Modeling the dynamic response of automobile sales in troubled times: a real-time Vector Autoregressive analysis with causality testing for Greece

In this paper, we investigate the factors that affect multi-segments automobile sales in Greece. Various relevant quantitative techniques have been employed, such as stationarity, causality and cointegration. A Vector Autoregressive (VAR) model was also developed and long-term impacts of the different variables of interest on car sales have been estimated through generalized impulse response functions (GIRF). The impact of the current financial crisis on the Greek automobile market was also taken into account. The results show that fuel prices Granger cause total car sales. The results also indicate the absence of long run cointegrating relationships among the variables. The full blown model shows that demand for new automobiles depends on the existing social, financial and political conditions of the local economy and that the various shocks observed have a temporary medium-run character on car sales, whereas the system is found to be stable

Impact of Economic Crisis on Passenger Transportation – Case of Travelling to the Greek Mainland from Crete

The Greek economic crisis of 2009 onwards has affected all aspects of social and economic life of the country, including transportation. The present study focuses on the impact of economic crisis on the long distance transportation between the island of Crete in Greece, the largest Greek island and one of the largest in the Mediterranean Sea, and the Greek mainland. A questionnaire survey was used to investigate the opinions of the Cretans on the way the economic crisis influenced their transportation to the Greek mainland. The results of the survey show that the frequency of the Cretans’ travels was significantly limited, owing to the increased direct or indirect associated cost, due to the economic crisis. Especially for those who struggled to make ends meet, the transportation to the mainland dropped to the bare essentials. Furthermore, the respondents deemed that the&nbsp;deregulation of the Greek maritime and airline markets was also to blame for the high fares, thus they favoured a regulated public transport sector and were against privatization. Inevitably, financially vulnerable individuals were the most preoccupied with these issues. A feeling of isolation and exclusion was revealed by the sample on occasions when the scheduled trips were cancelled by the operators due to exogenous parameters.</p

Ανάπτυξη μεθόδων εκτίμησης κατανομής της ζήτησης στα μέσα μαζικής μεταφοράς σε συνθήκες συμπληρωματικής λειτουργίας

In this thesis demand aspects of a multimodal public transportation system are investigated using econometric methods for analyzing non-stationary data. The case of the Athens public transport system, where different modes may operate in competition or cooperation, is used as a test bed. Two different but complementary aspects of public transport demand are analyzed. Ridership of each mode and share of each mode in total ridership. The econometric analysis adopted is based on cointegration and error correction techniques. This allows for treating non-stationary data, for determining short and long run elasticities and at the same time estimating the speed of adjustment towards long run equilibrium. According to the results, demand elasticities with respect to the explaining factors are significantly different for the different modes. Short run elasticities appear to be lower than the long run ones both in the models explaining ridership and in the models explaining the share of each mode, because short run elasticities are governed by resistance to change.Σκοπός της διατριβής είναι να διερευνηθούν οι παράγοντες που επηρεάζουν τόσο τις βραχυχρόνιες όσο και τις μακροχρόνιες μεταβολές στη ζήτηση ενός συστήματος αστικών συγκοινωνιών, το οποίο αποτελείται από πολλά συνεργαζόμενα μέσα. Η μελέτη της ζήτησης πραγματοποιείται με δύο προσεγγίσεις. Στην πρώτη, εξετάζονται οι παράγοντες που ερμηνεύουν την μηνιαία επιβατική κίνηση του κάθε μέσου. Στη δεύτερη, αναλύεται το ποσοστό (μερίδιο) της συνολικής ζήτησης που καλύπτει το κάθε Μέσο Μαζικής Μεταφοράς. Η μεθοδολογική προσέγγιση που χρησιμοποιήθηκε βασίζεται στις οικονομετρικές μεθόδους της Συνολοκλήρωσης και Δυναμικού Υποδείγματος Διόρθωσης Λαθών, οι οποίες επιτρέπουν την αξιόπιστη ανάλυση μη στάσιμων χρονολογικών σειρών και επιπλέον παρέχουν τις βραχυχρόνιες και μακροχρόνιες ελαστικότητες, καθώς και την ταχύτητα σύγκλισης στην μακροχρόνια κατάσταση ισορροπίας. Οι ελαστικότητες της ζήτησης ως προς τους ερμηνευτικούς παράγοντες είναι σημαντικά διαφορετικές για τα διάφορα Μ.Μ.Μ. Tόσο στα μοντέλα της επιβατικής κίνησης όσο και στα μοντέλα του μεριδίου αγοράς κάθε μέσου οι βραχυχρόνιες ελαστικότητες είναι μικρότερες από τις αντίστοιχες μακροχρόνιες

Development of Methods for Estimating Public Transport Shares under Complementary Operating Conditions

135 σ.Σκοπός της διατριβής είναι να διερευνηθούν οι παράγοντες που επηρεάζουν τόσο τις βραχυχρόνιες όσο και τις μακροχρόνιες μεταβολές στη ζήτηση ενός συστήματος αστικών συγκοινωνιών, το οποίο αποτελείται από πολλά συνεργαζόμενα μέσα που λειτουργούν συμπληρωματικά. Η ανάλυση της ζήτησης σε ένα σύστημα αστικών συγκοινωνιών παρέχει χρήσιμη πληροφόρηση για την αποτελεσματική λήψη αποφάσεων που αφορούν στην λειτουργία και στην ανάπτυξη υποδομών του συστήματος. Η ζήτησή εκφράζεται ως συνάρτηση λειτουργικών και μακροοικονομικών παραγόντων (τιμή εισιτηρίου, ΑΕΠ, τιμή βενζίνης, δείκτης ανεργίας, πωλήσεις Ι.Χ.) και ο βαθμός της μεταβολής της ζήτησης λόγω μεταβολής κάποιων παραγόντων εκτιμάται μέσω του μεγέθους της ελαστικότητας της ζήτησης. Η μελέτη της ζήτησης για το κάθε μέσο μαζικής μεταφοράς του συστήματος αστικών συγκοινωνιών της πόλης των Αθηνών πραγματοποιείται με δύο προσεγγίσεις. Στην πρώτη, εξετάζονται οι παράγοντες που ερμηνεύουν την μηνιαία επιβατική κίνηση του κάθε μέσου. Στη δεύτερη, αναλύεται το ποσοστό (μερίδιο αγοράς) της συνολικής ζήτησης που καλύπτει το κάθε μέσο μαζικής μεταφοράς. Ο προσδιορισμός του μεριδίου αγοράς κάθε μέσου επιτρέπει τη λεπτομερέστερη ανάλυση σε ότι αφορά στον ειδικό ρόλο κάθε μέσου σε ένα ολοκληρωμένο σύστημα αστικών συγκοινωνιών. Η ζήτηση κάθε μέσου χωριστά, καθώς και το μερίδιο αγοράς κάθε μέσου στο σύνολο της επιβατικής κίνησης αναλύονται εφαρμόζοντας τις οικονομετρικές μεθόδους της Συνολοκλήρωσης και Δυναμικού Υποδείγματος Διόρθωσης Λαθών, οι οποίες επιτρέπουν την ανάλυση μη στάσιμων χρονολογικών σειρών. Η μεθοδολογία αυτή απαλλάσσει από τα προβλήματα που η απλή παλινδρόμηση παράγει στην περίπτωση των μη στάσιμων χρονολογικών σειρών (φαινομενικές συσχετίσεις, μεροληπτικές εκτιμήσεις) και επιπλέον παρέχει τις βραχυχρόνιες και μακροχρόνιες ελαστικότητες, καθώς και την ταχύτητα σύγκλισης στην μακροχρόνια κατάσταση ισορροπίας. Τα στάδια της μεθοδολογίας αποτελούν ο έλεγχος στασιμότητας των μεταβλητών, ο έλεγχος ύπαρξης συνολοκλήρωσης μεταξύ των μη στάσιμων μεταβλητών, η εκτίμηση του Υποδείγματος Διόρθωσης Λαθών και η εφαρμογή στατιστικών ελέγχων για να διαπιστωθεί αν το Υπόδειγμα Διόρθωσης Λαθών που εκτιμήθηκε είναι κατάλληλο. Τέλος, στις περιπτώσεις που παρατηρείται Αυτοσυσχέτιση (Autocorrelation) ή/και Aυτοπαλίνδρομη υπό συνθήκη Ετεροσκεδαστικότητα (Autoregressive Conditional Heteroskedasticity-ARCH) στα κατάλοιπα, εκτιμώνται καινούρια Υποδείγματα Διόρθωσης Λαθών προκειμένου να αντιμετωπιστούν οι σχετικές επιπτώσεις. Σύμφωνα με τα αποτελέσματα, οι βασικοί παράγοντες που επηρεάζουν στατιστικώς σημαντικά την επιβατική κίνηση κάθε μέσου τόσο στην μακροχρόνια όσο και στη βραχυχρόνια περίοδο είναι το ΑΕΠ, η τιμή του εισιτηρίου και η τιμή της βενζίνης. Από τα Μέσα Μαζικής Μεταφοράς που εξετάστηκαν το μετρό και ο ηλεκτρικός σιδηρόδρομος εμφανίζουν τις μεγαλύτερες ελαστικότητες, ενώ το λεωφορείο παρουσιάζεται ιδιαίτερα ανελαστικό. Το γεγονός ότι οι ελαστικότητες της ζήτησης ως προς τους ερμηνευτικούς παράγοντες είναι σημαντικά διαφορετικές για τα διάφορα Μ.Μ.Μ. αναδεικνύει τη χρησιμότητα της ανάλυσης της ζήτησης για κάθε μέσο χωριστά. Τα αποτελέσματα δείχνουν επίσης ότι η τιμή του εισιτηρίου, το ΑΕΠ και η συνολική επιβατική κίνηση αποτελούν τους κυριότερους παράγοντες που καθορίζουν το μερίδιο αγοράς κάθε μέσου. Οι ελαστικότητες ως προς την τιμή του εισιτηρίου παρουσιάζονται ιδιαίτερα αυξημένες στα μοντέλα των μεριδίων αγοράς σε σύγκριση με τα μοντέλα ανάλυσης της επιβατικής κίνησης, καθώς απεικονίζουν με μεγαλύτερη ευαισθησία τις υποκαταστάσεις που προκύπτουν από μια μεταβολή της τιμής του εισιτηρίου. Τέλος, όπως αναμενόταν, τόσο στα μοντέλα της επιβατικής κίνησης όσο και στα μοντέλα του μεριδίου αγοράς κάθε μέσου οι βραχυχρόνιες ελαστικότητες είναι μικρότερες από τις αντίστοιχες μακροχρόνιες, επειδή οι συνέπειες κάθε μεταβολής απαιτούν χρόνο για να φτάσουν στην πλήρη ωρίμανσή τους.In this thesis demand aspects of a multimodal public transportation system are investigated using econometric methods for analyzing non-stationary data. The case of the Athens public transport system, where different modes may operate in competition or cooperation, is used as a test bed. Demand analysis is a necessary condition for efficient decision making in a public transport system; network expansion, pricing policies, subsidy and operational decisions are based on demand analysis. Demand is expressed as a function of operational and macroeconomic factors (fare, GDP, fuel price, unemployment, car and motorcycle sales) and the impact of each factor on demand is expressed through the elasticity concept. Two different but complementary aspects of public transport demand are analyzed. Ridership of each mode and share of each mode in total ridership. The above two issues provide useful information regarding effective policy measures. Demand analysis for each mode separately allows for identifying competition and substitution effects and produces more accurate demand elasticities. The analysis of the share of each transport mode in a multimodal urban public transport system is a key factor that explains the relative position of each mode in the system. It may also be a useful index for making investment decisions concerning the public transport infrastructure and for allocating subsidies. The econometric analysis adopted is based on cointegration and error correction techniques. This allows for treating non-stationary data, for determining short and long run elasticities and at the same time estimating the speed of adjustment towards long run equilibrium. Briefly, the method consists of the following modules: First, a unit root test is applied to test non-stationarity. Second, a cointegration test is performed to evaluate long run caused relation. Third, an error correction method is used to evaluate short run responses. Finally, in the cases that exists autocorrelation and/or autoregressive conditional heteroskedasticity on the residuals, new error correction models are developed to account for these effects. A model with correction for autocorrelation is used to correct serial correlation on the residuals and an ARCH model is used to capture changes in variability of the time series. According to the results, fare GDP and gasoline price are the main factors that affect PT ridership both in the short and in the long run. Of the different modes, metro and urban rail show the highest elasticities with respect to the factors examined, while bus appears to be quite inelastic. The fact that demand elasticities with respect to the explaining factors are significantly different for the different modes demonstrates the merits of demand analysis for each mode separately. Results also indicate that fare GDP and total ridership are the main determinants of public transport mode shares. In the ridership model GDP is the factor that shows the highest elasticities, while in the shares model fare is the factor that shows the highest elasticity. This is because the substitution effects between different PT modes resulting from an increase in fares are more clearly recorded in the share models. Finally, as expected, short run elasticities appear to be lower than the long run ones both in the models explaining ridership and in the models explaining the share of each mode, because short run elasticities are governed by resistance to change.Χριστίνα Π. Μηλιώτ

Estimating multimodal public transport mode shares in Athens, Greece

We analyze market shares for each public transport mode in total public transport ridership for the multimodal public transportation system of Athens, Greece. This analysis provides useful information for making investment decisions concerning the public transport infrastructure and for allocating subsidies. Due to the non-stationary properties of the data, cointegration techniques are applied to investigate the long run equilibrium relationships. Error Correction Models are implemented to estimate short run dynamics as well as the speed of adjustment from the short to the long run. Results suggest that fare and GDP are the main determinants of the public transport mode shares both in the short and in the long run. Findings also indicate the role of total ridership fluctuations in explaining variations in public transport mode shares

Examining Indian students’ bilingual profiles in Greece

This study investigates the bilingual profiles of Indian students in the Greek context. Indian students comprise a minority group that is highly underexamined in the Greek literature, in contrast to other minority groups in Greece (e.g., Albanians, see Chatzidaki Maligkoudi, 2013; Mattheoudakis et al., 2020). In particular, this article examines the bilingual profiles and the language behavior of sixteen (16) Indian students in the Greek educational context, namely in two schools in Attica. Content analysis of their interviews was applied, with the aim of exploring their bilingual profiles, their patterns of language choice and use as well as their attitude towards their home languages. Our findings reveal that the main language of communication adopted in the school environment (outside and inside the classroom) is Greek. On the other hand, Hindi or Punjabi are mostly used in the home environment, mainly due to the fact that mothers have a low proficiency in Greek. The results of our study also highlight how our participants perceive the role of host classes in their integration into the educational process

Factors affecting bus bunching at the stop level: A geographically weighted regression approach

Efficient operation of bus networks is vital for urban centers. Unfortunately, factors such as uneven passenger loads and congestion hinder the adherence to posted schedules, leading to reliability issues. Most notably, bus bunching has been identified as a significant reliability problem, impacting both users and operators. Bus bunching is treated as a route-level problem in the relevant literature, while spatial patterns in explanatory factors are overlooked. Diverging from the typically performed route-level analysis, this study exploits Automatic Vehicle Location data to investigate factors affecting bus bunching at the network level, while taking into account their spatial variability. For this purpose, a Geographically Weighted Regression Model is applied to model bus bunching, using bus stop and network attributes as explanatory variables. Results for approximately 360 bus stops in Athens, Greece underline the superiority of the proposed model to Ordinary Least Squares Regression and corroborate the presence of spatial variability in the factors affecting bus bunching. Indeed, the number of traffic lanes at the stop level is positively associated with bunching in heavy traffic segments, whereas a higher number of lanes is negatively linked to bunching in less congested regions. Further, the number of bunching occurrences generally increases with the number of routes serving each stop, as well as with the distance from subway stops in the outer parts of the city. Such findings highlight the need to consider spatial structures in relevant models and can help improve their reliability and accuracy.ISSN:2046-0449ISSN:2046-043

Identifying spatio-temporal patterns of bus bunching in urban networks

The objective of this paper is to identify hot spots of bus bunching events at the network level, both in time and space, using Automatic Vehicle Location (AVL) data from the Athens (Greece) Public Transportation System. A two-step spatio-temporal clustering analysis is employed for identifying localized hot spots in space and time and for refining detected hot spots, based on the nature of bus bunching events. First, the Spatio-Temporal Density Based Scanning Algorithm with Noise (ST-DBSCAN) is applied to distinguish bunching patterns at the network level and subsequently a k++means algorithm is employed to distinguish different types of bunching clusters. Results offer insights on specific time periods and route segments, where bus bunching events are more likely to occur and, also, on how bus bunching clusters change over time. Further, headway deviation analysis reveals the differences in the characteristics of the various bunching event types per line, showing that routes running on shared corridors experience more issues while underlying causes may vary per line. Collectively, results can help guide practice toward more flexible solutions and control strategies. Indeed, depending on the type of spatio-temporal patterns detected, appropriate improvements in service planning and real-time control strategies may be identified in order to mitigate their negative effects and improve quality of service. In light of emerging electric public transport systems, the proposed framework can be also used to determine preventive strategies and improve reliability in affected stops prior to the deployment of charging infrastructure

How did the COVID-19 pandemic impact traveler behavior toward public transport? The case of Athens, Greece

The COVID-19 outbreak led to significant changes in daily commuting. As lockdowns were imposed to metropolitan areas throughout the globe, travelers refrained heavily from using public transport, to maintain social distancing. Based on data from Athens, Greece, this paper investigates the anticipated, post-pandemic behavior of travelers with respect to public transport use. Focus is given on analyzing those factors that affect post-pandemic recovery time of public transport users, i.e. the time travelers would refrain from using public transport, following a gradual exit from the pandemic outbreak and relaxation of lockdowns. The analysis is performed using both a clustering algorithm and a discrete duration model. Both methodologies highlighted the fact that the frequency of using public transport before the pandemic along with the travelers’ age, influence their behavior in terms of recovery time. Results from the discrete duration model suggest also that self-employed and travelers who mostly use private vehicles, are less likely to use public transport after the outbreak. Concerning the psychological factors that shape COVID-19 safety-related perceptions that affect public transport use, travelers who would be willing to use protection gear when traveling with are also less likely to return to public transport. Findings of this study could be useful for policy making, suggesting that efficient marketing strategies toward promoting public transport usage in a post-pandemic era should focus on travelers with specific socio-demographic and travel characteristics