TUM Living Lab Connected Mobility

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The German automotive industry faces major challenges through new mobility concepts, digital business models and strong international competitors of digital mobility services (Google, Apple).

To support the digital transformation in the area of Smart Mobility and Smart City, the TUM Living Lab Connected Mobility (TUM LLCM) research project was initiated, funded by the Bavarian Ministry of Economic Affairs, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government. The project bundles the relevant research, implementation, and innovation skills of the Technical University of Munich in the fields of informatics and transport research.

TUM LLCM contribution to the development of digital mobility services within the urban space, Source:

Besides the scientific research, another significant achievement of the project is the networking of already established and currently arising mobility providers, service providers, developers and users on a personal, organizational and technical level. Thus, the project contributes to the establishment of a mobility ecosystem, which is necessary for the success of the mobility platform. Thereby, smaller companies and start-ups are enabled to develop their own digital mobility services with reduced financial, organizational and technical effort.

The TUM Living Lab Connected Mobility thus simplifies and accelerates the exchange regarding the development of digital mobility services between university, industry and end-users. The university contributes to this digital ecosystem with current research findings from key areas of digital mobility platforms such as data analysis, app development, service monitoring, platform governance and efficient and legally secure integration of other partners. It draws on the established cooperation between TUM, the local industry, but also the local start-up scene to account for practical demands in the field of digital mobility platforms from the beginning.

TU München shows four projects of TUM Living Lab Connected Mobility at Hypermotion:

The mobility ecosystem is currently changing in a rapid pace. New mobility players enter the mobility environment, such as Google and Apple, developing autonomous cars. Additionally, mobility users are demanding mobility as a service, which give rise to new business opportunities for established, but also new mobility players. Through the digitalization of mobility, which enables the mobility as a service as one aspect, the influence of technology companies is growing. These companies collaborate with automotive manufacturer and automotive supplier, receive funding of these or even are purchased.
This subproject addresses the documentation, modelling and visualization of the connected mobility ecosystem.

In a first prototype, the ecosystem of the TUM Living Lab Connected Mobility project is used. The relevant entities with their key attributes and relations are documented and visualized.

The aim of this subproject is a connected mobility ecosystem explorer, providing different views and thereby information of the connected mobility ecosystem. Two relevant use cases will be addressed: first, a public accessible ecosystem explorer to foster communication and collaboration between actors, and second, the firm-internal competitor analysis.

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Air pollutants, greenhouse gases and noise are three road transportation related emissions that must be reduced due to their direct effects on health and their regional/global impacts on the environment. While air pollutants cause several health problems (e.g. irritations, respiratory or cardiovascular problems and cancer), greenhouse gases cause regional and global problems (e.g. extreme weather conditions, water or food shortages and climate change). In addition, high noise emissions, which are often ignored, cause health problems such as high blood pressure, sleeping disorders and cardiovascular disease.

There are several methods and measures to reduce road transportation related emissions covering different policy fields including planning, regulations, vehicle technologies and traffic operation. One of the policy instruments that focuses on reducing air pollutant emissions is eco-sensitive traffic management (ETM). In Europe, NOx and PM10 are the two major air pollutants to which road transport majorly contribute. Although the average air pollutant emission levels have been decreasing in the last decades, due to new standards and regulations in Europe, the limits defined by European Air Quality Standards and World Health Organization are often exceeded in central urban areas with high traffic volumes and/or congestion (i.e. hotspots) (Figure 1). ETM is a dynamic traffic management application where specific measures are activated due to current and/or projected high air pollution levels for a specific area and for a defined time period.

ntensive discussions about environmental management of urban traffic began after the second world war with the increase in population as well as car ownership and initially started as planning of access restrictions for certain areas. In time with increasing traffic volumes, shifted importance from safety to environmental issues, increasing awareness and stricter emission regulations static measures became increasingly important and together with the development of intelligent transport systems, it became possible to apply complementary dynamic measures. The state of the art analysis that focused on studies and applications conducted in the last decades in Germany showed that emission generation is mostly influenced by traffic volumes and compositions, although there are several external effects (wind, weather, radiation, building structure,…etc.) that can affect measured emission levels. It also showed that the quality and effectiveness of ETM systems are remarkably affected by the availability of data, aggregation levels and user acceptance.

The main objective of the sub-project 4.2 is to develop innovative traffic management scenarios and technologies which are adapted dynamically according to the emission levels in order to reduce road transport related emissions in urban areas. Within this scope, it is important to contribute to the state of the art in eco-sensitive traffic management. Corresponding sub-goals are listed below:

  • Enhancement of data availability, to improve the accuracy of emission level assessment and projection,
  • Integration of electric vehicles (EVs), which generate zero local exhaust emissions and less noise at low speeds (e.g. in urban areas) into ETM systems and related traffic management measures,
  • Consideration of the reduction potential of noise and greenhouse gas emissions in ETM Systems,
  • Assessment of the impacts of the increasing share of EVs in traffic composition on emission reduction, as well as evaluation of the possible effects of EV related ETM measures on promotion of electric mobility.

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Location Based Services (LBS) (e.g. Navigation devices) are widely used in outdoor space in our daily lives. The main components of LBS are a localization method and a map. However, humans spend approximately 80% of their time in indoor environments, where there is a lack of localization technologies and maps. The main reason is that established localization technologies, such as GPS cannot deliver reliable data in indoor space due to weak signal received through building walls. Additionally, indoor places cannot be mapped as scalable as outdoor places. Even though various approaches have been suggested for indoor localization based on different technologies, such as BLE Beacons, WiFi RSS, GSM antennas, magnetic field fingerprint, light, UWB, and dead reckoning, the problem of a scalable mapping hasn’t been efficiently addressed. The last years’ techniques for crowd-sourced indoor mapping have been suggested, either based on volunteered geographic information, crowdsourcing with smartphone cameras, or even based on the simple movement of humans in indoor places [1], [2], [3]. The last works as follows; Measurements from embedded mobile device sensors are collected while users moving naturally inside buildings. The collected data can then be used for estimating the traces of users. Movements can be seen as motion constraints while different kind of POIs (e.g. doors, stairs etc.) as landmarks in a SLAM algorithm. The traces consist of various steps which, if annotated by their exact locations, can produce a point cloud of the required surface.

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The transportation system is a critical infrastructure for the movement of people and goods. However, major events, including both unexpected incidents and planned special events put its reliability at high risk. In order to reduce the adverse impact of such situations, traffic management authorities and emergency reposnce units must make decision and implement measures in a very limited time. These measures are in most cases are derived from checklists and manuals which are not necessarily optimal. Figure 1 implies that better traffic management could eventually expedite the recovery of the network performance back to the pre-incident situation. Admittedly, it is vital to develop traffic management strategies that take into account the spatiotemporal characteristics of the event.

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