VACANCY INTERNSHIP PROJECT We are looking for students interested in carrying out their internship project at DAT.Mobility
Extending applicability of an agent-based destination choice model Problem description Recently, DAT.Mobility has added the agent based travel demand model BRUTUS to OmniTRANS transport planning software (see https://tinyurl.com/y96m54kp , https://www.mobilitymodeling.com/brutus/ and https://www.dat.nl/omnitrans/ for more info). This model describes the number of trip chains along with their destinations and modes for all persons and households within the study area. For the destination choice, BRUTUS uses a multinomial logit model to determine the location of each activity conducted, thereby constructing trip chains. Currently, the utility function of a destination consists of the number of activities available at the destination plus the travel time from the current location to the destination and the travel time from the destination to the location of the next activity in the chain. Experiences with the agent-based model have shown that this utility function reproduces destination choices well, but only within a limited range of destinations (and thus trip lengths). Therefore, for application, the agent-based model is coupled with an aggregated model forming a hybrid travel demand model. The hybrid model describes the total mobility pattern, constructed from the agent-based model describing the short trips and the aggregated model describing the long trips.
Internship / Master thesis assignment The goal of this research is to improve and extend the applicability of the agent based travel demand model. Ideally this research should lead to an agent-based approach describing the total mobility pattern by itself. It seems natural to do this by transformations of the variables within the utility function and / or extension of the utility function with variables that better capture the destination choice sensitivities on different trip lengths, but other methods should be looked in to as well. Therefore, a literature research into trip destination choice modelling within agent-based travel demand models should be conducted to complete the overview of possible solution methods, possibly supplemented by your own ideas or methods. Good starting points might be (Janzen and Axhausen, 2017; Saleem et al., 2018). Furthermore, the student should get acquainted with the current BRUTUS implementation. After the literature research, one or some of the different solutions should be implemented and tested, bearing in mind that data requirements of both estimation and application context, scalability and calculation time are the most important selection criteria. With respect to the latter two criteria, a switch of estimation software from mLogit (https://cran.rproject.org/web/packages/mlogit/index.html ) to Biogeme (http://biogeme.epfl.ch/) is part of the assignment. As BRUTUS is programmed in R, a good environment for prototyping is available, allowing to directly see the effect of changes to the implementation. If a suitable method has been found and implemented, a showcase comparing its results, sensitivity and calculation times to the current implementation should be developed.
Research group DAT.Mobility Deventer Daily supervisors: Ir. Luuk Brederode (DAT.Mobility, Delft University of Technology) / Bernike Rijksen Msc. (DAT.Mobility) When interested in this internship or Masters thesis assignment on extending the applicability of agent based modelling, please contact Ir. Luuk Brederode (lbrederode@DAT.nl, +31 (0) 627369830)
References Janzen, M., Axhausen, K.W., 2017. Destination and mode choice in an agent-based simulation of long-distance travel demand, in: 17th Swiss Transport Research Conference (STRC 2017). STRC. Saleem, M., Västberg, O.B., Karlström, A., 2018. An Activity Based Demand Model for Large Scale Simulations. Procedia Comput. Sci., The 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018) / The 8th International Conference on Sustainable Energy Information Technology (SEIT-2018) / Affiliated Workshops 130, 920–925. https://doi.org/10.1016/j.procs.2018.04.090