Data to the people: a review of public and proprietary data for transport models

Figures & data

Figure 1. Overview of the production and operational flow of the data, partly inspired by OECD (2019).

Figure 2. The trend of articles in SCOPUS²² published from 2005 to 2019 with the keywords: (top) “public data”, “publicly available data”, “open data”, and “open government data”; (bottom) combinations of “publicly available data” (left) and “open data” (right) with transport domain keywords.

Figure 3. Public availability/ openness attributes (Extending the concentric shell model by Backx (2003), English translation by van Loenen and Grothe (2014), in the top right corner).

Table 1. Data Classification.

Data Type	Data Provider	Openness attribute					Data Category	Examples
		Legally attainable	Accessible	Affordable	Usable	Distributable
MPND	Telecom operator/ Data intermediaries (SaaS**)	✓	✓	-	✓*	-	NP-1	OD matrices derived by data intermediaries are offered as a premium service
Social Media	Social media platforms	✓	✓	✓^+	✓*	✓*	NP-1/ P-1	Premium access / Free access
Smart card	Transit operator	✓	-	✓^+	✓*^,^$	-	NP-2	Shared selectively for research purposes only.
Bluetooth	Traffic operators	✓	-/ ✓	✓	✓*^,^$	✓*	NP-2/ P-1/2	Aggregated information such as flow, travel time are shared
GNSS -derived AVL	Navigation service providers, OEMs, Commercial fleets	✓	✓	-	✓*	-	NP-1	Vehicle level information is seldom shared publicly
		✓	✓	✓^+	✓^$	✓*	P-1	Aggregated traffic data as premium or free service
GTFS	Transit operator	✓	✓	✓^+	✓^$	✓*	P-2	Stop locations and schedules, sometimes real-time data too
GBFS	Shared mobility provider	✓	✓	✓	✓^$	✓	P-2	Bike-share data
VGI	Crowdsourced	✓	✓	✓	✓^$	✓	P-2	OpenStreetMap

*Depends on the terms and conditions/ license of data (re-) use and sharing.

**Software as a Service

⁺ Could be offered as a free or a premium product/ service.

^$ Data may or may not be usable depending on the data format and end-user requirements.

Figure 4. Public availability and applications of the prominent datasets used in transport modelling.

Table 2. SWOT Analysis.

Data	Strengths	Weaknesses	Opportunities	Threats	References
Mobile phone network data	Spatial-temporal coverage over all modes, large sample size	Needs ground truth for scaling factors, representativeness issues, missing sociodemographic attributes	Numerous applications in demand, supply and traffic modelling	Strict data anonymisation for privacy protection; proprietary and commercial nature leads to sharing averseness	(Caceres et al., 2007; Rojas et al., 2016; Tolouei et al., 2017)
Smart card data	Disaggregate trip and fare data, spatial-temporal trends over a long duration	Only for closed transit systems, sample representation, missing sociodemographic attributes and context	Transit modelling, impact of disruptions on travel behaviour	Data not universally accessible, often requires complementary data, such as GTFS or AVL	(Bagchi & White, 2005; H. Faroqi et al., 2018; Pelletier et al., 2011)
GNSS data and AVL	Primary source of movement and traffic data	GNSS data suffers from signal loss and errors, detector-AVL has limited observability, lack sociodemographic attributes	Primary or secondary data (validation) in modelling tasks	GNSS-AVL primarily controlled by private companies, privacy-sensitive. Dedicated hardware and costs for detector-AVL	(Gadziński, 2018; Sandim et al., 2016)
Social media data (Free version)	Contextual, disaggregate and geotagged information	Sample bias, textual data requires processing, majority of data not free	Trip destination, purpose and activity space analysis	Evolving privacy issues affect data availability, commercialisation and major control by private companies	(Chaniotakis & Antoniou, 2015; Grant-Muller et al., 2015; Rashidi et al., 2017)
VGI	High spatial coverage, a rich environment of tools and programmes	Varying quality by region, lack of data validation	Land use and trip attractions for travel demand models, transport networks	Large amount of data can cloud completeness issues, limit reliability. Depends on continuing participation of contributors	(Bast et al., 2015; Senaratne et al., 2017)
GTFS	Standardised format	Only works for regular, schedule-based transit, fragmented/ aggregated feeds, validation before use	Detailed spatial-temporal modelling of public transport	Risk of being used only internally in companies without providing data to public	(Fransen et al., 2015; Kickhöfer et al., 2016; Kujala et al., 2018)
GBFS	Standardised format, Real-time information	Historical data not available, only station information, missing trip attributes	Behavioural analysis of shared and emerging micro-mobility services	Scalability depends on participation of private or government service providers	(North American Bikeshare Association, 2015)

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