Internet of Smart-Cameras for Traffic Lights Optimization in Smart Cities

Smart and decentralized control systems have recently been proposed to handle the growing traffic congestion in urban cities. Proposed smart traffic light solutions based on Wireless Sensor Network and Vehicular Ad-hoc NETwork are either unreliable and inflexible or complex and costly. Furthermore, the handling of special vehicles such as emergency is still not viable, especially during busy hours. Inspired by the emergence of distributed smart cameras, we present a novel approach to traffic control at intersections. Our approach uses smart cameras at intersections along with image understanding for real-time traffic monitoring and assessment. Besides understanding the traffic flow, the cameras can detect and track special vehicles and help prioritize emergency cases. Traffic violations can be identified as well and traffic statistics collected. In this paper, we introduce a flexible, adaptive and distributed control algorithm that uses the information provided by distributed smart cameras to efficiently control traffic signals. Experimental results show that our collision-free approach outperforms the state-of-the-art of the average user's waiting time in the queue and improves the routing of emergency vehicles in a cross congestion area.

[1]  S.K. Singh,et al.  Smart real-time traffic congestion estimation and clustering technique for urban vehicular roads , 2016, 2016 IEEE Region 10 Conference (TENCON).

[2]  Dhananjay Singh,et al.  Developing NovaGenesis architecture for internet of things services: Observation, challenges and ITMS application , 2014, 2014 International Conference on Information and Communication Technology Convergence (ICTC).

[3]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[4]  Gerhard P. Hancke,et al.  A Survey on Urban Traffic Management System Using Wireless Sensor Networks , 2016, Sensors.

[5]  Dea van Lierop,et al.  On time and ready to go: An analysis of commuters’ punctuality and energy levels at work or school , 2017 .

[6]  Qi Wang,et al.  Traffic congestion analysis: A new Perspective , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Judy McKay,et al.  Alleviating traffic congestion around our cities; how can supply chains address the issue? , 2014 .

[8]  A. Aisha Al-Abdallah,et al.  Real-time traffic surveillance using ZigBee , 2010, 2010 International Conference On Computer Design and Applications.

[9]  Isabelle M. Demeure,et al.  A distributed algorithm for adaptive traffic lights control , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[10]  Hossam M. Sherif,et al.  Real time traffic accident detection system using wireless sensor network , 2014, 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[11]  J. McKay,et al.  Investigating ‘anywhere working’ as a mechanism for alleviating traffic congestion in smart cities , 2019, Technological Forecasting and Social Change.

[12]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[13]  T. S. B. Sudarshan,et al.  Intelligent traffic management with wireless sensor networks , 2013, 2013 ACS International Conference on Computer Systems and Applications (AICCSA).

[14]  Alex Zelinsky,et al.  Learning OpenCV---Computer Vision with the OpenCV Library (Bradski, G.R. et al.; 2008)[On the Shelf] , 2009, IEEE Robotics & Automation Magazine.

[15]  Sinan Salman,et al.  Alleviating road network congestion: Traffic pattern optimization using Markov chain traffic assignment , 2018, Comput. Oper. Res..

[16]  Ali M. Shatnawi,et al.  Intelligent Traffic Light Flow Control System Using Wireless Sensors Networks , 2010, J. Inf. Sci. Eng..

[17]  Christophe Bobda,et al.  Distributed Embedded Smart Cameras: Architectures, Design and Applications , 2014 .

[18]  Danwei Wang,et al.  Distributed traffic signal control for maximum network throughput , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[19]  Gang Qu,et al.  A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy , 2019, IEEE Transactions on Intelligent Transportation Systems.

[20]  G. Matthews,et al.  The slow and the furious: Anger, stress and risky passing in simulated traffic congestion , 2016 .

[21]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[23]  Sébastien Faye,et al.  Multiple intersections adaptive traffic lights control using a wireless sensor networks Contrôle adaptatif des feux de circulation sur de multiples intersections à l'aide d'un réseau de capteurs sans fil , 2013 .

[24]  Qi Qi,et al.  Wireless sensor networks in intelligent transportation systems , 2009, Wirel. Commun. Mob. Comput..

[25]  Jiannong Cao,et al.  Distributed Mutual Exclusion Algorithms for Intersection Traffic Control , 2015, IEEE Transactions on Parallel and Distributed Systems.

[26]  Isabelle Demeure,et al.  A distributed algorithm for multiple intersections adaptive traffic lights control using a wireless sensor networks , 2012, UrbaNe '12.

[27]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[28]  G. Padmavathi,et al.  A Study on Vehicle Detection and Tracking Using Wireless Sensor Networks , 2010, Wirel. Sens. Netw..

[29]  Anilloy Frank,et al.  IoT based Smart Traffic density Control using Image Processing , 2019, 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC).