Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality

Author:

Gao Song1,Wang Yaoli1,Gao Yong1,Liu Yu1

Affiliation:

1. Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, China

Abstract

In this study we estimate urban traffic flow using GPS-enabled taxi trajectory data in Qingdao, China, and examine the capability of the betweenness centrality of the street network to predict traffic flow. The results show that betweenness centrality is not a good predictor variable for urban traffic flow, which has, theoretically, been pointed out in existing literature. With a critique of the betweenness centrality as a predictor, we further analyze the characteristics of betweenness centrality and point out the ‘gap’ between this centrality measure and actual flow. Rather than considering only the topological properties of a street network, we take into account two aspects, the spatial heterogeneity of human activities and the distance-decay law, to explain the observed traffic-flow distribution. The spatial distribution of human activities is estimated using mobile phone Erlang values, and the power law distance decay is adopted. We run Monte Carlo simulations to generate trips and predict traffic-flow distributions, and use a weighted correlation coefficient to measure the goodness of fit between the observed and the simulated data. The correlation coefficient achieves the maximum (0.623) when the exponent equals 2.0, indicating that the proposed model, which incorporates geographical constraints and human mobility patterns, can interpret urban traffic flow well.

Publisher

SAGE Publications

Subject

General Environmental Science,Geography, Planning and Development

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