Smart City Gnosys

Smart city article details

Title	Flowcasting: A Dynamic Machine Learning Based Dashboard For Bike-Sharing System Management
ID_Doc	26705
Authors	Avignone A.; Napolitano D.; Cagliero L.; Chiusano S.
Year	2024
Published	18th IEEE International Conference on Application of Information and Communication Technologies, AICT 2024
DOI	http://dx.doi.org/10.1109/AICT61888.2024.10740417
Abstract	Prompted by increasing citizens' demand, the rapid evolution of smart- and micro-mobility continues to shape the landscape of urban transportation services. In light of their practical benefits in terms of environmental sustainability, public health, and traffic congestion mitigation, smart cities manage mobility services by tracking user demand and service utilization over time. Leveraging this data is crucial for discerning current patterns and anticipating future trends, thus improving service provision. In this context, we propose a new interactive dashboard for the advanced analysis of spatio-temporal data acquired from bike-sharing systems. Our goal is to show on an interactive map the city areas with the highest current and future users' demand and a simulation of the routes suitable for redistributing bikes across stations according to their predicted occupancy level. We leverage a clustering algorithm to identify the areas with currently highest bike demand and a forecasting approach to predict users' demand trends. Thanks to multi-resolution time and path management, end-users can exploit the dashboard to support their decisions regarding resource shaping. We showcase the FlowCasting's capabilities on a opensource dataset collecting BlueBikes data in Boston (U.S.). The online demo is available at the following link: https://flowcasting.streamlit.app/ © 2024 IEEE.
Author Keywords	Data Analytics; Interactive Dashboard; Machine Learning; Smart Mobility

Similar Articles

Id	Similarity	Authors	Title	Published
24000	0.918	Subramanian M.; Cho J.; Veerappampalayam Easwaramoorthy S.; Murugesan A.; Chinnasamy R.	Enhancing Sustainable Transportation: Ai-Driven Bike Demand Forecasting In Smart Cities	Sustainability (Switzerland), 15, 18 (2023)
29019	0.909	Yang, JJ; Guo, BZ; Wang, ZH; Ma, YL	Hierarchical Prediction Based On Network-Representation-Learning-Enhanced Clustering For Bike-Sharing System In Smart City	IEEE INTERNET OF THINGS JOURNAL, 8, 8 (2021)
33156	0.908	Onen S.; Ggaliwango M.; Mugabi S.; Nabende J.	Interpretable Machine Learning For Intelligent Transportation In Bike-Sharing	2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023 (2023)
18245	0.905	Yang X.; Xu Y.; Zhou Y.; Song S.; Wu Y.	Demand-Aware Mobile Bike-Sharing Service Using Collaborative Computing And Information Fusion In 5G Iot Environment	Digital Communications and Networks, 8, 6 (2022)
35054	0.904	Manai M.; Sellami B.; Yahia S.B.	Leveraging Data For Better Bike Sharing: A Methodology For Terminal Availability Prediction	Procedia Computer Science, 246, C (2024)
1411	0.9	Lin Y.-C.	A Demand-Centric Repositioning Strategy For Bike-Sharing Systems	Sensors, 22, 15 (2022)
20781	0.899	Ali A.; Salah A.; Bekhit M.; Fathalla A.	Divide-And-Train: A New Approach To Improve The Predictive Tasks Of Bike-Sharing Systems	Mathematical Biosciences and Engineering, 21, 7 (2024)
33149	0.896	Rühmann S.; Leible S.; Lewandowski T.	Interpretable Bike-Sharing Activity Prediction With A Temporal Fusion Transformer To Unveil Influential Factors: A Case Study In Hamburg, Germany	Sustainability (Switzerland) , 16, 8 (2024)
37370	0.892	Shir B.; Prakash Verma J.; Bhattacharya P.	Mobility Prediction For Uneven Distribution Of Bikes In Bike Sharing Systems	Concurrency and Computation: Practice and Experience, 35, 2 (2023)
52829	0.888	Chai J.; Song J.; Fan H.; Xu Y.; Zhang L.; Guo B.; Xu Y.	St-Bikes: Predicting Travel-Behaviors Of Sharing-Bikes Exploiting Urban Big Data	IEEE Transactions on Intelligent Transportation Systems, 24, 7 (2023)