Data-Analysis

Analysis of Vodafone Users' fluxes : data analysis project for the 1st year course Laboratory of Computational Physics at the MSc in Physics of Data

Urban Mobility Analysis: Padova Traffic Flows

Statistical analysis of human mobility patterns in Padova, Italy using anonymized mobile carrier data to inform urban planning and transportation infrastructure decisions.

🎯 Objective

This project analyzes urban mobility patterns in Padova using Vodafone network data to identify critical bottlenecks in local transportation and recommend infrastructure improvements. The analysis covers visitor flows, commuter patterns, and distance-based mobility relationships across Italian provinces.

📊 Dataset Overview

Data Source: Vodafone Italia mobile network analytics
Period: February - May 2018 (4 months)
Geographic Scope: Padova and surrounding Italian provinces
Privacy Protection: Aggregated data with minimum 30-unit threshold

Core Data Files

File	Description
day_od.csv	Origin-destination flows by day of week
distinct_users_day.csv	Unique user counts by origin location
codici_istat_comune.csv	Municipality code mappings
codici_istat_provincia.csv	Provincial code mappings
codici_nazioni.csv	National code mappings

External Data Integration

• ISTAT population statistics by province
• Geographic distances between Padova and Italian provinces
• Highway network topology (A13, A4 corridors)

🔍 Analysis Framework

1. Data Preprocessing

• CSV file validation and standardization
• Geographic code matching with ISTAT references
• Data quality assessment and missing value handling

2. International Visitor Analysis

• Ranking of top 20 foreign countries by visitor volume
• Temporal patterns in international tourism flows

3. Domestic Mobility Patterns

• Provincial visitor rankings weighted by population density
• Identification of primary catchment areas

4. Highway Corridor Analysis

Core transportation network evaluation across three major axes:

• South Corridor (A13): Bologna-Roma direction
• West Corridor (A4): Milano-Torino direction
• Northeast Corridor (A4): Venezia-Trieste direction

Analysis Components:

• Mid-range mobility focus (regional scale)
• Weekend vs. weekday flow differentiation
• Commuter vs. visitor pattern separation
• Infrastructure investment prioritization

5. Distance-Mobility Relationship

• Distribution analysis of visitor volume by origin distance
• Statistical model identification and fitting
• Regression analysis for visitor prediction

6. Predictive Modeling

• Distance-based visitor flow regression
• Model validation and residual analysis
• Identification of top 5 anomalous provinces (over/under-performing)

🎯 Research Applications

Urban Planning

• Traffic flow optimization
• Public transport route planning
• Commercial district development

Transportation Engineering

• Highway capacity planning
• Intersection improvement prioritization
• Multi-modal connectivity enhancement

Economic Development

• Tourism infrastructure investment
• Business location optimization
• Regional economic integration analysis

📊 Data Privacy & Ethics

This analysis uses aggregated, anonymized mobile network data with strict privacy protections:

• No individual user identification possible
• Compliant with EU GDPR regulations
• Used solely for academic/planning purposes

Data Sources

• Vodafone Italia (Primary mobility data)
• ISTAT (Demographic and geographic data)
• Italian Ministry of Infrastructure

📚 References

[1] Italian National Institute of Statistics (ISTAT) - Provincial Demographics
[2] Vodafone Analytics - Urban Mobility Insights Methodology
[3] European Commission - Digital Single Market Strategy
[4] Padova Municipality - Urban Mobility Plan 2018-2025

📄 License

This project is licensed under the MIT License. Data usage complies with Vodafone Italia data sharing agreements and GDPR requirements.