Abstract
On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-a-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.
Highlights
Approaches to vehicle detection systems employed conventional inroad sensor leveraging technologies like infrared, ultra sound, magnetism and video streaming
To summarize: a) The signal phase is proportional to the CWT; b) The upper bound defines maximum time of a green phase to ensure starvation of green phase to a direction for unreasonably longer time; c) The green phase has a lower bound to consider vehicle startup time, acceleration and time to cross the distance of traffic junction; d) Signal phase are assigned to all directions in a single signal cycle
The difference between the two is equilibrium is delayed by a cycle of signal phase in case of 5:1
Summary
Approaches to vehicle detection systems employed conventional inroad sensor leveraging technologies like infrared, ultra sound, magnetism and video streaming. These technologies are not cost-effective from installation and maintenance aspects; have poor accuracy and repeatability; require space and power. Thereafter, Adhoc networks like VANET, MANET and connected vehicles were evaluated. This paper proposes real time data acquisition to address vehicular traffic congestion employing in-vehicle GPS system, traffic routers, traffic servers, big-data incorporating infrastructure data, knowledge system for travel history & behavioral history, high end computing engine and communication network.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
