A mixed behaviour equilibrium model with mode choice and its application to the endogenous demand of automated vehicles

This study develops a mixed behavioural equilibrium model with explicit consideration of mode choice (MBE-MC) in a transportation system where fully automated vehicles (AV) coexist with conventional human-driven vehicles (HV). For the mode choice, travellers select among three options, following a logit modal split: driving their private HV, or taking an AV mobility service provided by either a firm or the government. For the route choice, the HV drivers follow the random utility maximisation principle while central agents route the AV passengers following the Cournot Nash (firm agent) or Social Optimal (government agent) principles. We consider two types of travel costs (i.e. travel time and monetary travel cost) to characterise the new features (e.g. expanded link capacity and reduced value of time) of the mixed AV–HV transportation system. We model the MBE-MC problem in a combined mode–route choice framework and formulate it as a route-based variational inequality (VI) problem. We show the equivalence between the VI formulation and the MBE-MC problem, and the existence of a solution to the MBE-MC problem. Then, we modify a partial linearisation algorithm for solving the proposed model. Numerical results validate the equilibrium conditions and show the efficacy of the new model in capturing the features of the mixed AV–HV transportation system. The impact patterns of different parameters on (1) the network performance in terms of AV share and system cost and (2) on the solution efficiency are analysed.     

Application of evidential reasoning approach and OWA operator weights in road safety evaluation considering the best and worst practice frontiers

Data envelopment analysis (DEA) has recently become relatively popular with road safety experts. Therefore, various decision-making units (DMUs), such as EU countries, have been assessed in terms of road safety performance (RSP). However, the DEA has been criticized because it evaluates DMUs based only on the concept of self-assessment, and, therefore does not provide a unique ranking for DMUs. Therefore, cross efficiency method (CEM) was developed to overcome this shortcoming. Peer-evaluations in addition to self-evaluation have made the CEM to be recognized as an effective method for ranking DMUs. The traditional CEM is based only on the standard CCR (Charnes, Cooper and Rhodes) model, and it evaluates DMUs according to their position relative to the best practice frontier while neglecting the worst practice frontier. However, the DMUs can also be assessed based on their position relative to the worst practice frontier. In this regard, the present study aims to provide a double-frontier CEM for assessing RSP by taking into account the best and worst frontiers simultaneously. For this purpose, the cross efficiency and cross anti-efficiency matrices are generated.Even though a weighted average method (WAM) is most frequently used for cross efficiency aggregation, the decision maker's (DM) preference structure may not be reflected. For this reason, the present study mainly focuses on the evidential reasoning approach (ERA), as a nonlinear aggregation method, rather than the linear WAM. Equal weights are often used for cross efficiency aggregation; consequently, the effect of the DM's subjective judgments in obtaining the overall efficiency is ignored. In this respect, the minimax entropy approach (MEA) and the maximum disparity approach (MMDA) are applied for determining the ordered weighted averaging (OWA) operator weights for cross efficiency aggregation. The weighted cross efficiencies and cross anti-efficiencies are then aggregated using the ERA. Finally, the proposed method, called DF-CEM-ERA, is used to evaluate the RSP of EU countries as well as Serbian police departments (PDs).     

Dynamic efficiency evaluation of Italian judicial system using DEA based Malmquist productivity indexes

The aim of this study is the empirical analysis of the Italian judicial system, measuring its efficiency and productivity. For this purpose, in details, the Data Envelopment Analysis (DEA) and Malmquist indexes were used, since they are recognized by the current literature as successful techniques to evaluate the performance of decision making units, namely the courts of law in our field of application.The statistical data on the administration of justice in Italy, as reported in Dossier n. 11 May 2013, Senate Research Services, Research Office on Institutional Issues, Justice and Culture, XVII legislature, shows that the efficiency crisis of justice in Italy began in the 1970s of the last century, aggravating during the ‘80s, and reaching its most critical moment during the 1990s. Several studies emphasize the relevance of the effects of inefficiency upon the judicial system on the credit and financial markets.The present analysis, using data that covers a wide time span and is disaggregated at district level, has set the goal of measuring the efficiency of the individual Italian judicial offices while assessing the progress of productivity in its components, by the means of technological progress and scale efficiency. The efficiency analysis that was carried out transcends the aspect of judicial taxation, considering how the passage of time has impacted on judicial efficiency.Considering the judges and judicial administration employed, in the new, pending and finished cases during the years ranging from 2011 to 2016, the results highlighted a distinct heterogeneity among courts, depending on their geographical location. The five-year period that was considered, included the years in which the judicial geographical distribution reform entered into force, so to better comprehend how this complex reform influenced the recovery of efficiency of the judicial offices. Furthermore, by breaking down the index into change in efficiency and change in technology, this work offers a further glimpse into judiciary organization. The application of combined DEA method and Malmquist indexes for evaluating court efficiency in a large time frame showed very interesting and useful results, relevant for judicial administration.     

The long-run performance of acquiring firms in mergers and acquisitions: Does managerial ability matter?

This paper examines the association between the managerial ability of acquiring firms and their long-term performance after mergers and acquisitions (M&As). Based on M&A data for U.S. firms from 2000 to 2012, we find that acquiring firms with higher managerial ability achieve better long-term operating performance and stock returns. We also find that the positive effect of managerial ability on long-term performance is more pronounced when acquirers and target firms belong to the same industry. The result suggests that managers who have higher ability to manage their firms, i.e., to generate higher revenues for given resources, are more capable of achieving higher synergy benefits and better post-acquisition performance in same-industry acquisitions than in cross-industry acquisitions.     

数据的要素化过程及其与传统产业数字化的融合机制研究

从生产过程和价值创造来看,数据具备了传统生产要素所具备的功能,但又具有区别于传统生产要素的新特征。数据作为一种新型生产要素,对于数字产业化的高速发展和传统产业数字化进程的深入推进,具有关键作用。数据要素的形成和发展一方面需要与数字产业化、产业数字化融合发展进程相适应;另一方面,数据要素的形成过程本身又是与传统产业数字化进程的一个深度技术融合过程。因而,多样化和异质性的数据要素,其实质乃是数字技术与传统产业专用性技术融合的结果,其间的融合机制表现为深化、促进和多元性。     

Economic interpretations of DEA

Research on productive efficiency at the firm level has developed as an important and active strand of research the last decades, both within operations research, management science and economics. Two apparently different definitions of efficiency are examined, but it is shown that when both estimation methods are based on solving linear programming problems the definitions of efficiency are identical. The purpose of the paper is to give the basic ideas of efficiency analyses using DEA as a tool for researchers not so familiar with efficiency analysis and DEA. The concept of shadow prices is given special attention.     

Analyzing tactical control strategies for aircraft arrivals at an airport using a queuing model

This paper proposes to analyze control strategies for arrival air traffic at an airport using a classical queuing model. The parameters of our model are estimated by means of a data-driven analysis of two years of radar tracks and flight plans for arrival flights at Tokyo International Airport from 2016 to 2017. Our results show that increasing the capacity with one or two more aircraft in the airspace up to 60 NM around the airport significantly mitigates arrival delays, even when assuming future, increased arrival traffic volumes. The outcomes of this study provide insights into the effectiveness of arrival control strategies and are seen as a means to recommend scenarios to be further analyzed with human-in-the-loop simulations.     

Q‐FACTORS IN EMPIRICAL ASSET PRICING: A REVIEW AND SYNTHESIS

The latest development in the asset pricing literature is the emergence of empirical asset pricing models comprising q‐factors (profitability and investment factors) in conjunction with other factors. However, as in the case of the older empirical models, there is scepticism regarding the application of these newer factor models consisting of q‐factors because of the debate surrounding the explanatory power of these empirically inspired asset pricing models. This review attempts to synthesize studies pertaining to the four alternative explanations of the asset pricing models comprising the q‐factors (profitability and investment) – the data snooping hypothesis, the risk‐based explanation, the irrational investor behaviour explanation and the interpretation that suggest that the combination of the risk‐free asset and the factors comprising the model span the mean‐variance efficient tangency portfolio that prices the universe of assets.     

Locating a Biorefinery in Northern Spain: Decision Making and Economic Consequences

Biofuels are emerging as a prominent renewable and sustainable energy sources in developed countries. In this sense, this paper presents a case study in which a biorefinery has to be sited in Northern Spain. Thus, the strategic decision of locating such a facility is deeply investigated through strategic policy evaluation. Then, tactical decisions ranging from purchasing and transportation policies to storage protocols are carried out. Only local and limited biomass can be harvested for supplying the biorefinery through a heterogeneous vehicle fleet. Moreover two different and mutually exclusive storage strategies are evaluated: direct supply from crops to biorefinery and using intermediate-collectors. Additionally, crop exploitation factors and biorefinery sizes are used to generate several scenarios in which the strategic decision of location as well as all the tactic decisions are made. Some mixed integer linear programming models are proposed to figure out all relevant decision problems.The results suggest that the Northwest study area as the best option to locate the biorefinery and recommend the intermediate-collector storage strategy. Moreover, the key information about critical biomass, crops and times are also provided.     

Why you should use high frequency data to test the impact of exchange rate on trade

This study suggests that testing the impact of exchange rate on trade should be done using high-frequency data. Using different data frequencies for identical periods and specifications between the US and Canada, we show that low-frequency data might suppress and distort the evidence of the impact of exchange rate on trade in the short run and the long run.