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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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A deep embedded clustering method for location-specific driving safety profiling using trajectory data.

Ankit Kumar Kushwaha¹, Himanshu Kumar², Hardik Arora¹

¹Department of Civil and Environmental Engineering, IIT Patna, Bihta-801103, Bihar, India.

Scientific Reports

|November 25, 2025

Summary

This summary is machine-generated.

This study introduces a location-based machine learning model to classify driver behavior risk at specific road segments. It identifies driving patterns across multiple vehicle types, enhancing road safety and enabling targeted interventions for hotspots.

Keywords:

Driver behavior classification Location-based analysis Multi-vehicle analysis Road safety Trajectory data

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Area of Science:

Road safety
Machine learning
Transportation engineering

Background:

Traditional driver behavior analysis focuses on individual profiles, neglecting crash concentration at specific locations.
Understanding location-specific driving patterns is crucial for effective road safety interventions.

Purpose of the Study:

To develop a machine learning framework for classifying location-specific driving behavior risk.
To create a unified model for analyzing diverse vehicle classes simultaneously.
To identify high-risk driving hotspots and inform targeted safety measures.

Main Methods:

Utilized trajectory data from various vehicle classes in Chennai, India.
Applied Principal Component Analysis (PCA) for dimensionality reduction.
Employed K-Means, DBSCAN, Mean Shift, and Deep Embedded Clustering (DEC) for behavior classification.
Evaluated clustering performance using Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI).

Main Results:

Deep Embedded Clustering (DEC) demonstrated superior performance in classifying location-based driving behaviors.
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) yielded the least effective clustering.
The unified model successfully analyzed cross-modal behavior patterns.

Conclusions:

A location-based, unified machine learning model offers a robust approach to understanding and mitigating driving risks.
This framework has significant potential for real-world deployment to improve driver awareness and roadway safety.
Identifying specific driving hotspots enables targeted interventions for enhanced road safety.