Abstract
Objectives
Traffic management is challenging during construction because of the effects of traffic congestion, travel time, delay, and queue length. Long-term work zones on urban roads lead to many problems such as speed, inconvenience, and economic losses to drivers, which are focused on in previous studies.
Methods
Moreover, due to the construction work zone (CWZ), the impact on environmental factors such as air quality and noise levels was not focused on. Because of the building work zones, this research focused on comprehending how traffic congestion measurements and environmental factors affect urban traffic management.
Findings
The present research uses TransCAD to estimate air pollution due to increased traffic in the urban areas. Furthermore, three nonlinear AI-based models (ANFIS, FFNN, and SVR) and one linear black box model were developed to predict the noise level in the city, in which each contained the total traffic and speed as well as the ratio of heavy vehicles in the traffic.
Novelty
For traffic control, a variety of techniques are available, including video data analysis, infrared sensors, inductive loop detection, wireless sensor networks, etc. These are all practical techniques for efficient traffic management. It is necessary to conduct studies on the amount of traffic, the topography, accidents, time delays, and the level of safety offered in the work area. Construction operations are facilitated by the implementation of traffic flow, and during this process, long-term CWZs are inevitable. Therefore, the proposed model accomplishes the goal, namely that only analytical research and a few traffic diverter signs point drivers to alternate routes to their destinations.