Real-time dynamic traffic light timing adaptation algorithm and simulation software

Dissanayake, D.T; Senanayake, S.M.R; Divarathne, H.K.D.W.M.N.R.; Samaranayake, B.G.L.T.

doi:10.1109/iciinfs.2009.5429797

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…The results demonstrated that an effective signal control scheme could minimize air pollution by up to 50%. Dissanayake et al also used GA to optimize signal timing and performed comparative experiments under the same geometry and traffic conditions on different simulation platforms [76]. It was reported that GA yielded a robust traffic light setting, thereby substantially improving the throughput and travel time.…”

Section: Related Workmentioning

confidence: 99%

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

et al. 2020

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Intelligent traffic control at urban intersections is vital to ensure efficient and sustainable traffic operations. Urban road intersections are hotspots of congestion and traffic accidents. Poor traffic management at these locations could cause numerous issues, such as longer travel time, low travel speed, long vehicle queues, delays, increased fuel consumption, and environmental emissions, and so forth. Previous studies have shown that the mentioned traffic performance measures or measures of effectiveness (MOEs) could be significantly improved by adopting intelligent traffic control protocols. The majority of studies in this regard have focused on mono or bi-objective optimization with homogenous and lane-based traffic conditions. However, decision-makers often have to deal with multiple conflicting objectives to find an optimal solution under heterogeneous stochastic traffic conditions. Therefore, it is essential to determine the optimum decision plan that offers the least conflict among several objectives. Hence, the current study aimed to develop a multi-objective intelligent traffic control protocol based on the non-dominated sorting genetic algorithm II (NSGA-II) at isolated signalized intersections in the city of Dhahran, Kingdom of Saudi Arabia. The MOEs (optimization objectives) that were considered included average vehicle delay, the total number of vehicle stops, average fuel consumption, and vehicular emissions. NSGA-II simulations were run with different initial populations. The study results showed that the proposed method was effective in optimizing considered performance measures along the optimal Pareto front. MOEs were improved in the range of 16% to 23% compared to existing conditions. To assess the efficacy of the proposed approach, an optimization analysis was performed using a Synchro traffic light simulation and optimization tool. Although the Synchro optimization resulted in a relatively lower signal timing plan than NSGA-II, the proposed algorithm outperformed the Synchro optimization results in terms of percentage reduction in MOE values.

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Section: Related Workmentioning

confidence: 99%

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

et al. 2020

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“…This algorithm considers a number of traffic factors such as traffic volume, waiting time, vehicle density, etc., to determine green light sequence and the optimal green light duration. In [17] the authors propose an algorithm for vehicle detection, using magneto resistive sensors, in order to control road traffic. The system developed is able to detect vehicles and then to calculate the appropriate duration of traffic light cycles.…”

Section: Wireless Sensor Network For Road Traffic Managementmentioning

confidence: 99%

A self-powered wireless sensor network for dynamic management of queues at traffic lights

Collotta

Denaro

Scatà

et al. 2014

Transport and Telecommunication Journal

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The dynamic management of traffic light cycles is a really interesting research issue considering modern technologies, which can be used in order to optimise road junctions and then improve living conditions of the roads. Wireless sensor networks represent the most suitable technology, as they are easy to deploy and manage. The data relating to road traffic flows can be detected by the sensor network and then processed through the innovative approach, proposed in this work, in order to determine the right green times at traffic lights. Although wireless sensor networks are characterized by very low consumption devices, the continuous information transmission reduces the life cycle of the whole network. To this end, the proposed architecture provides a technique to power the sensor nodes based on piezoelectric materials, which allow producing potential energy taking advantage of the vibration produced by the passage of vehicles on the road.

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“…Indeed, the driver through his cell phone can receive traffic information of an interested area. The main goal of [19] is to design and implement an algorithm and, through several simulations, to evaluate road traffic management and control. The proposed system is able to detect ferrous objects in movement, e.g.…”

Section: Road Monitoring Through Wsns In Literaturementioning

confidence: 99%

Wireless Sensor Networks to Improve Road Monitoring

Collotta¹,

Pau²,

Salerno³

et al. 2012

Wireless Sensor Networks - Technology and Applications

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Real-time dynamic traffic light timing adaptation algorithm and simulation software

Cited by 6 publications

References 1 publication

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

A self-powered wireless sensor network for dynamic management of queues at traffic lights

Wireless Sensor Networks to Improve Road Monitoring

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