Initial public offerings in the port industry: exploring the determinants of underpricing

The capital-intensive nature of the port industry and the long payback period of port investments traditionally determine high financial needs for operating the business. Due to the growing financial requirements for developing greenfield mega-projects and feeding international terminal operators’ overseas expansion, equity markets are expected to extend their role in promoting the industry. In this perspective, the underpricing phenomenon, which represents a direct cost for the issuer during initial public offering (IPO) and discourages companies from going public, is neglected by port literature. The paper explores the antecedents of IPO underpricing in ports. Building upon extant finance and port literature, firm characteristics, transaction features, and country-specific variables are tested as determinants of IPOs’ initial returns. For the aim of this study, 58 port-related IPOs taking place on international stock exchanges are examined, and ordinary least squares regression analysis is performed. The study offers unprecedented understandings of the phenomenon and supports port managers in facing financial challenges. Before launching the IPO, financial executives are invited to carefully identify time-window opportunities, and to monitor underwriters’ reputation when selecting financial partners. Findings bring insights for policymakers, as the public listing of port companies emerges a viable option for implementing port reform.     

Car Parameters Identification by Handling Manoeuvres

A methodology is presented for identifying car parameters by studying the vehicle's handling. The methodology is based on the Kalman filter and makes it possible to determine the parameters on the basis of the results obtained from standard, on-road handling manoeuvres.     

Are millennials more multimodal? A latent-class cluster analysis with attitudes and preferences among millennial and Generation X commuters in California

Transportation - Millennials tend to use a variety of travel modes more often than older birth cohorts. Two potential explanations for this phenomenon prevail in the literature. According to the...     

Do millennials value travel time differently because of productive multitasking? A revealed-preference study of Northern California commuters

Transportation - Millennials, the demographic cohort born in the last two decades of the twentieth century, are reported to adopt information and communication technologies (ICTs) in their everyday...     

Natural and anthropogenic hydrocarbons in the water column of the Ross Sea (Antarctica)

In the framework of the Italian Research Programme in Antarctica (PNRA) an oceanographic cruise was carried out between December 2000 and February 2001 in the Western Ross Sea. n-Alkanes and PAHs concentrations were analysed in sea-water samples collected at different depths at four sites (D, B, CA2, and CA). Vertical profiles of pressure, temperature, salinity and fluorescence were also performed in order to distinguish the water masses present in the sampling areas. n-Alkanes concentrations were consistent with those found in the same area during previous expeditions and showed higher values in the particulate due to the low temperatures. n-Alkanes profiles exhibited different features in the dissolved and particulate phases. The dissolved phase was characterised by a bimodal distribution with two maxima at C16 and C24 and prevalent compounds in the range C15–C32 whereas particulate was strongly dominated by long chain n-alkanes C23–C34 with C24 (Site B, CA and CA2) or C32 (Site B) as Cmax. CPI values and Pr/Ph ratios suggested a prevalent biogenic origin for aliphatic hydrocarbons. n-Alkanes vertical distributions were affected by the dynamic of the pack-ice melting and phytoplankton activity as well by the chemical–physical properties of water masses. Higher n-alkanes values were found in the High Salinity Shelf Waters (HSSW), lower n-alkanes values were found in the Antarctic Surface Waters (AASW) and Modified Circumpolar Deep Water (MCDW).The low PAHs concentration levels confirmed the still pristine character of the Antarctic environment. Neither particular trend in total PAHs concentrations along the water column and nor correlations with the most characteristic water masses of the investigated area of the Ross Sea were observed, except that total PAH higher concentrations were generally observed in correspondence of HSSW.     

A modelling system to simulate goods movements at an urban scale

The paper reports a modelling system to simulate goods movements at an urban scale. It allows joint analysis of choices made by end-consumers (assumed to be families) and retailers. These movements are examined at two levels: analysis of commodity flows, in terms of quantity, generated by the consumption of commodities; analysis of commodity flows, in terms of vehicles, due to restocking. The first level allows us to calculate the goods quantity flows due to consumption and restocking; the second level allows us to determine the service, vehicles used and target time, as well as the route chosen for restocking sales outlets in order to estimate vehicle flows on the urban/metropolitan transportation network. The modelling system is a multi-step model and considers a disaggregated approach for each decisional level.     

Projecting travelers into a world of self-driving vehicles: estimating travel behavior implications via a naturalistic experiment

Automated driving technologies are currently penetrating the market, and the coming fully autonomous cars will have far-reaching, yet largely unknown, implications. A critical unknown is the impact on traveler behavior, which in turn impacts sustainability, the economy, and wellbeing. Most behavioral studies, to date, either focus on safety and human factors (driving simulators; test beds), assume travel behavior implications (microsimulators; network analysis), or ask about hypothetical scenarios that are unfamiliar to the subjects (stated preference studies). Here we present a different approach, which is to use a naturalistic experiment to project people into a world of self-driving cars. We mimic potential life with a privately-owned self-driving vehicle by providing 60 h of free chauffeur service for each participating household for use within a 7-day period. We seek to understand the changes in travel behavior as the subjects adjust their travel and activities during the chauffeur week when, as in a self-driving vehicle, they are explicitly relieved of the driving task. In this first pilot application, our sample consisted of 13 subjects from the San Francisco Bay area, drawn from three cohorts: millennials, families, and retirees. We tracked each subject’s travel for 3 weeks (the chauffeur week, 1 week before and 1 week after) and conducted surveys and interviews. During the chauffeur week, we observed sizable increases in vehicle-miles traveled and number of trips, with a more pronounced increase in trips made in the evening and for longer distances and a substantial proportion of “zero-occupancy” vehicle-miles traveled.     

Calibrating aggregate travel demand models with traffic counts: Estimators and statistical performance

Traffic counts on network links constitute an information source on travel demand which is easy to collect, cheap and repeatable. Many models proposed in recent years deal with the use of traffic counts to estimate Origin/Destination (O/D) trip matrices under different assumptions on the type of "a-priori" information available on the demand (surveys, outdated estimates, models, etc.) and the type of network and assignment mapping (see Cascetta & Nguyen 1988). Less attention has been paid to the possibility of using traffic counts to estimate the parameters of demand models. In this case most of the proposed methods are relative to particular demand model structures (e.g. gravity-type) and the statistical analysis of estimator performance is not thoroughly carried out. In this paper a general statistical framework defining Maximum Likelihood, Non Linear Generalized Least Squares (NGLS) and Bayes estimators of aggregated demand model parameters combining counts-based information with other sources (sample or a priori estimates) is proposed first, thus extending and generalizing previous work by the authors (Cascetta & Russo 1992). Subsequently a solution algorithm of the projected-gradient type is proposed for the NGLS estimator given its convenient theoretical and computational properties. The algorithm is based on a combination of analytical/numerical derivates in order to make the estimator applicable to general demand models. Statistical performances of the proposed estimators are evaluated on a small test network through a Monte Carlo method by repeatedly sampling "starting estimates" of the (known) parameters of a generation/distribution/modal split/assignment system of models. Tests were carried out assuming different levels of "quality" of starting estimates and numbers of available counts. Finally NGLS estimator was applied to the calibration of the described model system on the network of a real medium-size Italian town using real counts with very satisfactory results in terms of both parameter values and counted flows reproduction.     

Home-work carpooling for social mixing

Transportation - Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban...