The paper positions the passenger sea ports in the context of cruise tourism on the basis of e-services they offer. The e-services of eleven passenger ports are categorized and then quantitatively evaluated by binary and ranking approaches. In general, the port e-services might be categorized according to their functionality as navigational, ship and passenger-related ones, logistics, business, marketing, entertainment, security, safety, environmental, etc. These services can be bidirectional informational and/or transactional. In this paper, only those port e-services related directly to the passengers’ needs, within the frame of cruise tourism, are taken into consideration and categorized as core, or as value-added ones, and as informational and/or transactional ones. Then, each of them is assigned an appropriate binary value (0/1), depending on whether the considered passenger port offers the related e-service or not. These values are employed in the evaluation of the analyzed passenger port e-services offered, and as a base for their positioning. The appropriate weights coefficients, obtained by ranking (Saaty method), were used in the process of the considered port final positioning on the cruise tourism e-market. Some additional analyses and recommendations in the direction of further positioning and promotion of the port of Kotor (Montenegro), as rising cruise tourism port (destination), are given as well.

In the field of geoinformation and transportation science, the shortest path is calculated on graph data mostly found in road and transportation networks. This data is often stored in various database systems. Many applications dealing with transportation network require calculation of the shortest path. The objective of this research is to compare the performance of Dijkstra shortest path calculation in PostgreSQL (with pgRouting) and Neo4j graph database for the purpose of determining if there is any difference regarding the speed of the calculation. Benchmarking was done on commodity hardware using OpenStreetMap road network. The first assumption is that Neo4j graph database would be well suited for the shortest path calculation on transportation networks but this does not come without some cost. Memory proved to be an issue in Neo4j setup when dealing with larger transportation networks.

This paper presents a dynamic traffic assignment (DTA) model for urban multi-modal transportation network by con­structing a mesoscopic simulation model. Several traffic means such as private car, subway, bus and bicycle are con­sidered in the network. The mesoscopic simulator consists of a mesoscopic supply simulator based on MesoTS model and a time-dependent demand simulator. The mode choice is si­multaneously considered with the route choice based on the improved C-Logit model. The traffic assignment procedure is implemented by a time-dependent shortest path (TDSP) al­gorithm in which travellers choose their modes and routes based on a range of choice criteria. The model is particularly suited for appraising a variety of transportation management measures, especially for the application of Intelligent Trans­port Systems (ITS). Five example cases including OD demand level, bus frequency, parking fee, information supply and car ownership rate are designed to test the proposed simulation model through a medium-scale case study in Beijing Chaoy­ang District in China. Computational results illustrate excel­lent performance and the application of the model to analy­sis of urban multi-modal transportation networks.

This study proposes a behavioural intention model that integrates information quality, response time, and system accessibility into the original technology acceptance model (TAM) to investigate whether system characteristics affect the adoption of Web-based advanced traveller information systems (ATIS). This study empirically tests the proposed model using data collected from an online survey of Web-based advanced traveller information system users. Con­firmatory factor analysis (CFA) was performed to examine the reliability and validity of the measurement model, and structural equation modelling (SEM) was used to evaluate the structural model. The results indicate that three system characteristics had indirect effects on the intention to use through perceived usefulness, perceived ease of use, and attitude toward using. Information quality was the most im­portant system characteristic factor, followed by response time and system accessibility. This study presents implica­tions for practitioners and researchers, and suggests direc­tions for future research.

The assumption about travellers’ route choice behaviour has major influence on the traffic flow equilibrium analysis. Previous studies about the travellers’ route choice were mainly based on the expected utility maximization theory. However, with the gradually increasing knowledge about the uncertainty of the transportation system, the researchers have realized that there is much constraint in expected util­ity maximization theory, because expected utility maximiza­tion requires travellers to be ‘absolutely rational’; but in fact, travellers are not truly ‘absolutely rational’. The anticipated regret theory proposes an alternative framework to the tra­ditional risk-taking in route choice behaviour which might be more scientific and reasonable. We have applied the antici­pated regret theory to the analysis of the risk route choosing process, and constructed an anticipated regret utility func­tion. By a simple case which includes two parallel routes, the route choosing results influenced by the risk aversion degree, regret degree and the environment risk degree have been analyzed. Moreover, the user equilibrium model based on the anticipated regret theory has been established. The equivalence and the uniqueness of the model are proved; an efficacious algorithm is also proposed to solve the model. Both the model and the algorithm are demonstrated in a real network. By an experiment, the model results and the real data have been compared. It was found that the model re­sults can be similar to the real data if a proper regret degree parameter is selected. This illustrates that the model can better explain the risk route choosing behaviour. Moreover, it was also found that the traveller’ regret degree increases when the environment becomes more and more risky.