Smart City Gnosys
Smart city article details
| Title | Data-Driven Mobility And Transport Planning In Municipalities: Smart Solutions For Limited Resources |
|---|---|
| ID_Doc | 17449 |
| Authors | Höhne E.; Teich T.; Scharf O.; Leonhardt S.; Schlachte M.; Trommer M.; Mewes C.; Kraus M.; Bergelt S.; Queck-Hänel S. |
| Year | 2024 |
| Published | Lecture Notes in Networks and Systems, 1028 LNNS |
| DOI | http://dx.doi.org/10.1007/978-3-031-61905-2_4 |
| Abstract | The research paper highlights the importance of data-driven approaches in urban and mobility development for local communities. Tailor-made solutions, based on a precise understanding of local challenges, are essential, especially for smaller or financially weaker municipalities. Implementing a data-gathering infrastructure, particularly within Smart City concepts, often presents challenges due to limited technical and financial resources, as well as a lack of expertise. To address these issues, a video-based traffic detector was developed to enable flexible and minimally invasive traffic measurement. This technology differs from traditional AI models through its miniaturization and application to less powerful embedded systems. The ‘Machine Learning on the edge’ approach was tested in Zwickau, Saxony, Germany, where its robustness and reliability were confirmed by comparison with manual traffic counts. The field study demonstrated that reliable data collection is possible even with minimal technical resources. This contrasts with traditional traffic detection technologies, which rely on induction loops and compute-intensive systems. The novel approach offers a cost-efficient alternative, particularly suitable for minor townships with limited fiscal capacity. The study's conclusions recommend promoting data-based approaches in municipal planning, investing in cost-effective technologies like video-based traffic detectors, emphasizing scalability and adaptability, and continuously monitoring and improving data solutions. This enables the implementation of Smart City concepts even in financially challenged municipalities, contributing to the improvement of local quality of life. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
| Author Keywords | AI model; Tiny Machine Learning; video-based detection |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
32238  | 0.9 | Jain V.; Mitra A. | Integrative Hybrid Information Systems For Enhanced Traffic Maintenance And Control In Bangalore: A Synchronized Approach | Hybrid Information Systems: Non-Linear Optimization Strategies with Artificial Intelligence (2024) |
| 35056 | 0.899 | Mahmud S.; Day C.M. | Leveraging Data-Driven Traffic Management In Smart Cities: Datasets For Highway Traffic Monitoring | The Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems (2022) |
| 46591 | 0.897 | Deveshwar P.; Singh T.; Sharma Y.; Bidwe R.V.; Hiremani V.; Devadas R.; Shah K. | Revolutionizing Smart Cities: A Data-Driven Traffic Monitoring System For Real-Time Traffic Density Estimation And Visualization | Lecture Notes in Networks and Systems, 1075 LNNS (2025) |
| 3836 | 0.897 | Shams A.; Schekelmann A.; Mülder W. | A Proof Of Concept For Providing Traffic Data By Ai Based Computer Vision As A Basis For Smarter Industrial Areas | Procedia Computer Science, 201, C (2022) |
| 38883 | 0.891 | Kumar A.; Batra N.; Mudgal A.; Yadav A.L. | Navigating Urban Mobility: A Review Of Ai-Driven Traffic Flow Management In Smart Cities | 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024 (2024) |
| 5789 | 0.89 | Ranka S.; Rangarajan A.; Elefteriadou L.; Srinivasan S.; Poasadas E.; Hoffman D.; Ponnulari R.; Dilmore J.; Byron T. | A Vision Of Smart Traffic Infrastructure For Traditional, Connected, And Autonomous Vehicles | Proceedings - 2020 International Conference on Connected and Autonomous Driving, MetroCAD 2020 (2020) |
| 58610 | 0.886 | Manjaiah D.H.; Praveena Kumari M.K.; Harishkumar K.S.; Bongale V. | Traffic Jam Detection Using Regression Model Analysis On Iot-Based Smart City | Lecture Notes in Networks and Systems, 653 LNNS (2023) |
| 51664 | 0.886 | Mahrez Z.; Sabir E.; Badidi E.; Saad W.; Sadik M. | Smart Urban Mobility: When Mobility Systems Meet Smart Data | IEEE Transactions on Intelligent Transportation Systems, 23, 7 (2022) |
| 53682 | 0.883 | Aráuz J. | Supporting Mobility Planning In Small Cities And Communities With Low-Power, Machine Learning Based Sensing | Procedia Computer Science, 185 (2021) |
| 50152 | 0.882 | Sarker I.H. | Smart City Data Science: Towards Data-Driven Smart Cities With Open Research Issues | Internet of Things (Netherlands), 19 (2022) |