MIT Unit Affiliation:
Lab Affiliation(s):
Intelligent Transportation Systems (ITS)
Post Doc Sponsor / Advisor:
Moshe Ben-Akiva
Areas of Expertise:
  • Operations Research
  • Transportation
  • Discrete Choice Modeling
Date PhD Completed:
November, 2013
Expected End Date of Post Doctoral Position:
July 1, 2017

Bilge Atasoy

  • Post Doctoral

MIT Unit Affiliation: 

  • Civil and Environmental Engineering

Lab Affiliation(s): 

Intelligent Transportation Systems (ITS)

Post Doc Sponsor / Advisor: 

Moshe Ben-Akiva

Date PhD Completed: 

Nov, 2013

Top 3 Areas of Expertise: 

Operations Research
Discrete Choice Modeling

Personal Statement: 

Dr. Bilge Atasoy has been a Postdoctoral Associate with the ITS Lab since December 2013. Prior to joining MIT, Bilge obtained her PhD from Ecole Polytechnique Federale de Lausanne (EPFL), where she worked with Professor Michel Bierlaire in the TRANSP-OR Laboratory. She received her BSc. and MSc. degrees in Industrial Engineering from Bogazici University, Turkey. Her main research interests are transportation planning problems, application of operations research techniques in various areas, particularly transportation, and the integration of discrete choice models and optimization problems

Bilge was the project manager for the sponsored research project with Fujitsu Laboratories in which we developed an innovative Flexible Mobility on Demand (FMOD) transportation system. She worked on the real-time optimization framework in which passengers are offered an optimized assortment of travel options upon their request that is based on a choice model. Bilge also developed optimization models that take into account future demand estimation while optimizing the system for the received requests.

Her recent work on FMOD has been accepted in the Journal of Transportation Research Part C: Emerging Technologies. Bilge presented this work at the International Paratransit Conference in October 2014, the INFORMS Annual Meeting in November 2014, and the TRB Annual Meeting in January 2015.

Additionally, Bilge is the project manager for our project entitled ‘Real-time Optimization on Managed Lanes Based on Prediction of Demand and Traffic Conditions’, which is sponsored by Ferrovial S.A. through the MIT Energy Initiative. In addition to her research, Bilge is highly active in teaching. She is a teaching fellow for 1.202 Demand Modeling and head lab instructor for 14.61s Discrete Choice Analysis: Predicting Demand and Market Shares.  

Expected End Date of Post Doctoral Position: 

July 1, 2017


Research Projects: 

Ongoing projects:

Mobility Electronic Market for Optimized Travel (MeMOT): An app-based travel incentive tool offering realtime personalized information and token rewards to be exchanged for goods & services, since May 2016: Working on the user optimization component that provides an optimized menu of options to travelers.

Real-time toll optimization on managed lanes based on prediction of demand and traffic conditions, collaborative project with CINTRA, since October 2014, Project manager: Working on the development of real-time toll optimization platform with students.

Flexible Mobility on Demand (FMOD), Project manager: Developed assortment optimization models for the flexible mobility on demand system, conducted simulation experiments for the analysis of the system, now working on enhancements to the FMOD system.

Thesis Title: 

Integrated supply-demand models for the optimization of flexible transportation systems

Thesis Abstract: 

 This thesis investigates methodologies for improving the demand responsiveness of transportation

systems through flexibility. The methodologies propose advances both in demand

and supply models having a focus on supply-demand interactions. The demand side enables

to understand the underlying travel behavior and is important to identify the most important

aspects of flexibility that needs to be offered with new transportation alternatives. Supply

models that integrate supply-demand interactions lead to more efficient and flexible decision

support tools with integrated decision problems. Furthermore the supply models enable to

understand the impact of flexibility on transportation operations with appropriate representation

of flexibility aspects. The main study area of the thesis is air transportation however we

believe that the methodological contributions of the thesis are not limited to any mode and

have the potential to provide improvements in various systems.

In the context of demand modeling, advanced demand models are studied. In the first place,

hybrid choice models are developed in the context of a mode choice studymotivated by a rich

data set. Attitudes and perceptions of individuals are integrated in choice modeling framework

and an enhanced understanding of preferences is obtained. Secondly, an air itinerary choice

model is developed based on a real dataset. A mixed revealed preferences (RP) and stated

preferences (SP) dataset is used for the estimation of the demand model. A demand model is

obtained with reasonable demand elasticities due to the existence of the SP data.

Advances in demand models can be exploited early in the planning phase when deciding

on the capacity. For this matter an integrated airline scheduling, fleeting and pricing model

is studied with explicit supply-demand interactions represented by the air itinerary choice

model. The integrated model simultaneously decides on schedule design, fleet assignment,

pricing, spill, and seat allocation to each class. Several scenarios are analyzed in order to

understand the added-value of the integrated model. It is observed that the simultaneous

decisions on capacity and revenue bring flexibility in decision making and provide higher

profitability compared to state-of-the art models. The main reference model is called the

sequential approach that solves the planning and revenue problems sequentially representing

the current practice of airlines.

The explicit integration of the demand model brings nonlinearities which cannot be characterized

as convexity/concavity. For the solution of themodel a heuristic method is implemented

which iteratively solves two sub-problems of the integrated model. The first sub-problem

is an integrated schedule planning model with fixed prices and the second sub-problem

is a revenue management problem with fixed capacity. The heuristic is found to provide 

 better quality feasible solutions, in considerably reduced computational time, compared to

the mixed integer nonlinear solver BONMIN. Local search techniques are embedded in the

heuristic method which enable to obtain better feasible solutions compared to the sequential

approach in reasonable computational time even for instances that are similar to real flight


In order to reduce the complexity of the problem a logarithmic transformation of the logit

model is proposed. The transformation results with a stronger formulation of the revenue

problem. Price is the only explanatory variable of the logit model that is defined as a decision

variable of the optimization model. However the methodology is flexible for other specifications.

The reformulation of the model is again a mixed integer non-convex problem however

as illustrated with examples and the airline case study, the model can be handled easier. In

order to obtain valid bounds on the revenue a piecewise linear approximation is proposed for

the non-convexities in the model.

In the last part of the thesis, we focus on analyzing the impact of flexibility by a new design

of aircraft called Clip-Air. The main property of Clip-Air is the flexible capacity due to the

decoupling of the wing and the capsules (cabin). One, two, or three capsules can be attached

under the wing and the configuration of Clip-Air can be adapted to the demand volume. Clip-

Air is the main motivation for the contributions of the thesis in the context of supply modeling.

The developed integrated models are therefore used in order to carry out a comparative

analysis between Clip-Air and standard aircraft. It is found that Clip-Air utilizes the available

capacity more efficiently and carries more passengers with less allocated capacity for several

scenarios. A sensitivity analysis is performed for different realizations of cost figures. In a

nutshell it is observed that the solutions are improved as the level of flexibility is increased,

in other words as wemove fromstandard systems to flexible alternatives and from classical

planning models to integrated models with explicit representation of demand.

Top 5 Awards and honors (name of award, date received): 

Best Ph.D. Thesis Award - Swiss Operations Research Society (SVOR/ASRO) prize for an outstanding theoretical or applied Ph.D. Thesis in the field of Operations Research among the theses finalized between 2012-2014.

5 Recent Papers: 

Atasoy, B., Ikeda, T., Song, X. and Ben-Akiva, M. E. (2015), "The Concept and Impact Analysis of a Flexible Mobility on Demand System", Transportation Research Part C: Emerging Technologies, 56, 373-392.

Atasoy, B., Ikeda, T. and Ben-Akiva, M. E. (2015), "Optimizing a Flexible Mobility on Demand System", Transportation Research Record, No. 2536, 76-85.

Atasoy, B., Salani, M., and Bieriare, M. (2014), "An Integrated Airline Scheduling, Fleeting, and Pricing Model for a Monopolized Market", Computer-Aided Civil and Infrastructure Engineering - Special issue on Computational Methods for Advanced Transportation Planning, 29(2), 76-90.

Atasoy, B., Salani, M., Bierlaire, M., and Leonardi, C. (2013), "Impact analysis of a flexible air transportation system", European Journal of Transport and Infrastructure Research, 13 (2), 123-146.

Atasoy, B., Güllü, R, and Tan, T. (2012), "Optimal Inventory Policies with Non-stationary Supply Disruptions and Advance Supply Information", Decision Support Systems - Special issue on Information Issues in Supply Chain and in Service System Design, 53 (2), 269-281.

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