Evaluating the Emission of CO2 at Traffic Intersections with the Purpose of Reducing Emission Rate, Case Study: The University of Nigeria, Nsukka

Nathan David, Chinedu Duru
Traffic congestion is a major problem around the world that results in slower speeds, increased trip time, and a longer queuing of vehicles. The production and use of fuels for vehicles results in emissions of greenhouse gases (GHSs), besides carbon dioxide, which include methane and nitrous oxide. Traffic lights that wirelessly keep track of vehicles could reduce journey time and fuel consumption thereby reducing carbon emissions. In view of the importance of vehicles as an emitter of GHGs, namely CO2, with the growing concern about climate change, this paper aims to explore the emission of CO2 from vehicles at a traffic intersection for the purpose of reducing emission rate. Realizing this reduction, points to the implementation of an Advanced Traffic Management System (ATMS) with Wireless Sensor Networks (WSNs) on the road network of a region will be discussed. With such a technology, a region can experience lower queue lengths at an intersection and therefore lower CO2 emission surrounding the area. The University of Nigeria, Nsukka (UNN) is used as a case study in exploring this phenomenon which over the years has seen a drastic increase on the amount of cars on the campus area. With the assumption that an ATM system with WSNs is deployed on the UNN campus area, the paper looks into the traffic dynamics that makes it possible to evaluate CO2 emission at traffic light intersections to ensure a cleaner environment. Throughout the paper, it will be made clear that with the relevant equation of CO2 emission and the arrival time per vehicle, CO2 emission rate can be evaluated at a traffic intersection depending on the volume of cars at the intersection. With such evaluation, further analysis can be made on ways to actually reduce CO2 emission and techniques for implementation with an ATM system.

Keywords

CO2 emission, intersection, traffic dynamics, arrival times, wait times, vehicles

References

Bovik, A., Waller, S. T., Heath, R., & Vishwanath, S. (2010). Wireless visual sensor networks for urban traffic management. University of Texas, Austin.

Collotta, M., Pau, G., Salerno, V. M., & Scata, G. (2012). A Novel Road Monitoring Approach Using Wireless Sensor Networks. 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems. doi:10.1109/cisis.2012.37

Duru, L., & David, N. (2016). A Realisation on the Deployment of Smart Traffic Lights with Wireless Sensor Networks in an Effort to Ensure Safety on the Roads of The University of Nigeria, Nsukka Campus Area. International Journal of Scientific & Engineering Research (IJSER),7(4).

Duru, C., & David, N. (2014). Evaluating the Deployment Problem of a Wireless Sensor Network on the University of Nigeria, Nsukka Campus for the Detection and Tracking of Vehicles. International Journal of Scientific & Engineering Research (IJSER),5(10).

Goel, A., Ray, S., & Chandra, N. (2012). Intelligent Traffic Light System to Prioritized Emergency Purpose Vehicles Based on Wireless Sensor Network. International Journal of Computer Applications,40(12), 36-39. doi:10.5120/5019-7352.

Guan, X., Sengupta, R., Krishnan, H., & Bai, F. (2007). A Feedback-Based Power Control Algorithm Design for VANET. 2007 Mobile Networking for Vehicular Environments. doi:10.1109/move.2007.4300806

Li, C., & Shimamoto, S. (2011). Dynamic Traffic Light Control Scheme for Reducing CO2 Emissions Employing ETC Technology. International Journal of Managing Information Technology,2(1). doi:10.5121/ijmit.2012.4201

Luttinen, T. (1996). Statistical analysis of vehicle time headways. Otaniemi: Teknillinen korkeakoulu.

Ramanathan, B. V. (1993). Actuated signal network simulation program. Institute of Transportation Studies University of California Irvine.

Renfrew, D., & Yu, H. (2009). Traffic signal control with ant colony optimization: A thesis(Unpublished master's thesis).

Shih, P. (2013). Traffic Signal Control with Swam Intelligence Ant Colony Optimization(Master's thesis, Faculty of California Polytechnic State University). San Louis Obispo.

Toepfer, H., Chervakova, E., Goetze, M., Hutschenreuther, T., Nikolić, B., & Dimitrijević, B. (2015). Application of wireless sensors within a traffic monitoring system. 2015 23rd Telecommunications Forum Telfor (TELFOR),236-241.

Union of Concerned Scientists. (n.d.). Car Emissions and Global Warming. Retrieved August, 2017, from https://www.ucsusa.org/clean-vehicles/car-emissions-and-global-warming#.XCIk_1wzZPZ

Yates, R., & Goodman, D. (2004, July 26). Probability and Stochastic Processes, second Edition[PDF]. Wiley.

Yousef, K. M., Al-Karaki, J. N., & Shatnawi, A. M. (2010). Intelligent Traffic Light Flow Control System Using Wireless Sensor Networks. Journal of Information Science and Engineering,753-768.

Zhou, B., Cao, J., Zeng, X., & Wu, H. (2010). Adaptive Traffic Light Control in Wireless Sensor Network-Based Intelligent Transportation System. 2010 IEEE 72nd Vehicular Technology Conference - Fall. doi:10.1109/vetecf.2010.5594435

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