Analyzing Pedestrian and Bicycle Activity Patterns in Downtown Los Angeles Using Python

Document Type

Conference Proceeding

Publication Date

7-2025

Abstract

This study examines urban mobility patterns in Los Angeles, focusing on pedestrian and cyclist behavior through a comprehensive analysis of data that includes counts of male and female pedestrians, bikers, scooter riders, and helmet usage statistics. The dataset encompasses key variables that highlight transportation trends on weekdays and weekends, enabling a nuanced understanding of gender differences in active transportation. Our findings reveal total pedestrian counts of 2,500 males and 3,000 females, along with 1,200 biking trips and 800 scooter rides, with a notable 70% helmet compliance rate among cyclists. Analyzing the top 10 locations with the highest pedestrian counts by gender further provides insights into areas of high demand and potential safety needs. Through location-based analysis and trend examination, this research offers data-driven recommendations for urban planning and public policy aimed at promoting safer, more inclusive, and sustainable transportation options. The findings underscore the importance of recognizing diverse mobility patterns to support Los Angeles’s goals of enhancing public health, safety, and environmental sustainability in urban design and infrastructure planning.

Comments

Part of the book series: Lecture Notes in Networks and System

Included in the following conference series: International Conference on Advances in Computing Research

DOI

10.1007/978-3-031-87647-9_21

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