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Thesis . 2026
License: CC BY
Data sources: Datacite
ZENODO
Thesis . 2026
License: CC BY
Data sources: Datacite
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Advancing Road Traffic Safety: Deep Learning for Traffic Modeling and Strategy

Authors: Wang, Chenxi;

Advancing Road Traffic Safety: Deep Learning for Traffic Modeling and Strategy

Abstract

This dataset was created for conflict and accident modeling. Real-time traffic data were first collected through a traffic API and then integrated with the microscopic traffic simulation platform Simulation of Urban MObility to reconstruct detailed vehicle trajectories. The simulation produced trajectory files in XML format, which were subsequently converted into a CSV file (trajectories_with_angle(april_may_june).csv). Using Python-based data processing and analysis, surrogate safety indicators were then computed from the trajectory data. This process generated the final dataset ttc_lttb_final_split_by_type_yaw(april_may_june).csv, which is intended for subsequent algorithm development and modeling tasks related to traffic conflict and accident analysis. Due to privacy and safety considerations, the complete set of reconstructed pre-crash trajectories cannot be fully disclosed. Therefore, a representative example dataset (case_1.csv) is provided to illustrate the structure and format of the reconstructed trajectory data.

This dataset is provided for demonstration and research reference purposes only. Unauthorized commercial use of the dataset is strictly prohibited. Researchers intending to use this dataset for academic publications are encouraged to contact the author and properly cite the dataset using the provided DOI.

The dataset includes processed trajectory data and computed surrogate safety indicators for research and algorithm development purposes.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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