Using Mobile Phone Data to Observe and Understand Mobility Behavior, Territories, and Transport Usage

Chrétien, Julie; Néchet, Florent Le; Leurent, Fabien; Yin, Biao

doi:10.1016/b978-0-12-812647-9.00003-2

Cited by 4 publications

(3 citation statements)

References 115 publications

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“…Correlation analysis has been mobility services [5]. Mobile phone data have been used and analysed extensively in recent research papers [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Correlation Analysis Method of Customisation and Semi-Personalisation in Mobility as a Service

Csiszár

2022

PROMTT

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Mobility as a Service (MaaS) has been proposed as a user-centric, data-driven and personalised service. However, full personalisation is not available yet. Customisation settings are developed in mobile applications, and several semi-personalised functionalities are also involved. The quantitative analysis of relation between these two could be the reference for further development tendency of interface functions in mobile applications. Thus, the research objective is identified as: the quantitative correlation analysis between semi-personalisation functionalities and customisation settings. Accordingly, the multi-criteria qualitative analysis method is applied to identify the assessment aspects regarding mobile applications. The scoring method is also introduced. Then the correlation quantitative analysis method is applied to calculate the correlation coefficient. We have assessed 25 MaaS applications regarding determined aspects. The correlation coefficients for each application together with the overall coefficient are calculated, the assessment results are summarised, and the correlation tendency is interpreted. According to assessment results, the correlation between customisation settings and semi-personalisation is not strong at current stage. Selected MaaS mobile applications are customisation setting oriented applications. Fewer manual selections are expected in further personalised services. Our results facilitate development of further personalised functions in MaaS mobile applications.

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“…Correlation analysis has been mobility services [5]. Mobile phone data have been used and analysed extensively in recent research papers [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Correlation Analysis Method of Customisation and Semi-Personalisation in Mobility as a Service

Csiszár

2022

PROMTT

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“…Con el propósito de tener resultados más precisos, se calibra el modelo con base en las especificidades de la localidad: las condiciones climáticas en la ciudad, origen y destino de los desplazamientos, participación modal de los viajes, comercio informal, flujos peatonales y vehiculares (medidas en campo). Para el levantamiento de información georreferenciada en campo se utilizaron celulares (Chrétien et al, 2018) y la aplicación KoBoCollect (Pham et al, 2022).…”

Section: Materiales Y Métodosunclassified

Simulador para recrear las dinámicas urbanas de Tena con Netlogo, antes de la intervención del espacio público

Chicaiza Ortiz,

Chérrez Rodas,

Carrillo Pineda

et al. 2022

Eidos

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Las intervenciones del espacio público en las ciudades amazónicas del Ecuador se realizan sin un proceso de planificación que evalúe previamente el impacto sobre las dinámicas urbanas y su incidencia en los indicadores de la calidad del espacio público. Este trabajo busca recrear el impacto de las intervenciones urbanas en las dinámicas de la ciudad de Tena, específicamente en el sector con mayor comercio de la avenida 15 de Noviembre y su incidencia en la calidad del espacio público, previa a la intervención del espacio en mención. Los datos de los diferentes elementos que forman parte de la ciudad (sombra, mobiliario, usos en planta baja, cruces peatonales, paradas de buses, áreas verdes, comercio formal e informal, anchos de aceras y vías) sus vínculos e influencia en la dinámica urbana se cargan en el programa Netlogo. Con el propósito de tener resultados más aproximados a la realidad se calibra el modelo con base en las especificidades de la localidad, por ejemplo, las condiciones climáticas en la ciudad de Tena. Entre los resultados más destacables, se obtuvo la estructura de un modelo en Netlogo de la principal avenida de Tena, que describirá la dinámica urbana, que constituirá una herramienta para definir las mejores alternativas antes de intervenir el espacio público. La presente investigación es pionera en el contexto amazónico ecuatoriano y marca un precedente para futuros proyectos en la región.

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“…At the aggregation level of home zones, total daily trip lengths were provided. A comprehensive survey on mobile phone data applied to mobility analysis can be found in our previous work ( 21 ).…”

Section: Related Workmentioning

confidence: 99%

Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones

Yin

Leurent

2021

Transportation Research Record: Journal of the Transportation R

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Data mining techniques can extract useful activity and travel information from large-scale data sources such as mobile phone geolocation data. This paper aims to explore individual activity-travel patterns from samples of mobile phone users using a two-week geolocation data set from the Paris region in France. After filtering the data set, we propose techniques to identify individual stays and activity places. Typical activity places such as the primary anchor place and the secondary place are detected. The daily timeline (i.e., activity-travel program) is reconstructed with the detected activity places and the trips in-between. Based on user-day timelines, a three-stage clustering method is proposed for mobility pattern analysis. In the method framework, activity types are first identified by clustering analysis. In the second stage, daily mobility patterns are obtained after clustering the daily mobility features. Activity-travel topologies are statistically investigated to support the interpretation of daily mobility patterns. In the last stage, we analyze statistically the individual mobility patterns for all samples over 14 days, measured by the number of days for all kinds of daily mobility patterns. All individual samples are divided into several groups where people have similar travel behaviors. A kmeans++ algorithm is applied to obtain the appropriate number of patterns in each stage. Finally, we interpret the individual mobility patterns with statistical descriptions and reveal home-based differences in spatial distribution for the grouped individuals.

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Using Mobile Phone Data to Observe and Understand Mobility Behavior, Territories, and Transport Usage

Cited by 4 publications

References 115 publications

Correlation Analysis Method of Customisation and Semi-Personalisation in Mobility as a Service

Correlation Analysis Method of Customisation and Semi-Personalisation in Mobility as a Service

Simulador para recrear las dinámicas urbanas de Tena con Netlogo, antes de la intervención del espacio público

Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones

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