AllAboard: Visual Exploration of Cellphone Mobility Data to Optimise Public Transport

The deep penetration of mobile phones offers cities the ability to opportunistically monitor citizens’ mobility and use data-driven insights to better plan and manage services. With large scale data on mobility patterns, operators can move away from the costly, mostly survey based, transportation planning processes, to a more data-centric view, that places the instrumented user at the center of development. In this framework, using mobile phone data to perform transit analysis and optimization represents a new frontier with significant societal impact, especially in developing countries. In this paper we present AllAboard, an intelligent tool that analyses cellphone data to help city authorities in visually exploring urban mobility and optimizing public transport. This is performed within a self contained tool, as opposed to the current solutions which rely on a combination of several distinct tools for analysis, reporting, optimisation and planning. An interactive user interface allows transit operators to visually explore the travel demand in both space and time, correlate it with the transit network, and evaluate the quality of service that a transit network provides to the citizens at very fine grain. Operators can visually test scenarios for transit network improvements, and compare the expected impact on the travellers’ experience. The system has been tested using real telecommunication data for the city of Abidjan, Ivory Coast, and evaluated from a data mining, optimisation and user prospective.

1 Introduction

Transportation planning and management is often a top priority for many cities around the world. Increased urbanisation, as well as traffic congestion and associated pollution push several cities to invest in building new public transport networks, or extending their existing ones. Usually, transit authorities involved in planning tasks have to rely on several semi-automatic steps [1]. Poor availability of data to create quantitative demand models is a major challenge. Currently, this is addressed by conducting travel surveys and using census information, and several experts are in charge of interpreting the results of the surveys to build a mathematical model of travel demand [2]. This is then imported into GIS tools to be visualised, and numerical tools such as Excel or R are used to process the data to extract statistics. However, these tools are not very suited to specific applications, such as transit planning, and so are not very flexible, and difficult to reuse on a periodic basis to evaluate how various transit key performance indicators (KPIs) vary over time. Even once a travel demand model is built, a procedure to come up with an optimised route requires some manual intervention, as candidate routes have to be chosen by experts. It is then rather difficult to quickly test several scenarios, and get indicative results on the impact of proposed changes to the network.