The potential role of ICT options to enhance co-modality and decarbonise passenger transport in Europe Arno Schroten CE Delft The project is partially funded by the European Commission

OPTIMISM OPTIMISM: Optimising Passenger Transport Information to Materialize Insights for Sustainable Mobility Aim: Providing a set of strategies and policy measures for integrating and optimising (passenger) transport systems, particularly focussed on co-modality and ICT solutions for transport. Four key objectives:  Gather, analyse and harmonize the national travel data statistics  Identify and classify the key factors/drivers defining passenger transport  Analyse the impacts of ICT options enhancing co-modality, especially focussed on their decarbonisation potential  Develop recommendations on strategies and policy measures for the integration and optimisation of the (passenger) transport system

OPTIMISM OPTIMISM: Optimising Passenger Transport Information to Materialize Insights for Sustainable Mobility Aim: Providing a set of strategies and policy measures for integrating and optimising (passenger) transport systems, particularly focussed on co-modality and ICT solutions for transport. Four key objectives:  Gather, analyse and harmonize the national travel data statistics  Identify and classify the key factors/drivers defining passenger transport  Analyse the impacts of ICT options enhancing co-modality, especially focussed on their decarbonisation potential  Develop recommendations on strategies and policy measures for the integration and optimisation of the (passenger) transport system

Overview of the presentation Objectives of the analysis Scope of the analysis Identified ICT options and selected best practices Mobility impacts of best practices Decarbonisation potential of best practices Conclusions

Objectives of the analysis To provide an overview of ICT options enhancing co-modality To identify best practices To estimate the impacts of the best practices on mobility patterns To estimate the EU-wide decarbonisation potential of the best practices

Scope of the analysis Co-modality: umbrella term for both multimodal and intermodal transport  Multimodal transport refers to the use of different modes of transport at different opportunities (e.g. trips)  Intermodal transport refers to the use of different modes of transport within one route/trip The following categories of ICT options enhancing co-modality are distinguished:  Travel information services  Mobility services  Transport management services Analysis is based on ICT options already implemented or options which are planned to be implemented in the near future (up to 2020) Impacts are estimated for 2020 and 2030 Only passenger transport is considered

Identified ICT options Travel information services  Static route planners  Dynamic and real-time route planners  Personalised travel information  Infrastructure bounded travel information for public transport  Infrastructure bounded travel information for road transport  In-vehicle travel information Mobility services  E-ticketing  Mobile phone ticketing  Multimodal smart cards  Mobile phone payments  Bicycle sharing services  Car sharing services  Demand Responsive Transport systems Transport management systems  Public transport management systems  General transport management systems

Identified ICT options Travel information services  Static route planners  Dynamic and real-time route planners  Personalised travel information  Infrastructure bounded travel information for public transport  Infrastructure bounded travel information for road transport  In-vehicle travel information Mobility services  E-ticketing  Mobile phone ticketing  Multimodal smart cards  Mobile phone payments  Bicycle sharing services  Car sharing services  Demand Responsive Transport systems Transport management systems  Public transport management systems  General transport management systems Mobile payment devices

Mobility impacts: approach Based on a thorough literature review (ex-ante and ex-post evaluation studies) Empirical evidence is scarce (particularly for mobile payment devices) Only evidence on modal shift effects, not on effects on total transport demand (rebound effects) Additionally, data on long-term market penetration rates is gathered Due to scarce data availability the analysis is carried out for just one ‘reference’ country

Mobility impacts: results OptionModal Shift From To Market penetration rate Reference Country Personal travel information Car: -3% to - 11% Bus: 78% Train: 22% 2020: 30% 2030: 75% UK Bus: -2%Car: 90% Train: 10% Car sharing schemes Car: -2 to - 2.5% Bus: 90% Train: 10% 2020: 0,5% 2030: 1 – 1.5% Germany Mobile payment devices Car: -1 to - 2.5% Bus: 92% Train: 8% 2020: 40% 2030: 100% UK

Decarbonisation potential: approach (1) Modal shift

Decarbonisation potential: approach (2) Scaling country effects to EU-level Use of an assignability factor:  Indicates which share of the effects for the reference country could be expected for other countries  Based on relevant country-specific indicators:  GDP  Urbanisation  Modal share of public transport  Etc.  For countries with a high assignability factor the CO 2 reduction potential will be higher

Decarbonisation potential: results (1) ICT option Personalised travel information 0.5 – 2%1 – 4 % Car sharing services0.01% Mobile payment devices0.2 – 0.5%0.5 – 1% Decarbonisation potential of the various ICT options (% of passenger transport CO 2 emissions in the BAU-scenario)

Conclusions All three ICT options considered in this analysis significantly enhance co-modality Particularly personalised travel information and mobile payment devices significantly contribute to the decarbonisation of the transport system  However, rebound effects (impacts on total transport demand) are not taken into account  Empirical evidence on mobility impacts of these options is rather scarce and hence the uncertainty in the estimates is rather large