Assessment of Cross‐Border Spillover Effects of National Transport Infrastructure Plans: An Accessibility Approach

Abstract

Traditional transport infrastructure assessment methodologies rarely include the full range of strategic benefits for the transportation system. One of these benefits is the contribution to cross‐border integration, critical for the European integration process. However, this is a key issue in strategic planning and decision‐making processes, as its inclusion may increase the probability of large‐scale transport infrastructure projects being funded. This paper presents a methodology for the measurement of the contribution of transport infrastructure plans to European integration. The methodology is based on the measurement of the improvement in network efficiency in cross‐border regions of neighbouring countries, via accessibility calculations in a Geographical Information System support. The methodology was tested by applying it to the ambitious road and rail network extensions included in the Spanish Strategic Transport and Infrastructure Plan (PEIT) 2005–2020. The results show significant and important network efficiency improvements of the PEIT outside the Spanish border. For the road mode, while the Spanish average accessibility improvement accounts for 2.6%, average improvements in cross‐border regions of France and Portugal are of 1.8%. And for the rail mode, the corresponding Spanish value is 34.5%, whereas in neighbouring regions it accounts for 20.2%. These results stress the significant importance of this strategic benefit and the consequent need for its inclusion in strategic planning processes. Finally, the paper identifies the potential of the methodology when applied at different administrative levels, such as the local or state levels.     

Stochastic frontier estimation of budgets for Kuhn–Tucker demand systems: Application to activity time-use analysis

We propose a stochastic frontier approach to estimate budgets for the multiple discrete–continuous extreme value (MDCEV) model. The approach is useful when the underlying time and/or money budgets driving a choice situation are unobserved, but the expenditures on the choice alternatives of interest are observed. Several MDCEV applications hitherto used the observed total expenditure on the choice alternatives as the budget to model expenditure allocation among choice alternatives. This does not allow for increases or decreases in the total expenditure due to changes in choice alternative-specific attributes, but only allows a reallocation of the observed total expenditure among different alternatives. The stochastic frontier approach helps address this issue by invoking the notion that consumers operate under latent budgets that can be conceived (and modeled) as the maximum possible expenditure they are willing to incur. The proposed method is applied to analyze the daily out-of-home activity participation and time-use patterns in a survey sample of non-working adults in Florida. First, a stochastic frontier regression is performed on the observed out-of-home activity time expenditure (OH-ATE) to estimate the unobserved out-of-home activity time frontier (OH-ATF). The estimated frontier is interpreted as a subjective limit or maximum possible time individuals can allocate to out-of-home activities and used as the time budget governing out-of-home time-use choices in an MDCEV model. The efficacy of this approach is compared with other approaches for estimating time budgets for the MDCEV model, including: (a) a log-linear regression on the total observed expenditure for out-of-home activities and (b) arbitrarily assumed, constant time budgets for all individuals in the sample. A comparison of predictive accuracy in time-use patterns suggests that the stochastic frontier and log-linear regression approaches perform better than arbitrary assumptions on time budgets. Between the stochastic frontier and log-linear regression approaches, the former results in slightly better predictions of activity participation rates while the latter results in slightly better predictions of activity durations. A comparison of policy simulations demonstrates that the stochastic frontier approach allows for the total out-of-home activity time expenditure to either expand or shrink due to changes in alternative-specific attributes. The log-linear regression approach allows for changes in total time expenditure due to changes in decision-maker attributes, but not due to changes in alternative-specific attributes.