Network Analysis of Urban Traffic with Big Bus Data

TL;DR

This study analyzes urban traffic in Helsinki using big bus data, identifying key traffic areas, differentiating bus and urban traffic causes, and proposing improvements through traffic simulation to reduce congestion.

Contribution

It introduces a network-based analysis of bus data to identify traffic hotspots and distinguishes bus traffic from overall urban traffic, offering targeted improvement strategies.

Findings

Betweenness centrality identifies major traffic areas.

Bus traffic is not a primary cause of urban congestion.

Better bus scheduling can reduce overall traffic congestion.

Abstract

Urban traffic analysis is crucial for traffic forecasting systems, urban planning and, more recently, various mobile and network applications. In this paper, we analyse urban traffic with network and statistical methods. Our analysis is based on one big bus dataset containing 45 million bus arrival samples in Helsinki. We mainly address following questions: 1. How can we identify the areas that cause most of the traffic in the city? 2. Why there is a urban traffic? Is bus traffic a key cause of the urban traffic? 3. How can we improve the urban traffic systems? To answer these questions, first, the betweenness is used to identify the most import areas that cause most traffics. Second, we find that bus traffic is not an important cause of urban traffic using statistical methods. We differentiate the urban traffic and the bus traffic in a city. We use bus delay as an identification of the urban traffic, and the number of bus as an identification of the bus traffic. Third, we give our solutions on how to improve urban traffic by the traffic simulation on road networks. We show that adding more buses during the peak time and providing better bus schedule plan in the hot areas like railway station, metro station, shopping malls etc. will reduce the urban traffic.