Analysis and Modelling of the Relationship between Stopped and Control Delays by Differential Evolution Algorithm

Abstract

Abstract:Two delay definitions namely stopped delay and control delay are commonly used to determine the level of service of a signalized intersection. In this research, we first developed a number of new models to describe the relationship between stopped and control delays using Differential Evolution (DE) algorithm. In these models, we considered four cycle lengths of 60, 90, 120 and 150 seconds with the green ratio ranging from 0.35 to 0.60. Taking the saturation flow as 1800 vph, five different traffic volumes were considered ranging from 600 to 1400 vph with 200 vph increments. Three analysis periods were selected as 15, 30 and 60 minutes. The data needed for model development and verification were obtained from a four leg intersection by using CORSIM simulation. In the second part of the study, the effect and sensitivity of cycle length, traffic volumes and green ratio parameters on the conversion ratio (D s /D c ) to relate stopped and control delays were evaluated based on the selected parameter ranges.The results revealed that the conversion ratio cannot be accepted constant usually taken as 0.76 for practical purposes since it is dependent on changing operation and traffic conditions. Therefore, it needs to be determined with the use of an appropriate model considering variable traffic conditions as functions of volume, cycle length and green ratio.

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

“…DE and AIS (artificial immune system) are some other heuristic search optimization techniques that have been recently used successfully for traffic engineering applications [65][66][67][68][69]. AIS is inspired by human biological immune system.…”

A Differential Evolution Algorithm‐Based Traffic Control Model for Signalized Intersections

“…Genetic Algorithm (GA), Harmony Search Algorithm (HS), Particle Swarm Optimization Algorithm (PSO), Ant Colony Optimization Algorithm (ACO), Arti cial Bee Colony Algorithm (ABC), and Di erential Evolution (DE) are some of the most used metaheuristic optimization methods for the solution of the problems. Previous studies in the literature showed that more e ective solutions can be achieved by using the Di erential Evolution algorithm rather than the other metaheuristic optimization algorithms [33][34][35][36][37]. us, it is preferred in this study.…”