About Me

I am now working as a postdoc researcher at ETH Zurich. I am currently working with Prof. Martin Raubal at the Mobility Information Engineering Lab. Before that, I worked as a postdoctoral fellow in the Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, collaborated with Prof. Mei-Po Kwan, Dr. Xintao Liu and Dr. Lilian Pun. I received my doctor's degree from the School of Remote Sensing and Information Engineering, Wuhan University, supervised by Prof. Kun Qin.

My research interests focuse on:

  • Urban mobility
  • Transportation GIS
  • Machine learning
  • Trajectory data analysis and mining
  • Mobility-oriented health GIS

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Journal publications

Zhao, P., Liu, X., Shi, W., Jia, T., Li, W. and Chen, M., 2018. An empirical study on the intra-urban goods movement patterns using logistics big data. International Journal of Geographical Information Science, pp.1-28.

Zhao, P., Liu, X., Kwan, M.P. and Shi, W., 2018. Unveiling cabdrivers' dining behavior patterns for site selection of 'taxi canteen' using taxi trajectory data. Transportmetrica A: Transport Science, pp.1-24.

Zhao, P., Kwan, M.P. and Zhou, S., 2018. The uncertain geographic context problem in the analysis of the relationships between obesity and the built environment in Guangzhou. International journal of environmental research and public health, 15(2), p.308.

Wang, Y., Qin, K., Chen, Y. and Zhao, P., 2018. Detecting anomalous trajectories and behavior patterns using hierarchical clustering from taxi GPS data. ISPRS International Journal of Geo-Information, 7(1), p.25.

Qin, K., Wang, Y., Zhao, P., Xu, W., and Xu Y., 2018. Spatiotemporal clustering and analysis of behavior trajectory. Chinese Journal of Nature, 40(3), p.177-182. (in Chinese)

Zhao, P., Liu, X., Shen, J. and Chen, M., 2017. A network distance and graph-partitioning-based clustering method for improving the accuracy of urban hotspot detection. Geocarto International, pp.1-23.

Zhao, P., Kwan, M.P. and Qin, K., 2017. Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel. Journal of Transport Geography, 62, pp.122-135.

Zhao, P., Qin, K., Ye, X., Wang, Y. and Chen, Y., 2017. A trajectory clustering approach based on decision graph and data field for detecting hotspots. International Journal of Geographical Information Science, 31(6), pp.1101-1127.

Zhao, S., Zhao, P. and Cui, Y., 2017. A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China. Physica A: Statistical Mechanics and its Applications, 478, pp.143-157.

Jiao, C., Chen, S. and Zhao, P., 2016. An improved DOM seam line searching algorithm based on structual information. Science of Surveying and Mapping, 1, p.037. (in Chinese)

Zhao, P. Jia, T., Qin, K., Shan, J. and Jiao, C., 2015. Statistical analysis on the evolution of OpenStreetMap road networks in Beijing. Physica A: Statistical Mechanics and its Applications, 420, pp.59-72.

Proceeding publications

Zhao, P., Qin, K., Zhou, Q., Liu, C. and Chen, Y., 2015. Detecting hotspots from taxi trajectory data using spatial cluster analysis. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(4), p.131.

Zhao, P. and Zhao, S., 2016. Understanding urban traffic flow characteristics from the network centrality perspective at different granularities. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41.


I am currently working on the following projects.

COMMIT - Context-Aware Mobility Mining Tools

This project is motivated by the fact that in order to gain insights into the guiding principles of human mobility, computational methods for context-aware, longitudinal analyses of human mobility behavior are needed, and can be expected to have high societal significance and relevance for a broad range of scientific disciplines and related industrial sectors. Its aim is to directly address these issues and develop a comprehensive open source framework, which includes generalizable and reusable methods explicitly targeted at the spatio-temporal analysis of semantically enriched longitudinal movement data sets

SCCER Mobility

The Swiss Competence Center for Energy Research - Efficient Technologies and Systems for Mobility (SCCER Mobility) aims at developing the knowledge and technologies essential for the transition from the current fossil fuel based transportation system to a sustainable one, featuring minimal CO2 output, primary energy demand as well as virtually zero-pollutant emissions. Research carried out within SCCER Mobility is organized into five different Capacity Areas, focusing on battery systems, fuel cells and increasing the efficiency of internal combustion engines, minimization of vehicular energy demand, energy infrastructure for future mobility as well as the assessment of entire mobility systems.

SBB Green Class

In the context of integrated mobility offers currently gaining in importance, SBB is collaborating with BMW Switzerland to develop a combined mobility solution, and test it with 140 test customers in a one-year pilot project. We provide the lead scientific support for this new research project SBB Green Class, and will use surveys, interviews and spatio-temporal movement data analyses to examine how such a comprehensive multi-modal mobility offer influences people's mobility behavior.


Postdoc research, ETH Zurich

Mobility Information Engineering Lab, ETH Zurich

Institute of Cartography and Geoinformation

Chair of Geoinformation Engineering


+41 44 6333277

HIL D 54.2, Stefano-Franscini-Platz 5, 8093 Zurich, Switzerland