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When traveling in an autonomous car, the travel time can be used for performing activities other than driving. This paper distinguishes users' work-related and home-related activities in autonomous cars and proposes an activity-based bottleneck model to investigate travelers' behavior in the morning commute, shedding light on how the scope to undertake in-vehicle activities affects travelers' trip-timing preferences and decisions, and therewith social welfare. These welfare effects can be expected to depend on the optimality of both the market for trips, and the market for vehicles. We therefore consider different supply regimes for automobiles, and un-priced congestion versus queue-eliminating road pricing. We reveal analytically the relationship between users' various in-vehicle activities and trip timing choices by autonomous and normal car users. Three supply regimes for autonomous cars are investigated: welfare-maximizing public supply, competitive marginal cost supply, and profit-maximizing private supply. Pricing rules under different supply regimes are compared analytically, and the relative efficiencies in terms of the welfare gains are compared numerically. Results show that travelers' in-vehicle activity choices have significant impacts on the travel patterns, congestion externality, supply decisions and the associated welfare effects.

We examine the relationship between the total size of an airline and its service quality by analysing over 4.8 million domestic flights within the USA in 2016. The total size of an airline is measured by its total market share, total amount of assets or total number of full-time equivalent employees. Delays are a widely used proxy for service quality and the most common category of airline customer complaints. Numerous regressions have been estimated using arrival delay time and whether a flight arrives on time as dependent variables. The regressors of main interest were the total airline size and the degree of competition on the route and airport. We control for weather, congestion, date, and characteristics of the airport, flight and airplane. The results suggest that the larger the total size of an airline, the smaller its average delay time and delay occurrence. Hence, larger airlines seem to offer a higher quality in terms of delays. We also find that an origin airport with less competition may lead to more delays. Surprisingly, a less competitive route may reduce delays.

This paper analyzes the effect of carrier collaboration on fleet capacity, fleet structures in terms of the number and the size of vehicles, and load factors. The model features complementary networks, scheduling, price elastic demands, and demand uncertainty. For the case of a given number of vehicles, the analysis shows that carrier collaboration increases vehicle sizes (thus, fleet capacity) if marginal seat costs are low while fleet capacity remains unchanged if marginal seat costs are high. If both vehicle sizes and vehicle numbers can be varied, then collaboration will always increase vehicle numbers and fleet capacity, while the effects on vehicle sizes and, thus, also load factors, are ambiguous and therewith hard to predict. Numerical simulations indicate that collaboration increases expected load factors also when the number of vehicles is endogenous.

‘Robot cars’ are cars that allow for automated driving. They can drive closer together than human driven ‘normal cars’, and thereby raise road capacity. Obtaining a robot car instead of a normal car can also be expected to lower the user’s value of time losses (VOT), because travel time can be used for other activities than driving. With a mix of normal and robot car users, the VOT is therefore (more strongly) heterogeneous. We study the effect of robot cars on social welfare for a number of market organizations: private monopoly, perfect competition and public supply. Increasing the share of robot cars raises average capacity (especially if robot cars drive concentrated in time), but may hurt existing robot car users as the switchers’ lowered VOT will increase their bottleneck-congestion externality. When the capacity effect dominates, buying a robot imposes a net positive externality, but otherwise, it causes a net negative externality. Numerical analysis suggests that a net positive externality is more likely; nevertheless, for a small, but still plausible, capacity effect a net negative externality results. With a positive (negative) externality, marginal cost provision tends to lead to an undersupply of robot cars, and a public supplier needs to subsidise (tax) robot car purchase in order to maximise welfare. A monopolist supplier ignores the externality and tends to add a mark-up to its price. This almost always leads to a substantial undersupply.

This paper analyzes efficient pricing at a congested airport dominated by a single firm. Unlike much of the previous literature, we combine a dynamic (bottleneck) model of congestion and a vertical structure model that explicitly considers the role of airlines and passengers. We show that when a Stackelberg leader interacts with a competitive fringe, charging the congestion toll that is derived for fully atomistic carriers to both leader and fringe yields the first-best outcome. This holds regardless of the leader's internalization of congestion in the unregulated equilibrium, and regardless of the assumed demand substitution pattern between firms. This result implies that the financial deficit under optimal pricing may be less severe than what earlier studies suggest. Finally, we show that there are various alternative toll regimes that also induce the welfare maximizing outcome, and therefore widen the set of choices for regulators.

We study road supply by competing firms between a single origin and destination. In previous studies, firms simultaneously set their tolls and capacities while taking the actions of the others as given in a Nash fashion. Then, under some widely used technical assumptions, firms set a volume/capacity ratio that is socially optimal, and thus the level of travel time or service quality is socially optimal. We find that this result does not hold if capacity and toll setting take place in separate stages, as then firms want to limit the toll competition by setting lower capacities; or when firms set capacities one after another in a Stackelberg fashion, as then firms want to limit their competitors' capacities by setting higher capacities. In our Stackelberg competition, the firms that act last have few if any capacity decisions to influence. Hence, they are more concerned with the toll-competition substage, and set a higher volume/capacity ratio than sociall y optimal. The firms that act first care more about their competitors' capacities that they can influence: they set a lower volume/capacity ratio. So the first firms to enter have a too short travel time from a social perspective, and the last firms a too long travel time. The average private travel time is shorter than socially optimal. Still, in our numerical model, for three or more firms, welfare is higher under Stackelberg competition than under Nash competition, because of the larger total capacity and lower tolls.

In most dynamic traffic congestion models, congestion tolls must vary continuously over time to achieve the full optimum. This is also the case in Vickrey's (1969) 'bottleneck model'. To date, the closest approximations of this ideal in practice have so-called 'step tolls', in which the toll takes on different values over discrete time intervals, but is constant within each interval. Given the prevalence of step tolling schemes they have received surprisingly little attention in the literature. This paper compares two step-toll schemes that have been studied using the bottleneck model by Arnott, de Palma and Lindsey (1990) and Laih (1994). It also proposes a third scheme in which late in the rush hour drivers slow down or stop just before reaching a tolling point, and wait until the toll is lowered from one step to the next step. Such behaviour has indeed been observed in reality. Analytical derivations and numerical modelling show that the three tolling schemes have different optimal toll schedules and reduce total social costs by different percentages. These differences persist even in the limit as the number of steps approaches infinity. Braking lowers the welfare gain from tolling by 14% to 21% in the numerical example. Therefore, preventing or limiting braking seems important in designing step-toll systems.