Mobile crowd sensing based dynamic traffic efficiency framework for urban traffic congestion control

Mobile Crowd Sensing (MCS) is a developing category of Internet of Things applications that are used in Intelligent Transportation System to improve the transport system. In today's metropolitan life, traffic congestion in busy hours is the main problem due to which many commuters are unable to reach their destination on time, motorist time and extra fuel are wasted. In this paper, the MCS-based Dynamic Traffic Efficiency Framework (MCS-DTEF) for traffic congestion control is presented. The basic characteristics of MCS-DTEF are: traffic detection can be performed without deploying sensor devices on both sides of the road. The traffic data can be collected in real time, traffic flow can be predicted and managed according to current situations which reducing fuel consumption and driving time. Simulation of Urban Mobility simulator is used for evaluation. Meaningful and reliable statistical data is obtained by the traffic flow which is built according to Normal Traffic Conditions (NTC's) in the first scenario. In the second simulation scenario, the MCS-DTEF mechanism is applied to assign a time slot to a requested vehicle and dynamically allocate the fastest path to each vehicle which is introduced to avoid traffic congestion. To validate the results of the proposed framework technique by comparing with NTC's and real-world traffic flow in three different situations morning, afternoon and evening are examined and tested to explore the traffic usage parameter. The results show that the MCS-DTEF strategy demonstrates significantly improved traffic flow performance in terms of reducing time to destination, fuel consumption and increased vehicle average speed.