Commuting Pattern Recognition Using a Systematic Cluster Framework

Rongrong Hong, Wenming Rao, Dong Zhou, Chengchuan An, Zhenbo Lu and Jingxin Xia
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Rongrong Hong: Intelligent Transportation Systems Research Center, Southeast University, Nanjing 211189, China
Wenming Rao: Intelligent Transportation Systems Research Center, Southeast University, Nanjing 211189, China
Dong Zhou: Intelligent Transportation Systems Research Center, Southeast University, Nanjing 211189, China
Chengchuan An: Intelligent Transportation Systems Research Center, Southeast University, Nanjing 211189, China
Zhenbo Lu: Intelligent Transportation Systems Research Center, Southeast University, Nanjing 211189, China
Jingxin Xia: Intelligent Transportation Systems Research Center, Southeast University, Nanjing 211189, China

Sustainability, 2020, vol. 12, issue 5, 1-20

Abstract: Identifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters’ route feature is not fully considered or not accurately characterized. In this study, a systematic framework considering the route feature for commuting pattern recognition was developed for urban road networks. Three modules are included in the proposed framework. These modules were proposed based on automatic license plate recognition (ALPR) data. First, the temporal and spatial features of individual vehicles were extracted based on the trips detected by ALPR sensors, then a hierarchical clustering technique was applied to classify the detected vehicles and the ratio of commuting trips was derived. Based on the ratio of commuting trips, the temporal and spatial commuting patterns were investigated, respectively. The proposed method was finally implemented in a ring expressway of Kunshan, China. The results showed that the method can accurately extract the commuting patterns. Further investigations revealed the dynamic temporal-spatial features of commuting patterns. The findings of this study demonstrate the effectiveness of the proposed method in mining commuting patterns at urban traffic networks.

Keywords: commuting pattern; commuter feature extraction; hierarchical clustering; automatic license plate recognition data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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