| Project ID |
BITS-SRIP/9CE5C2/2026 |
| Project Title |
AI and NLP-Based Analysis of Crash Causes at High-Risk Cycling Road Segments |
| Project Description |
This project focuses on understanding why crashes are more frequent at selected high-risk cycling locations. Based on prior analysis, one or more high-risk road segments will be chosen for detailed investigation.
The study will examine cyclist trajectory data to understand movement patterns such as speed behaviour, stopping patterns, route usage, and time-of-day variations within the selected segment. Incident text data will be analysed to identify commonly reported crash circumstances, conflicts with vehicles, infrastructure-related issues, and environmental factors.
By combining behavioural patterns and incident descriptions, the project aims to identify the key reasons for the higher crash occurrence at these locations. The objective is to determine the major contributing factors and provide location-specific safety insights.
Expected outcomes
Behavioural analysis of cyclist movement within the selected high-risk segment
Identification and categorisation of crash causes from incident text data
Understanding of site-specific factors contributing to frequent crashes
Identification of the dominant safety issue at the location
Visual summaries and a concise technical report
Good quality work may be developed further for a journal paper. |
| Project Discipline |
Civil Engineering (Transportation) / Computer Science / Data Science / Artificial Intelligence |
| Faculty Name |
VINAYAK MALAGHAN |
| Department |
Department of Civil Engineering |
