ITF researchers used actual transport data from Lisbon, Portugal, to modal the impact of two concepts: “TaxiBots”, self-driving vehicles shared simultaneously by several passengers (ride sharing) and “AutoVots”, which pick-up and drop-off single passengers sequentially (car sharing).

Other findings of the study include:

• A TaxiBot system with high-capacity public transport will result in 6% more car-kilometers travelled than today, because these services would have to replace not only those provided by private cars and traditional taxis but also all those provided by buses. An AutoVot system in the absence of high-capacity public transport will nearly double (+89%) car-kilometers travelled. This is due to repositioning and servicing trips that would otherwise have been carried out by public transport.

• A TaxiBot system in combination with high-capacity public transport uses 65% fewer vehicles during peak hours. An AutoVots system without public transport would still remove 23% of the cars used today at peak hours. However, overall vehicle-kilometers travelled during peak periods would increase in comparison to today. For the TaxiBot with high-capacity public transport scenario, this increase is relatively low (9%). For the AutoVot car sharing without high capacity public transport scenario, the increase is significant (103%). While the former remains manageable, the latter would not be.

  Spatial distribution of the variation of peak hour travel volumes for TaxiBot system in Lisbon. (weekday 8-9 a.m., TaxiBot plus high-capacity public transport scenario, vehicle-kilometers). Source: ITF. Click to enlarge.

• In all cases examined, self-driving fleets completely remove the need for on-street parking. This is a significant amount of space, equivalent to 210 soccer fields or nearly 20% of the curb-to-curb street space in the model city. Additionally, up to 80% of off-street parking could be removed, generating new opportunities for alternative uses of this valuable space.

• An AutoVot fleet requires more vehicles than a TaxiBot system to provide the same level of mobility. AutoVots also require considerably more repositioning travel to deliver that mobility. Around 18% more TaxiBots and 26% more AutoVots are needed in scenarios without high-capacity public transport, compared to scenarios where shared self-driving vehicles are deployed alongside high-capacity public transport. Without public transport, 5,000 additional cars are required for the TaxiBot system and another 12,000 in the AutoVot system. Car-kilometers travelled would increase by 13% and 24% respectively.

• If only 50% of car travel is carried out by shared self-driving vehicles and the remainder by traditional cars, total vehicle travel will increase between 30% and 90%. This holds true irrespective of the availability of high-capacity public transport. Looking only at traffic during peak hours, the overall number of cars required increases in all but one scenario, namely TaxiBots with high-capacity public transport.

From the results, the ITF researchers posited a number of policy insights:

• Self-driving vehicles could change public transport as we currently know it. For small and medium-sized cities it is conceivable that a shared fleet of self-driving vehicles could completely obviate the need for traditional public transport.

• The potential impact of self-driving shared fleets on urban mobility is significant. It will be shaped by policy choices and deployment options Transport policies can influence the type and size of the fleet, the mix between public transport and shared vehicles, and ultimately, the amount of car travel, congestion and emissions in the city.

• Active management is needed to lock in the benefits of freed space. Shared vehicle fleets free up significant amounts of space in a city. Prior experience indicates that this space must be proactively managed in order to ensure these benefits are fully realized. Management strategies can include restricting access to this space by allocating it to specified commercial or recreational uses, such as delivery bays, bicycle tracks or enlarged footpaths. Freed-up space in off-street parking could be used for urban logistics purposes, such as distribution centers.

• Improvements in road safety are almost certain; environmental benefits will depend on vehicle technology. The deployment of large-scale self-driving vehicle fleets will likely reduce both the number of crashes and crash severity, despite increases in overall levels of car travel. Environmental impacts remain tied to per-kilometer emissions and thus will be dependent on the adoption of more fuel-efficient and less polluting technologies. TaxiBots and AutoVots are in use 12 hours and travel nearly 200 kilometers per day, compared to 50 minutes and 30 kilometers for privately-owned cars today. More intense use means shorter vehicle lifecycles and thus quicker adoption of new, cleaner technologies across the car fleet.

• New vehicle types and business models will be required. A drastic reduction in the number of cars needed would significantly impact car manufacturer business models. New services will develop under these conditions, but it is unclear who will manage them and how they will be monetized. The role of authorities, both regulatory and fiscal, will be important in guiding developments or potentially maintaining market barriers. Innovative maintenance programs could be part of the monetization package developed for these services.

• Public transport, taxi operations and urban transport governance will have to adapt. Shared self-driving car fleets will directly compete with urban taxi and public transport services, as currently organized. Such fleets might effectively become a new form of low capacity, high quality public transport. This is likely to cause significant labor issues, ITF suggests. Governance of transport services, including concession rules and arrangements, will have to adapt.

• Mixing fleets of shared self-driving vehicles and privately-owned cars will not deliver the same benefits as a full TaxiBot/AutoVot fleet—but still remains attractive. In all fleet-mixing scenarios, overall vehicle travel will be higher. Also, vehicle numbers will increase in three out of four peak hour scenarios. Improved traffic flow of automated cars could mitigate congestion up to a point. However, the public policy case for self-driving fleets alone (without high-capacity public transport) may be difficult to make based solely on space and congestion benefits, due to the increase in overall travel volumes. Nonetheless, even in mixed scenarios, shared self-driving fleets could be a cost- effective alternative to traditional forms of public transport, if the impacts of additional travel are mitigated. “All in” deployment of shared self-driving fleets may be easier in circumscribed areas such as business parks, campuses, islands, as well as in cities with low motorization rates.

The “Urban Mobility System Upgrade: How self-driving cars could change city traffic” project has been carried out with the Corporate Partnership Board of the International Transport Forum.

Resources

• Urban Mobility System Upgrade: How shared self-driving cars could change city traffic