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Long-term relationship estimation and coupling/decoupling analysis between motorway traffic and Gross Value Added. Specification of an ARDL cointegration approach and application to the Italian case study

100%

Pampigna A. , Mauro R.

tom Vol. 60, iss. 4

39--56

As transportation is an activity derived from spatial complementarities between a certain supply at an origin and a certain demand at a destination, according to a general axiom it seems that economic activities entail transport demand. In this perspective, an essential analysis deals with the quantification of the relationships between transport demand and certain socioeconomic variables. Elasticity is a concept widely used in transport economics as a measure of the responsiveness of transport demand concerning different factors represented as independent variables in an econometric model and coupling/decoupling concepts have been proposed in literature. This paper deals with the estimation of elasticities of motorway traffic demand based on Gross Value Added (GVA), and the consequent investigation of coupling/decoupling situation. The analysis is based on the application of an Autoregressive-Distributed Lag (ARDL) cointegration model with the F-bound test and of the related Error Correction model. Starting from the general ARDL model and the methodology for the verification of its robustness, the same model is applied to the Italian toll road network. The time series of GVA for goods and services and the overall length of the toll network from 1995 to 2019 are considered as explanatory variables of the total annual distance traveled by light and heavy vehicles. The various tests in the ARDL framework show a cointegration between the variables, under the fulfillment of all the diagnostic requirements. In this way, the long-term elasticities and the short-term adjustment dynamics are estimated separately for the goods and services components of GVA, and light and heavy vehicles. Starting from stable estimates of elasticities, the long-term coupling and decoupling effects between motorway traffic of light and heavy vehicles and the national production of goods and services can be shown. The paper, as well as providing an updated picture of the Italian situation, identifies a methodological framework that can be transferred to other contexts for a sector of great interest to investors, such as the motorway sector. All this can be useful to meet the needs of numerous stakeholders, who want to deepen the links between the economic cycle and traffic demand on toll motorways.

Low computational complexity algorithm for recognition highly corrupted QR codes based on Hamming-Lippmann neural network

88%

Kvyetnyy R. N. , Ivanov Y. Y. , Pivoshenko V. V. , Kulyk Y. A. , Knysh B. P. , Smolarz A. , Muslimov K. , Turgynbekov Y.

2019

tom R. 95, nr 4

162--166

This article describes the architecture of the Hamming-Lippmann neural network and the math of the modified learning-recognition algorithm and presents some practical aspects for using it for solving an image recognition task. We have created software using C# programming language, that utilized this network as an additional error-correcting procedure, and have solved the task of recognition highly corrupted QR codes (with a connection to the database). Experimental results, of finding the optimal parameters for this algorithm, are presented. This neural network doesn’t require time-consuming computational procedures and large amounts of memory, even for high-resolution and big size images.

W tym artykule opisano architekturę sieci neuronowej Hamminga-Lippmanna oraz matematykę zmodyfikowanego algorytmu rozpoznawania uczenia się, a także przedstawiono kilka praktycznych aspektów korzystania z niej w celu rozwiązania zadania rozpoznawania obrazu. Stworzyliśmy oprogramowanie wykorzystujące język programowania C #, który wykorzystał tę sieć jako dodatkową procedurę korekty błędów i rozwiązaliśmy zadanie rozpoznawania wysoce uszkodzonych kodów QR (w połączeniu z bazą danych). Przedstawiono wyniki eksperymentalne poszukiwania optymalnych parametrów dla tego algorytmu. Opisywana neuronowa nie wymaga czasochłonnych procedur obliczeniowych i dużej ilości pamięci, nawet w przypadku obrazów o wysokiej rozdzielczości i dużych rozmiarach.