In Revenue Management (RM) systems, information censoring and the interaction between the forecasting and optimization stages, increases the costs and complexity of performance analysis using historical data. An affordable and suitable alternative is using simulations, but appropriate behavioral models must be considered. In the following document, we discuss and test the implementation of a dynamic air transport market simulator, designed to analyze RM systems. The simulator replicates the behavior of passengers that book seats offered in multiple flights by different airlines. We use discrete choice models to replicate the demand behavior, accounting for preferences and decision rule heterogeneity, and including a temporal evolution of the preference throughout the selling horizon. To replicate the supply behavior, a number of airlines modify the price and quantity of different fare classes offered in each flight, using a variety of RM forecasting, un-constraining, and optimization techniques. The simulator allows analysts to study the economic benefit of RM systems under predefined assumptions in an artificial and controlled environment. This increases the benefits obtained by the correct selection of context-appropriate RM systems and the likelihood of successfully implementing new and complex systems. We test and showcase the simulator performance, studying the entrance of a new airline in a competitive context. We generate, implement and evaluate different RM strategies in response to the introduction of new competition, and discuss the results, highlighting the interpretability and accuracy of the proposed framework.

By installing wireless sensors such as Bluetooth or Wi-Fi at a specific set of intersections and/or roadways it is possible to detect the passage of vehicles equipped with these technologies. However, Wi-Fi or Bluetooth-equipped vehicles are not necessarily detected by every sensor they pass by, as the detection probability depends on several factors, such as weather, nearby infrastructure, or the vehicle’s speed. To address this lack of perfect information, we propose a methodology to infer the most likely route used by a vehicle between two successive detections. The methodology consists of three stages. The first stage entails constructing a graph of the road network and the location of the sensors. The second stage consists of using the wireless data to calibrate the distribution of dwell time at each node and travel time for each link of the graph defined in the first stage. The third and final stage consists of convoluting node and link time distributions between successive detections to obtain an aggregate time distribution for each potential route. A Bayesian inference is then applied based on the travel time observed for each vehicle and the number of missed detections, to determine the probability of each alternative route. The methodology is tested through microsimulations, showing a prediction performance of over 90% in most favorable scenarios tested. When compared to a benchmark approach to infer routes, the proposed methodology provides better results when the network’s sensory density is low and data available are reduced.

Service reliability has an important impact on the satisfaction stated by public transport users with the service they receive. The main source of unreliability is found in headway variance, which also affects waiting times and distributes passengers unevenly across vehicles. However, it is still unclear how headway irregularity, with its impact in waiting, crowdedness and reliability, affect travellers’ service satisfaction. Different stated preference studies have identified non-linear impacts produced by overcrowding. However, none of these studies is directly related to users’ satisfaction evaluation. In this study, we investigate the existence of this non-linearity in users’ satisfaction caused by both the crowding level and the number of denied boardings through a post-service satisfaction survey of bus and metro users. An Ordered Logit Model was estimated, accounting for sample heteroscedasticity and preference heterogeneity. Overall, there is a significant and negative perception of the bus mode, keeping all other attributes equal. For users under 35 years old, comfort experienced almost always plays an important role in service satisfaction, while for those over 35 years old women are significantly more sensitive to this attribute. Most important, crowding has a negative and non-linear impact on how passengers evaluate their travel satisfaction. Using a Likert-type scale, this curve is convex. This relationship between crowding and satisfaction might bias service planning and delivery if performance indicators associated to service are not properly weighted by the number of passengers served. Improving level of service indicators in this direction might provide public transport agencies a clearer and more accurate perception of the actual users’ experience.