SOMOBIL – Improving Public Transport Planning Through Mobile Phone Data Analysis

Elias, D. C.; Nadler, Friedrich; Stehno, Johannes; Krösche, Jens; Lindorfer, Manuel

doi:10.1016/j.trpro.2016.05.370

Cited by 7 publications

(5 citation statements)

References 1 publication

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“…The algorithm was then adjusted to attain population levels that were very similar to the official statistics. Similar methods of scaling GSM data to the total population have been applied in other transport studies (5,17,21).…”

Section: Gsm Datamentioning

confidence: 99%

“…To design public transport services in this dynamic environment, smartcard data are often used to analyze mobility patterns (4). These data, however, only provide information on public transport travel demand, neglecting overall travel demand, although this should be taken into account by public transport operators (5). Travel habit surveys are traditionally used to collect data for estimating and analyzing the demand for transport (6,7).…”

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confidence: 99%

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Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

Regt

Cats

Oort

et al. 2017

Transportation Research Record: Journal of the Transportation R

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The public transport industry faces challenges in catering to the variety of mobility patterns and corresponding needs and preferences of passengers. Travel habit surveys provide information on overall travel demand as well as its spatial variation. However, that information often does not include information on temporal variations. By applying data fusion to smartcard and Global System for Mobile Communications (GSM) data, researchers were able to examine spatial and temporal patterns of public transport usage versus overall travel demand. The analysis was performed by contrasting different spatial and temporal levels of smartcard and GSM data. The methodology was applied to a case study in Rotterdam, Netherlands, to analyze whether the current service span is adequate. The results suggested that there is potential demand for extending public transport service on both ends. In the early mornings, right before transit operations are resumed, an hourly increase in visitor occupancy of 33% to 88% was observed in several zones, showing potential demand for additional public transport services. The proposed data fusion method was shown to be valuable in supporting tactical transit planning and decision making and can easily be applied to other origin-destination transport data.

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Section: Gsm Datamentioning

confidence: 99%

mentioning

confidence: 99%

Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

Regt

Cats

Oort

et al. 2017

Transportation Research Record: Journal of the Transportation R

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“…Data pocházející z dopravních průzkumů obsahují velké množství detailních informací, avšak jejich vypovídací schopnost je omezena velikostí vzorku a časového úseku; naproti tomu big data nabízejí nové možnosti výzkumu individuální mobility z mnoha perspektiv a v širokém kontextu (Chen et al 2016) a mohou být proto významným zdrojem informací pro dopravní plánování a politiku (TMIP 2013). Jejich největší výhodou je to, že nejsou výběrová, ale zahrnují veškeré obyvatele (samozřejmě s omezením na vlastníky mobilního telefonního přístroje) v jednom časovém úseku a v celé zkoumané oblasti; tím umožňují provést komplexní, holistický výzkum (Elias et al 2016). Mezi hlavní výhody využití big data patří: (i) nižší náklady pořízení než sběr pomocí dotazníkových průzkumů (při srovnatelném rozsahu šetření), (ii) větší vzorek respondentů a širší prostorový kontext šetření, (iii) aktuální data a možnost rychlé aktualizace, (iv) možnost dlouhodobého Přes výše zmíněné výhody má použití big data také své nevýhody a omezení, které plynou především z toho, že tato data nejsou primárně určena pro výzkum spotřebitelského chování a vyžadují proto poměrně náročnou úpravu a zpracování.…”

Section: Vývoj Metodologie a Modelování V Dopravní Politiceunclassified

Železnice a evropská dopravní politika – ideje, strategie, nástroje a jejich aplikace

Kvizda¹,

Paleta²,

Seidenglanz³

et al. 2018

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“…Mobile phone data have been used for transport planning (Elias, Nadler, Stehno, Krosche, & Lindorfer, ; Liu et al, ), traffic measurement (Dong et al, ; Hongyan & Fasheng, ; Steenbruggen, Tranos, & Rietveld, ) and modelling (Oliveira, Viana, Naveen, & Sarraute, ), trajectory evaluation (Bonnel, Hombourger, Olteanu‐Raimond, & Smoreda, ; Chen, Bian, & Ma, ), and travel time predictions (Woodard et al, ). The general problems in urban planning (Jonge, Pelt, & Roos, ; Ricciato, Widhalm, Pantisano, & Craglia, ) and analysis (Lee et al, ), land use (Ríos & Muñoz, ), and smart city development (Steenbruggen, Tranos, & Nijkamp, ) can all be addressed with the help of mobile phone data analysis.…”

Section: Introductionmentioning

confidence: 99%

Mobile phone data statistics as a dynamic proxy indicator in assessing regional economic activity and human commuting patterns

et al. 2020

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Various studies demonstrate that data on mobile phone use are useful when analysing problems in the fields of human activity or population dynamics, including tourism, transportation planning, public administration, etc. However, one of the biggest challenges is related to the restrictions contained in the General Data Protection Regulation that force the use of statistics about mobile operator client activities instead of allowing the analysis of mobile operator data. Therefore, a data analytics approach that does not involve information on the mobility of particular persons was developed, providing economically relevant data on aggregate mobility while protecting personal data. The activity data aggregation was conducted at 15-min intervals in the area of each cellular base station; "activity" is defined as the number of outgoing and incoming calls and sent and received text messages (short message service or SMS) and, in some instances, as the count of unique users. The case study examines all of Latvia's municipalities, analysing the economic activity level in each municipality in comparison to the mobile phone activity

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SOMOBIL – Improving Public Transport Planning Through Mobile Phone Data Analysis

Cited by 7 publications

References 1 publication

Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

Železnice a evropská dopravní politika – ideje, strategie, nástroje a jejich aplikace

Mobile phone data statistics as a dynamic proxy indicator in assessing regional economic activity and human commuting patterns

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