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Efficient Mobility: Lessons on Dynamic Pricing and Sustainable Passenger Service

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Smart Urban Mobility

Part of the book series: MPI Studies on Intellectual Property and Competition Law ((MSIP,volume 29))

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Abstract

The Internet of Things is increasingly delivering high-precision mobility data. This makes it possible to fine-tune the use of scarce mobility goods and services and thus achieve higher system efficiency. In the future, it will be feasible to put a price tag on even the smallest traffic areas and auction them off to the highest bidder. This will enable congestion-free driving, search-free parking, and a trouble-free switching from road to rail and vice versa. Many contractual decisions will be made automatically on the basis of previously determined preferences, without the individual noticing anything. At the same time, the increase in data created in this system will also lead to new manipulation potential. The intelligent legislator will try to make the opportunities of the Internet of Things possible while at the same time limiting the associated risks.

Martin Fries, PD Dr., is Lecturer at the Ludwig-Maximilians-Universität, Munich, Germany.

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Notes

  1. 1.

    Increasingly, it is not the free decision of car manufacturers, but a legal requirement that vehicles can send and receive data. For example, Parliament and Council Regulation (EU) 2015/758 of 29 April 2015 concerning type-approval requirements for the deployment of the eCall in-vehicle system based on the 112 service and amending Directive 2007/46/EC [2015] OJ L123/77, arts 4ff requires car manufacturers to equip vehicles with an automatic emergency call system and thus to establish some sort of a permanently available communication option with State-run institutions. Aside from that, Parliament and Council Regulation (EU) 2019/631 of 17 April 2019 setting CO2 emission performance standards for new passenger cars and for new light commercial vehicles, and repealing Regulations (EC) No 443/2009 and (EU) No 510/2011 [2019] OJ L111/13, art 7 requires the EU Member States to collect carbon dioxide emission data from new cars in the near future.

  2. 2.

    See, eg, the apps provided by Easypark (<https://easypark.de/en> accessed 16 July 2020), parkingpay (<https://parkingpay.ch> accessed 16 July 2020), and ParkNow (<https://www.your-now.com/de/our-solutions/park-now> accessed 16 July 2020).

  3. 3.

    Exemplary proposals for the technical implementation of these ideas can be found at Aditya Vikram Agarwal, Navneet Verma and Sanjeev Kumar, ‘Intelligent Decision Making Real-Time Automated System for Toll Payments’ in Basant Tiwari and others (eds), Proceedings of International Conference on Recent Advancement on Computer and Communication: ICRAC 2017 (Springer 2018) 223; Segun I Popoola and others, ‘A Cloud-Based Intelligent Toll Collection System for Smart Cities’ in Pushpak Bhattacharyya and others (eds), Smart and Innovative Trends in Next Generation Computing Technologies (Springer 2018) 653.

  4. 4.

    Garrett Hardin, ‘The Tragedy of the Commons’ (1968) 162 Science 1243.

  5. 5.

    The exception proves the rule. The Estonian capital Tallinn is one of the rare examples where public transportation has been free for local residents since 2013.

  6. 6.

    In Singapore, since 1998, motorists are charged a toll on their account, the amount of which depends on the time of day when they drive which route. Several European cities have since followed this example. A detailed analysis of the Oslo toll cordon is provided by James Odeck and Morten Welde, ‘Economic Evaluation of Intelligent Transportation Systems Strategies: the Case of the Oslo Toll Cordon’ (2010) 4 IET Intelligent Transport Systems 221.

  7. 7.

    See, eg, Ram Singh, ‘Economic Efficiency’, Encyclopedia of Law and Economics (living edition, 2018) <https://link.springer.com/referenceworkentry/10.1007/978-1-4614-7883-6_206-1> accessed 16 July 2020, with further references.

  8. 8.

    The theory neglects unequal wealth, which would ideally be compensated at the level of income and wealth taxation. If this approach is not followed, it is possible to multiply the offers made in the auction by an inverse income or wealth factor.

  9. 9.

    In theory, auction processes could even be extended to the driving style. For example, if a driver going 130 mph has a higher space-time-consumption than someone driving at 70, she could be allowed to do so at a proportionate higher price.

  10. 10.

    Such a special lane for vehicles is well-known, eg, in Norway, California and Canada. Some central European States provide separate bus lanes with a similar goal. The economic problem with such lanes is, however, an often obvious underuse, as these lanes remain unused for most of the time.

  11. 11.

    This is especially true for large US cities like Los Angeles, San Francisco and Washington, D.C. For details of the conceptual framework, see Shobhit Shanker and Syed Masud Mahmud, ‘An Intelligent Architecture for Metropolitan Area Parking Control and Toll Collection’ [2005] IEEE Proceedings 723.

  12. 12.

    One of the early adopters of distance-related prices was the Californian train operator Caltrain which implemented a tag-on-tag-off procedure to calculate travel fares already ten years ago.

  13. 13.

    See Ronald H Coase, ‘The Problem of Social Cost’ [1960] The Journal of Law & Economics 1.

  14. 14.

    For details, see below Sect. 5.

  15. 15.

    See Committee on Legal Affairs, Report with recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL), 2017) <https://www.europarl.europa.eu/doceo/document/A-8-2017-0005_EN.html> accessed 16 July 2020. A similar, even more decisive proposal was made quite some time ago by Steffen Wettig and Eberhard Zehender, ‘A Legal Analysis of Human and Electronic Agents’ (2004) 12 AI & Law 111.

  16. 16.

    Gerhard Wagner, ‘Robot liability’ [2019] Working Paper No 2 des Forschungsinstituts für Recht und digitale Transformation <http://www.rewi.hu-berlin.de/de/lf/oe/rdt/pub/working-paper-no-2> accessed 16 July 2020, with further references.

  17. 17.

    Max Raskin, ‘The Law and Legality of Smart Contracts’ (2017) 1 Georgetown Law Technology Review 304; Alexander Savelyev ‘Contract Law 2.0: “Smart” Contracts as the Beginning of the End of Classic Contract Law’ (2017) 26 Information & Communications Technology Law 116.

  18. 18.

    If, however, non-personal data is concerned, the legislative approach is quite the opposite, as the regulatory aim of the EU is the maximum free flow of data; see, eg, the Parliament and Council Regulation (EU) 2018/1807 of 14 November 2018 on a framework for the free flow of non-personal data in the European Union [2018] OJ L 303/59.

  19. 19.

    As to the compatibility of this approach with Parliament and Council Regulation (EU) 2016/679 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) [2016] OJ L119/1 (GDPR), art 22, see Michèle Finck, ‘Smart Contracts as a Form of Solely Automated Processing Under the GDPR’ (2019) 9 International Data Privacy Law 1.

  20. 20.

    Details provided by Robert Brauneis and Ellen P Goodman, ‘Algorithmic Transparency for the Smart City’ (2017) 20 Yale Journal of Law & Technology 103, 122ff.

  21. 21.

    Bart Custers and others, ‘Data on the Move: Privacy of Road Pricing’ (2010) 63 The Journal of Navigation 51.

  22. 22.

    For details on this phenomenon, see Robert H Strotz, ‘Myopia and Inconsistency in Dynamic Utility Maximization’ (1955-56) 23 The Review of Economic Studies 165; David Laibson, ‘Golden Eggs and Hyperbolic Discounting’ (1997) 112 The Quarterly Journal of Economics 443.

  23. 23.

    See above n 6.

  24. 24.

    From the multitude of statistics see only <https://climate.nasa.gov/vital-signs/carbon-dioxide/> accessed 16 July 2020 and <https://data.worldbank.org/topic/climate-change> accessed 16 July 2020.

  25. 25.

    Andrew Torre, ‘Externalities’, Encyclopedia of Law and Economics (living edition, 2014) <https://link.springer.com/referenceworkentry/10.1007/978-1-4614-7883-6_40-1> accessed 16 July 2020, with further references.

  26. 26.

    Recent research from British Columbia proves the economic prediction that timid carbon taxes have hardly any measurable effects; Felix Pretis, ‘Does a Carbon Tax Reduce CO2 Emissions? Evidence from British Columbia’ [2019/2020] Working Paper <https://ssrn.com/abstract=3329512> accessed 16 July 2020.

  27. 27.

    Such neutralization effects have been observed in various European countries; see Boqiang Lin and Xuehui Li, ‘The Effect of Carbon Tax on per Capita CO2 Emissions’ (2011) 39 Energy Policy 5137. See, eg, § 9(1) No 4 of the German Income Tax Act (EStG) which establishes a tax privilege for commuters which will be increased from 2021 to offset the burden of a new carbon dioxide tax (that is already insufficient in itself).

  28. 28.

    Parliament and Council Reg (EU) 2019/631, art 7 setting CO2 emission performance standards for new passenger cars and for new light commercial vehicles.

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Fries, M. (2020). Efficient Mobility: Lessons on Dynamic Pricing and Sustainable Passenger Service. In: Finck, M., Lamping, M., Moscon, V., Richter, H. (eds) Smart Urban Mobility. MPI Studies on Intellectual Property and Competition Law, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61920-9_12

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