cycling-safety-analysis

Analysis of vehicle-to-cyclist passing distances in traffic using distance sensors, with traffic safety insights.

https://github.com/wuihee/cycling-safety-analysis

Science Score: 36.0%

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Repository

Analysis of vehicle-to-cyclist passing distances in traffic using distance sensors, with traffic safety insights.

Basic Info
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  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 3 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

Traffic Data Collection

Table of Contents

  1. Introduction
  2. Notebooks
  3. Installation

Introduction

Context

In Singapore, in the face of bicycle related traffic accidents, traffic rules require cars to overtake bicycles at a minimum distance of 1.5m.

Goals

The goal of this project is to collect data on the passing distance of cars to bicycles. I use various sensors to measure the distance of passing vehicles. The sensors were mounted a bike which was used to ride around Singapore roads and collect data.

Installation

Clone Repo

bash git clone https://github.com/wuihee/cycling-safety-analysis.git

Create Virtual Environment

bash python -m venv .venv

Install Dependencies

bash pip install -r requirements.txt

Install Package Locally

This will allow you to import cycling_safety_analysis and run .py files in scripts locally.

bash pip install -e .

Notebooks

Basic Tests

The results and analysis of each sensor's baseline capabilities in a static environment.

Outdoor Tests

Analysis and conclusions of the vehicle-to-cyclist passing distance data collected by each sensor.

  • TOF Sensor: TOF sensor unreliable due to high amount of spurious data generated.
  • Laser Sensor: Laser sensor too slow if not used with windows software.
  • LIDAR Sensor: Successful automated data collection with AI camera! Also showcases sample automated data analysis with DBSCAN cluster identification.

Owner

  • Name: Wuihee
  • Login: wuihee
  • Kind: user

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Dependencies

requirements.txt pypi
  • ipympl *
  • matplotlib *
  • numpy *
  • openpyxl *
  • pandas *
  • scikit-learn *
  • wheel *
setup.py pypi