Driving behavior performance from tunnel main road to underground merging area: A real vehicle study based on speed and lateral offset

Multi-entry underpass road tunnels feature long entrance downhill sections and underground merging areas where main and secondary roads converge. These complex driving environments can lead to variations in driver speed and lateral offset, increasing the risk of traffic accidents. Therefore, this study aims to analyze the speed and lateral offset characteristics in different tunnel sections and their impact on traffic safety, providing support for traffic control and safety improvements in multi-entry underpass tunnels. This study conducted real-vehicle natural driving tests using test vehicles equipped with an inertial navigation system and Mobileye. Based on changes in tunnel alignment and road parameters, the study divided the test sections into five segments: tunnel external section, entrance downhill section, entrance internal section, underground merging section, and tunnel internal section. By analyzing the speed variation trends, lateral offset characteristics, and their interrelationships across these sections, a standardized relative deviation fraction was introduced to quantitatively compare driving behavior in key sections, revealing differences in driving patterns and potential safety risks across different road segments. The speed growth rate in the entrance downhill section was the highest at 15.09%. In contrast, drivers in the underground merging section had the lowest average speed at 54.057 km/h and the highest speed dispersion. The underground merging section had the lowest rate of lateral offset change but the highest dispersion in lane offset within this section. Conversely, the entrance downhill section showed the smallest dispersion, with a standard deviation of only 0.111. In addition, research found that the driving distance in each road section is positively correlated with vehicle speed and negatively correlated with lane offset. Through real-vehicle tests, this study analyzed the speed, lateral offset, and driving safety characteristics of different sections in multi-entry tunnels. The results indicate that the entrance downhill section and underground merging section pose higher driving risks, as fluctuations in speed and lateral offset contribute to driving instability. These findings reveal the driving risks associated with specific sections of multi-entry underpass road tunnels and provide important references for tunnel traffic management and safety optimization.

Publication Details

Journal
Figshare
Published
2025-06-12
DOI
https://doi.org/10.6084/m9.figshare.29307008
Primary Topic
Mycorrhizal Fungi and Plant Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Driving behavior performance from tunnel main road to underground merging area: A real vehicle study based on speed and lateral offset

Figshare
Mycorrhizal Fungi and Plant Interactions
article

Driving behavior performance from tunnel main road to underground merging area: A real vehicle study based on speed and lateral offset

article en

Abstract

Multi-entry underpass road tunnels feature long entrance downhill sections and underground merging areas where main and secondary roads converge. These complex driving environments can lead to variations in driver speed and lateral offset, increasing the risk of traffic accidents. Therefore, this study aims to analyze the speed and lateral offset characteristics in different tunnel sections and their impact on traffic safety, providing support for traffic control and safety improvements in multi-entry underpass tunnels. This study conducted real-vehicle natural driving tests using test vehicles equipped with an inertial navigation system and Mobileye. Based on changes in tunnel alignment and road parameters, the study divided the test sections into five segments: tunnel external section, entrance downhill section, entrance internal section, underground merging section, and tunnel internal section. By analyzing the speed variation trends, lateral offset characteristics, and their interrelationships across these sections, a standardized relative deviation fraction was introduced to quantitatively compare driving behavior in key sections, revealing differences in driving patterns and potential safety risks across different road segments. The speed growth rate in the entrance downhill section was the highest at 15.09%. In contrast, drivers in the underground merging section had the lowest average speed at 54.057 km/h and the highest speed dispersion. The underground merging section had the lowest rate of lateral offset change but the highest dispersion in lane offset within this section. Conversely, the entrance downhill section showed the smallest dispersion, with a standard deviation of only 0.111. In addition, research found that the driving distance in each road section is positively correlated with vehicle speed and negatively correlated with lane offset. Through real-vehicle tests, this study analyzed the speed, lateral offset, and driving safety characteristics of different sections in multi-entry tunnels. The results indicate that the entrance downhill section and underground merging section pose higher driving risks, as fluctuations in speed and lateral offset contribute to driving instability. These findings reveal the driving risks associated with specific sections of multi-entry underpass road tunnels and provide important references for tunnel traffic management and safety optimization.

Figshare
Sustainable cities and communities
Openalex Percentile: Top 59%
Mycorrhizal Fungi and Plant Interactions
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.