Sustainability at Spanish ports specialized in liquid bulk: evolution in times of crisis (2010–2015)

In Spain, 28 Port Authorities of general interest moved more than 168 thousand tonnes of liquid bulk (34% of overall traffic) in 2015. Almost 82% of this amount corresponded to eight ports (G-8) that have a refinery within their facilities. This unique degree of specialization and concentration makes this set of ports an ideal sample to analyse the evolution of their sustainability levels, particularly during the crisis started in 2008 and onwards. A proprietary methodology based on Multidimensional Synthetic Indices has been used. The comparison of the findings obtained for 2010 and 2015 allows a diagnosis of the evolution of port sustainability measured through the economic, institutional, environmental and social dimensions to be established, as well as a study of the patterns of behaviour that each port has followed in this issue.     

Assessing the impacts of driving environment on driving behavior patterns

Transportation - Considering the role of behavioral and environmental factors on road accidents and traffic intensities, the characterization of vehicle use and driver behavior opens new...     

Adolescents and their aspirations for private car-based transport

Transportation - The need to transition away from the current car-dominated transport system is well documented in sustainability, health and transportation literatures. Despite growing interest in...     

Reassessing the commuting penalty for immigrants: new evidence from Spain

Transportation - This article examines the differences in commuting length between native and immigrant employees in Spain, a relevant issue since immigrants' longer commuting times may, among...     

What are the determinants of the willingness to share rides in pooled on-demand services?

Transportation - Simulation studies suggest that pooled on-demand services (also referred to as Demand Responsive Transport, ridesharing, shared ride-hailing or shared ridesourcing services) have...     

Valuing Freight Transport Time using Transport Demand Modelling: A Bibliographical Review

The value of time for freight transport is of major importance in infrastructure-related cost–benefit analysis and yet its study has been largely neglected when compared with its passenger counterpart. In fact, one of the attributes that could decide the profitability of a project is how much can be saved if freight vehicles use new infrastructure. Despite being the primary benefit of most investments in transport infrastructure, researchers have not yet reached agreement over either the size or the nature of the values of time that should be used when evaluating projects. This article provides a review of the estimation of freight value of time through transport demand modelling and extant empirical evidence on this topic. Similarly, the bibliographic review of studies undertaken has allowed us to pinpoint the most critical issues when modelling freight transport demand and the position of various research teams regarding these aspects. Such issues include identifying the decision-maker, heterogeneity in the transport flows and transport attributes considered by decision-makers.     

Short Sea Shipping in Supply Chains. A Strategic Assessment

The feasibility of implementing short sea shipping (SSS) between two specific ports has been typically approached on the operative level, in terms of time and cost for a specific origin–destination pair. This paper focuses on the strategic level. Particularly, its goal is to study the potential of SSS in different industrial sectors by analysing their freight-distribution strategies. To achieve the objective, some standard freight-distribution strategies are defined based on the requirements of the industry, the demand and the product being transported. The information is complemented with a survey of more than 100 Spanish companies distributing internationally. As a result it is concluded that there are economies of scale related to the size of the carrier (the ship) and its capacity to absorb the variability in demand. This potential increases with the cargo value. Additionally, a framework of supply/distribution chains is provided which can help to identify potential customers/sectors and the feasibility of new potential shipping lines. The value of this paper resides in its approach (strategic, instead of operational/tactical), and the solid groundwork point provided for future researchers.     

Lightweight Occupancy Estimation on Freeways Using Extended Floating Car Data

Advanced traffic management systems rely heavily on technology to perform accurate estimations of the current state of the traffic as well as its short-term evolution. The objectives are improving traffic flow and enhancing road safety. Their success is based on accurate monitoring of two key variables, specifically speed and occupancy. The latter of the two has, to date, received significantly less attention from the scientific community. In this work we present a lightweight method to perform “on-line” occupancy estimation. We first propose three occupancy measurements calculated from data collected by a floating car: vehicle count, percentage of stop time, and headway. We then extend these discrete values to a continuous estimation of occupancy in space and time. The proposed estimators are based on a pairwise linear regression of each of the previously calculated measurements over certain references obtained from other floating cars or magnetic loop detectors. The method has been calibrated and validated under real traffic conditions and data. Despite the ease of implementation, the method is able to reproduce the occupancy values generated by the actual loop detectors, achieving promising results, with estimation errors down to 6.52%, even before multivehicle systems are considered.     

Generating twins to build weekly time use data from multiple single day OD surveys

Origin-Destination surveys, which are regularly conducted in many cities (to calibrate transport models), contain indirect information on individual time use that can be recovered through the declared trip purpose. Although this data source is very rich, it has two limitations for the calibration of time use models: the level of disaggregation regarding time use is constrained by the definition of trip purposes, and the information gathered on different time periods is usually obtained from different individuals. In this paper we propose a new method to overcome the second limitation, transforming the original daily observations into individual-weeks. For every working day observation we build Saturday and Sunday “twins” as a convex combination of observed weekend individuals such that the distance between the attributes of the working day individual and the synthetic twin is minimized. We applied this procedure to the Santiago OD survey, and generated a database of weekly observations particularly rich for model calibration and segmentation.     

Influence of the weather on mode choice in corridors with time-varying congestion: a mixed data study

Studies that link human behaviour to the influence of weather have historically been conducted in such fields as tourism, marketing and leisure. In most studies that jointly examine weather and the mode of transport, only open-air transportation has been considered (for example, bicycle, motorcycle or walking). This focus, together with the habitual use of data collected with automatic devices and a lack of studies that analyse this issue using stated preference data, are the main reasons motivating this paper. This paper aims to analyse the influence of weather and the density of traffic on the choice of transport mode. A case study is conducted in an access/egress corridor located in the city of Barcelona (Spain). Two data sources were used: revealed preference and stated preference data. Modelling techniques using mixed data enabled the stronger features from both data sources to be captured. Finally, we discuss how the selection of different alternative specific constants in models estimated using mixed data could generate unrealistic forecasting results if environmental changes are expected in the actual market.