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.

中文翻译：

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智慧城市绿色交通管理系统中基于绩效的问题分析

目的

本文介绍了一种智能信息辅助通信运输框架（II-CTF），以减少交通拥堵，数据可靠性和环境影响。

设计/方法/方法

II-CTF的主要关注点是使用当前的运输服务来减轻公共交通拥堵，这有助于提高危险情况下的数据可靠性，并避免驾驶员无法做出合理反应时发生事故。该程序使用机器学习帮助，根据运动模式和车辆分类预测最佳路线，这有助于最大程度地减少交通拥堵。

发现

在II-CTF中，调度交通优化有助于减少能源和交通管理人员面临的许多挑战，包括路线优化，平均等待时间以及交通拥堵，交通拥堵以及交通拥堵造成的环境影响。

创意/价值

II-CTF的定义旨在克服运输环境中的一些难题，使所有人的运输更简单，更安全，更高效，更环保。