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Category: Case Studies

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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:

Categories
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

https://www.tum-create.edu.sg/content/towards-ultimate-public-transport-system-0 and https://www.tum-create.edu.sg/research/rapid-road-transport.

A publication of our findings will follow.

Categories
Case Studies

Large-Scale Modelling of Electric Public Bus Operation and Charging

Large-Scale Modelling of
Electric Public Bus Operation
and Charging

Advanced evaluation of electrification pathways for public transport at scale

Background

Background

With climate change concerns in mind, many transportation authorities in the world are pushing towards the electrification of road transport in the coming decades. In particular, public transport networks are considered for early electrification. Converting an existing public bus system to operate with electric buses can be challenging, as existing bus schedules might not be directly feasible with the limited range of electric buses. Moreover, the charging infrastructure for an electric bus fleet involves the sharing of chargers between many buses, thus necessitating finding the appropriate charging strategy and the corresponding number of chargers to install.

CityMoS Application

With CityMoS, entire public bus networks can be simulated with the desired fleet composition and charging infrastructure for depot-charging and/or opportunity-charging at end-stations. Optimization of the number of chargers through simulation-driven optimization ensures the feasibility of the operation at each stage of the optimization. Trip dispatching verifies that sufficient energy is available to the vehicles, otherwise they are directed to a charging station to replenish their battery. The energy demand of both service trips and off-service trips is accounted for and depends on the driving profile, air-conditioning load and the number of passengers in the bus.

Outcomes

In the SITEM project, we applied this model and built a digital twin of the entire bus network of Singapore, which consists of more than 500 bus routes and 5800 buses. In cooperation with the local transport authority, several electrification scenarios have been evaluated and compared. The impact on the power grid of the electrification of the bus fleet was assessed and recommendations were derived.

Categories
Case Studies

CiLo Charging

CiLoCharging

Optimized integration of charging, logistics, energy and
traffic management for the operation of electric vehicles in
logistics depots close to cities

City Logistics Charging (CiLo Charging)

City Logistics Charging (CiLoCharging)

The project focuses on the development, prototypical implementation and evaluation of a corresponding solution both in a simulation and in a field trial at the site of a newly built terminal. Leading partners are working in the individual domains together with recognized research institutions to develop an optimized, flexible and demand-oriented solution for requirements-based integration.

Process

The CiLoCharging project aims to enable an optimized, flexible and demand-oriented solution for the use of electric vehicles in the distribution service of a logistics terminal from an economic, technical and environmental perspective by taking into account the requirements from the domains of energy, logistics, charging infrastructure and mobility management.
In order to be able to adequately take into account the framework conditions typical for general cargo logistics and to ensure the scalability of the fleet terminals in an economical manner, both charging management must be integrated into the existing logistics processes and smart energy management must be provided for integrating electrified logistics terminals into the electrical distribution network.

Application of CityMoS in CiLoCharging Project

The use of a powerful simulation platform enables the cost-efficient exploration of large parameter space and the analysis of a wide variety of what-if scenarios. CityMoS serves as the digital twin of the entire logistics operation including depot, fleet vehicles, other traffic and in the target area (Frankfurt am Main). The covered topics include:
  • Study of various fleet parameters (fleet composition, vehicle types, cargo space, battery sizes)
  • Study of various depot parameters (number of charging stations, charging speed, auxiliary consumers)
  • Research into the effect of second-life battery use as local energy storage
  • Evaluation of novel vehicle-to-grid communication
  • Connect to existing fleet management tools
  • Analysis of all fleet relevant metrics (delivery delay, electricity costs, etc.)
In collaboration with:
Categories
Case Studies

SITEM

SITEM

Singapore Integrated Transport and Energy Model

Citymos and Mesmo-01 1

Commissioned by the Prime Minister’s Public Sector Science and Technology Policy and Plans Office (S&TPPO) and in collaboration with all relevant agencies, researchers from TUMCREATE and A*STAR’s Institute of High Performance Computing (IHPC) set out to create the first high-fidelity, island-wide simulation of electric vehicle (EV) transport in Singapore, called SITEM, short for Singapore Integrated Transport and Energy Model.

SITEM integrates multiple aspects of mobility and energy modelling, including the movements of individual vehicles, drivers’ decisions where and when to charge, as well as the interaction of EV charging demand with the capacity of the power grid. The project conducts a comprehensive analysis of projected electric vehicle charging patterns and energy demand, which will support policymaking on Singapore’s budget 2040 vision for all vehicles to run on cleaner energy. Such initiative will greatly contribute towards Singapore’s decarbonisation commitments.

SITEM Project Builds on Two Primary Simulation Technologies Developed by TUMCREATE in Singapore:

“

This collaboration between the research teams and relevant government agencies allows for integration of the best from two research institutes to address national level challenges.”

Er Pang Chung Khiang, Group Chief Systems Officer of S&TPPO, PMO
  • CityMoS, the City Mobility Simulator utilises high-performance computing techniques to enable high-detail transport simulation of the entire island of Singapore, while maintaining short turnaround times. This enables the efficient exploration of wide parameter spaces.

  • Multi Energy System Modelling and Optimisation (MESMO) is an advanced software framework that combines simulation of electrical grids and optimisation techniques to mitigate the grid impact of distributed energy resources (such as photovoltaics) and new types        of loads (such as EV charging).

The live coupling of the mobility simulator CityMoS and the power grid simulator MESMO provides insights into the inter- dependencies of both systems. These advanced scenario modelling capabilities have enabled regulatory agencies to explore and evaluate various pathways to vehicle electrification. For example, given that private electric cars will generally park longer than the actual duration required for the car to fully charge, smart charging management can help reduce grid infrastructure upgrade costs without compromising the overall energy provisioned to the electric cars. 

SITEM makes it possible to estimate the efficiency gains from such systems and to model their impact alongside or in combination with other mechanisms such as incentive-based demand shifting, smart scheduling, and local energy storages.

Medal
Winner of the 2022 Ministry of Trade and Industry (MTI) Borderless Silver Award.
In collaboration with:
In collaboration with:
Categories
Case Studies

MoVES Lab

MoVES Lab

Mobility in Virtual Environments at Scale (MoVES)

The City Mobility Simulator (CityMoS), developed by TUMCREATE, is a next-generation transport planning tool which incorporates high-performance computing techniques to offer microscopic detail at city scale. The digital twin platform has been used in various government and industry projects to support infrastructure planning, traffic flow optimisation as well as public transport and commercial fleet management.

CityMoS is further enhanced in the MoVES (Mobility in Virtual Environments    at Scale) Lab, a partnership between TUMCREATE and Huawei to conduct cutting-edge research that will enhance transport system simulation and improve urban planning in smart cities. The lab aims to develop new simu-lation models that can accurately reflect complex real-world scenarios with a high level of realism. These enhanced simulations can then be used as a base to support traffic flow planning and traffic light control, contributing to more efficient transport solutions.

Frame 1720
Frame 1720 (1)
Citymos image 1

Holistic Transport System Exploration

Instead of focusing only on traffic properties, CityMoS fully considers the mobility of people, including commute behaviour and mode choice. With models for public transport including rail networks and buses, as well as taxis and private traffic, CityMoS can provide insights into the transport system inaccessible to traditional traffic simulators.

 

 

Greener Transport Systems

CityMoS provides insights into a range of environmental metrics such as emissions or noise. The simulator is a great platform to study the effects of electrification not only on the environment but also on the operations of vehicle fleets, and through co-simulation, on the power grid. It can therefore be used to support the planning of charging infrastructure at scale and help cities transit to a carbon-free future.

Commercial Fleet Analysis

The charging and energy management of electric fleets and the move away from conventional combustion engine vehicles comes with challenges. CityMoS can serve as a digital twin to analyse vehicle assignment, fleet compositions, charging strategies, and demand prediction, making it a powerful companion for existing fleet management tools.

Intelligent Transport Systems

Connected vehicles and autonomous driving can change the way we move. CityMoS can be used to study the effect of mixed traffic, i.e. how humans and self-driving cars share the road. In the future, it will be possible to integrate CityMoS into the traffic control centre of a modern city to provide traffic forecasting and recommendations for traffic flow control strategies.

In partnership with: Huawei
Categories
Case Studies

Case Studies – Individual Project

CiLo Charging
We have created a new product that will help
developers and companies create websites
for their startups quickly and easily.

Objective

We have created this demo version in order to show you the structure of Startup Framework. It has some of the components from the full version, two great samples (Bootstrap themes) and documentation. You can find 2 images of a Macbook and an iPad, which you can use in your project. We hope you will like your first introduction Startup Framework. The most important part of Startup Framework are the samples or Bootstrap themes and templates. The samples consist of a set of few pages you can use «as is» or add new blocks from UI Kit.

Outcomes

We have created this demo version in order to show you the structure of Startup Framework. It has some of the components from the full version, two great samples and documentation. You can find images of a Macbook and an iPad, which you can use in your project.

Operation Optimisation

PUBLIC TRANSPORT

  • Bus stops, termini, depots, railway, underground, etc​.​
  • Individual passengers with origins and destinations, choosing their combination of transport modes​, including walking and transfer times

FLEET SIMULATION

  • Support of multi-shift taxi simulation​
  • Private hire vehicles​
  • Taxi passenger demand​
  • Commercial fleets (e.g. delivery fleets, carriers)​

MOBILITY SIMULATION

  • Private vehicles (cars, motorbikes), taxis, private hire cars, buses, trains, commercial traffic​
  • Behavioral models (driving behavior, route choices, decision making)​
  • Detailed electric and internal combustion engine models

 

 

3D VISUALISATION

  • Overlay of statistics, heatmaps, road colouring, vehicle information​
  • Features a touchscreen demo version and a full version​
  • Colour templates for corporate ident​ity for easy integration
Not only can you create and track projects, you can also work on road-maps, map them to your OKRs and ensure your entire team is aligned on the most important work. Overlay of the statistics, heatmaps, road colouring, vehicle information​. entire team is aligned on the most also important work. Not only can you create and track projects, you can work on roadmaps, map them to your OKRs and Entire team is can aligned on the most important you can also work.
Ensure your entire team is aligned on the most important work overlay of the statistics, heatmaps, road colouring, vehicle information​. entire team is aligned on the most important work. Not only can you create and track projects, you can also work on roadmaps, map them to your OKRs and ensure your entire team is aligned on the most important Work overlay of the statistics, heatmaps, road colouring, vehicle information​. Entire team is aligned on the most important work.