Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2016

Abstract

Given the changing dynamics of mobility patterns and rapid growth of cities, transport agencies seek to respond more rapidly to needs of the public with the goal of offering an effective and competitive public transport system. A more data-centric approach for transport planning is part of the evolution of this process. In particular, the vast penetration of mobile phones provides an opportunity to monitor and derive insights on transport usage. Real time and historical analyses of such data can give a detailed understanding of mobility patterns of people and also suggest improvements to current transit systems. On its own, however, mobile geolocation data has a number of limitations. We thus propose a joint telco-and-farecard-based learning approach to understanding urban mobility. The approach enhances telecommunications data by leveraging it jointly with other sources of real-time data. The approach is illustrated on the First- and last-mile problem as well as route choice estimation within a densely-connected train network.

Keywords

Big data, Mobility, Public transport route choice

Discipline

Numerical Analysis and Scientific Computing | Transportation

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, August 13-17

First Page

589

Last Page

598

ISBN

9781450342322

Identifier

10.1145/2939672.2939723

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/2939672.2939723

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