Contact Us

Use the form on the right to contact us.

You can edit the text in this area, and change where the contact form on the right submits to, by entering edit mode using the modes on the bottom right. 

1 Create Way
Singapore, 138602

Research project within TUM CREATE. Focussing on modelling and optimisation of architecture and infrastructure, urban systems simulation like traffic and power are the main research interests. Apart from that, a cognitive systems group deal with human computer interaction. 


Visiting Internship of Henning Uck

David Eckhoff

During the past six weeks, Henning Uck, a Master Thesis student from Flensburg University in Germany, has paid us a visit and carried out a part of the research for his Master Thesis at AIDA. Writing his thesis in an industrial placement together with the consultancy firm P3 Energy & Storage, Henning intends to assess the potentials for innovative technologies and business models in the context of energy and mobility of future cities.

Given the area-interlinking character of our group and considering our approach of future cities as a holistic system, we could introduce him to the entire profile of research projects and programs within TUMCREATE. Thus, he was able to connect to various researchers within TUMCREATE and learn about their views on technology innovation.

For the AIDA group, the focus was set on learning which technologies are enabling complex simulations such as CityMoS, e.g.,  the concept of parallel computing and its significance. Furthermore, the understanding of potentials for other applications and the opportunities that arise from CityMoS were part of the stay.

Apart from the working part, there was also plenty of time for socializing with the team, exchanging perspectives and experiencing Singapore as a life-sized Smart City laboratory.

The insights Henning got will feed back into his Master's thesis. At the same time, we hope this collaboration to be just the beginning of a working relationship between our research team and P3.

CityMoS and Veins - Car to Car Communication

Michael Wagner

Veins (Vehicles in Network Simulation) is an open source framework for simulating wireless network communication between road-going vehicles and infrastructure, such as traffic lights. Veins originated as a specialized application of the OMNeT++ network simulation library, which supports highly detailed network models covering the complete network stack down to the physical layer. SUMO is the first traffic simulation that supports coupling with Veins, which is solved by the so called TraCI protocol.

The TraCI Protocol

The protocol defines how a TCP connection is established between Veins and a traffic simulation, as well as the contents of the packages sent between both ends. The traffic simulation acts as the TraCI server, and waits for a network simulation (Veins), acting as a TraCI client, to initiate the TCP connection between both. Once the connection is established Veins takes control of the simulation flow by creating a continuous feedback loop between traffic and communication to accurately portray the effects of communication on the urban system.

As of version 1.5.5 CityMoS received its own implementation of the TraCI protocol, which allows it to run as a TraCI server in three different ways:

Blocking server

$ ./CityMoS --traci

Running as a TraCI server without additional parameters prompts CityMoS to start up and wait until an instance of Veins has connected. No simulation work will be done before that.

Timed blocking server

$ ./CityMoS --traci = 5

Running as a TraCI server with an additional time argument prompts CityMoS to start up, perform simulation until the given time and then to wait for incoming Veins connections. Once established, the traffic and network simulations are running in unison. This is the most common use case of simulation coupling where the CityMoS side is guaranteed some warm-up time until it reaches a steady state. Doing so ensures that recorded data is free of artifacts that may occur during the startup phase of the simulation

Parallel server

$ ./CityMoS --traci = -1

Running as a TraCI server with any negative parameters prompts CityMoS to run the simulation while at the same time waiting for an incoming Veins connection. In this mode Veins can take charge of the traffic simulation at any time

Region of Interest

In many use cases it is not necessary or useful for the network simulation to geographically cover the entirety of the traffic simulation. For example, the user might only be interested in the effects on a certain district in town, save computational resources to increase performance or reduce the impact of boundary conditions by excluding the border areas from the feedback loop.

Veins, and by extension the TraCI protocol, allow the user to define a region of interest, which delimits the geographic area of the traffic simulation that will be considered for the network simulation.

Visitors and talks

David Eckhoff

The Singapore-ETH Centre invited David to give a presentation on how to acquire funding and how to write proposals. The audience of about 40 people consisted of associates from ETH and TUMCREATE as well as external early career researchers. The talk was well perceived as shown by a vivid and fruitful discussion afterwards.

The same day, we hosted 40 of Roland Berger's top-performing consultants and gave a presentation on the smart city and how intelligent transport systems can be simulated. With an attendance of some 60 people, the CREATE theatrette was well filled. 

On Friday, we welcomed a visiting delegation from Porsche around their executive board member Detlev von Platen. In a small circle, AIDA presented their research and discuss the possible role of the automotive OEM in the future smart city.


The Simulation Toolchain - CityMoS Suite

Michael Wagner

A simulation platform as complex and extensive as CityMoS runs the risk of being difficult to understand and cumbersome to use. Setting up a simulation experiment requires comprehensive input data and a detailed configuration file.

Input data comprises the following:

  • road and routing networks
  • intersection definitions
  • itineraries for road-going vehicles
  • optional content for public transport, e.g.: bus stops and bus line itineraries

The configuration file fulfills two functions; it organises all simulation input and provides definitions for vehicle and behaviour models.

The CityMoS Suite is designed to offer the users of CityMoS a starting point for creating simulation experiments from scratch and guide them through the process of setting up and using CityMoS. Currently under development the suite unites the Network Editor and the Config Designer, two major parts of the current CityMoS toolchain, in one single application. This will lead to an improved and more natural workflow for editing and creating simulation experiments.

First page of the Config Designer wizard

The Config Designer guides the user in form of a wizard-like app through the config creation process. Several pages of forms and menus support creating and choosing a suitable configuration for the user.

Network Editor default view

The Network Editor is a comprehensive toolbox for creating, editing and analysing every kind of input data for a CityMoS simulation. Its eponymous feature is to support the creation and editing of road and routing networks. Traffic lights and intersection phases can then be defined on top of the networks. Furthermore its extended features offer tools for managing agent itineraries, public transport including bus lines, schedules and depot functionality. Finally integrity checks and analysis tools provide helpful statistics and insights about the road network properties to help ensure the correctness of the simulation.

TUMCREATE and AEC - Interactive Virtual Research Lab

Daniel Zehe

Urban mobility needs to be analysed holistically to understand how the infrastructure and people interface and interact, and the impact it has on ecology and society. Therefore, TUMCREATE, together with Ars Electronica Future Lab, has developed an Interactive Virtual Research Lab within the Deep Space 8K – a state-of-the-art interactive 3D visualisation platform. Using this research lab, future mobility concepts can be simulated, social acceptance of new technologies can be assessed and the impact of new transportation concepts on the infrastructure, like road, energy grid and vehicle fleets can be studied.

Investigating Future Urban Mobility

If a city is viewed from above, it is a world in motion: trains carry people to and from work, taxis circulate, trucks deliver goods and carry away garbage, pedestrians walk down city blocks, cyclists zip through traffic. In other words, mobility is the bloodstream of our cities and is essential for urban life. Technological innovations in the form of electrification, connectivity, and autonomy are on the horizon. The combination of urban expansion and rapid innovation will inevitably drive significant changes – what will the consequences be for future urban mobility?

  The simulation of an urban landscape within Deep Space 8K and a team member walking the virtual city. Source: TUMCREATE + Ars Electronica

The simulation of an urban landscape within Deep Space 8K and a team member walking the virtual city. Source: TUMCREATE + Ars Electronica

Urban mobility needs to be analysed holistically to understand how the infrastructure and people interface and interact, and the impact it has on ecology and society. Hence, the Interactive Virtual Research Lab within the Deep Space 8K was concieved. Using this infrastructure, future mobility concepts can be simulated, social acceptance of new technologies can be assessed and the impact of new transportation concepts on the infrastructure, like road, energy grid and vehicle fleets can be studied. 

Furthermore, the 3D immersive and responsive environment of this novel tool is equipped with human-in-the-loop simulation for conducting research on how people react to new technologies like autonomous vehicles (AV). Human responses to various AV communication strategies can be easily sensed, tested and verified, without necessarily working on prototypes and conducting tests in real world situations, which are much more time, effort and finance intensive.

  The perspective of the oculus rift wearer is displayed in 3D on the 16×9 metre display behind. A scenario with a human interacting with an AV on a pedestrian crossing. Source: TUMCREATE + Ars Electronica

The perspective of the oculus rift wearer is displayed in 3D on the 16×9 metre display behind. A scenario with a human interacting with an AV on a pedestrian crossing. Source: TUMCREATE + Ars Electronica

This research tool allows us to have an impressive, detailed visualization and to experience the vehicle concepts, mobility systems and operation strategies “live” as it were, and at city level. This rich visualization makes it understandable and transparent to scientists, students and people from all backgrounds.

  Humans interacting with virtual pedestrians within the urban simulation. Source: TUMCREATE + Ars Electronica

Humans interacting with virtual pedestrians within the urban simulation. Source: TUMCREATE + Ars Electronica


The project was done in collaboration with Ars Electronica Future Lab, Ars Electronica Linz GmbH & Co KG, Ars-Electronica-Straße 1, 4040 Linz - Austria.


Future Urban Mobility - TUMCREATE 2017

A Simulation-based Heuristic for City-scale Electric Vehicle Charging Station Placement

Jiajian Xiao

Electric Vehicles (EVs) play an important role towards a more sustainable transportation system. Sufficient charging infrastructure is, however, needed in order to accommodate their power demand and increase EV adoption. In this study, a simulation-based approach for charging station (CS) placement using an agent-based traffic simulation is proposed. The heuristic's objective is to achieve sufficient network coverage to keep charging related inconvenience within an acceptable range while minimising the overall number of CSs. For this purpose, the algorithm identifies locations at which the charging procedure seamlessly integrates into the drivers' itineraries, thus minimising detours and waiting times. At the same time, the algorithm attempts to maximise the utilisation of each CS throughout the day in order to minimise the number of CSs. The methodology is demonstrated at the example of Singapore. The investigation shows that the charging demand of 20,000 EVs can be covered with approximately 2,500 CSs by accepting average detours no greater than 410 metres and average waiting time below 10 minutes. This number can be further reduced by relaxing the inconvenience criterion. 

The main components of the proposed methodology are a charging behaviour model and a CS placement algorithm. The first one determines under what conditions a driver decides to recharge the vehicle’s battery, the latter aims for placing CSs in a way which best suits the drivers’ charging needs.

For charging behaviour, two types of charging behaviours are defined depending on the battery usage:

  • Mandatory Charging: charge when battery is not sufficient

At the end of each trip, an estimation is made to check if the remaining energy is enough to support the next trip + some safety margin.  And mandatory charging is triggered when the remaining energy is less than the estimation

We introduce a queuing behaviour. When a mandatory charge is required and the CS is currently occupied, a vehicle will queue up for charging. We assume an unlimited queue for each CS.

  • Convenience Charging: charge during parking

Convenience charging is modelled by considering a trade-off between remaining the energy and the distance to the nearest CS. 

CS placement algorithm removes unused CS and merges nearby CSs with less usage.

The combined usage of two CSs is computed by the following formula. If the combined usage is below a certain threshold, these two CS will be combined.


And the new CS will be created at the place which uses the idea of centre of mass

We run the experiment with CityMoS. The best result we achieved is:

With 20,000 EVs, we achieve a distribution of CSs as illustrated by the figure. 

  • Approximately 2,500 CSs
  • Average detour 410 meters
  • Average queuing time of 10 minutes
Screen Shot 2017-12-21 at 15.51.20.png

CityMoS 1.6 - Nasi Lemak

Daniel Zehe

CityMoS 1.6 is the third large feature release of CityMoS. In the tradition of naming CityMoS releases after items from the Singaporean cuisine, this release is called Nasi Lemak.

In addition to bug fixes and overall improvements to the stability and usability, this release introduces the ability to add custom dwelling time models for the busses, as well as an improved traffic assignment model. This optional feature improves the route choice model of vehicles in order to resemble human decision making then navigating through a traffic system.

In addition to the new features in CityMoS Nasi-Lemak, improvements to existing functionality were added. This includes the introduction of shape points for individual lanes. This allows modeling the curvature of a lane independently of the geometry of the associated Link. One effect of this alteration in road geometry modeling is a significant reduction in overall network size and complexity. Before, additional road links had to be introduced to resemble the curvature of a road. Appart for a reduction in network file size, the routing performance due to reduced graph complexity can be improved. A modularization of the lane changing allows for custom lane change behavior models to be implemented in the future. 

The CityMoS 3D front-end introduced with CityMoS 1.5 has improved road texture renderings as well as traffic signal models at intersections. Custom themes for styling the visual output can be added either manually or by using the CityMoS Configuration editor.

CityMoS with Car Models.png


The tooling for creating experiments with the CityMoS Configuration Editor and creating and modifying networks has been updated to versions 1.4 and 1.5 respectively. The CityMoS Network Editor supports the editing of the newly introduced lane shape points as well as better keyboard hotkey support for quickly changing between different tools.
CityMoS Configuration Editor supports multiple bus lines as well as general usability improvements to the workflow to work with the changed input file structures for CityMoS 1.6. It also offers the user the possibility to create configurations that work with CityMoS 3D.

PhD Thesis: Load Balancing and Synchronisation for Parallel Agent-based Road Traffic Simulation

yadong xu

In June 2017, Xu Yadong obtained his Ph.D in the School of Computer Science and Engineering at Nanyang Technological University. His PhD topic focuses on novel algorithms to accelerate the execution of microscopic traffic simulation using high performance computers. This is particularly useful in the studies where real-time decision making is critical, or a large number of simulation runs are required.

Read More