ABSTRACT
Predicting vehicle flow is crucial for traffic management but is often limited by the scope of sensors. In contrast, extensive mobile network coverage enables us to utilize counts of mobile users' network activities (cellular traffic) on roadways as a proxy for vehicle flow. However, cellular traffic counts, which encompass various user types, may not directly align with vehicle flow. To address this issue, we present a new task: utilizing cellular traffic to predict vehicle flow in camera-free areas. This is supported by our Tel2Veh dataset, which comprises extensive cellular traffic and sparse vehicle flows. To tackle this task, we propose a two-stage framework. It first independently extracts features from multimodal data, and then integrates them using a graph neural network (GNN)-based fusion to generate predictions of vehicle flow in camera-free areas. We pioneer the fusion of telecom and vision-based data, paving the way for future expansions in web-based applications and systems.
Supplemental Material
- Nir Aharon, Roy Orfaig, and Ben-Zion Bobrovsky. 2022. BoT-SORT: Robust associations multi-pedestrian tracking. arXiv preprint arXiv:2206.14651 (2022).Google Scholar
- Gianni Barlacchi, Marco De Nadai, Roberto Larcher, Antonio Casella, Cristiana Chitic, Giovanni Torrisi, Fabrizio Antonelli, Alessandro Vespignani, Alex Pentland, and Bruno Lepri. 2015. A multi-source dataset of urban life in the city of Milan and the Province of Trentino. Scientific data (2015).Google Scholar
- Shaked Brody, Uri Alon, and Eran Yahav. 2022. How Attentive are Graph Attention Networks?. In International Conference on Learning Representations.Google Scholar
- Weiwei Jiang. 2022. Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications (2022), 117163.Google ScholarDigital Library
- ChungYi Lin, Shen-Lung Tung, and Winston H Hsu. 2023. Pay Attention to Multi-Channel for Improving Graph Neural Networks. In Proc. of ICLR.Google Scholar
- ChungYi Lin, Shen-Lung Tung, Hung-Ting Su, and Winston H Hsu. 2024. TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling. arXiv preprint arXiv:2401.03138 (2024). Accepted by AAAI 2024. To appear.Google Scholar
- Chung-Yi Lin, Hung-Ting Su, Shen-Lung Tung, and Winston H. Hsu. 2021. Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic. In Proc. of CIKM.Google Scholar
- Zhihan Lv, Yuxi Li, Hailin Feng, and Haibin Lv. 2021. Deep learning for security in digital twins of cooperative intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems (2021).Google Scholar
- Philip Sedgwick. 2012. Pearson's correlation coefficient. Bmj, Vol. 345 (2012).Google ScholarCross Ref
- Xu Wang, Zimu Zhou, Fu Xiao, Kai Xing, Zheng Yang, Yunhao Liu, and Chunyi Peng. 2018. Spatio-temporal analysis and prediction of cellular traffic in metropolis. IEEE Transactions on Mobile Computing (2018).Google Scholar
- Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph WaveNet for Deep Spatial-Temporal Graph Modeling.. In Proc. of IJCAI.Google ScholarCross Ref
- Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, and Junbo Zhang. 2020. Urban flow prediction from spatiotemporal data. Information Fusion (2020).Google Scholar
- Junchen Ye, Zihan Liu, Bowen Du, Leilei Sun, Weimiao Li, Yanjie Fu, and Hui Xiong. 2022. Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting. In Proc. of KDD. 2296--2306.Google ScholarDigital Library
- Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, and Qi Zhang. 2020. Multivariate time-series anomaly detection via graph attention network. In Proc. of ICDM. 841--850.Google ScholarCross Ref
Index Terms
- Tel2Veh: Fusion of Telecom Data and Vehicle Flow to Predict Camera-Free Traffic via a Spatio-Temporal Framework
Recommendations
CTCam: Enhancing Transportation Evaluation through Fusion of Cellular Traffic and Camera-Based Vehicle Flows
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementTraffic prediction utility often faces infrastructural limitations, which restrict its coverage. To overcome this challenge, we present Geographical Cellular Traffic (GCT) flow that leverages cellular network data as a new source for transportation ...
Fine-Grained Traffic Flow Prediction of Various Vehicle Types via Fusion of Multisource Data and Deep Learning Approaches
Both road users and road administrators are keen to know traffic flow of fine-grained vehicle type. Successful prediction on the traffic flow of heavy, medium and small vehicle could contribute to the improvement of travel safety and efficiency. However, ...
A simulation study on prioritizing connected freight vehicles at intersections for traffic flow optimization (industrial paper)
SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information SystemsDue to the importance of road freight, there is a significant cost of delaying freight vehicles on the road. In this work, we focus on freight vehicle optimization by reducing delays at intersections. Our simulation study evaluates the effectiveness of ...
Comments