Impact of Driver Anticipation on Traffic at a Ramp in Foggy Conditions

A new macroscopic traffic model is proposed based on visibility distance and the time to collision (TTC) in foggy conditions. The TTC is obtained from real traffic data. It is shown that the proposed model is hyperbolic and has a well-posed solution. Further, it is string stable. The proposed and Payne–Witham (PW) models are implemented in MATLAB R2019b using the first-order upwind numerical scheme and evaluated over a 3000 m circular road with a ramp at 1500 m. The results show that the proposed model can effectively characterize traffic in poor visibility, while the PW model provides unrealistic results. Thus, the proposed model can be used to accurately predict traffic behavior and alleviate congestion in foggy conditions.

Authors

Institutions

Publication Details

Journal
Mathematics
Published
2025-09-04
DOI
https://doi.org/10.3390/math13172855
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Impact of Driver Anticipation on Traffic at a Ramp in Foggy Conditions

Khurram Shehzad Khattak, Thomas Aaron Gulliver, Zawar Hussain Khan
Mathematics
Traffic control and management
article

Impact of Driver Anticipation on Traffic at a Ramp in Foggy Conditions

Khurram Shehzad Khattak, Thomas Aaron Gulliver, Zawar Hussain Khan
article en

Abstract

A new macroscopic traffic model is proposed based on visibility distance and the time to collision (TTC) in foggy conditions. The TTC is obtained from real traffic data. It is shown that the proposed model is hyperbolic and has a well-posed solution. Further, it is string stable. The proposed and Payne–Witham (PW) models are implemented in MATLAB R2019b using the first-order upwind numerical scheme and evaluated over a 3000 m circular road with a ramp at 1500 m. The results show that the proposed model can effectively characterize traffic in poor visibility, while the PW model provides unrealistic results. Thus, the proposed model can be used to accurately predict traffic behavior and alleviate congestion in foggy conditions.

MathematicsVol. 13(17)
University of Victoria (CA), University of Ha'il (SA), University of Engineering and Technology Peshawar (PK)
Openalex Percentile: Top 71%
Traffic control and management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.