Reservations (and Pricing)

Notes and Links

No free rides
Let's get one thing straight: Travelers will pay to ride on CAV Systems. How much they will pay will depend on what the service is worth to them and how much will be needed to sustain the system. This point is the subject of Davius' Ninth Commandment at right. Davius here implies that without riders willing to pay for rides, there is no assurance that CAV Systems will survive. Government could shake down taxpayers for money to pay for free-riders but, this would doom CAV Systems to sub-standard coverage, quality, and service. Instead, Davius insists that CAV Systems meet the needs and expectations of travelers willing to pay their way.
Basic access
At each node,

1. CAV certification for corridor travel will be verified,
2. traffic load will be monitored, and
3. corridor access will be controlled.

To gain access to a CAVWAY, a CAV will have to be certified. It will be charged an access fee, like a toll on a toll road. As on today's toll roads, that fee will be assessed automatically.
To maintain continuous traffic flow, a CAVWAY will not allow traffic loads to exceed corridor capacity. However, if and when demand for access exceeds capacity, the result might be long queues for CAVWAY access; this would simply transfer time wasted in traffic jams to time wasted in access queues. To mitigate such delays, CAV Systems will use policies to level demand over time of day and routes, based on points of entry and exit.
One such policy, congestion pricing, involves varying prices to influence travelers and reduce demand for access to crowded routes at peak travel times.
Under congestion pricing, CC will price reservations according to demand for access at various times on various routes. A CAV will be able to reserve CAVWAY access for travel on a specific route at a specific time for a quoted price (based on supply and demand). During high demand periods, CAVs holding reservations will have priority of access; CAVs without reservations might be denied access until demand receded.
A second aspect of pricing will affect organizations which offer public transportation. These organizations, to meet costs, can be expected to pass access and reservation fees on to customers. Congestion pricing can be expected to affect traveler choices and, over time, to promote more effective use of capacity.
The example at right illustrates how such pricing can be useful in bringing corridor-access supply and demand into balance.

Davius' Ninth Commandment
Make CAV Systems sustainable so that they may endure.
Resolving Congestion
One approach to CAV-System congestion is to 1) set user fees relatively high for typically high-demand routes, days, and times and 2) adjust fees slowly, over days, weeks, and months, to reflect changing demand patterns as travelers integrate commute prices into decisions regarding travel times. Congestion pricing, which depends on decisions by travelers, has been shown to be effective in reducing traffic demand during peak travel hours. However, results vary according to locations and situations and require continual monitoring. Further speculation is beyond the scope of this discussion.

Example concerning congestion pricing
Suppose that many sports fans are expected for the big game. N(e), the ideal node to exit the corridor, and T(a), the ideal arrival time, are expected to be in high demand. The further fans exit from N(e) and the more their arrival time deviates from T(a), the lower the expected demand. Therefore, congestion pricing would indicate a high price close to N(e) and T(a), declining for fans exiting further from N(e) at times earlier or later than T(a).
Similar situations arise during work-related commutes in urban areas. Traffic flow is normally heavy in one direction on workday mornings and in the other direction in the evening. This situation differs from the big game in two ways: during urban-area commutes, there are likely to be several origins and several destinations in high demand; and access demands are highly likely to follow daily time patterns.

Neither reservations nor pricing have been characterized in CSIM. However, over time, as more is learned about how to model commuter behavior in response to pricing, CSIM will become an effective venue in which to model congestion-pricing algorithms. Note the implication that the simulation writer will learn by observing traveler behavior rather than from simulation results. The simulation should be an effective way of documenting such observations.