The OPTIMISM project: Data gaps and guidelines for harmonised travel statistics Panayotis Christidis, European Commission, JRC

2 ♦ OPTIMISM – Optimising Passenger Transport Information to Materialize Insights for Sustainable Mobility ♦ Co-funded by Framework Programme 7 ♦ Project Coordinator: Eleni Anoyrkati, Coventry University Enterprises Ltd ♦ Duration: 24 months ♦ Start: 1st October 2011

OPTIMISM consortium Coventry University Enterprises Ltd (CUE) Zurich University of Applied Sciences (ZHAW) Signosis Sprl (SIGNOSIS) Deutsches Zentrum Fuer Luft-Und Raumfahrt Ev (DLR) Forum of European National Highway Research Laboratories (FEHRL) Laboratorio Nacional de Engenharia Civil (LNEC) Road and Bridge Research Institute (IBDiM) University College Dublin (UCD) Sapienza Università di Roma - Centre for Transport and Logistics (CTL) Transport & Mobility Leuven (TML) CE Delft (DELFT) European Commission - Joint Research Centre (EC-JRC)

Our goals in OPTIMISM: Identify key factors of travel behaviour at national level Analyze methodologies for national statistical data Explore the needs for harmonizing national travel data Assess various methods of harmonizing statistical data Develop a methodology to collect and report data on travel behaviour Provide guidelines and recommendations for harmonizing differences of statistical data and scientific analysis on travel behaviour

Background MEST (Methods for European Surveys of Travel Behaviour), 1999: CATI & CAPI, GPS for the future TEST (Technologies for European Surveys of Travel Behaviour), 1999: computer based travel diaries, GIS DATELINE (Design and Application of a Travel Survey for European Long-distance Trips Based on an International Network of Expertise ), 2003: CATI & CAPI, 2-phase, multi-day/ multi-destination trips ETIS (European Transport Information System), 2005: Data needs, agent based system ETIS+, ongoing: Policy & models, new data collection methods COST/ SHANTI: Survey harmonization with new technologies COMPASS: ICT solutions

OPTIMISM approach Need for EU survey: Monitor progress towards goals of WP on Transport Review of national surveys: Variables, format, structure Gaps Harmonization potential New methods New survey Harmonization guidelines

Why to harmonise data collection systems 7 Time-series analyses: comparability of data over time Cross-country analyses: comparability of data over and between regions, countries, etc. Harmonisation = methodology that allows for the repeatable collection of a consistent set of mobility related information, using National Travel Surveys, that allows for the comparison of specific aspects of transport policies over time and between countries.

Methodology: a first outline based on the analysis of existing NTSs, allowing for the analysis of EU policies. Not a definitive NTS Repeatable: over time/ across countries Consistent: allow for the collection of a set of data that, together with data from other data sources, allows for the making production of sound scientific analyses Mobility related information: more than only just trip information … it also includes parameters that can influence mobility Using National Travel Surveys: the focus lies on the NTS, not on other data sources … although they are required to complete a consistent image of the mobility situation. What is harmonisation (according to OPTIMISM)?

The purpose of the data collection The data that is collected  The parameter definitions  The data grouping  The data format (quantitative vs qualitative)  The units of measurement The sampling  The sampling frequency of NTS collection: how frequently is an NTS taken?  The sampling composition: who is asked to complete the NTS? In order to: Analyse the effect of a policy or measure over time. Compare the mobility situation between countries. Harmonisation needed at different levels:

OPTIMISM: outline for a standardised NTS To allow for the (scientifically sound) analysis of the effects of a selection of passenger transport policies that can be identified for all EU Member States. Within OPTIMISM 14 key initiatives were identified in 7 domains linked to passenger transport. For all of these the parameter definitions, data grouping, data format and units of measurement were proposed Sampling: The sampling frequency: preferential yearly data acquisition (max: 5- yearly) The sampling composition: population registry as a starting point, number of items can be estimated using Cochran‘s formula

Variables analysed: 9 types of information groups were identified, divided over 6 groups of parameters.  Demographic information: 11 parameters, all suited for NTS data collection  Emissions and cost: 5 parameters, non suited for NTS data collection  Vehicle usage: 2 parameters, 1 suited for NTS data collection  General mobility information: 23 parameters, 21 suited for NTS data collection  Infrastructure, quality of mobility & multi-modality: 13 parameters, 8 suited for NTS data collection  Accidents, injuries and fatalities: 9 parameters, 6 suited for NTS data collection

See Deliverable 2.2 for details Demographic information Mostly available, but major differences exist in the amount of information collected: only for the respondent, or for the entire related family. Vehicle usage Car occupancy rates are only moderately collected. General mobility information Basic information (vkm, pkm) is readily available, but more detailed information on the trip motives, regions, fuel types, etc. is not. Infrastructure, quality of mobility & multi-modality Information on trip duration is available, but not for time loss, congestion, travel management systems, multi-modality options, assisting technologies, etc. Accidents, injuries and fatalities Accidents and injuries are not usually reported. Overview of results:

New methods Few applications at EU level Issues of representativeness Privacy/ legal concerns Results from other projects Explore more sources/ cases

14 Automated data collection  GPS data collection  Measuring equipment in the infrastructure  Counting systems  Large databases with quantitative data describing in detail how mobility evolves in numbers, but more seldom why it evolves in a specific direction Non-automated data collection (or only partially automated)  National Travel Surveys (NTS)  Specialised and focussed surveys & questionnaires  Databases combining quantitative and qualitative data, describing partially how mobility evolves, but also allowing for a qualitative interpretation of these evolutions. Overview of new methods reviewed

The use of NTS in the future Some general findings and ideas, for discussion: The format that is used to collect data through NTS:  Traditional formats: pen & paper, phone interviews, etc.  Future opportunities: smartphone, dedicated applications, etc. (but at what cost?) The type and amount of data that is collected  Automated data collection techniques aim at gathering a high quantity of information on quantitative parameters.  Relatively cheap (per data unit), but high amounts of data can work be counter- productive, and focussing on quantitative information doesn’t always reveal why something happens.  Traditional formats aim at gathering information on both quantitative and qualitative parameters from a relative limited amount of persons (depending on the NTS, in the range of a couple of thousand households).  Relatively expensive (per data unit), data processing is sometimes time- consuming, but allows to learn why something happens

NTS not suitable for: Emissions information  CO2 emissions per vehicle, person, fuel type Cost information  Internalisation of external costs: how and how much of it? Vehicle usage  Trip based car occupancy (too detailed for a NTS)  Trip chain composition (too detailed for a NTS)  Trip based origin-destination information (too detailed for a NTS) Infrastructure & network  Rail network length & usage  Congestion: location, length & time loss  ICT options: the availability and use of travel management systems Safety  The number of fatalities (also per region, per vehicle type)

OPTIMISM recommendations An EU methodological framework for NTS can be developed without changing current practices significantly Initial list of variables developed, can be used as a starting point Too early to abandon traditional instruments (pen and paper, telephone and face-to-face interviews, etc.) Alternative sources can enrich NTS: analyses of travel patterns and validation New technologies (e.g. GPS, GSM, GIS technologies, internet, smart cards etc..): their potential role in data collection should be further investigated

Contact: Panayotis CHRISTIDIS European Commission, Joint Research Centre Institute for Prospective Technological Studies Edificio Expo, C/ Inca Garcilaso 3 E SEVILLA, Spain Phone number: Thank you for your attention!