Turning Movement Count Data Integration Methods for Intersection Analysis and Traffic Signal Design

Abstract

Traffic simulation is widely used for modeling, planning, and analyzing different strategies for traffic control and road development in a cost-efficient manner. In order to perform an intersection simulation, random vehicle trip data are typically applied to an intersection network, making them unrealistic. In this paper, we address this issue by presenting two different methods of incorporating actual turning movement count (TMC) data and comparing their similarity for intersection simulation and analysis. The TMC of three intersections in Las Vegas are estimated separately for one hour using a developed vision-based tracking system and they are incorporated into Simulation of Urban MObility (SUMO) for estimating traffic measurements and traffic signal design. t-tests with a 95% confidence interval on the simulation variables demonstrate the importance of using a route-based creation method which injects vehicles into a simulation environment based on the frame-level departure time. The intersection analyses and comparisons are performed based on estimated traffic measurements such as travel time, density, lane density, occupancy, and normalized waiting time. Since the critical edge of each intersection network is identified based on a higher normalized waiting time, new traffic signal designs are suggested based on the actual critical turning movements and improvements in vehicle travel time are achieved to better accommodate the actual traffic demand.

Enhancing Urban Intersection Efficiency: Utilizing Visible Light Communication and Learning-Driven Control for Improved Traffic Signal Performance

“…Reinforcement learning (RL) [26] represents a category within the machine learning (ML) framework, wherein an agent undergoes a learning process by actively engaging with an environment [27]. The RL algorithm is very suitable for automatic control [28] and, therefore, a promising approach to intelligent traffic light control.…”

From novice to expert: advancing step-by-step simulation guideline for urban logistics with an open-source simulation tool

“…Most of the relevant papers focus on presenting the capability and suitability of the SUMO software for simulating different urban mobility challenges, such as traffic light optimisation and environmental impact simulation. The challenges addressed are mostly related to the dataset, incorporation of historical data, prediction accuracy, and the advantages of sensor systems for collecting necessary data (Abidin et al ., 2015; Ilarri et al ., 2022; Rapelli et al ., 2022; Shokrolah Shirazi, Chang and Tayeb, 2022). Few directly mention challenges in developing SUMO models for field experts.…”