Bilge Atasoy

Attitudes towards mode choice in Switzerland

We integrate latent attitudes of the individuals into a transport mode choice model through latent variable and latent class models. Psychometric indicators are used to measure these attitudes. The aim of the inclusion of attitudes is to better understand the underlying choice preferences of travelers and therefore increase the forecasting power of the choice model. We first present an integrated choice and latent variable model, where we include attitudes towards public transport and environmental issues, explaining the utility of public transport. Secondly, we present an integrated choice and latent class model, where we identify two segments of individuals having different sensitivities to the attributes of the alternatives, resulting from their individual characteristics. The calibration of these types of advanced models on our sample has demonstrated the importance of attitudinal variables in the characterization of heterogeneity of mode preferences within the population.

Optimal inventory policies with non-stationary supply disruptions and advance supply information

We consider the production/inventory problem of a manufacturer (or a retailer) under non-stationary and stochastic supply availability. Although supply availability is uncertain, the supplier would be able to predict her near future shortages - and hence supply disruption to (some of) her customers - based on factors such as her pipeline stock information, production schedule, seasonality, contractual obligations, and non-contractual preferences regarding other manufacturers. We consider the case where the information on the availability of supply for the near future, which we refer to as advance supply information (ASI), is provided by the supplier. The customer demand is deterministic but non-stationary over time, and the system costs consist of fixed ordering, holding and backorder costs. We consider an all-or-nothing type of supply availability structure and we show the optimality of a state-dependent (s,S) policy. For the case with no fixed ordering cost we prove various properties of the optimal order-up-to levels and provide a simple characterization of optimal order-up-to levels. For the model with fixed ordering cost, we propose a heuristic algorithm for finding a good ordering strategy. Finally, we numerically elaborate on the value of ASI and provide managerial insights.

An Integrated Airline Scheduling, Fleeting, and Pricing Model for a Monopolized Market

In airline schedule planning models, the demand and price information are usually taken as inputs to the model. Therefore, schedule and capacity decisions are taken separately from pricing decisions. In this article, we present an integrated scheduling, fleeting, and pricing model for a single airline where these decisions are taken simultaneously. This integration enables to explicitly model supply and demand interactions and make superior decisions. The model refers to a monopolized market. However, competing airlines are included in the model as a reference for the pricing decisions. The pricing decision is formulated through an itinerary choice model which determines the demand of the alternative itineraries in the same market according to their price, travel time, number of stops, and the departure time of the day. The demand model is estimated based on real data and is developed separately for economy and business classes. The seat allocation for these classes is optimized according to the behavior of the demand. The choice model is also used to appropriately model the spill and recapture effects. The resulting model is evaluated with different illustrations and the added value of the integrated approach is analyzed compared to a sequential approach. Results over a set of representative instances show that the integrated model is able to make superior decisions by jointly adjusting capacity and pricing.

Optimizing a Flexible Mobility on Demand System

This paper analyzes an innovative transportation concept called Flexible Mobility on Demand (FMOD), which provides personalized services to passengers. FMOD is a demand-responsive system: a list of travel options is provided in real-time for each passenger request. The system provides passengers with flexibility to choose from a menu that is optimized in an assortment optimization framework. For operators, there is flexibility in terms of vehicle allocation to service types: taxi, shared taxi, and minibus. The allocation of the available feet to these three services is carried out dynamically and is based on demand and supply so that vehicles can change roles during the day. The FMOD system is built on a choice model that enables it to analyze the consumer surplus. This paper describes the FMOD system and presents simulation results for a network in Tokyo. For FMOD, three models that maximize profit, consumer surplus, or both are considered. FMOD is compared with its counterpart, which does not have flexibility in vehicle allocation under different scenarios. The results show that FMOD improves profit and consumer surplus with a reasonable real-time performance for the considered network.

Personalized Menu Optimization with Preference Updater: A Boston Case Study

This paper presents a personalized menu optimization model with preference updater in the context of an innovative Smart Mobility system that offers a personalized menu of travel options with incentives for each incoming traveler in real time. This Smart Mobility system can serve as a major travel demand management system that encourages energy-efficient travel options. The personalized menu optimization is built on a logit mixture model that captures each individual traveler’s choice behavior. The personalized menu optimization model is enhanced with a preference updater that can update the estimates of individual traveler’s preference parameters when new choice data is received. To illustrate the advantages of the proposed methodology, a case study is presented based on real travelers and trips in the greater Boston area from the Massachusetts Travel Survey data. The case study consists of two parts. In the first part, the personalized menu optimization with preference updater is tested in a setting where the travelers are new to the system and their preferences are updated through preference updater. A comparative analysis of the performance of the proposed method with preference updater is presented against the method without preference updater. In the second part, the benefit of using individual level preference parameters instead of population level preference parameters in the personalized menu optimization model is analyzed. The case study shows that the proposed method can outperform the hit rates of its two counterparts.

Alternative Activity Pattern Generation for Stated Preference Surveys

We present a systematic method for generating activity-driven, multi-day alternative activity patterns that form choice sets for stated preference surveys. An activity pattern consists of information about an individual’s activity agenda, travel modes between activity episodes, and the location and duration of each episode. The proposed method adjusts an individual’s observed activity pattern using a hill-climbing algorithm, an iterative algorithm that finds local optima, to search for the best response to hypothetical system changes. The multi-day approach allows for flexibility to reschedule activities on different days and thus presents a more complete view of demand for activity participation, as these demands are rarely confined to a single day in reality. As a proof-of-concept, we apply the method to a multi-day activity-travel survey in Singapore and consider the hypothetical implementation of an on-demand autonomous vehicles service. The demonstration shows promising results, with the algorithm exhibiting overall desirable behavior with reasonable responses. In addition to representing the individual’s direct response, the use of observed patterns also reveals the propagation of impacts, that is, indirect effects, across the multi-day activity pattern.

An Innovative Concept for Paratransit: Flexible Mobility on Demand

Originality/value We consider a business model for FMOD that offers flexibility to the operator in terms of who provides resources. The resources are managed with dedicated and non-dedicated services. The experiment indicates that operators can determine the size of the dedicated fleet based on an objective function that maximizes operator profit and passenger satisfaction.

Integrated schedule planning with supply-demand interactions for a new generation of aircrafts

We present an integrated schedule planning model where the decisions of schedule design, fleeting and pricing are made simultaneously. Pricing is integrated through a logit demand model where itinerary choice is modeled by defining the utilities of the alternative itineraries. Spill and recapture effects are appropriately incorporated in the model by using a logit formulation similar to the demand model. Furthermore class segmentation is considered so that the model decides the allocation of the seats to each cabin class. We propose a heuristic algorithm based on Lagrangian relaxation to deal with the high complexity of the resulting mixed integer nonlinear problem.