Machine learning based accident prediction in secure IoT enable transportation system

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.

[1]  Hesham A. Rakha,et al.  Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data , 2015, IEEE Transactions on Intelligent Transportation Systems.

[2]  Ruimin Li,et al.  Real-time traffic accidents post-impact prediction: Based on crowdsourcing data. , 2020, Accident; analysis and prevention.

[3]  Madhar Taamneh,et al.  Severity Prediction of Traffic Accident Using an Artificial Neural Network , 2017 .

[4]  Sotiris Karabetsos,et al.  A Review of Machine Learning and IoT in Smart Transportation , 2019, Future Internet.

[5]  Debasish Jena,et al.  MagTrack: Detecting Road Surface Condition using Smartphone Sensors and Machine Learning , 2019, TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON).

[6]  Carsten Maple,et al.  A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments , 2019, Sensors.

[7]  Hongbo Zhu,et al.  Vehicle Accident Risk Prediction Based on AdaBoost-SO in VANETs , 2019, IEEE Access.

[8]  Bhabendu Kumar Mohanta,et al.  An ECC based Lightweight Authentication Protocol For Mobile Phone in Smart Home , 2018, 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS).

[9]  Ting Fu,et al.  Crash prediction based on traffic platoon characteristics using floating car trajectory data and the machine learning approach. , 2019, Accident; analysis and prevention.

[10]  Amirfarrokh Iranitalab,et al.  Comparison of four statistical and machine learning methods for crash severity prediction. , 2017, Accident; analysis and prevention.

[11]  Abdulhamit Subasi,et al.  Traffic accident detection using random forest classifier , 2018, 2018 15th Learning and Technology Conference (L&T).

[12]  Khan Muhammad,et al.  Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm , 2019, Appl. Soft Comput..

[13]  Neetesh Kumar,et al.  Fuzzy Inference Enabled Deep Reinforcement Learning-Based Traffic Light Control for Intelligent Transportation System , 2021, IEEE Transactions on Intelligent Transportation Systems.

[14]  Houbing Song,et al.  Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS) , 2018, Sensors.

[15]  Luis Sánchez-Fernández,et al.  Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving , 2017 .

[16]  Azzedine Boukerche,et al.  Machine Learning-based traffic prediction models for Intelligent Transportation Systems , 2020, Comput. Networks.

[17]  Chun Liu,et al.  Research on black spot identification of safety in urban traffic accidents based on machine learning method , 2019, Safety Science.

[18]  Dhananjay Kalbande,et al.  Machine learning approach for predicting bumps on road , 2015, 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT).

[19]  Bhabendu Kumar Mohanta,et al.  DecAuth: Decentralized Authentication Scheme for IoT Device Using Ethereum Blockchain , 2019, TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON).

[20]  Shouhuai Xu,et al.  A Safety and Security Architecture for Reducing Accidents in Intelligent Transportation Systems , 2018, 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[21]  Iman Aghayan,et al.  Prediction for traffic accident severity: comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods , 2012 .

[22]  Yi Yang,et al.  Smart community security monitoring based on artificial intelligence and improved machine learning algorithm , 2020, J. Intell. Fuzzy Syst..

[23]  Yibo Ai,et al.  Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments , 2018, Inf..

[24]  Erdogan Dogdu,et al.  A real-time autonomous highway accident detection model based on big data processing and computational intelligence , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[25]  Sambit Bakshi,et al.  A memetic algorithm using emperor penguin and social engineering optimization for medical data classification , 2019, Appl. Soft Comput..

[26]  Seyed Mohammad Hossein Hasheminejad,et al.  Traffic accident severity prediction using a novel multi-objective genetic algorithm , 2017 .

[27]  Helai Huang,et al.  Statistical and machine-learning methods for clearance time prediction of road incidents: A methodology review , 2020, Analytic Methods in Accident Research.

[28]  Praveen Edara,et al.  Traffic Flow Forecasting for Urban Work Zones , 2015, IEEE Transactions on Intelligent Transportation Systems.