Tracing Accident Time Using High-Resolution Traffic Data

Around the world, the analysis of highway accident data is employed to improve safety measures. In the accident data sets, the time of the accident is often the moment the accident is reported to the control center. However, this may not accurately reflect the pre- and post-accident traffic conditions. This study aims to enhance accuracy by utilizing high-resolution temporal and spatial traffic data to analyze the traffic pattern leading up to the reported accident time. By examining the traffic flow condition from upstream to downstream and considering the traffic pattern at the upstream detector of the accident, a more comprehensive understanding of accidents can be achieved. The identified accidents are then summarized based on accident rates, vehicles involved, and accident type, aligned with the corresponding traffic conditions.

Authors

Institutions

Publication Details

Journal
Transportation research procedia
Published
2025-01-01
DOI
https://doi.org/10.1016/j.trpro.2024.12.092
Citations
1
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
1.01
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tracing Accident Time Using High-Resolution Traffic Data

Takashi Oguchi, Jaya Varshini Kala, Ryo Saito, Yusuke Sakayori
1 citations
Transportation research procedia
Traffic Prediction and Management Techniques
1.01
article

Tracing Accident Time Using High-Resolution Traffic Data

Takashi Oguchi, Jaya Varshini Kala, Ryo Saito, Yusuke Sakayori
article en
1 citations

Abstract

Around the world, the analysis of highway accident data is employed to improve safety measures. In the accident data sets, the time of the accident is often the moment the accident is reported to the control center. However, this may not accurately reflect the pre- and post-accident traffic conditions. This study aims to enhance accuracy by utilizing high-resolution temporal and spatial traffic data to analyze the traffic pattern leading up to the reported accident time. By examining the traffic flow condition from upstream to downstream and considering the traffic pattern at the upstream detector of the accident, a more comprehensive understanding of accidents can be achieved. The identified accidents are then summarized based on accident rates, vehicles involved, and accident type, aligned with the corresponding traffic conditions.

Transportation research procediaVol. 82
National Highway Traffic Safety Administration (US), The University of Tokyo (JP)
Gender equality
Openalex Percentile: Top 29%
Traffic Prediction and Management Techniques
1.01
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.

Tracing Accident Time Using High-Resolution Traffic Data — Takashi Oguchi, Jaya Varshini Kala, et al. · Transportation research procedia (2025) | TGRS Research Map | TGRS