Dynamic OD Matrix Extraction Method of Urban Residents Based on Cell Phone Signaling Data

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Dynamic OD Matrix Extraction Method of Urban Residents Based on Cell Phone Signaling Data

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[1]TIAN Zhao,ZHANG Qianzhong,ZHAO Xuan,et al.Dynamic OD Matrix Extraction Method of Urban Residents Based on Cell Phone Signaling Data[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):46-54.[doi:10. 13705 / j. issn. 1671-6833. 2024. 03. 006]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 03 Page number: 46-54 Column: Public date: 2024-04-20

Title:: Dynamic OD Matrix Extraction Method of Urban Residents Based on Cell Phone Signaling Data

Author(s):: TIAN Zhao ¹; ZHANG Qianzhong¹; ZHAO Xuan¹; CHEN Bin ²; SHE Wei ¹; YANG Yanfang ³; 4; 1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China; 3. China Academy of Transportation Sciences, Beijing 100029, China; 4. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing 100029, China

Keywords:: city travel; intelligent traffic system; cell phone signaling data; dynamic origin-destination matrix; dwell point identification; spatial-temporal characteristics analysis

CLC:: TU998U491

DOI:: 10. 13705 / j. issn. 1671-6833. 2024. 03. 006

Abstract:: Previous surveys of urban resident travel were hindered by prolonged durations and insufficient granularity in traffic zone divisions, which impeded the timely and accurate acquisition of travel data. To address this issue, this study proposed a method for extracting the dynamic origin-destination ( OD) matrix of urban residents based on mobile phone signaling data. Firstly, methods to address two complex types of noise inherent in the signaling data: ping-pong switching data and drifting data were proposed. Specifically, a window thresholdbased detection and equivalent location replacement method for ping-pong switching data was proposed, as well as a complex drift point detection and marking method for drifting data. Secondly, an enhanced ST-DBSCAN clustering algorithm was proposed, which incorporated a temporal isochronization method to integrate temporal and spatial information, enabling the identification of dwell points during travel. Finally, a road network with key nodes was established using geographic information system ( GIS) , aligning resident travel OD with the network nodes to effectively derive the dynamic OD matrix of urban residents. Experimental results showed that the enhanced STDBSCAN clustering algorithm outperformed the traditional ST-DBSCAN, improving clustering efficiency by 6. 10% and identification speed by 5. 26%. Furthermore, the dynamic OD matrix extraction method based on the enhanced ST-DBSCAN clustering algorithm achieved approximately 16. 98% and 21. 55% reductions in mean squared error compared to the conventional statistical methods and the second-order statistical methods, respectively. By applying the proposed dynamic OD matrix extraction method to the case of Beijing, this study was able to conduct timely and effective analyses of daily and peak travel patterns of urban residents.

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