@inproceedings{BurgerSarkarKirschetal., author = {Burger, David and Sarkar, Amit 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} } @inproceedings{DuennweberSarkarPuthiyadathetal., author = {D{\"u}nnweber, Jan and Sarkar, Amit and Puthiyadath, Vimal Kumar and Barde, Omkar}, title = {A Tale of Four Cities - Improving Bus and Waste Collection Schedules in Practical Smart City Applications}, series = {Computer Information Systems and Industrial Management}, booktitle = {Computer Information Systems and Industrial Management}, editor = {Saeed, Khalid and Dvorsk{\´y}, Jiř{\´i}}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-84340-3}, doi = {10.1007/978-3-030-84340-3_30}, pages = {369 -- 380}, abstract = {Computer-based Improvements of waste collection and public transport procedures are often a part of smart city initiatives. When we envision an ideal bus network, it will primarily connect the most crowded bus stops. Similarly, an ideal waste collection vehicle will arrive at every container exactly at the time when it is fully loaded. Beyond doubt, this will reduce traffic and support environmentally friendly intentions like waste separation, as it will make more containers manageable. A difficulty of putting that vision into practice is that vehicles cannot always be where they are needed. Knowing the best time for arriving at a position is not insufficient for finding the optimal route. Therefore, we compare four different approaches to optimized routing: Regensburg, Christchurch, Malaysia, and Bangalore. Our analysis shows that the best schedules result from adapting field-tested routes frequently based on sensor measurements and route optimizing computations.}, language = {en} } @inproceedings{StadlerSarkarDuennweber, author = {Stadler, Timo and Sarkar, Amit and D{\"u}nnweber, Jan}, title = {Bus Demand Forecasting for Rural Areas Using XGBoost and Random Forest Algorithm}, series = {CISIM2021: 20th International Conference on Computer Information Systems and Industrial Management Applications, September 24-26 2021, Ełk, Poland}, booktitle = {CISIM2021: 20th International Conference on Computer Information Systems and Industrial Management Applications, September 24-26 2021, Ełk, Poland}, editor = {Saeed, Khalid and Dvorsk{\´y}, Jiř{\´i}}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-84340-3}, doi = {10.1007/978-3-030-84340-3_36}, pages = {442 -- 453}, abstract = {In recent years, mobility solutions have experienced a significant upswing. Consequently, it has increased the importance of forecasting the number of passengers and determining the associated demand for vehicles. We analyze all bus routes in a rural area in contrast to other work that predicts just a single bus route. Some differences in bus routes in rural areas compared to cities are highlighted and substantiated by a case study data using Roding, a town in the rural district of Cham in northern Bavaria, as an example. Data collected and we selected a random forest model that lets us determine the passenger demand, bus line effectiveness, or general user behavior. The prediction accuracy of the selected model is currently 87\%. The collected data helps to build new mobility-as-a-service solutions, such as on-call buses or dynamic route optimizations, as we show with our simulation.}, subject = {{\"O}ffentlicher Personennahverkehr}, language = {en} }