TY - CHAP A1 - Dünnweber, Jan A1 - Sarkar, Amitrajit A1 - Puthiyadath, Vimal Kumar A1 - Barde, Omkar ED - Saeed, Khalid ED - Dvorský, Jiří T1 - A Tale of Four Cities - Improving Bus and Waste Collection Schedules in Practical Smart City Applications T2 - Computer Information Systems and Industrial Management N2 - 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. KW - Smart city KW - Bus schedules KW - Waste management Y1 - 2021 SN - 978-3-030-84340-3 U6 - https://doi.org/10.1007/978-3-030-84340-3_30 SP - 369 EP - 380 PB - Springer CY - Cham ER - TY - CHAP A1 - Stadler, Timo A1 - Sarkar, Amitrajit A1 - Dünnweber, Jan ED - Saeed, Khalid ED - Dvorský, Jiří T1 - Bus Demand Forecasting for Rural Areas Using XGBoost and Random Forest Algorithm T2 - CISIM2021: 20th International Conference on Computer Information Systems and Industrial Management Applications, September 24-26 2021, Ełk, Poland N2 - 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. KW - Transportation KW - Rural mobility KW - Prediction KW - Öffentlicher Personennahverkehr KW - Ländlicher Raum KW - Verkehrsnachfrage KW - Prognosemodell KW - Random Forest Y1 - 2021 SN - 978-3-030-84340-3 U6 - https://doi.org/10.1007/978-3-030-84340-3_36 SP - 442 EP - 453 PB - Springer CY - Cham ER -