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CityMos suite Driver Model Callibration Dynamic Traffic Assignment Network Manipulation

test tutorial 1

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News

CityMoS at the Automotive Charging & Battery ASEAN 1st International Conference

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.

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Papers

Causality and Consistency of State Update Schemes in Synchronous Agent-based Simulations (A52)

@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}, }

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Papers

AVDM: A hierarchical command-and-control system architecture for cooperative autonomous vehicles in highways scenario using microscopic simulations (A51)

@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}, }

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Papers

Optimal Railway Disruption Bridging Using Heterogeneous Bus Fleets (A53)

@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}, }

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Papers

A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions (A54)

@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}, }

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Award-Winning Projects and Papers How has CityMoS been Utilised Media Coverage News Where has CityMoS been Featured

CityMoS Website Launch Announcement

CityMoS Website Launch Announcement

We are proud to announce the launch of our CityMoS website and even prouder to share with you, that CityMoS, our City Mobility Simulator, has matured from a research project to a market-ready mobility simulation product.

Visit our website at https://citymos.net/ for easy access to essential information on CityMoS, our high-performance digital twin solution for city-scale transport systems. Learn more about its capabilities and application areas, and explore our Case Studies, that feature how CityMoS is being used in complex real-world scenarios.   

CityMoS combines beyond state-of-the-art modelling and simulation research with parallel computing techniques to deliver answers to a wide range of mobility related what-if questions. CityMoS paves the way for transitioning to greener and most-efficient transport systems by providing enhanced simulations that reflect complex real-world scenarios.   

Today, we are looking back at more than 10 years of CityMoS software development and tremendous advancements by a team of computer scientists, transport engineers and designers. We continue building onto our achievements and will leverage our experience and expertise to support your next mobility project.  

Reach out to us via info@citymos.net or get in touch via social media to find the answers to your mobility related questions.     

Follow us on social media to find out more about CityMoS and see what we are up to.  

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Case Studies

Traffic Emission Modelling for Singapore

Traffic Emission
Modelling for Singapore

Using simulation to understand traffic emissions

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:
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Case Studies

Bus Bridging Services

Bus Bridging Services

Study of optimal fleet composition in the event of
disruption to train services in Singapore

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.

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.

More information on the topic can be found in these research papers:

In partnership with:

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Case Studies

Dynamic Autonomous Road Transit

Electric and Autonomous
Shuttle Buses

Using CityMoS to study the impact and optimize the deployment
of new modes of public transport

Electric Autonomous Shuttle Buses

With CityMoS, cities and operators can now accurately evaluate and support the integration of autonomous shuttle buses into their transportation systems. Autonomous shuttle buses represent the future of public transit, offering sustainable, efficient, and accessible mobility solutions. But integrating them seamlessly into existing infrastructure requires meticulous planning and evaluation. That’s where CityMoS, the City Mobility Simulator, comes in. Our software enables city planners, transportation authorities, and operators to simulate various scenarios, assessing factors like shuttle and terminal configuration, route optimization, traffic flow, passenger demand, as well as safety and comfort considerations. View the video on the left for more insights and to see TUMCREATE’s DART model in action in CityMoS. Below find a testimonial from our project partner as well as an example of what we have evaluated for the deployment of a shuttle bus in Singapore.
„CityMoS helped us to optimize the operational KPIs of autonomous electric vehicle fleets in public transport systems. Its capabilities are unique in the market, offering a wide variety of functionality from microscopic to macroscopic transport planning tasks. It is easy to use and facilitates insightful conclusion through comprehensive reporting.“, Emma Nagel, ZF Friedrichshafen AG CAE Engineer

Study of Large-Scale Transport Systems

Large-Scale
Transport Systems

With a growing urban population and an increasing demand in public transport, research into novel public transport solutions becomes increasingly important. Simulation studies can offer an insight into the performance, bottlenecks, and potentials of the public transport system and act as an enabler to improve the efficiency and thereby the passenger experience. With CityMoS, we present a novel methodology for the modelling and simulation of large-scale public transport systems, including passenger assignment, routing, as well as bus and train operation. Contrary to related work in the field, we utilize high-performance parallel computing to follow a microscopic approach, simulating each passenger and vehicle individually to provide more flexibility in the decision-making process.

Process

The selected area highlights the deployment area for the DART system. We modelled this area in detail in CityMoS, including existing bus lines, the mass rapid transit network as well as the proposed DART corridors. Entry and exit points to the target area (highlighted in triangles) were modelled to carry all passenger demand expected to enter and exit the area. Passenger demand was modelled on a zonal basis and mode assignment was carried out during run-time.

We modelled the entire public transport network in the form of a directed graph where nodes represent bus stops, platforms, MRT stations, etc and edges denote connectivity with an assigned travel time. We created 98 of these graphs to capture how connections change over time, with each graph occupying around 2.5mb of memory. With the help of contraction hierarchies, each query only took a few microseconds to execute, allowing us to simulate behaviour and preferences for every single commuter.

Outcomes

CityMoS provided insights into passenger choices as well as the performance of the system. Furthermore, we investigated how experiences made by passengers affect their next choices and how those would impact the transportation system. Our simulation study supported the original hypothesis that DART can alleviate bus crowding and improve commute times for passengers.
For more information on the DART system, please visit

A publication of our findings will follow.