TY - JOUR
T1 - Discovering traffic bottlenecks in an urban network by spatiotemporal data mining on location-based services
AU - Lee, Wei Hsun
AU - Tseng, Shian Shyong
AU - Shieh, Jin Lih
AU - Chen, Hsiao Han
PY - 2011/12
Y1 - 2011/12
N2 - Discovering traffic bottlenecks and taking action to alleviate congestion to enhance the performance of a traffic network are the most important tasks for the advanced traffic management system in the intelligent transportation system. However, traffic bottlenecks are affected by several factors and vary with spatial and temporal environments, which makes them difficult to define and discover. This paper proposes a three-phase spatiotemporal traffic bottleneck mining (STBM) model, including several spatiotemporal traffic patterns and STBM algorithms that use the raw data of location-based services to discover urban network spatiotemporal traffic bottlenecks. This paper implements an STBM prototype system based on a taxi dispatching system in a Taipei, Taiwan, urban network. The experimental results show that the congestion prediction capability of the proposed heuristic methods (congestion-propagation heuristic) is up to 79.6% during workdays and 72.1% on weekends, which outperforms other methods (e.g., the congestion-converge heuristic, the congestion-drop heuristic, and congested object item), and the discovered spatiotemporal bottlenecks match the travelers' experience.
AB - Discovering traffic bottlenecks and taking action to alleviate congestion to enhance the performance of a traffic network are the most important tasks for the advanced traffic management system in the intelligent transportation system. However, traffic bottlenecks are affected by several factors and vary with spatial and temporal environments, which makes them difficult to define and discover. This paper proposes a three-phase spatiotemporal traffic bottleneck mining (STBM) model, including several spatiotemporal traffic patterns and STBM algorithms that use the raw data of location-based services to discover urban network spatiotemporal traffic bottlenecks. This paper implements an STBM prototype system based on a taxi dispatching system in a Taipei, Taiwan, urban network. The experimental results show that the congestion prediction capability of the proposed heuristic methods (congestion-propagation heuristic) is up to 79.6% during workdays and 72.1% on weekends, which outperforms other methods (e.g., the congestion-converge heuristic, the congestion-drop heuristic, and congested object item), and the discovered spatiotemporal bottlenecks match the travelers' experience.
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U2 - 10.1109/TITS.2011.2144586
DO - 10.1109/TITS.2011.2144586
M3 - Article
AN - SCOPUS:82455209065
SN - 1524-9050
VL - 12
SP - 1047
EP - 1056
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 4
M1 - 5766752
ER -