SAGA: An Activity-based Multi-modal Mobility ScenarioGenerator for SUMO
In this paper, we define a workflow and a toolchain to support fast mobility scenario prototyping based on open data and open-source software. SAGA is an activity-based multi-modal mobility scenario generator for the Simulation ofUrban MObiltiy (SUMO). Starting from an OpenStreetMap (OSM) file, SAGA extracts the data required to build an amulti-modal scenario, and in a step-by-step fashion, generates the configurations needed to execute it, including the intermediate steps required to refine the scenario with additional data, allowing the iterative improvement of realism and representativeness. The workflow implemented, extended, and automated by SAGA was developed while hand-crafting the Monaco SUMO Traffic (MoST) Scenario. Based on the fast prototyping capabilities added by SAGA, the creation of a multi-modal mobility scenario is readily achievable, and the incremental process to fine-tune it is supported by a workflow instead of being solely based on expert knowledge and experience. Based on previous experience, the generation of the first working prototype of a city-scale multi-modal mobility scenario may take months of work and expert knowledge. SAGAautomatically generates such a prototype, and all the intermediate configuration files are made available for further iterative improvements.
Li, Yanying and Voege, Tom. Mobility as a service (MaaS): challenges of Implementation and Policy Required. Journal of Transportation Technologies, 7(02):95–106, 2017.
Silva, Bhagya Nathali and Khan, Murad and Han, Kijun. Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38:697–713, 2018.
Pangbourne, Kate and Stead, Dominic and Mladenovi´c, Miloˇs and Milakis, Dimitris. The case of mobility as a service: A critical reflection on challenges for urban transport and mobility governance. In Governance of the smart mobility transition, pages 33–48. Emerald Publishing Limited, 2018.
Mooney, Peter and Minghini, Marco and others. A review of OpenStreetMap data. 2017.
Pablo Alvarez Lopez and Michael Behrisch and Laura Bieker-Walz and Jakob Erdmann and YunPang Flötteröd and Robert Hilbrich and Leonhard Lücken and Johannes Rummel and Peter Wagner and Evamarie Wießner. Microscopic Traffic Simulation using SUMO. In The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE, 2018.
Toader, Bogdan and Cantelmo, Guido and Popescu, Mioara and Viti, Francesco. Using Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 993–998. IEEE, 2018.
S. Yeung, H. M. A. Aziz, and S. Madria. Activity-Based Shared Mobility Model for Smart Transportation. In 2019 20th IEEE International Conference on Mobile Data Management (MDM), pages 599–604, June 2019.
Bowman, John L and Ben-Akiva, Moshe E. Activity-based disaggregate travel demand model system with activity schedules. Transportation research part a: policy and practice, 35(1):1–28, 2001.
Lara Codeca and J´erˆome H¨arri. Monaco SUMO Traffic (MoST) Scenario: A 3D Mobility Scenario for Cooperative ITS. In SUMO 2018, SUMO User Conference, Simulating Autonomous and Intermodal Transport Systems, Berlin, GERMANY, 05 2018.
Saxena, Pratiksha and Choudhary, Abhinav and Kumar, Sanchit and Singh, Satyavan. Simulation Tool for Transportation Problem: TRANSSIM. In Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications, pages 111–130. IGI Global, 2016.
Horni, Andreas and Nagel, Kai and Axhausen, Kay W. The multi-agent transport simulation MATSim. Ubiquity Press London, 2016.
Adnan, Muhammad and Pereira, Francisco C and Azevedo, Carlos Miguel Lima and Basak, Kakali and Lovric, Milan and Raveau, Sebasti´an and Zhu, Yi and Ferreira, Joseph and Zegras, Christopher and Ben-Akiva, Moshe. SimMobility: A multi-scale integrated agent-based simulation platform. In 95th Annual Meeting of the Transportation Research Board Forthcoming in Transportation Research Record, 2016.
Schweizer, Joerg and Rupi, Federico and Filippi, Francesco and Poliziani, Cristian. Generatingc activity based, multi-modal travel demand for SUMO. EPiC Series in Engineering, 2:118–133, 2018.
Basu, Rounaq and Araldo, Andrea and Akkinepally, Arun Prakash and Nahmias Biran, Bat Hen and Basak, Kalaki and Seshadri, Ravi and Deshmukh, Neeraj and Kumar, Nishant and Azevedo, Carlos Lima and Ben-Akiva, Moshe. Automated mobility-on-demand vs. mass transit: a multi-modal activity-driven agent-based simulation approach. Transportation Research Record, 2672(8):608–618, 2018.
Zehe, Daniel and Nair, Suraj and Knoll, Alois and Eckhoff, David. Towards CityMoS: A Coupled City-Scale Mobility Simulation Framework. 5th GI/ITG KuVS Fachgespr¨ach Inter-Vehicle Communication, 2017:03, 2017.
Xu, Yadong and Aydt, Heiko and Lees, Michael. SEMSim: A distributed architecture for multiscale traffic simulation. In Proceedings of the 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation, pages 178–180. IEEE Computer Society, 2012.
Cantelmo, Guido and Viti, Francesco. Incorporating activity duration and scheduling utility into equilibrium-based Dynamic Traffic Assignment. Transportation Research Part B: Methodological, 126:365–390, 2019.
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Copyright (c) 2022 Lara Codeca, Jakob ERDMANN, Vinny CAHILL, Jerome Haerri
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