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Identifying Important Nodes in Trip Networks and Investigating Their Determinants.

Ze-Tao Li¹, Wei-Peng Nie¹, Shi-Min Cai¹

¹Compleχ Lab, Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China.

Entropy (Basel, Switzerland)

|June 28, 2023

Summary

Analyzing taxi trip data reveals distinct urban transportation network structures. Chengdu exhibits a hierarchical multi-center network, unlike New York City, offering insights into urban planning.

Keywords:

centrality index distance trip network participation index travel pattern urban structure

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Area of Science:

Transportation Geography
Urban Dynamics
Network Analysis

Background:

Identifying travel patterns and significant urban locations is vital for transportation geography and social dynamics research.
Taxi trip data provides a rich source for understanding urban mobility and network structures.

Purpose of the Study:

To analyze taxi trip data from Chengdu and New York City to understand urban travel patterns.
To construct and compare long- and short-distance trip networks in both cities.
To identify critical nodes and analyze network structures in relation to trip distance and socio-economic factors.

Main Methods:

Probability density distribution of trip distance to create distinct networks.
PageRank algorithm for identifying critical nodes.
Centrality and participation indices for node categorization and influence analysis.

Main Results:

A clear hierarchical multi-center network structure was observed in Chengdu's taxi trip data.
No comparable hierarchical multi-center phenomenon was evident in New York City's taxi trip data.
Significant differences in network structures were found between the two cities, linked to trip distance and socio-economic factors.

Conclusions:

Trip distance significantly impacts important nodes within urban transportation networks.
Network structures differ substantially between cities, influenced by socio-economic factors.
Findings offer insights for urban planning, policy-making, and distinguishing between long and short taxi trips.