A Study on the Profitability of Airlines by Using Demand Forecasting Models

2019 ◽  
Vol 7 (1) ◽  
pp. 79-97
Author(s):  
Hwi-Young Lee ◽  
Keyword(s):  
Demand Forecasting ◽  
Forecasting Models

Assessing the Evolution of Transit User Behavior from Smart Card Data

2019 ◽  
Vol 2673 (4) ◽  
pp. 184-194 ◽  
Author(s):  
Alexis Viallard ◽  
Martin Trépanier ◽  
Catherine Morency
Keyword(s):  
Smart Card ◽  
User Behavior ◽  
Demand Forecasting ◽  
Computation Time ◽  
Experimental Methodology ◽  
Travel Patterns ◽  
Forecasting Models ◽  
Smart Card Data ◽  
Clustering Quality ◽  
Similar Computation

There is a huge potential for exploiting information centered on individual transit users’ behavior through longitudinal smart card data. This is particularly true for cities like Gatineau, Canada, where the bus system serves passengers with different travel patterns. Understanding the evolution of these patterns marks an important point in improving transit demand forecasting models. Indeed, better models can help transit planners to create optimized networks. This paper proposes a comparison of a traditional and an experimental methodology aiming to identify the evolution of travel structure among transit users. These methodologies are based on the clustering of multi-week travel patterns derived from a large sample of smart card transactions (35.4 million). Representing users’ behavior, these patterns are constructed using the number of trips made by every card on each day of a week. Behavior vectors are defined by seven components (one for each day) and are clustered using a K-means algorithm. The experimental week-to-week method consists in clustering the population on each week, while using the clustering results from the previous week as seed. This latter approach makes it possible to observe the evolution of users’ behaviors and also has a better clustering quality in a similar computation time than the traditional method.

scholarly journals Improving the Performance of Water Demand Forecasting Models by Using Weather Input

2014 ◽  
Vol 70 ◽  
pp. 93-102 ◽  
Author(s):  
M. Bakker ◽  
H. van Duist ◽  
K. van Schagen ◽  
J. Vreeburg ◽  
L. Rietveld
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Forecasting Models ◽  
Water Demand Forecasting
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