Abstract
In order to better understand the role of transportation convenience in location preferences, as well as to uncover transportation system patterns that span multiple modes of transportation, we analyze 500 locations in the Tokyo area using properties of their multimodal transportation networks. Multiple sets of measures are used to cluster regions by their transportation features and to classify them by their synergistic properties and dominant mode of transportation. We use twelve measures collected at five different radii for five distinct combinations of transportation networks to rank locations by their transportation characteristics. We introduce an additional 114 scores derived from the 300 measures to assess, among other things, access to public transportation, the effectiveness of each mode of transportation, and synergies among the modes of transportation. Additionally, we leverage those scores to classify our locations as being train-centric, bus-centric, or car-centric and to uncover geographic patterns in these characteristics. We find that business hubs, despite having low populations, are so conveniently reachable via train and road systems that they consistently achieve the highest sociability and convenience scores. Suburban regions have more serviceable bus systems, but lower connectivity overall resulting in lower reachable populations despite greater local populations. Even though Tokyo has the largest and densest public transportation system in the world we find that the road network consistently dominates the train and bus networks for all accessibility measures.
Highlights
Because transportation systems are so naturally seen as graphs/networks they are a common subject for graph theory and network analysis – including the original Königsberg bridge problem
Most studies of transportation networks focus on one mode: typically train (Derrible and Kennedy 2009; Derrible 2012), road (Crucitti et al 2006), or air (Guimera et al 2005) (for a review of how network theory has been applied to transportation systems see (Derrible and Kennedy 2011)) focusing on one mode allows for simpler analyses of structural patterns and similarities among cities, it is insufficient for characterizing how people use a transportation system
While most applications of machine learning to transportation networks aim at traffic prediction, flow efficiency, rerouting and robustness, we are interested in public transportation accessibility
Summary
Because transportation systems are so naturally seen as graphs/networks they are a common subject for graph theory and network analysis – including the original Königsberg bridge problem. The current work analyzes the transportation system of the Greater Tokyo Area (Tokyo, Kanagawa, Chiba, and Saitama prefectures) integrating the train, bus, and road systems along with a geographical hexagonal grid foundation. As such it includes (2019) 4:97 highly urbanized areas, suburban areas, rural areas, desolate mountainous areas, and everything in between. Rather than focus on purely structural features, we perform an analysis that combines demographic data with geographically modified network methods This is done at multiple time and distance scales in order to assess a variety of transportation and sociological characteristics such as transportation access limitations, synergies among distinct modes, transportation mode importance, and heterogeneity in transportation effectiveness
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