A Simulation Framework for a Real-Time Demand Responsive Public Transit System

Transit systems have encountered a radical change in the recent past as a result of the digital disruption. Consequently, traditional public transit systems no longer satisfy the diversified demands of passengers and hence, have been complemented by demand responsive transit solutions. However, we identify a lack of simulation tools developed to test and validate complex scenarios for real-time demand responsive public transit. Thus, in this paper, we propose a simulation framework, which combines complex scenario creation, optimization algorithm execution and result visualization using SUMO, an open source continuous simulator. In comparison to a state-of-the-art work, the proposed tool supports features such as varying vehicle capacity and driving range, immediate and advance passenger requests and maximum travel time constraints. Further, the framework follows a modular architecture that allows plug-and-play support for external modules.

[1]  Andreas Rekersbrink MICROSCOPIC TRAFFIC SIMULATION BY FUZZY LOGIC.. , 1995 .

[2]  Dipak Ghosal,et al.  VGSim: An integrated networking and microscopic vehicular mobility simulation platform , 2009, IEEE Communications Magazine.

[3]  BarcelíJ.,et al.  Microscopic traffic simulation , 2005 .

[4]  R Clavel The role of intelligent transport systems for demand responsive transport , 2009 .

[5]  Gordon D. B. Cameron,et al.  PARAMICS—Parallel microscopic simulation of road traffic , 1996, The Journal of Supercomputing.

[6]  Matti Pursula,et al.  SIMULATION OF TRAFFIC SYSTEMS : AN OVERVIEW , 1998 .

[7]  Emilio Frazzoli,et al.  On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment , 2017, Proceedings of the National Academy of Sciences.

[8]  Thomas Mayer,et al.  An open-source discrete event simulator for rich vehicle routing problems , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[9]  Paul Schonfeld,et al.  Optimizing dial-a-ride services in Maryland: Benefits of computerized routing and scheduling , 2015 .

[10]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[11]  José Manuel Viegas,et al.  Formulating a New Express Minibus Service Design Problem as a Clustering Problem , 2015, Transp. Sci..

[12]  Thambipillai Srikanthan,et al.  Hybrid Genetic Algorithm for an On-Demand First Mile Transit System Using Electric Vehicles , 2018, ICCS.

[13]  Rosaldo J. F. Rossetti,et al.  A HLA-based multi-resolution approach to simulating electric vehicles in simulink and SUMO , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[14]  Justin Dauwels,et al.  Development of a simulation platform to implement vehicle routing algorithms for large scale fleet management systems , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[15]  Maria Grazia Speranza,et al.  A simulation study of an on-demand transportation system , 2018, Int. Trans. Oper. Res..

[16]  Michal Maciejewski,et al.  City-wide shared taxis: A simulation study in Berlin , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).