Road section navigation attribute mining

doi:10.11947/j.AGCS.2024.20220649

Abstract

Abstract: A large number of automatic or semi-automatic road extraction methods have sprung up, but the generated products usually lacked navigation attribute information, such as road hierarchies, road speed limits, etc., which restrict intelligent navigation services such as "main road priority" and "speed limit alert". Therefore, taking road sections as the unit of analysis and considering the strong correlation between adjacent upstream and downstream road sections, a method for mining attribute information of road hierarchies and road speed limits was proposed. First, we preprocessed tracks and road networks and realized the connection between track points and road sections. Then, multi-modal features were designed based on the understanding of the data and the task. Finally, the random forest was used to recognize road hierarchies and road speed limits, taking into account the information of the target road segments and their upstream and downstream adjacency information. Compared with single-class features, the integration features of road networks and trajectories improve the classification accuracy of road hierarchies and road speed limits; compared with the classification of road hierarchies and road speed limits considering only the target road segment, the classification of road hierarchies and road speed limits considering spatial adjacency information is more effective.

Key words: crowd-sourced trajectories, navigation attribute mining, multi-mode feature fusion, spatial adjacency information, road hierarchies recognition, road speed limits recognition

CLC Number:

P208

ZHANG Caili, XIANG Longgang, LI Yali, GAO Songfeng, PAN Chuanjiao. Road section navigation attribute mining[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(2): 367-378.

References

[1] YIN Yifang, VARADARAJAN J, WANG Guanfeng, et al. A multi-task learning framework for road attribute updating via joint analysis of map data and GPS traces[C]//Proceedings of 2020 Web Conference. Taipei:ACM Press, 2020:2662-2668.
[2] 杨航, 张鑫淼, 杨冲. 基于卷积神经网络的公路限速牌识别方法[J]. 地理空间信息, 2016,14(1):31-33. YANG Hang, ZHANG Xinmiao, YANG Chong.Highway speed limit sign identification method based on convolutional neural network[J]. Geospatial Information, 2016,14(1):31-33.
[3] GARBERN J, GADIRAJU R. Factors affecting speed variance and its influence on accidents[J]. Transportation Research Record, 1989(1213):64-71.
[4] 刘祥敏. 普通公路限速影响因素与预测模型研究[D]. 济南:山东建筑大学, 2020. LIU Xiangmin. Study on the influencing factors and prediction model of speed limit of ordinary highway[D].Jinan:Shandong Jianzhu University, 2020.
[5] 宋博. 考虑环境影响的高速公路限速值研究[D]. 西安:长安大学, 2017. SONG Bo. Study on speed limit value of freeway considering environmental impact[D].Xi'an:Changan University, 2017.
[6] PASCALE A, DEFLORIO F, NICOLI M, et al. Motorway speed pattern identification from floating vehicle data for freight applications[J]. Transportation Research Part C:Emerging Technologies, 2015, 51:104-119.
[7] 唐炉亮, 杨雪, 任畅, 等. 轨迹大数据挖掘与高时空精度道路众包测图[M]. 北京:科学出版社, 2019. TANG Luliang, YANG Xue, REN Chang, et al. Trajectory big data mining and road crowdsourcing mapping with high temporal and spatial accuracy[M]. Beijing:Science Press, 2019.
[8] CHEN Banqiao, DING Chibiao, REN Wenjuan, et al. Automatically tracking road centerlines from low-frequency GPS trajectory data[J]. ISPRS International Journal of Geo-Information, 2021, 10(3):122.
[9] 张彩丽, 向隆刚, 李雅丽, 等. 基于出租车轨迹的可导航路网构建[J]. 测绘学报, 2021, 50(12):1650-1662.DOI:10.11947/j.AGCS.2021.20200470. ZHANG Caili, XIANG Longgang, LI Yali, et al. Construction of navigable road network based on taxi trajectories[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(12):1650-1662. DOI:10.11947/j.AGCS.2021.20200470.
[10] ZHANG Caili, XIANG Longgang, LI Siyu, et al. An intersection-first approach for road network generation from crowd-sourced vehicle trajectories[J]. ISPRS International Journal of Geo-Information, 2019, 8(11):473.
[11] SILVANO A P, KOUTSOPOULOS H N, FARAH H. Free flow speed estimation:a probabilistic, latent approach. Impact of speed limit changes and road characteristics[J]. Transportation Research Part A:Policy and Practice, 2020, 138:283-298.
[12] 韩宝睿, 宋婉璐, 于晓妹, 等. 城市道路同向窄路段车速统计特性研究[J]. 武汉理工大学学报(交通科学与工程版), 2020, 44(5):818-822. HAN Baorui, SONG Wanlu, YU Xiaomei, et al. Study on statistical characteristics of vehicle speed in urban narrow road section with the same direction[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2020, 44(5):818-822.
[13] 陆建, 孙祥龙, 戴越. 普通公路车速分布特性的回归分析[J]. 东南大学学报(自然科学版), 2012, 42(2):374-377. LU Jian, SUN Xianglong, DAI Yue. Regression analysis on speed distribution characteristics of ordinary road[J]. Journal of Southeast University (Natural Science Edition), 2012, 42(2):374-377.
[14] 马晶晶, 兰凤民, 刘金鑫. 基于车速预测的新建城市道路标志限速分析[J]. 交通与运输, 2020, 36(4):49-53. MA Jingjing, LAN Fengmin, LIU Jinxin. The sign limited-speed of new urban road based on vehicle speed prediction[J]. Traffic & Transportation, 2020, 36(4):49-53.
[15] 孙祥龙, 陆建, 戴越. 普通公路车速分布特性影响因素分析[J]. 交通信息与安全, 2012, 30(1):5-9. SUN Xianglong, LU Jian, DAI Yue. Analysis of factors influencing speed distribution at ordinary highway[J]. Journal of Transport Information and Safety, 2012, 30(1):5-9.
[16] 赵媛媛, 申雷霄. 基于速度一致性的限速标志限速合理性研究[J]. 交通科技, 2019(3):93-96. ZHAO Yuanyuan, SHEN Leixiao. Research on speed limit rationality of speed limit sign based on speed consistency[J]. Transportation Science & Technology, 2019(3):93-96.
[17] 涂家伟. 基于浮动车技术的道路交通特性分析及系统实现[D]. 天津:天津大学, 2009. TU Jiawei. Analysis and system implementation of the road traffic characteristics based on floating car technology[D].Tianjin:Tianjin University, 2009.
[18] LI Jun, QIN Qiming, HAN Jiawei, et al. Mining trajectory data and geotagged data in social media for road map inference[J]. Transactions in GIS, 2015, 19(1):1-18.
[19] 廖律超, 蒋新华, 林铭榛, 等. 基于交通轨迹数据挖掘的道路限速信息识别方法[J]. 交通运输工程学报, 2015, 15(5):118-126. LIAO Lüchao, JIANG Xinhua, LIN Mingzhen, et al. Recognition method of road speed limit information based on data mining of traffic trajectory[J]. Journal of Traffic and Transportation Engineering, 2015, 15(5):118-126.
[20] VAN WINDEN K, BILJECKI F, VAN DER SPEK S. Automatic update of road attributes by mining GPS tracks[J]. Transactions in GIS, 2016, 20(5):664-683.
[21] YIN Yifang, TRANA, ZHANG Ying, et al. Multimodal fusion of satellite images and crowdsourced GPS traces for robust road attri-bute detection[C]//Proceedings of the 29th International Conference on Advances in Geographic Information Systems. Beijing:ACM Press, 2021:107-116.
[22] 张彩丽, 向隆刚, 李雅丽, 等. 顾及路网与轨迹多模特征的道路等级分类研究[J]. 地球信息科学学报, 2022, 24(10):1925-1940. ZHANG Caili, XIANG Longgang, LI Yali,et al. A research on road type classification considering the multi-mode features of road network and trajectories[J].Journal of Geo-Information Science, 2022, 24(10):1925-1940.
[23] XU Yongyang, XIE Zhong, WU Liang, et al. Multilane roads extracted from the OpenStreetMap urban road network using random forests[J]. Transactions in GIS, 2019, 23(2):224-240.
[24] ALSAHFI T, ALMOTAIRI M, ELMASRI R, et al. Road map generation and feature extraction from GPS trajectories data[C]//Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science.Chicago:ACM Press, 2019:1-10.
[25] SINNOTT R W. Virtues of the haversine[J]. Sky and Telescope,1984, 68:159.