|Table of Contents|

Contrastive of car-following model based on multinational empirical data(PDF)

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

Issue:
2024年2期
Page:
89-100
Research Field:
交通工程
Publishing date:

Info

Title:
Contrastive of car-following model based on multinational empirical data
Author(s):
XU Zhi-gang1;  WEI Lu-ying1;  LIU Zhi-guang1;  LIU Zhang-qi2;  QIN Kong-jian3
(1. School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; 2. Shaanxi Automobile Holding Group Co., Ltd., Xi'an 710299, Shaanxi, China; 3. Beijing CATARC Auto Test Center Co., Ltd., Beijing 100176, China)
Keywords:
traffic engineering;  microscopic traffic flow;  car following model;  S3 model;  HighD dataset;  simulated annealing method
PACS:
U491.112
DOI:
10.19721/j.cnki.1671-8879.2024.02.009
Abstract:
In order to describe the car-following(CF)behavior of vehicles more accurately, and to study the influence of car-following behavior data from different countries on the calibration of car-following models, as well as the accuracy of various car-following models in simulating car-following behavior, three datasets was selected as a traffic flow dataset from CHD dataset from a section of the South Second Ring Road in Xi'an, China, the U.S. NGSIM dataset, and the German HighD dataset. The Gazis-Herman-Rothery(GHR)model, the intelligent driver model(IDM), and the newly proposed S-shaped three-parameters car-following model(S3)were used for model calibration and error analysis. Acceleration, speed difference between the leading and following vehicles, position difference between the leading and following vehicles, and the speed of the following vehicle were used as input parameters. A combination of cross-correlation analysis and simulated annealing methods were employed for data fitting. The performance of the fitted models was evaluated using the root mean square error(RMSE)of acceleration, speed and displacement. The results show that for the car-following behavior in the three different countries' datasets, the S3 microscopic model shows the best calibration performance, with the lowest average RMSE for all three datasets compared to the other two car-following models. Due to the high overall data collection accuracy and large data volume of the German HighD dataset, it exhibits the best performance and lowest error in car-following behavior calibration, regardless of the car-following model used. The research results are of great significance for the selection of car-following models and parameter optimization in traffic simulation software, and hold important value for the choice of datasets for car-following model calibration.4 tabs, 10 figs, 30 refs.

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Last Update: 2024-03-01