Abstract
Nowadays, road traffic arises from the increased population because of employment in urban areas of all places. This creates to be a challenging aspect, uses certain measures to solve then and thereby the authorized people in the situation. The varying types of services related to traffic streams are pedestrian census, turning movement census, and others. The highlighting features are tabulated based on traffic management techniques. The proposed study on agglomerative clustering on traffic data has been satisfied with its enhanced features. Agglomerative clustering is an extensive model of hierarchical clustering, a bottom-up approach that combines the similarities of samples in clusters. A quite number of methods to find optimal clusters in different ways are briefly discussed to support the clustering. Experiments studied on California-Traffic solution-Data from SWITRS conducted with traffic data prove the optimal number of clusters formed, its validity using the method of clustering accuracy and pasteurization of time series on traffic data are shown in different categories.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
N.C. Benitez, R. Aquino-Santos, P. Magna-Espinoza et al., Traffic congestion system through connected and vehicles and big data. Sensors (2016)
S. Guo, Y. Lin, S. Li, Z. Chen, H. Wan, Deep Spatial-temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans. Intell. Transp. Syst. (2019)
VTRAC-Intelligent Traffic Solutions
G.P. Siddamma, S. Madhavanavar, Different techniques used in traffic control system: an introduction. IJERT
W. Weijermars, E. Van Berkum, Analyzing Highway Flow Patterns Using Cluster Analysis (IEEE, 2005), pp. 831–836
W.C. Ku, G.R. Jagadeesh, A. Prakash, T. Srikanthan, A Clustering–Based Approach for Data-Driven Imputation of Missing Traffic Data (IEEE, 2016)
M. Y. Choong, L. Angeline, R.K.Y. Chin, K.B. Yeo, K.T.K. Teo, Vehicle trajectory clusteriung for traffic intersection surveillance, in International Conference on Consumer Electronics-Asia (IEEE, 2016)
V. Pattanaik, M. Singh, P.K. Gupta, S.K. Singh, Smart real-time traffic congestion estimation and clustering technique for urban vehicular roads (IEEE, 2016)
M. Aven, Daily Traffic Flow Pattern Recognition by Spectral Clustering (CMC Senior Theses, 2017), pp. 1–16
S. Yang, J. Wu, G. Qi, K. Tian, Analysis of Traffic state variation patterns for urban road network based on spectral clustering. Adv. Mech. Eng. (2017)
M. Billah, A. Maskooki, F. Rahman, J.A. Farrell, roadway feature mapping from point cloud data: a graph-based clustering approach, in Intelligent Vehicles Symposium (IEEE, 2017)
W. Liu, G. Qin, Y. He, F. Jiang, Distributed cooperative reinforcement learning—based traffic signal control that integrates V2X Networks Dynamic Clustering, in Transactions on Vehicular Technology (IEEE, 2017)
G. Shen, C. Chen, Q. Pan, S. Shen, Z. Liu, Research on Traffic Speed Prediction by Temporal Clustering Analysis and Convolutional Neural Network with Deformable Kernels (IEEE Access, 2018), pp. 51756–51765
S. Hosain Sumit, S. Akhter, C-means Clustering and Deep-neuro-fuzzy Classification for Road Weight Measurement in Traffic Management System (Springer, 2018)
Y. Chang, B. Su, Construction of the driving cycle of vehicles queuing at toll station, in International Conference on Intelligent Transportation Systems (ITSC) (IEEE, 2018)
W. Xu, J. Li, An Improved Algorithm for Clustering Uncertain Traffic Data Srtreams Based on Hadoop Platform (World Scientific, 2019), pp. 1950203-1–1950203-19
T. Bandaragoda, D. De Silva et.al. Trajectory Clustering of road traffic in urban environments using incremental machine learning in combination with hyper dimensional computing, in International Transportation Systems Conference (IEEE, 2019)
H. Watanobe, T. Maly, J. Wallner, G. Prokop, Cluster-linkage analysis in traffic data clustering for development of advanced driver assistance systems (ICICT, 2020), pp. 54–59
W. Qi, B. Lanfeldt, Q. Song, L. Guo, A. Jamlaipour, Traffic differentiated clustering routing in DSRC and C-V2X Hybrid vehicular networks. Transaction on Vehicular Technology (IEEE,2020)
W. Chen, F. Zhou Guo, F.-Y. Wang, A survey of traffic data visualization. Transactions on Intelligent Transportation Systems (IEEE, 2015) pp. 1–15
docs.rapidminer.com/latest/studio/operators/modeling/segmentation/agglomerative-clustering.html
J. Yang, D. Parikh, D. Batra, Joint unsupervised learning of deep representations and image clusters (2016)
S. Nawrin, Md.R. Rahman, S. Akhter, Exploring K-means with interval validity indexes for data clustering in traffic management system. IJACSA (2017)
medium.com/@masraudheena/4-best-ways-to-find-optimal-number-of-clusters-for-clustering-with-python-code-706199f9957C
tutorialspoint.com/machine-learning-with-python/machine-learning-with-python-analysis-of-silhouette-score.htm
kaggle.com/alexgude/California-traffic-collision-data-from-switrs
machinelearningmastery.com/time-series-data-visualization-with-python
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Senthilarasi, S., Kamalakkannan, S. (2021). Unsupervised Deep Learning on Spatial-Temporal Traffic Data Using Agglomerative Clustering. In: Suma, V., Chen, J.IZ., Baig, Z., Wang, H. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1395-1_56
Download citation
DOI: https://doi.org/10.1007/978-981-16-1395-1_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1394-4
Online ISBN: 978-981-16-1395-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)