Random and fuzzy utility models for road route choice

The objective of this work is the specification, calibration and validation of a route choice model distinguishing the phases of set perception and route choice among the alternatives belonging to the chosen set. Route generation is performed with a selective multi-criteria approach; route choice is performed using random and fuzzy utility models. As regards the fuzzy utility models, a new specification of a fuzzy model is introduced. Random and fuzzy utility models are compared and applied on a test road network and were specified and calibrated for national road freight transport on a real Italy-wide network.     

Passenger airline choice behavior for domestic short-haul travel in South Korea

When the Seoul-Busan line of South Korea's high-speed rail system, dubbed the Korea Train Express (KTX), entered service in November 2010, it was expected that this line would compete with air transport services for short-haul domestic journeys. Therefore this is a study about passengers' choice behavior when traveling short-haul domestic routes in South Korea. It utilizes MNL and NL logit models with selected variables, and the data required for the analyses were gathered through Stated Preference (SP) Techniques. The main SP survey was conducted for three weeks at departure lounges in Incheon International Airport in May 2012. The results reveal that fare, access time and journey time are significantly important with respect to passenger choice. The results further indicate that business travelers are more willing to pay than non-business travelers to reduce access and journey time. It is also noteworthy that reducing access time is more important than reducing journey time for short-haul domestic travelers. The conclusion is that it is significantly important for airline planners or local authorities that want to increase their local market share to invest in relatively fast access modes.     

Developing advanced route choice models for heavy goods vehicles using GPS data

This paper presents a novel application in route choice modelling using Global Positioning System (GPS) data, focussing on heavy goods vehicles which typically make longer journeys with decisions potentially underpinned by different priorities from those used by car drivers. The scope of the study is larger than many previous ones, using the entire road network of England. Making use of the error components model put forward for route choice by Frejinger and Bierlaire (2007), the work reveals low elasticities in response to changes in travel time, reflecting the limited opportunity for avoiding specific roads on long distance journeys by heavy goods vehicles.     

Shortest path or anchor-based route choice: a large-scale empirical analysis of minicab routing in London

Understanding and modelling route choice behaviour is central to predicting the formation and propagation of urban road congestion. Yet within conventional literature disagreements persist around the nature of route choice behaviour, and how it should be modelled. In this paper, both the shortest path and anchor-based perspectives on route choice behaviour are explored through an empirical analysis of nearly 700,000 minicab routes across London, United Kingdom. In the first set of analyses, the degree of similarity between observed routes and possible shortest paths is established. Shortest paths demonstrate poor performance in predicting both observed route choice and characteristics. The second stage of analysis explores the influence of specific urban features, named anchors, in route choice. These analyses show that certain features attract more route choices than would be expected were individuals choosing route based on cost minimisation alone. Instead, the results indicate that major urban features form the basis of route choice planning – being selected disproportionately more often, and causing asymmetry in route choice volumes by direction of travel. At a finer scale, decisions made at minor road features are furthermore demonstrated to influence routing patterns. The results indicate a need to revisit the basis of how routes are modelled, shifting from the shortest path perspective to a mechanism structured around urban features. In concluding, the main trends are synthesised within an initial framework for route choice modelling, and presents potential extensions of this research.     

Small community airport choice behavior analysis: A case study of GTR

The issue of airport selection attracts considerable attention. However, most studies focus on using advanced discrete choice models to analyze airport choice behavior in metropolitan areas with several closely located, competing airports. This paper addresses passengers’ choice behavior in selecting between local small community airports and more distant major commercial airport. It looks at factors affecting air travelers’ airport choice behavior in cities with small community air service. Data relating to the Golden Triangle Regional Airport in Mississippi is used in logistic regressions to identify the key factors that influence air travelers’ airport choices. Ticket price, experience with Golden Triangle Regional Airport and flight schedule were found to be the strongest effects.     

A strategic network choice model for global container flows: specification, estimation and application

Container flows have been booming for decades. Expectations for the 21st century are less certain due to changes in climate and energy policy, increasing congestion and increased mobility of production factors. This paper presents a strategic model for the movement of containers on a global scale in order to analyse possible shifts in future container transport demand and the impacts of transport policies thereon. The model predicts yearly container flows over the world’s shipping routes and passing through 437 container ports around the world, based on trade information to and from all countries, taking into account more than 800 maritime container liner services. The model includes import, export and transhipment flows of containers at ports, as well as hinterland flows. The model was calibrated against observed data and is able to reproduce port throughput statistics rather accurately. The paper also introduces a scenario analysis to understand the impact of future, uncertain developments in container flows on port throughput. The scenarios include the effects of slow steaming, an increase in land based shipping costs and an increased use of large scale infrastructures such as the Trans-Siberian rail line and the opening of Arctic shipping routes. These scenarios provide an indication of the uncertainty on the expected port throughputs, with a particular focus on the port of Rotterdam in the Netherlands.     

A scenario planning approach for the flood emergency logistics preparation problem under uncertainty

This paper aims to develop a decision-making tool that can be used by government agencies in planning for flood emergency logistics. In this article, the flood emergency logistics problem with uncertainty is formulated as two stochastic programming models that allow for the determination of a rescue resource distribution system for urban flood disasters. The decision variables include the structure of rescue organizations, locations of rescue resource storehouses, allocations of rescue resources under capacity restrictions, and distributions of rescue resources. By applying the data processing and network analysis functions of the geographic information system, flooding potential maps can estimate the possible locations of rescue demand points and the required amount of rescue equipment. The proposed models are solved using a sample average approximation scheme. Finally, a real example of planning for flood emergency logistics is presented to highlight the significance of the proposed model as well as the efficacy of the proposed solution strategy.     

Destination choice and the identity of places: A disaggregated analysis for different types of visitor population environment in the Netherlands

Individual behaviour relates to the ecological structure or identity of places. In this study, we investigated the relationship between the space–time ecologies of different types of visitor population environment in the Netherlands and destination choice. Multinomial logistic regression modelling for car users was applied to determine the relative importance of various personal and household attributes on choosing for a particular purpose a particular type of visitor population environment during a particular time period of the day. The attributes with the strongest link to the space–time ecologies of destination environments proved to be age, educational level, car ownership, and household income and type.