European Transport / Trasporti Europei (2006) 34/XII

This study applies a new intelligent transportation methodology for transforming informal operations concepts for narrow passageway systems into system-level designs, which will formal enough to support automated validation of anticipated component- and system-level behaviours. Models and specifications of behaviour are formally designed as labelled transition systems. Each object is the management system is assumed to have behaviour that can be defined by a finite state machine; thus, the waterway management system architecture is modelled as a network of communicating finite state machines. Architecture-level behaviours are validated using the Labelled Transition System Analyzer (LTSA). We exercise the methodology by working step by step through the synthesis and validation of a high-level behaviour model for a vessel passing through a waterway network (i.e., canal).

The effect of traffic volume and its composition on Passenger Car Equivalency (PCE) of different vehicle types in a mixed traffic stream is investigated taking an urban mid-block section as the case study. The reduction in stream speed caused by marginal increment in traffic volume by a vehicle type is compared with that of caused by an old technology car, for the estimation of PCE of that vehicle type. A Neural Network (NN) approach is explored for capturing the underlying non-linear effects of traffic volume and its composition level on the stream speed. It is found that PCE of a vehicle type varies in a non-linear manner with total traffic volume and compositional share of that vehicle type in the traffic stream. The speed model using NN technique alone could establish the variation of PCE with vehicle type, traffic volume and its composition.

In this paper a new index for measuring the timetable co-ordination of an airline hub is proposed, with application to a sample of European hubs. This index is both quite accurate and easy to use, so that it may prove a useful schedule analysis tool for airline managers. In section 1 of this paper, the definition of “wave-system structure” and “ideal wave” are given. In section 2 the problem of measuring hub connectivity and hub timetable co-ordination is discussed. Then, both the so-called “weighted indirect connection number”, which is an index for measuring hub connectivity, and the “connectivity ratio”, which is an index for measuring hub timetable co-ordination, are described, in section 3 and 4 respectively. In section 5, a new index for measuring hub timetable co-ordination is illustrated: the “weighted connectivity ratio”. Some examples of hub timetable co-ordination measures performed with the new index are reported in section 6.

In this paper the importance of service quality attributes for public transport is estabilished by Importance Value calculation. Attribute weights (IV) are calculated by a specific empirical procedure in which a rate is assigned to each attribute according to the preferences of passengers. Finally, a Service Quality Index (SQI) for measuring the effectiveness of supplied services is calculated according to the main service quality attributes and their weights. This index can be useful to planners to choose more appropriate public transport agencies. Futhermore, it can be used by transport agencies to improve supplied service regarding more convenient service quality attributes.

We present behavioral models designed to capture the response of drivers to real-time traffic information. In 2003, we have conducted a survey in Switzerland in order to collect both Revealed Preferences (RP) and Stated Preferences (SP) about choice decisions in terms of route and mode. The RP data contains socio-economic characteristics of the individuals in our samples, their actual usage of ITS as well as their actual route and mode choice behavior. The SP data provide us with stated route and mode choices when drivers are faced with different hypothetical choice situations involving real-time information about the state of the network. First we present a Mixed Binary Logit model with panel data to analyze the drivers’ decisions when traffic information is provided during their trip by the mean of Radio Data System (RDS) or Variable Message Signs (VMS). This model is referred to en-route choice model. Second we present Nested Logit models capturing the behavior of drivers when they are aware of traffic conditions before their trip. These last models allow to predict pre-trip route choice decisions with regard to route and mode when traffic information is available. The calibrated models are subsequently included in a simulator which predicts travelers’ behavior in specific scenarios (described by adjustable parameters) allowing the sensitivity analysis of the demand with regard to the variations of various parameters. In this paper, we discuss the results of the estimation process, including some comments about the Value of Travel Time Savings (VTTS) and present some scenarios developed with our simulator.