Analysis of performance-based issues in green transportation management systems in smart cities

作者：Liang Chen, Prathik Anandhan, Sivasathya Pradha Balamurugan · 发表于：The Electronic Library · 年份：2020 · DOI：10.1108/el-07-2020-0205 · 被引用次数：12 · 研究领域：Traffic Prediction and Management Techniques、IoT and Edge/Fog Computing、Transportation and Mobility Innovations

Purpose In this paper, an intelligent information assisted communication transportation framework (II-CTF) has been introduced to reduce congestion, data reliability in transportation and the environmental effects. Design/methodology/approach The main concern of II-CTF is to mitigate public congestion using current transport services, which helps to improve data reliability under hazardous circumstances and to avoid accidents when the driver cannot respond reasonably. The program uses machine learning assistance to predict optimal routes based on movement patterns and categorization of vehicles, which helps to minimize congestion of traffic. Findings In II-CTF, scheduling traffic optimization helps to reduce the energy and many challenges faced by traffic managers in terms of optimization of the route, average waiting time and congestion of traffic, travel, and environmental impact due to heavy traffic collision. Originality/value The II-CTF definition is supposed to attempt to overcome some of the problems of the transportation environment that pose difficulties and make the carriage simpler, safer, more efficient and green for all.