An Intelligent and Dynamic Ride Sharing Service Minimizing Economic and Environmental Impacts for Urban Transportation Scenario

Abstract

newline In the current scenario, technological developments have modernized society, and vehicles with innovative ideas are introduced now and then. For the reason, there is an increased usage of cars. Traffic congestion is an unpredicted problem in the highway scenario. It is necessary for an intelligent transport service to analyze travel congestion and traffic density. Although many researchers have addressed the issue still there exist several challenges in the urban scenario. The major issue is road density, traffic congestion, time delay, fuel consumption, and pollutant emission. A Ride-sharing mechanism is an effective technology for utilizing the traveling resources. Dynamic ride-sharing has recently considered as the major area of research interest. In this research IRS-UTS: An Intelligent Ride-Sharing Service in Urban Transportation System address another distinct mode of travel is urban dynamic ride-sharing. In the first module, Dynamic HFF Algorithm for Urban Traffic Density Analysis and Efficient Ride-Sharing Service predicts the vehicle blockage and analyzes the density of a road network for comfy traveling. For the purpose, a novel dynamic-HHF algorithm is proposed to determine traffic congestion. This phase of the research minimizes fuel utilization. The second module Dynamic and Efficient Route Planning for Intelligent Ride-Sharing Service in Urban Scenario proposes a dynamic and effective route planning to reduce the average newlinev newlinetraveling distance for this it implements a novel Dynamic Path Planner (DPP) algorithm. Finally, the third phase Optimization in Dynamic Ride-Sharing Service based on Economic and Environmental Impacts performs service optimization of the proposed system by avoiding unoccupied trips and shared services balancing the load on the road network. This module utilizes an agent-based simulation model to evaluate service optimization in terms of average waiting time, average vehicle in-time, and the satisfied request. Thus the study analyzes the travel conditions, env