Road crowd-sensing with high spatio-temporal resolution in big data era.
Recommended citation: Luliang Tang, Zilong Zhao*, Xue Yang, Zihan Kan, Qingquan Li, et al. (2022). Road crowd-sensing with high spatio-temporal resolution in big data era. Acta Geodaetica et Cartographica Sinica, 51(6):1070- 1090. http://zilzhao.github.io/files/Road_crowdsensing.pdf
• Proposes a new method for road crowd-sensing with high spatio-temporal resolution based on static infrastructure ‘form’ and dynamic activity behavior ‘flow’.
• From the perspective of static road network ‘form’, the method takes ‘point-line-surface-body’ elements as the research context, and constructs a theoretical system of high-precision road map crowd-sensing.
• In terms of activity behavior ‘flow’, we break through the limitations of traditional point pattern analysis and develop a spatio-temporal modeling and multi-scale analysis method for spatial activity flow.
Recommended citation: Luliang Tang, Zilong Zhao*, Xue Yang, Zihan Kan, Chang Ren, Jie Gao, Chaokui Li, Xia Zhang, Qingquan Li. (2022). Road crowd-sensing with high spatio-temporal resolution in big data era. Acta Geodaetica et Cartographica Sinica, 51(6):1070-1090.