Trans2Vec: a representation learning framework for large-scale urban road networks coupled with vehicle movement patterns

doi:10.13474/j.cnki.11-2246.2023.0075

Abstract

Abstract: Efficient learning of representation of urban road network information is an important project for embedding various deep learning models to intelligently solve downstream traffic tasks. However, the existing learning framework are difficult to effectively couple the topology information of the urban road network and the transfer mode information carried, and cannot accurately match the characteristic requirements of downstream traffic tasks. In this reason, this paper develops a multi-view roaming segment representation vector learning framework, which takes the vehicle movement pattern as the core, integrates the topology structure for heterogeneous walk, and realizes the complete and efficient information embedding of large-scale road network. In the experimental part, taking Shenzhen as the case area, the effectiveness and advanced nature of the method in this paper are verified through the full urban road network and actual vehicle travel data.

Key words: Trans2Vec, urban road network, vehicle movement patterns, representation learning

CLC Number:

P208

DENG Min, CHU Guowei, CHEN Kaiqi, SHI Yan. Trans2Vec: a representation learning framework for large-scale urban road networks coupled with vehicle movement patterns[J]. Bulletin of Surveying and Mapping, 2023, 0(3): 74-78.

References

[1] DIMITRAKOPOULOS G, DEMESTICHAS P. Intelligent transportation systems[J]. IEEE Vehicular Technology Magazine, 2010, 5(1):77-84.
[2] WANG Mengxiang, LEE W C, FU Taoyang, et al. On representation learning for road networks[J]. ACM Transactions on Intelligent Systems and Technology, 2021, 12(1):1-27.
[3] JEPSEN T S, JENSEN C S, NIELSEN T D, et al. On network embedding for machine learning on road networks:a case study on the Danish road network[C]//Proceedings of 2018 IEEE International Conference on Big Data.Seattle, WA, USA:IEEE, 2018.
[4] LIU Kang, GAO Song, QIU Peiyuan, et al. Road2Vec:measuring traffic interactions in urban road system from massive travel routes[J]. ISPRS International Journal of Geo-Information, 2017, 6(11):321.
[5] WANG Hongjian, TANG Xianfeng, KUO Y H, et al. A simple baseline for travel time estimation using large-scale trip data[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2):1-22.
[6] WANG Mengxiang, LEE W C, FU Taoyang, et al. Learning embeddings of intersections on road networks[C]//Proceedings of the 27th ACM SigSpatial International Conference on Advances in Geographic Information Systems. New York:ACM, 2019:309-318.
[7] CUI Peng, WANG Xiao, PEI Jian, et al. A survey on network embedding[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(5):833-852.
[8] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088):533-536.
[9] PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk:online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. New York:ACM, 2014:701-710.
[10] GROVER A, LESKOVEC J.Node2vec:scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM, 2016:855-864.
[11] CLAUSET A, NEWMAN M J, MOORE C. Finding community structure in very large networks[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2004, 70(6):066111.
[12] CHEN Kaiqi, DENG Min, SHI Yan. A temporal directed graph convolution network for traffic forecasting using taxi trajectory data[J]. ISPRS International Journal of Geo-Information, 2021, 10(9):624.
[13] LI A Q, AHMED A, RAVI S, et al.Reducing the sampling complexity of topic models[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. New York:ACM, 2014:891-900.
[14] WANG Zheng, FU Kun, YE Jieping. Learning to estimate the travel time[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery&Data Mining.New York:ACM Press, 2018:858-866.

[1]	CHEN Guang, GAO Linying, ZENG Yixiao, CHEN Liangchao. Study on the scale of 3D real scene for demarcation and calibration of ecological protection red line [J]. Bulletin of Surveying and Mapping, 2023, 0(3): 89-93.
[2]	NI Xiaodong, LIANG Junda, WU Longxiang, LIU Muyang. A method of simplification of 3D model based on CityGML [J]. Bulletin of Surveying and Mapping, 2022, 0(12): 29-34.
[3]	ZHANG Tao, YANG Xiaofeng, QIN Kun, LI Feifei, LUO Wenshan. Analysis of path optimization algorithm using seagull theory [J]. Bulletin of Surveying and Mapping, 2022, 0(12): 110-115.
[4]	LI Wei, DU Fangjuan, RUAN Ou. Geographical detection of the allocation and influencing factors of different healthcare resources: take the main city of Guiyang as an example [J]. Bulletin of Surveying and Mapping, 2022, 0(12): 116-120,125.
[5]	ZOU Lei, YNAG Peng, CAO Wentao, XIE Gangsheng. Automated production solutions from existing topographic maps to geo-entities [J]. Bulletin of Surveying and Mapping, 2022, 0(12): 147-152.
[6]	LUO Guangfei, GONG Yingying, QIAN Chao, XIAO Han, ZHENG Libo, WANG Zhaoyuan, ZHANG Jintao, YE Linghao, LIU Yunbo. Study on geological disaster-prone zoning in Shaoxing city [J]. Bulletin of Surveying and Mapping, 2022, 0(12): 160-164,169.
[7]	GE Pengfei, LIU Hui, CHEN Mi, LI Yu, DING Ruili, LIU Fei. Monitoring land subsidence of Hebei section of Beijing-Xiong'an intercity railway by time-series InSAR [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 64-70.
[8]	Lü Zheng, SUN Qun, WEN Bowei, MA Jingzhen. A self-global-best-fit road network Stroke generation method [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 93-99.
[9]	ZHONG Qikang, WANG Zhiyi, WANG Na, XI Furui. Spatial differentiation characteristics and driving factors of landscape ecological risk in arid area of northern Shaanxi [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 100-106.
[10]	QIANG Dexia, MA Haizheng, ZHU Ziping, GOU Yanmei. Spatial distribution and analysis of debris flow in Jishishan county of Gansu province [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 107-111,117.
[11]	HAN Wenli, ZHANG Jixian, CHEN Haipeng, HUANG Haiying, ZHANG Libo, GE Juan, SHEN Jing, LU Yao. Discussion on quality inspection technology for new fundamental surveying and mapping [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 148-153.
[12]	TAO XiaoJing. Security protection technology and verification analysis of geographic information data based on TEA algorithm [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 154-157,167.
[13]	CAI Roudan. An intelligent identification method of user identity based on asynchronous trajectory [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 158-162,167.
[14]	ZHOU Ye, LIU Yunbo, ZHENG Libo, LONG Yangjun. Precision analysis of single tree parameter extraction for multi-platform point cloud data [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 168-172.
[15]	HE Yuqi, ZENG Yixiao, CHEN Guang, CHEN Liangchao. Research on the early warning and simulation of land space planning in mountainous city from the perspective of new surveying and mapping [J]. Bulletin of Surveying and Mapping, 2022, 0(4): 11-15.