Commuting Traffic Analysis Zone Recognition Using Improved K-means Algorithm

doi:10.3969/j.issn.1006-2475.2024.07.009

Abstract

Abstract: Commuting is a periodical and stable travel behavior of urban residents， which is an important research content of urban development planning and public transportation management. Taxi GPS trajectory data reflects urban traffic conditions and citizens’ travel patterns to a certain extent. Aiming at the problem of taxi regional commuting pattern recognition， a commuting traffic analysis zone recognition method based on improved K-means algorithm is proposed. This method mainly includes three steps： dividing traffic analysis zones， generating flow transfer matrix between traffic analysis zones， and identifying commuting traffic analysis zone pairs. Referring to the existing traffic analysis zones division methods， a bottom-up division method based on fine-grained elements is proposed. In the recognition model of commuting traffic analysis zone pairs， the traffic flow and its dispersion coefficient during peak hours are taken as input features， and the commuting traffic analysis zone pairs are identified based on the improved K-means algorithm. Finally， an experimental verification is carried out based on the Chongqing taxi GPS data set， and the experimental results show that the method is effective.

Key words: , GPS trajectory data； improved K-means algorithm； commuting traffic analysis zone recognition

CLC Number:

TP181

QIN Yang, ZHAN Yong, MING Luyao, YANG Shuqi, LAN Zhenyi. Commuting Traffic Analysis Zone Recognition Using Improved K-means Algorithm[J]. Computer and Modernization, 2024, 0(07): 63-68.

References

［1］胡雪春. 出租车GPS轨迹大数据在智能交通中的应用研究［J］. 大众标准化， 2020（24）：152-153.
［2］ DAI J C， LI R M， LIU Z Y， et al. Impacts of the introduction of autonomous taxi on travel behaviors of the experienced user： Evidence from a one-year paid taxi service in Guangzhou， China［J］. Transportation Research Part C： Emerging Technologies， 2021（130）. DOI： 10.1016/j.trc.2021.
103311.
［3］ FU C Y， ZHOU Y， XU C， et al. Spatial analysis of taxi speeding event using GPS trajectory data［C］// 2019 IEEE Intelligent Transportation Systems Conference （ITSC）. IEEE， 2019：122-127.
［4］黄敏，毛锋，钱宇翔. 基于出租车司机经验的约束深度强化学习算法路径挖掘［J］. 计算机应用研究， 2020，37（5）：1298-1302.
［5］ LIAO C W， CHEN C， XIANG C C， et al. Taxi-passenger’s destination prediction via GPS embedding and attention-based BiLSTM models［J］. IEEE Transactions on Intelligent Transportation Systems， 2022，23（5）：4460-4473.
［6］ YU W H. Discovering frequent movement paths from taxi trajectory data using spatially embedded networks and association rules ［J］. IEEE Transactions on Intelligent Transportation Systems， 2019，20（3）：855-866.
［7］ DAI G N. Deep learning method for citywide crowd flows prediction［C］// 2019 20th IEEE International Conference on Mobile Data Management （MDM）. IEEE， 2019：373-374.
［8］ LIU L B， QIU Z L， LI G B， et al. Contextualized spatial–temporal network for taxi origin-destination demand predictions ［J］. IEEE Transactions on Intelligent Transportation Systems， 2019，20（10）：3875-3887.
［9］ LIU T， WU W B， ZHU Y M， et al. Predicting taxi demands via an attention-based convolutional recurrent neural network ［J］. Knowledge-Based Systems， 2020，206. DOI： 10.1016/j.knosys.2020.106294.
［10］姜山，郭娅明，李萌. 基于Transformer的出租车旅行时间静态预测［C］// 第十七届中国智能交通年会（ITSAC 2022）论文集. 北京：机械工业出版社， 2022. DOI： 10.26914/c.cnkihy.2022.053548.
［11］ HUANG L Q， XU L Y. Research on taxi travel time prediction based on GBDT machine learning method［C］// 2018 8th International Conference on Instrumentation & Measurement， Computer， Communication and Control （IMCCC）. IEEE， 2018：718-722.
［12］ KWAK S， GEROLIMINIS N. Travel time prediction for congested freeways with a dynamic linear model［J］. IEEE Transactions on Intelligent Transportation Systems， 2021，22（12）：7667-7677.
［13］ LUO C Y， DAN T P， LI Y H， et al. Why-not questions about spatial temporal top-k trajectory similarity search ［J］. Knowledge-Based Systems， 2021，231. DOI： 10.1016/j.knosys.2021.107407.
［14］ WANG W Y， ZHANG Q， PENG Z L， et al. An empirical evaluation of different usage pattern between car-sharing battery electric vehicles and private ones ［J］. Transportation Research Part A： Policy and Practice， 2020，135：115-129.
［15］ FAN X L， XU C， TANG F， et al. CommuteShare： A ridesharing service for daily commuters using cross-domain urban big data［C］// 2018 IEEE International Conference on Web Services （ICWS）. 2018：298-301.
［16］ ZHU Y， TING K M， JIN Y， et al. Hierarchical clustering that takes advantage of both density-peak and density-connectivity［J］. Information Systems， IEEE，2021，103. DOI： 10.1016/j.is.2021.101871.
［17］ SUN L， QIN X Y， DING W P， et al. Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy［J］. Neurocomputing， 2022，473：159-181.
［18］ YAO W B， ZHANG M L， JIN S， et al. Understanding vehicles commuting pattern based on license plate recognition data［J］. Transportation Research Part C： Emerging Technologies， 2021，128（2）. DOI： 10.1016/j.trc.2021.103142.
［19］ BAI J W， LI Y M， LI J W， et al. Multinomial random forest ［J］. Pattern Recognition， 2022，122. DOI： 10.1016/j.patcog.2021.108331.
［20］付鑫，孙茂棚，孙皓. 基于GPS数据的出租车通勤识别及时空特征分析［J］. 中国公路学报， 2017，30（7）：134-143.
［21］ SOLIS P， ANDERSON J， RAJAJIPALAN S. Open geospatial tools for humanitarian data creation， analysis， and learning through the global lens of Youth Mappers ［J］. Journal of Geographical Systems， 2021，23（4）：599-625.
［22］ ZHANG J B， ZHENG Y， SUN J K， et al. Flow prediction in spatio-temporal networks based on multitask deep learning［J］. IEEE Transactions on Knowledge and Data Engineering， 2020，32（3）：468-478.
［23］ LIU X D， TIAN Y， Z ZHANG X Q， et al. Identification of urban functional regions in Chengdu based on taxi trajectory time series data ［J］. ISPRS International Journal of Geo-Information， 2020，9（3）. DOI： 10.3390/ijgi9030158.
［24］丁彦文，许捍卫，汪成昊. 融合OSM路网与POI数据的城市功能区识别研究［J］. 地理与地理信息科学， 2020，36（4）：57-63.
［25］诸葛雪玉，张文闯，谭娅琦，等. 基于POI空间聚类的交通小区划分方法改进［J］. 山西建筑， 2018，44（26）：7-8.
［26］郭允涛，白钊. 基于改进K-means算法的航空客户细分研究［J］. 电子技术， 2023，52（1）：49-51.
［27］ SAPUTRA M D， SAPUTRA D， OSWARI L D. Effect of distance metrics in determining K-value in K-means clustering using elbow and silhouette method［C］// Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications. Springer，2020. DOI： 10.2991/aisr.k.200424.051.

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