone networks for transportation applications. In: TRB 2010 Annual Meeting. 2010.

Chen C, Liu Y, Sun X, Cairano-Gilfedder DC, Titmus S. Automobile maintenance prediction using deep learning with GIS data. Procedia CIRP. 2019;81:447–52.

Liang F, Yu A, Hatcher GW, Yu W, Lu C. Deep leaning-based power usage forecast modeling and evaluation. Procedia Comput Sci. 2019;154:102–8.

Polson GN, Sokolov OV. Deep learning for short-term traffic flow prediction. Transp Res Part C. 2017;79:1–17.

Nguyen H, Kieu L-M, Wen T, Cai C. Deep learning methods in transportation domain: a review. IET Intell Transp Syst. 2018;12(9):998–1004.

Suhao L, Jinzhao L, Guoquan L, Tong B, Huiqian W, Yu P. Vehicle type detection based on deep learning in traffic scene. Procedia Comput Sci. 2018;131:564–72.

Yan M, Li M, He H, Peng J. Deep learning for vehicle speed prediction. Energy Procedia. 2018;152:618–23.

Raiyn J. Speed adaptation in urban road network management. Transp Telecommun. 2016;17(2):11–121.

Jan B, et al. Deep learning in big data analytics: a comparative study. Comput Electr Eng. 2017;75:1–13.

Jawhar I, Mohamed N, Usmani H. An overview of inter-vehicular communication systems, protocols and middleware. J Netw. 2013;8(12):2749–61.

Park CR, Homg JE. Urban traffic accident risk prediction for knowledge-based mobile multimedia service. Pers Ubiquit Comput. 2020. https://doi.org/10.1007/s00779-020-01442-y.

Raiyn J. Road traffic congestion management based on search allocation approach. Transp Telecommun. 2017;18(1):25–33.

Raiyn J. Developing vehicle locations strategy on urban road. Transp Telecommun. 2017;18(4):253–62.

Ramm K, Schwieger V. Mobile positioning for traffic state acquisition. J Location Serv. 2007;1(2):133–44.

Lv Y, Tang S. Real-time highway traffic accident prediction based on the K-nearest neighbor method. International conference on measuring technology and mechatronics automation. IEEE: Piscataway; 2010.

Xiaoqiang Z, Ruimin L, Xinxin Y. Incident duration model on urban freeways based on classification and regression tree. In: 2nd international conference on intelligent computation technology and automation, TRB annual meeting. 2010; 2: 526–528.

Wang J, Cehn R, He Z. Traffic speed prediction for urban transportation network: a path based deep learning approach. Transp Res Part C. 2019;100:372–85.

Wang Z, Murray-Tuite P. Modeling incident-related traffic and estimating travel time with a cellular automaton model. In: proceedings of transportation research board’s 89th annual meeting CD-ROM. DC; 10–14 Jan 2010.

Chrobok R, Kaumann O, Wahle J, Schreckenberg M. Different methods of traffic forecast based on real data. Eur J Oper Res. 2004;15:558–68.

Mishra A, Cohen A, Reichherzer T, Wilde T. Detection of data anomalies at the edge of pervasive IoT Systems. Computing. 2021. https://doi.org/10.1007/s00607-021-00927-9.

Chang W, Jung BC. Optimal transmission strategy without capacity loss at a primary user in cognitive radio networks over inter-symbol interference channels. IEEE Commun Lett. 2014;18(3):411–4.

Li J, Li S, Zhao F, Du R. Co-channel interference modeling in cognitive wireless networks. IEEE Trans Commun. 2014;62(9):3113–27.

Adnan A, Nordina S, Bahruddinb MAB, Alic M. How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle. Transp Res Part A. 2018;118:819–36.

Martinez-Diaz M, Soriguera F. Autonomous vehicles: theoretical and practical challenges. Transp Res Procedia. 2018;33:275–82.

Xiang W, Huang T, Wan W. Machine learning based optimization for vehicle-to-infrastructure communications. Futur Gener Comput Syst. 2019;94:488–95.

Ndashimye E, Ray KS, Sarkar N, Gutiérrez AI. Vehicle-to-infrastructure communication over multi-tier heterogeneous networks: a survey. Comput Netw. 2017;112:144–66.

Silva N, Shah V, Soares J, Rodrigues H. Road anomalies detection system evaluation. Sensors. 2018;18:1–20.

Wang T, Zhao J, Li P. An extended car-following model at unsignalized intersections under V2V communication environment. PLoS ONE. 2018;13(2):e0192787.

Sepulcre M, Gozalvez J. Context-aware heterogeneous V2X communications for connected vehicles. Comput Netw. 2018;136:13–21.

Choudhury A, Maszczyk T, Math CB, Li H, Dauwels J. An integrated simulation environment for testing V2X protocols and applications. Procedia Comput Sci. 2016;80:2042–52.

Weiß C. V2X communication in Europe—from research projects towards standardization and field testing of vehicle communication technology. Comput Netw. 2011;55:3103–19.

Jin Q, Wu G, Boriboonsomsin K, Barth M. Platoon-based multi-agent intersection management for connected vehicles. In: 16th international IEEE conference on intelligent transportation systems. 2013.

Gora P, Rüb I. Traffic models for self-driving connected cars. Transp Res Procedia. 2016;14:2207–16.

Li J, Dridi M, El-Moudni A. A cooperative traffic control of vehicle-intersection, (CTCVI) for the reduction of traffic delays and fuel consumption. Sensors. 2016;16:1–20.

Bergenhem C, Hedin E, Skarin D. Vehicle-to-vehicle communication for a platooning system. Procedia Soc Behav Sci. 2012;48:1222–33.

Jiang T, Chen H-H, Wu H-C, Yi Y. Channel modeling and inter-carrier interference analysis for V2V communication systems in frequency-dispersive channels. Mobile Netw Appl. 2010;14:4–12.

Sun W, Ström EG, Brännström F, Sou KC, Sui Y. Radio resource management for D2D-Based V2V communication. IEEE Trans Veh Technol. 2016;65(8):6636–50.

Xinran L, Xingwu L, Yuanhong W, Juhua P, Xiangliang Z. Detecting anomaly in traffic flow from road similarity analysis. Lecture notes in computer science. Berlin: Springer Nature; 2016. p. 92–104.

Du LL, Dao H, Li X-X. Information dissemination delay in vehicle-to-vehicle communication networks in a traffic stream. IEEE Trans Intell Transp Syst. 2015;16(1):66–80.

Jing Bai J, Chen Y. A deep neural network based on classification of traffic volume for short-term forecasting, Hindawi. Math Probl Eng. 2019. https://doi.org/10.1155/2019/6318094.

Nam VH, Dang HN. An improvement of traffic incident recognition by deep convolutional neural network. Int J Innov Technol Explor Eng (IJITEE). 2018;8(1):10–4.

Li R, Pereira FC, Ben-Akiva ME. Overview of traffic incident duration analysis and prediction. Eur Transp Res Rev. 2018;10(22):1–13. https://doi.org/10.1186/s12544-018-0300-1.

Lu N, Cheng N, Zhang N, Shen X, Mark JW. Connected vehicles: solutions and challenges. IEEE Internet Things J. 2014;1(4):289–99.

Iliopoulou C, Kepaptsoglou K. Combining ITS and optimization in public transportation planning: state of the art and future research paths. Eur Transp Res Rev. 2019;11(27):1–16. https://doi.org/10.1186/s12544-019-0365-5.

Zhanga Q, Yang TL, Chenc Z, Li P. A survey on deep learning for big data. Inf Fus. 2018;42:146–57. https://doi.org/10.1016/j.inffus.2017.10.006.

Raiyn J. Road traffic anomaly detection based on deep learning technology. In: 7th international conference on vehicle technology and intelligent transport systems (VEHITS). Apr 2021.

Wooldrige M, Jennings RN. Intelligent agents: theory and practice. Cambridge: Cambridge University Press; 2009.

Russel S, Norvig P. Artificial intelligence, a modern approach. 4th ed. London: Pearson Education Limited; 2021.

Raiyn J. Classification of road traffic anomaly based on travel data analysis. Int Rev Civ Eng (IRECE). 2021. https://doi.org/10.15866/irece.v12i6.20530.

Santhosh KK, Dogra DP, Roy PP. Anomaly detection in road traffic using visual surveillance: a survey. ACM Comput Surv. 2020;6(53):1–26.

Dogra DP, Roy PP, Mitra A. Video trajectory classification and anomaly detection using hybrid CNN-VAE Architثcture. IEEE transactions on Intelligent Transportation Systems, 2021.

Santhosh KK, Mohapatra S, Debi MS, Dogra DP, Roy PP, Kim Mitra GB. Computer vision-guided intelligent traffic signaling for isolated intersections. Expert Syst With Appl. 2019;134:267–78.

Santhosh KK, Dogra DP, Roy PP. Queuing theory guided intelligent traffic scheduling through video analysis using Dirichlet process mixture model. Expert Syst With Appl. 2019;118:169–81.