@inproceedings{ThelenEderMelzeretal., author = {Thelen, Simon and Eder, Friedrich and Melzer, Matthias and Nunes, Danilo Weber and Stadler, Michael and Rechenauer, Christian and Obergrießer, Mathias and Jubeh, Ruben and Volbert, Klaus and D{\"u}nnweber, Jan}, title = {A Slim Digital Twin For A Smart City And Its Residents}, series = {SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology, 2023, Hi Chi Minh, Vietnam}, booktitle = {SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology, 2023, Hi Chi Minh, Vietnam}, publisher = {ACM}, isbn = {979-8-4007-0891-6}, doi = {10.1145/3628797.3628936}, pages = {8 -- 15}, abstract = {In the engineering domain, representing real-world objects using a body of data, called a digital twin, which is frequently updated by "live" measurements, has shown various advantages over tradi- tional modelling and simulation techniques. Consequently, urban planners have a strong interest in digital twin technology, since it provides them with a laboratory for experimenting with data before making far-reaching decisions. Realizing these decisions involves the work of professionals in the architecture, engineering and construction (AEC) domain who nowadays collaborate via the methodology of building information modeling (BIM). At the same time, the citizen plays an integral role both in the data acquisition phase, while also being a beneficiary of the improved resource management strategies. In this paper, we present a prototype for a "digital energy twin" platform we designed in cooperation with the city of Regensburg. We show how our extensible platform de- sign can satisfy the various requirements of multiple user groups through a series of data processing solutions and visualizations, in- dicating valuable design and implementation guidelines for future projects. In particular, we focus on two example use cases concern- ing building electricity monitoring and BIM. By implementing a flexible data processing architecture we can involve citizens in the data acquisition process, meeting the demands of modern users regarding maximum transparency in the handling of their data.}, language = {en} } @inproceedings{BurgerSarkarKirschetal., author = {Burger, David and Sarkar, Amitrajit and Kirsch, Konstantin and D{\"u}nnweber, Jan}, title = {Combining Fill-Level Sensing with Route Optimization for a More Efficient Waste Collection}, series = {ECDG 2018 - European Conference on Digital Government, 2018, Santiago De Compostela, Spain}, booktitle = {ECDG 2018 - European Conference on Digital Government, 2018, Santiago De Compostela, Spain}, editor = {Bouzas Lorenzo, Ram{\´o}n}, isbn = {9781912764037}, pages = {24 -- 31}, abstract = {We tackle the problem that collecting all the waste of modern cities within the scheduled time spans becomes increasingly challenging, while the waste collection team is not supplemented accordingly in many places, e.g. by more staff or vehicles. Separating waste has become natural for urban populations, as recycling is ecologically necessary. However, the separation also leads to a continuously increasing number of containers which are logistically unmanageable without computer-aided collection scheduling. Regensburg recently introduced a new program for the collection of biological waste, which extends the private collection of such waste by a few hundred public biological waste containers. Computer support was pretty basic so far, i.e. the collection times were recorded and the routes were sporadically rescheduled manually in an Excel sheet. We show that much shorter waste collection routes can be found automatically using a dynamic version of the classic Ant Colony Optimization (ACO) algorithm for shortening the collection routes. Moreover, we sketch an loT (lnternet-of-Things) approach to "lntelligent Waste Containers" which we equip with ultrasonic sensors monitoring the fill levels, which allows us to skip poorly used containers during the collection and reposition them on demand. By means of a computer simulation with online visualization, we illustrate that our optimized and dynamically adapted collection routes lead to significant time savings. Indeed, more than twice the number of currently used containers can be collected within the same time. We also report about the first experiments that we conducted with real sensors. The presented work is the result of a cooperation between four contributing parties: 1) Technical University of Regensburg (OTH); 2) Ara Institute of Canterbury; 3) Regensburg's Governmental Department of Waste Management; and 4) an industry partner (kpit.com). We compare the outcome of our project with related work and we sketch some future perspectives and ideas for transforming Regensburg into an even more automatized, environmentally friendly "smart city".}, language = {en} }