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

Abstract

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.

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

“…Such data offer some opportunities for different services as it reflects the spatio-temporal patterns of the users' activities. These data have been used for trip analysis [25], [64], [65], detecting social events [66], urban sensing [24], city modeling [29], finding crowd trajectories [32], planning and modeling urban transport [65], [67], [68], [69], estimating an actual crowd size in an event [49], [53], detecting tourist spot [70], and so on. 2) RF and IR Based Data: RF data can be used to obtain important information about the crowd size and mobility.…”

“…Louail et al [67] and Isaacman et al [68] utilized the CDR data to pin down in the city's hotspot, spatial structure and the busiest points during particular hours of a day. Di Lorenzo et al optimized a transit network based on mobility patterns of people extracted from CDR data which can help government agencies to manage the transport network efficiently [69].…”

Cell phone big data to compute mobility scenarios for future smart cities

“…More recent papers has been published by Calabrese et al [17,18]. In [17], they describe the use of the Enhanced Cell-ID with Timing Advance (TA) algorithm to localize mobile phones and to compute behavioural mobility patterns of the monitored users in Rome.…”

“…the high cost of the probes, and the impact of the localization errors introduced by the TA in urban areas where cells are small). In [18], the authors show a visual representation of Origin/Destination flows to optimize Public Transport. This work is very recent (2016) and in line with the scenario and definitions we described in 2014 in the SUPERHUB European project (https://ec.europa.eu).…”

Sources of VGI for Mapping

“…These data have been analysed to investigate applications in areas such as transportation planning (Di Lorenzo et al, 2016), user behaviour (Bianchi et al, 2016), public health (Oliver et al, 2015), the spatial spread of diseases such as cholera (Bengtsson et al, 2015) or population displacement after a major disaster (Wilson et al, 2016).…”