Economics of Transportation最新文献

It is currently unknown in which city environments, automated vehicles could be deployed at reasonable speeds, given safety concerns. We analytically and numerically assess the impact of automation for optimal vehicle size, service frequency, fare, subsidy and degree of economies of scale, by developing a model that is applied for electric vehicles, with data from Chile and Germany, taken as illustrative examples of developed and developing countries. Automation scenarios include cases with partial driving cost savings and reduced running speed for automated vehicles. We find that a potential reduction in vehicle operating cost due to automation benefits operators, through a reduction of operator costs, and also benefits public transport users, through a reduction on waiting times and on the optimal fare per trip. The optimal subsidy per trip is also reduced. The benefits of vehicle automation are greater in countries where drivers’ salaries are larger.

It is well known that price and quantity regulation are not equivalent under uncertainty. This asymmetry has been a factor in the debate about whether to use taxes or Tradable Permit Schemes (TPS) for controlling greenhouse gas emissions. We analyze the allocative efficiency of a TPS for a congestible facility such as an airport, a road, a recreational area, or a museum that experiences supply and demand shocks. The number of permits issued cannot depend on the state. We compare the efficiency of a TPS and a congestion fee when the level of the fee is similarly constrained to be the same across states. When demand and cost curves are linear, a fee outperforms a TPS for several combinations of additive and/or multiplicative demand and cost shocks. More generally, the ranking depends on the nature and magnitude of demand and cost shocks, the elasticity of the cost function, and whether or not the permit requirement always binds. A TPS tends to perform well when first-best usage levels are similar across states. Analogously, a fee is relatively efficient if first-best fees are similar across states.

Car scrappage schemes are generally introduced to upgrade the car fleet to reduce environmental pressures from private transportation. The aim of this paper is to present a combined use of two counterfactual techniques (RDD and DiD) to quantify the impact of Italian car scrappage schemes of 2007–2009 on the deregistration of cars. The empirical assessment of the policy effectiveness is made possible by a discontinuity in the age of cars that were eligible for the subsidy. In the DiD model we also introduce a variable on ‘product innovation’ in the car market as a factor that determines scrappage timing. Results, based on detailed information on the car fleet and the deregistration of cars in Italy, suggest no impact for the 2007 and 2008 schemes and a large impact for the 2009 scheme, which was based on rather high incentives. Results are robust to both counterfactual approaches and to different tests.

In a controlled lab experiment, we investigate hypothetical biases in the value of time by comparing stated preference (SP) and revealed preference (RP) values attached to unexpected waiting times. The SP and RP choice sets are identical in terms of design with the only difference being that the RP choices have real consequences in terms of unexpected waiting times and monetary incentives. We find a substantial hypothetical bias with the average SP value of time being only 70% of the corresponding RP value. The bias is mainly driven by participants who have scheduling constraints during the time of the unexpected wait. Scheduling constraints are taken into account to a much lesser extent in the SP setting than in the RP setting, presumably because only in the latter, the consequences of ignoring them are costly. We find evidence that this effect is stronger for persons with relatively low cognitive ability.

At the end of 2005, Autostrade per l’Italia (ASPI) and the Italian traffic police progressively deployed along the Italian tolled motorway network an average speed enforcement system, named Safety Tutor, able to determine the average speed of vehicles over a long section to encourage drivers to comply with speed limits and improve safety. The aim of this study was to empirically test the extent to which Safety Tutor led to a reduction in both total and fatal accidents on Italian highways during the period of 2001–2017. To do so, we carried out a generalized difference-in-differences estimation using a unique panel dataset that exploits the heterogeneous accident data within all tolled motorway sectors in a quasi-experimental setting. To deal with the potential endogeneity of the non-random placement of Safety Tutor sites, we utilized an instrumental variable strategy by using the network of motorway sectors managed by ASPI and its controlled concessionaires from 2005 onwards (i.e., when the technology was available) as an instrument to predict Safety Tutor adoption. We found that a 10% increase in Safety Tutor coverage led to an average reduction in total accidents of 3.9%, whereas there is no evidence of a significant causal effect of Safety Tutor in reducing fatal accidents.

This study provides further empirical evidence on pricing by international airline alliances. The paper covers a long sample period, which runs from 1997 to 2016, and it supplements the usual USDOT fare data with confidential fare data reported by the foreign alliance partners of US carriers. The empirical results for connecting service match earlier findings, with alliances charging lower fares than nonaligned carriers. In contrast to almost all previous studies, the gateway-to-gateway results imply that, in the latter part of the sample period, granting antitrust immunity to two previously nonaligned carriers is equivalent to removing a competitor, with a consequent increase in fares. However, a simulation based on the results shows that this anticompetitive effect is more than offset by gains to connecting passengers, making alliances beneficial on balance.

This paper develops an economic model of driver behaviour and discusses the traffic safety implications when the driver listens to the radio, uses a mobile phone, and has passengers in the car. The main findings are that, even though engaging in the three distractions reduce the driver's concentration and his driving performance, they can improve the traffic safety situation for both himself and the passengers. By reasoning a little beyond the pure results of the model the paper also suggests that having access to the radio and a mobile phone can improve safety due to the driver being able to receive information about driving conditions and inform people about lateness. The safety implications of having passengers in the car will also depend on how much the driver is attached to the passengers; the more concerned he is about their safety, the more careful he becomes.