Spatio-Temporal Public Transport Mode Share Estimation and Analysis Using Mobile Network and Smart Card Data

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Fältnamn	Indikatorer	Metadata
000		03070naa a2200325 4500
001		oai:DiVA.org:kth-344060
003		SwePub
008		240229s2023 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3440602 URI
024	7	a https://doi.org/10.1109/ITSC57777.2023.104221992 DOI
040		a (SwePub)kth
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a kon2 swepub-publicationtype
100	1	a Cebecauer, Mateju KTH,Transportplanering4 aut0 (Swepub:kth)u1nb1u4p
245	1 0	a Spatio-Temporal Public Transport Mode Share Estimation and Analysis Using Mobile Network and Smart Card Data
264	1	b Institute of Electrical and Electronics Engineers (IEEE),c 2023
338		a print2 rdacarrier
500		a QC 20240301Part of ISBN 979-8-3503-9946-2
520		a Public transport plays a vital role in society and the urban environment. However, knowledge of its spatial and temporal shares is often limited to traditional travel surveys. Recently, there has been substantial progress in mobility data collection, including data from traffic, public transport, and mobile phones. Especially mobile network data is a large-scale and affordable source of high-level mobility records. Similarly, public transport smart cards or ticket validation data are being collected and made available in major cities. The contribution of this study is to unveil the potential of estimating public transport shares, by merging mobile and smart card data. Stockholm, Sweden, is used as a case study. We analyze and discuss spatio-temporal patterns of estimated public transport shares for Stockholm, using descriptive and cluster analysis. The typical representative day-types are revealed and analyzed. Finally, a regression analysis considering the weather and socioeconomic context is conducted. It provides a highly explanatory and predictive understanding of which factors impact the share of public transport in Stockholm. To conclude, combined mobile and smart card data offers a cost-efficient, large-scale, low spatio-temporal aggregation (capturing daily and hourly variations) alternative to traditional travel surveys for analyzing PT shares.
650	7	a TEKNIK OCH TEKNOLOGIERx Samhällsbyggnadsteknikx Transportteknik och logistik0 (SwePub)201052 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Civil Engineeringx Transport Systems and Logistics0 (SwePub)201052 hsv//eng
700	1	a Gundlegård, Davidu Linköping University4 aut
700	1	a Jenelius, Erik,c Docent,d 1980-u KTH,Transportplanering4 aut0 (Swepub:kth)u1x5t81f
700	1	a Burghout, Wilcou KTH,Transportplanering4 aut0 (Swepub:kth)u1x8efdz
710	2	a KTHb Transportplanering4 org
773	0	t 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)d : Institute of Electrical and Electronics Engineers (IEEE)g , s. 2543-2548q <2543-2548
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-344060
856	4 8	u https://doi.org/10.1109/ITSC57777.2023.10422199

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Av författaren/redakt...: Cebecauer, Matej; Gundlegård, Davi ...; Jenelius, Erik, ...; Burghout, Wilco

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Samhällsbyggnads ...; och Transportteknik ...

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Av lärosätet: Kungliga Tekniska Högskolan

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