@inproceedings{tan2020multithreadAIDA, title = {Multi-thread State Update Schemes for Microscopic Traffic Simulation}, author = {Tan, Wen Jun and Andelfinger, Philipp and Cai, Wentong and Xu, Yadong and Knoll, Alois and Eckhoff, David}, booktitle = {Proceedings of the 2020 Winter Simulation Conference (WSC)}, pages = {182–193}, address = {Orlando, FL, USA}, month = dec, year = {2020}, publisher = {IEEE}, doi = {10.1109/WSC48552.2020.9383962}, }
Author: citymos_user
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum
CityMoS at the Automotive Charging & Battery ASEAN 1st International Conference
We are proud to announce that our co-founder Dr David Eckhoff is included among the Top Speakers at the 1st International Conference for the Automotive Charging & Battery ASEAN. We are looking forward to being part of the event and connecting with leading industry experts.
Dr Eckhoff is a Principal Scientist and the Director of the MoVES laboratory at TUMCREATE and will speak about how digital twin and simulation software are utilised to support the transition to electric transportation, including a particular focus on modelling mobility aspects at a city scale. His research interests include privacy protection, smart cities, vehicular networks, and intelligent transportation systems with a particular focus on modelling and simulation. Find out more about Dr. David Eckhoff HERE.
@inproceedings{tan2021causalityAIDA, author = {Tan, Wen Jun and Andelfinger, Philipp and Eckhoff, David and Cai, Wentong and Knoll, Alois}, title = {Causality and Consistency of State Update Schemes in Synchronous Agent-based Simulations}, booktitle = {Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS)}, publisher = {ACM}, year = {2021}, month = may, address = {Virtual Event, USA}, doi = {10.1145/3437959.3459262}, }
@article{braud2021avdmAIDA, title = {{AVDM}: {A} hierarchical command-and-control system architecture for cooperative autonomous vehicles in highways scenario using microscopic simulations}, author = {Braud, Thomas and Ivanchev, Jordan and Deboeser, Corvin and Knoll, Alois and Eckhoff, David and Sangiovanni-Vincentelli, Alberto}, journal = {Autonomous Agents and Multi-Agent Systems}, volume = {35}, year = {2021}, month = apr, publisher = {Springer}, doi = {10.1007/s10458-021-09499-6}, }
@article{bojic2021optimalAIDA, author = {Bojic, Iva and Luo, Chunling and Li, Xinrong and Zehe, Daniel and Eckhoff, David and Ratti, Carlo}, journal = {IEEE Access}, title = {Optimal Railway Disruption Bridging Using Heterogeneous Bus Fleets}, year = {2021}, volume = {9}, month = jun, pages = {90656–90668}, doi = {10.1109/ACCESS.2021.3091576}, }
@article{ivanchev2021hierarchicalAIDA, author = {Ivanchev, Jordan and Deboeser, Corvin and Braud, Thomas and Knoll, Alois and Eckhoff, David and Sangiovanni-Vincentelli, Alberto}, journal = {IEEE Access}, title = {A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions}, year = {2021}, volume = {9}, month = aug, pages = {128393–128406}, doi = {10.1109/ACCESS.2021.3106455}, }
Traffic Emission
Modelling for Singapore
Using simulation to understand traffic emissions
- Case Studies
- Greener Transport Systems
- Traffic Emission Modelling for Singapore
Urban Climate Design and Management
Cooling Singapore is a multi-institutional, multidisciplinary project that aims to tackle urban heat, also known as the Urban Heat Island (UHI) effect. The goal of the project is to design and implement an island-wide Digital Urban Climate Twin (DUCT) of Singapore, by developing computational models (environ- mental, land surface, industrial, traffic, building energy) as well as regional and micro-scale climate models suitable for analysing UHI and Outdoor Thermal Comfort (OTC) aspects. The project will establish climate-informed urban design guidelines as a resource to planners and agencies.
TUMCREATE researchers are developing scientific models and numerical simulations for the Energy and Transport sectors as well as machine learning models for data analytics and informed decision making. Specifically, anthropogenic heat from buildings and traffic is evaluated with the use of those models to analyse ‘what-if’ scenarios (e.g. electric vehicles, energy efficient buildings), and to explore actions which can lead to the improvement of climate in Singapore.
Microscopic Traffic Emission Simulation
Cooling Singapore is a multi-institutional, multidisciplinary project that aims to tackle urban heat, also known as the Urban Heat Island (UHI) effect.
Project
Objective
Project Objective
The goal of the project is to design and implement an island-wide Digital Urban Climate Twin (DUCT) of Singapore, by developing computational models (environmental, land surface, industrial, traffic, building energy) as well as regional and micro-scale climate models suitable for analysing UHI and Outdoor Thermal Comfort (OTC) aspects. The project will establish climate-informed urban design guidelines as a resource to planners and agencies. TUMCREATE researchers are developing scientific models and numerical simulations for the Energy and Transport sectors as well as machine learning models for data analytics and informed decision making. Specifically, anthropogenic heat from buildings and traffic is evaluated with the use of those models to analyse ‘what-if’ scenarios (e.g. electric vehicles, energy efficient buildings), and to explore actions which can lead to the improvement of climate in Singapore.
In collaboration with:
Categories
Bus Bridging Services
Bus Bridging Services
Study of optimal fleet composition in the event of
disruption to train services in Singapore
- Case Studies
- Holistic Transport System Evaluation & Exploration
- Bus Bridging Services
Combining Optimisation with CityMoS
In the domains of fleet management and transport planning, often numerical optimisation is used to find an optimal solution to a given complex problem. These problems commonly include the assignment of vehicles to tasks under given constraints, the optimisation of routes, as well as finding the minimum resources required to fulfil a list of given requirements. The scenario as well as
the problem is formulated using mathematical equations which can then be solved using modern solvers such as CPLEX. For these optimisation problems to be manageable, they usually need to be simplified. Also, complex human behaviour and their interdependencies need to be expressed mathematically which also requires significant simplification, often to the extent where the validity of the obtained results can be questioned.
The combination of optimisation with a realistic simulator to study the feasibility, validity and efficacy of a proposed solution, or, in the case of multi-objective optimisation, a pareto set, significantly increases the fidelity and trustworthiness of the entire approach. Insights gained with the simulation can be fed back into the optimisation model (e.g., assumed travel times of buses) and, where possible, new constraints can be included to avoid finding solutions that would only work in the mathematical representation of the real world, but not in the real world itself.
The combination of optimisation with a realistic simulator to study the feasibility, validity and efficacy of a proposed solution, or, in the case of multi-objective optimisation, a pareto set, significantly increases the fidelity and trustworthiness of the entire approach. Insights gained with the simulation can be fed back into the optimisation model (e.g., assumed travel times of buses) and, where possible, new constraints can be included to avoid finding solutions that would only work in the mathematical representation of the real world, but not in the real world itself.
Case Study Singapore's MRT Network
We studied a hypothetical disruption of an MRT line with the goal to use bus bridging services to transport all affected passengers either to the next non-affected MRT station or their destination. We considered existing bus lines along the affected corrido r as well as 7 specific bridging lines. Given a maximum number of twenty buses, we studied the optimal fleet composition (double decker buses, articulated buses, single decker buses) and their assignment to the bus bridging lines.
This problem was formulated as a mathematical optimisation problem which could be solved in a few minutes of computation. The target area was modelled in CityMoS and CityMoS was extended to be able to read the bridging bus plans generated by the solver. The simulation helped to significantly improve the optimisation formulisation and highlighted cases where the mathematical simplification of the real world led to underestimation of travel times as highlighted in the graph on the right.
This problem was formulated as a mathematical optimisation problem which could be solved in a few minutes of computation. The target area was modelled in CityMoS and CityMoS was extended to be able to read the bridging bus plans generated by the solver. The simulation helped to significantly improve the optimisation formulisation and highlighted cases where the mathematical simplification of the real world led to underestimation of travel times as highlighted in the graph on the right.
More information on the topic can be found in these research papers:
- Iva Bojic, Chunling Luo, Xinrong Li, Daniel Zehe, David Eckhoff, and Carlo Ratti, “Optimal Railway Disruption Bridging Using Heterogeneous Bus Fleets,” IEEE Access, vol. 9, pp. 90656-90668, June 2021.
- Chunling Luo, Xinrong Li, Yuan Zhou, Aakil M. Caunhye, Umberto Alibrandi, Nazli Y. Aydin, Carlo Ratti, David Eckhoff, and Iva Bojic, “Data-driven disruption response planning for a Mass Rapid Transit system,” Proceedings of KES International Symposium on Smart Transportation Systems 2019 (KES-STS-19), St. Julians, Malta, June 2019, pp. 205-213.
In partnership with:
