Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm

Abstract

Current traffic signals in Jordan suffer from severe congestion due to many factors, such as the considerable increase in the number of vehicles and the use of fixed timers, which still control existing traffic signals. This condition affects travel demand on the streets of Jordan. This study aims to improve an intelligent road traffic management system (IRTMS) derived from the human community-based genetic algorithm (HCBGA) to mitigate traffic signal congestion in Amman, Jordan's capital city. The parameters considered for IRTMS are total time and waiting time, and fixed timers are still used for control. By contrast, the enhanced system, called enhanced-IRTMS (E-IRTMS), considers additional important parameters, namely, the speed performance index (SPI), speed reduction index (SRI), road congestion index (R i ), and congestion period, to enhance IRTMS decision. A significant reduction in congestion period was measured using E-IRTMS, improving by 13% compared with that measured using IRTMS. Meanwhile, the IRTMS result surpasses that of the current traffic signal system by approximately 83%. This finding demonstrates that the E-IRTMS based on HCBGA and with unfixed timers achieves shorter congestion period in terms of SPI, SRI, and R i compared with IRTMS.

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

“…It can easily adapt to different kinds of optimization problems by using parameter tuning and modifying the operations. Metaheuristic algorithms are divided into four classes: (1) Evolutionary Algorithms (EAs), such as Genetic Algorithm (GA) [7], Genetic Programming (GP) [8], and Differential Evolution [9]. (2) Human-based algorithms, such as Tabu search (TS) [10], Translation Lookaside Buffer (TLB) [11], and Socio-evolution and Learning Optimization (SELO) [12].…”