Work

Evaluating the characteristics of mixed traffic flow under the intelligent connected environment based on autonomous driving dataset

Year: 2026

Type: article

Abstract: Mixed traffic flow, consisting of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs), is expected to dominate future roadways. This study develops a heterogeneous traffic flow model in an intelligent connected environment to analyze macroscopic characteristics, including traffic capacity, critical density, and fundamental diagrams, under varying CAV penetration rates. Three car-following modes are considered: HDVs following HDVs, HDVs following CAVs, and CAVs following CAVs, modeled respectively by the Intelligent Driver Model (IDM), Adaptive Cruise Control (ACC), and Cooperative Adaptive Cruise Control (CACC). Model parameters are calibrated using genetic algorithms based on publicly available autonomous driving datasets. Calibration results demonstrate high predictive accuracy, with mean absolute percentage errors of 0.009% (distance) and 2.37% (speed) for IDM, 0.27% and 3.67% for ACC, and near-zero errors for CACC. Analysis of fundamental diagrams shows that increasing CAV penetration significantly enhances traffic flow efficiency, improves stability, and mitigates congestion. The findings provide theoretical guidance for optimizing mixed traffic operations and support the development of intelligent and connected transportation systems.

Source: Urban Lifeline

DOI: 10.1007/s44285-025-00058-z

Authors: Ying Hu, Xiaonian Shan, Changxin Wan

Institution: Hohai University

Language: English

Topic: Traffic control and management

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: diamond

Funders: Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Natural Science Research of Jiangsu Higher Education Institutions of China