|Table of Contents|

Modeling of human-driven vehicles and characteristics of heterogeneous traffic flow for freeway(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年04期
Page:
97-107
Research Field:
交通工程
Publishing date:
2024-07-10

Info

Title:
Modeling of human-driven vehicles and characteristics of heterogeneous traffic flow for freeway
Author(s):
CHENG Guo-zhu1;  LI Jin-yu1;  CHEN Yong-sheng1;  XU Liang2
(1. School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040,Heilongjiang, China; 2. School of Civil Engineering, Changchun Institute of Technology, Changchun 130012, Jilin, China)
Keywords:
traffic engineering;  heterogeneous traffic flow;  car-following model;  lane changing model;  traffic flow characteristic
PACS:
U491.1
DOI:
10.19721/j.cnki.1671-8879.2024.04.009
Abstract:
In order to better simulate the heterogeneous traffic flow consisting of connected and autonomous vehicles(CAVs)and human-driven vehicles(HDVs)on highways, research on the car-following and lane-changing behaviors of heterogeneous traffic flow on highways was conducted. Firstly, based on the NGSIM trajectory dataset, the micro-level traffic characteristics of human-driven vehicles were analyzed. According to the acceleration and deceleration strategies of drivers under the same headway conditions, they were classified into three types, conservative, moderate, and aggressive drivers. Next, considering the uncertainty in drivers' perception and judgment, along with the analysis of the characteristics of the judgment errors in headway and speed by drivers with different driving styles based on vehicle following data, and simultaneously incorporating the information utility theory to simulate the changes in drivers' perception of CAVs and their impact on driving decisions, an improved HDV following and lane-changing model was proposed on the basis of the intelligent driver model(IDM)and STCA lane-changing model. Finally, the improved model was compared and analyzed against the predictive results of the IDM model, and simulation analyses of the characteristics of heterogeneous traffic flow on highways were conducted using MATLAB. The results show that significant improvements in accuracy are observed with the improved model, a reduction in the mean absolute error MAE by 24.7%, in the mean squared error MSE by 11.9%, and an increase in the Pearson correlation coefficient PCCs by 2.6% for the conservative HDV following model. For the normal type, decreases in MAE by 45.6% and in MSE by 38.6%, with an increase in PCCs by 4.0%, are documented. The aggressive type exhibits decreases in MAE by 41.2%, in MSE by 45.9%, and a marginal increase in PCCs by 0.4%, indicating a more accurate simulation of vehicle following behavior by the improved HDV model. The proportion of HDVs in traffic has a minimal impact on speed, flow, and stability under near-free flow conditions. However, as density increases, the impact of HDV proportion also grows, reaching a peak at a critical density, after which the influence of vehicle composition on traffic flow decreases. At the same traffic flow density, a negative correlation is found between the proportion of HDVs and the speed, flow, and stability of the traffic stream. The research on heterogeneous traffic flow, particularly focusing on HDV car-following and lane-changing behaviors is enriched by the model. This holds significant reference value for traffic management and infrastructure design in scenarios involving the coexistence of HDVs and CAVs on freeways.3 tabs, 14 figs, 17 refs.

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Last Update: 2024-07-10