Abstract: The perceived public transport quality is an important driver that influences both customer satisfaction and mobility choices. The competition among transport operators needs to improve the quality of the services and identify which attributes are perceived as relevant by passengers. Among the “traditional” public transport quality attributes there are, for example: travel and waiting time, regularity of the services, and ticket price. By contrast, there are some “non-conventional” attributes that could significantly influence customer satisfaction jointly with the “traditional” ones. Among these, the beauty/aesthetics of the transport terminals (e.g. rail station and bus terminal) is probably one of the most impacting on user perception. Starting from these considerations, the point stressed in this paper was if (and how munch) the travel experience of the overall travel (e.g. how long is the travel, how many transport modes must be used) influences the perception of the public transport quality. The aim of this paper was to investigate the weight of the terminal quality (e.g. aesthetic, comfort and service offered) within the overall travel experience. The case study was the extra-urban Italian bus network. The passengers of the major Italian terminal bus were interviewed and the analysis of the results shows that about the 75% of the travelers, are available to pay up to 30% more for the ticket price for having a high quality terminal. A travel experience effect was observed: the average perceived transport quality varies with the characteristic of the overall trip. The passengers that have a “long trip” (travel time greater than 2 hours) perceived as “low” the overall quality of the trip even if they pass through a high quality terminal. The opposite occurs for the “short trip” passengers. This means that if a traveler passes through a high quality station, the overall perception of that terminal could be significantly reduced if he is tired from a long trip. This result is important and if confirmed through other case studies, will allow to conclude that the “travel experience impact" must be considered as an explicit design variable for public transport services and planning.
Abstract: Social-economic variables influence transportation
demand largely. Analyses of discrete choice model consider
social-economic variables to study traveler-s mode choice and
demand. However, to calibrate the discrete choice model needs to have
plenty of questionnaire survey. Also, an aggregative model is
proposed. The historical data of passenger volumes for high speed rail
and domestic civil aviation are employed to calibrate and validate the
model. In this study, models with different social-economic variables,
which are oil price, GDP per capita, CPI and economic growth rate,
are compared. From the results, the model with the oil price is better
than models with the other social-economic variables.
Abstract: In this paper discrete choice models, Logit and Probit
are examined in order to predict the economic recession or expansion
periods in USA. Additionally we propose an adaptive neuro-fuzzy
inference system with triangular membership function. We examine
the in-sample period 1947-2005 and we test the models in the out-of
sample period 2006-2009. The forecasting results indicate that the
Adaptive Neuro-fuzzy Inference System (ANFIS) model outperforms
significant the Logit and Probit models in the out-of sample period.
This indicates that neuro-fuzzy model provides a better and more
reliable signal on whether or not a financial crisis will take place.
Abstract: Discrete choice model is the most used methodology for studying traveler-s mode choice and demand. However, to calibrate the discrete choice model needs to have plenty of questionnaire survey. In this study, an aggregative model is proposed. The historical data of passenger volumes for high speed rail and domestic civil aviation are employed to calibrate and validate the model. In this study, different models are compared so as to propose the best one. From the results, systematic equations forecast better than single equation do. Models with the external variable, which is oil price, are better than models based on closed system assumption.
Abstract: The paper proposes an approach for design of modular
systems based on original technique for modeling and formulation of
combinatorial optimization problems. The proposed approach is
described on the example of personal computer configuration design.
It takes into account the existing compatibility restrictions between
the modules and can be extended and modified to reflect different
functional and users- requirements. The developed design modeling
technique is used to formulate single objective nonlinear mixedinteger
optimization tasks. The practical applicability of the
developed approach is numerically tested on the basis of real modules
data. Solutions of the formulated optimization tasks define the
optimal configuration of the system that satisfies all compatibility
restrictions and user requirements.