Self-driving vehicles as instruments for the coordination of mobility

Autonomous cars will change the way we think about traffic. Today traffic is primarily regarded as the result of the independent actions of thousands of drivers. A view from above on any city would show large numbers of vehicles pursuing their own trajectories through the maze of roads. The cities’ traffic management systems try their best to observe, identify and somewhat channel the grand flows.

At first glance, autonomous vehicles do not seem to change this situation very much. From above, self-driving cars will not be distinguishable from human driven cars and they too, will seek their individual paths through the maze of roads. The picture changes, however, when we consider fleets of self-driving cars. Recent statements by Ford, Uber, BMW and others clearly show that fleets of self-driving cars will emerge early and have the potential to capture a significant share of individual motorized mobility.

This introduces a crucial difference: Fleet vehicles no longer pursue their local optimum; rather than completing the individual trip as quickly as possible, fleet management will seek to maximize throughput for all of its vehicles – for the fleet as a whole. The operational goals of fleet management are therefore very much aligned with the traffic flow goals of a city as a whole.

Initially, autonomous fleet vehicles will be instruments which fleet management systems can use to understand, model and predict the detailed traffic situation. The vehicles will be used as sensors and relay important information to the fleet management system.

As fleets grow, fleet managers will find that the vehicles can be used to influence the flow of traffic. Many different strategies are possible (and their effectiveness varies greatly with the ratio of fleet cars to total number cars): fleet vehicles can purposefully slow down the build-up of traffic ahead of arteries which are in danger of clogging. Fleet cars can reliably calculate and selectively or pre-emptively use alternative routes. As the percentage of fleet vehicles in relation to total traffic grows, fleet vehicles may travel part of the way in more densely packed convoys. They may even change their acceleration behavior at stop-lights (using a somewhat faster acceleration pattern than the standard acceleration pattern of human drivers) which may or may not be copied by human drivers.

Because both city traffic managers and fleet managers will recognize early on that their interests are very much aligned, we can expect many ways in which both parties will cooperate. Fleet managers will make real-time traffic information gained via their cars used as sensors available to the city traffic managers. Fleets are likely to ask city traffic managers to adjust stop light phases to improve traffic flow (and fleets will provide the data and models to prove that these changes will be beneficial). We can expect that this will lead to much more real-time traffic management for stop lights and fleet vehicles may come to very directly influence traffic signals. Eventually, as the differences in driving behavior between human-driven and autonomous vehicles become more apparent and fleet vehicles exceed 20 percent of traffic (initially mostly likely in urban centers), we may find that cities will reserve some lanes or roads for self-driving vehicles because they are more effective at providing local mobility than individual cars, or because the throughput on autonomous-vehicle-only lanes can be twice the throughput of human-driven lanes (mostly due to shorter distances between vehicles and better reaction times/acceleration behavior at stop lights, in some cases also because two conventional lanes might be re-fitted into three narrower lanes for autonomous fleet vehicles).

But this is only the tip of the iceberg. Fleet managers will understand local traffic very well and want to avoid their most valuable resources to be stuck in traffic. They will be able to predict the actual duration for a trip at any given point in time and will aim to minimize trips which incur heavy congestion. Instead of just driving a customer every day to work at a time of his choosing, they will look for ways to reduce the peak load on the fleet. Ridesharing is only one of many approaches: Fleets will provide rewards to those who stay out of the rush hour (or add congestion pricing, which in turn will drive down congestion). They may find ways to systematically phase traffic flows in certain areas, work with employers and schools to adjust working hours, provide an in-car environment that allows workers to begin their work while commuting (and ensure employers’ approval), provide a reliable forecast of trip times (and a clear indication how expected trip times can be reduced by leaving earlier or later).

Time will tell which of these many possible actions will yield the most benefit (and through which other approaches fleets of self-driving vehicles will improve the overall traffic flow in a city). But it is obvious that fleets of autonomous vehicles will lead to a very different thinking about traffic. Where today we have thousands of actors all pursuing their own little traffic goals, these fleets will start us thinking about how traffic can be optimized not just locally but as a whole. It is clear that this optimization does not necessarily start when a trip begins, but potentially already before Рwhen a mobility demand for a trip from a certain location to another location in a certain time range  is known. Fleets will pave the way by optimizing their trips against the whole fleet. And the lessons we learn from managing trips for autonomous vehicle fleets will deeply change our thinking about traffic and how traffic should be organized.

Thus, autonomous vehicles not only drive themselves; they change the cost structure of mobility, which in turn enables shared¬† autonomous mobility services to grab a significant part of the market for motorized individual mobility. These shared services will necessarily implement a centralized perspective on mobility which requires finding (and negotiating) ways to optimize the mobility demands of large groups, even cities. In the end, we will likely think about all mobility – whether in a fleet vehicle, in privately owned autonomous or conventional car – from a perspective of global optimization. It won’t be long before our mayors, regulators and politicians will see the potential of self-driving vehicles for traffic management and begin to develop policies that lead traffic away from today’s heavily congested local optima towards structures that come much closer to the global optimum.

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