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Assessing HDBSCAN Implementation for Traffic Congestion Pattern Estimation in Bangkok with Taxi GPS Probe
Author(s)
Tony, Dio
Date Issued
January 1, 2023
Type
Conference Paper
Abstract
Traffic congestion is a major issue that is experienced globally in metropolitan cities. The phenomenon becomes more serious during peak hour as congestion increases and degrades the traffic networks. Each city possesses a unique traffic network, and the behaviour of its residents affects its traffic patterns. Therefore, a flexible congestion pattern identification approach is desirable. We proposed the employment of Hierarchical Density Based Spatial Clustering of Applications with Noise (HDBSCAN) to estimate traffic congestion propagation patterns through congestion length distribution. Global positioning System (GPS) probe of taxis were utilised to represent traffic pattern within Bangkok. The dataset was preprocessed into two successive timeframes, namely 'later' timeframe and 'prior' timeframe before being clustered. The identified congestion hotspots from both timeframes were transformed into a congestion area from which congestion lengths were extracted. Similarity measurements on congestion lengths distribution were conducted against Longdo Traffic's top 100 most congested roads list in Bangkok, showed encouraging results across all tests, with more than 90% similarity in one of the measurements, which indicated that HDBSCAN was feasible to make a key contribution to traffic management research.
Citation
18th International Conference on Artificial Intelligence and Natural Language Processing and International Conference on Artificial Intelligence and Internet of Things Isai Nlp 2023, 2023