Node Place Accessibility Ridership per Time (NPART) Model in the Hauts-de-France Region

Welcome to the GitHub repository dedicated to implementing an extended Node Place Accessibility Ridership per Time Model applied to the Hauts-de-France region. This project aims to provide a comprehensive framework for assessing the urban and transportation infrastructure within the region, leveraging spatial analysis to better understand the connectivity and accessibility of various places.

Project Overview

The Node Place Index Model is a critical tool for urban planners, geographers, and policymakers aiming to evaluate and enhance the mobility landscape of urban areas. By applying this model to the Hauts-de-France region, we seek to identify key areas of improvement, potential for development, and strategies for sustainable urban mobility.

This repository contains all the necessary Python codes used throughout the project stages. These resources are designed to be openly accessible, enabling others to replicate the study, contribute to its improvement, or adapt the methodology to their own regions. The code includes:

• 1. Codes for Generating Railway Stations Neighborhoods;
• 2. Codes for Spatial and Statistical Data Collection;
• 3. Codes for Statistical Analysis and Clustering.

1. Codes for Generating Railway Station Neighborhoods

• Pedestrian and Cycling Buffers 🚶
• Pedestrian and Cycling Isochrones 🚴

2. Codes for Spatial and Statistical Data Collection

Node (N) Transit 🚉	Place (P) Development 🏬	Accessibility (A) Oriented ⛲	Ridership per Time (RT) Demand 🎫
N1: Frequency (Weekdays, High-speed Rail) 🚄	P1: Population Density 🏠	A1: Length of Walking Paths 🚶	RT1: Off-Peak Hour Ridership (Weekdays, 12AM-6AM) 🎫
N2: Frequency (Weekends, High-speed Rail) 🚄	P2: Employment Density 🏢	A2: Intersection Density 🚦	RT2: Peak Hour Ridership (Weekdays, 6AM-10AM) 🎫
N3: Frequency (Weekdays, Regional Rail) 🚅	P3: Residential Land Use 📊	A3: Pedshed and Bikeshed Ratios 🚷	RT3: Off-Peak Hour Ridership (Weekdays, 10AM-3PM) 🎫
N4: Frequency (Weekends, Regional Rail) 🚅	P4: Commercial and Public Service Land Use 📊	A4: Length of Cycling Paths 🚲	RT4: Peak Hour Ridership (Weekdays, 3PM-8PM) 🎫
N5: Service Span (Weekdays) 🕖	P5: Office and Industry Land Use 📊	A5: Cycling Parking Capacity 🚲	RT5: Off-Peak Hour Ridership (Weekdays, 8PM-12AM) 🎫
N6: Service Span (Weekends) 🕖	P6: Green Space Land Use 📊	A6: Public Sharing Bicycles 🚲	RT6: Off-Peak Hour Ridership (Weekends) 🎫
N7: Commercial Rail Speed 🚥	P7: Proximity Points of Interest 🏫	A7: Metro and Tramway Stops 🚇
N8: Number of Directions ↔️	P8: Intermediate Points of Interest 🏫	A8: BRT and Bus Stops 🚍
N9: Degree Centrality 🔀	P9: Superior Points of Interest 🏫	A9: Motorized Speed Limit 🚗
N10: Closeness Centrality 🔀	P10: Residential Real Estate 💶	A10: Car Parking Area 🚗
N11: Betweenness Centrality 🔀	P11: Industrial, Commercial and Office Real Estate 💶	A11: Household Motorization 🚗
N12: Reachable Stations (One Hour) ✴️	P12: Share of affordable housing 🏨
N13: Stations to Lille 📍	P13: Income per Household 💰
N14: Travel Time to Lille 📍
N15: Stations to Paris 📍
N16: Travel Time to Paris 📍

Getting Started

To get started with the Node Place Model, you are encouraged to clone this repository and explore the provided Python scripts. Each script is accompanied by comments explaining its purpose, input data requirements, and the expected outputs, making it easier for newcomers to understand and engage with the project.

Contributors

Dylan Moinse

Ahad Amini Pishro

Alain L'Hostis

Shiquan Zhang

Ndeye Aïta Cissé

Xavier Lehmann

Liu Yuetong

Hu Qixiao

Olivier Theureaux

Heythem Adjeroud