1. Yanyan Xu, Riccardo Di Clemente, Marta C. Gonzalez, “Understanding Route Choice Behavior with Location-Based Services Data”, under review in EPJ Data Science.

2. Yanyan Xu, Luis E. Olmos, Sofiane Abbar, Marta C. Gonzalez, “Deconstructing laws of accessibility and facility distribution in cities”, Science Advances 6, no. 37 (2020): eabb4112. [Link].

3. Marco De Nadaia, Yanyan Xu, Emmanuel Letouze, Marta C. Gonzalez, and Bruno Lepri, “Socio-economic, built environment, and mobility conditions associated with crime: A study of multiple cities”, Scientific Reports, 10, 13871, 2020. [Link]

4. Mahendra Paipuri, Yanyan Xu, Marta C. Gonzalez, Ludovic Leclercq, “Estimating MFDs, trip lengths and path flow distributions in a multi-region setting using mobile phone data”, Transportation Research Part C: Emerging Technologies, 2020. [pdf]

5. Bin Wang, Cungang Wang*, Qian Zhang, Ying Su, Yang Wang, Yanyan Xu* , “Cross-Lingual Image Caption Generation based on Visual Attention Model”, IEEE Access 8: 104543-104554, 2020. [pdf]

6. Wuwei Lan⁺, Yanyan Xu^+,* , Bin Zhao, “Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach”, IJCAI, 2019 (Acceptance rate: 17.9%). [pdf]

7. Yanyan Xu, Shan Jiang, Ruiqi Li, Jiang Zhang, Jinhua Zhao, Sofiane Abbar, Marta C. Gonzalez, “Unraveling Environmental Justice in Ambient PM2.5 Exposure in Beijing: A Big Data Approach”, Computers, Environment and Urban Systems, 75: 12-21, 2019. [pdf] [Supplementary Information] [Visulization]

8. Yanyan Xu⁺, Serdar Colak⁺, Emre C. Kara, Scott J. Moura and Marta C. Gonzalez, “Planning for Electric Vehicle Needs by Coupling Charging Profiles with Urban Mobility”, Nature Energy. 3, 484–493, 2018. [link] [Supplementary Information]

9. Senyan Yang, Jianping Wu, Tao Yang, Yanyan Xu, “Revealing Heterogeneous Spatiotemporal Traffic Flow Patterns of Urban Road Network via Tensor Decomposition-based Clustering Approach”, accepted in Physica A: Statistical Mechanics and its Applications, 2019

10. Chang Yuan, Hui Chen*, Ju Liu, Di Zhu, Yanyan Xu* , “Robust Lane Detection for Complicated Road Environment Based on Normal Map”, IEEE Access, 6: 49679-49689, 2018 [pdf]

11. Dongxue Han, Hui Chen*, Changhe Tu, Yanyan Xu* , “View Synthesis using Foreground Object Extraction for Disparity Control and Image Inpainting”, Journal of Visual Communication and Image Representation, 56: 287-295, 2018 [pdf]

12. Xiaoqing Dai, Lijun Sun, Yanyan Xu* , “Short-term Origin-Destination based Metro Flow Prediction with Probabilistic Model Selection Approach”. Journal of Advanced Transportation, 2018. [pdf]

13. Jinzhao Yuan, Hui Chen*, Bin Zhao, and Yanyan Xu* , “Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections,” Journal of Computer Science and Technology 32(6), 1150-1161, 2017. [pdf]

14. Yanyan Xu, and Marta C. Gonzalez, “Collective benefits in traffic during mega events via the use of information technologies,” J. Roy. Soc. Interface, 14(129), 2017. [pdf] [Supplementary Information]

15. Yanyan Xu, Ruiqi Li, Shan Jiang, Jiang Zhang, and Marta C. Gonzalez, “Clearer skies in Beijing – revealing the impacts of traffic on the modeling of air quality,” Proc. of the 96th Annual Meeting of the Transportation Research Board, Washington, D.C., 2017. [pdf]

16. Rui Song, Hui Chen, Zhiguang Xiao, Yanyan Xu, and Reinhard Klette, “Lane detection algorithm based on geometric moment sampling,” Science in China Series F-Information Sciences (Chinese), 47(1), 2017. [pdf]

17. Yanyan Xu, Hui Chen, Qing-Jie Kong, Xi Zhai, and Yuncai Liu, “Urban Traffic Flow Prediction: A Spatio-Temporal Variable Selection Based Approach,” Journal of Advanced Transportation, vol. 50, pp. 489-506, 2016. [pdf]

18. Yanyan Xu, Qing-Jie Kong, Reinhard Klette, and Yuncai Liu, “Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 6, pp. 2457-2469, 2014. [pdf]

19. Yanyan Xu, Bin Wang, Qing-Jie Kong, Yuncai Liu and Fei-Yue Wang, “Spatio-temporal Variable Selection Based Support Vector Regression for Urban Traffic Flow Prediction,” Proc. of the 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2014. [pdf]

20. Qing-Jie Kong, Yanyan Xu, Shu Lin, Ding Wen, Fenghua Zhu and Yuncai Liu, “UTN-model based traffic flow prediction for parallel-transportation management systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1541-1547, 2013. [pdf]

21. Yanyan Xu, Qing-Jie Kong, and Yuncai Liu, “A Spatio-Temporal Multivariate Adaptive Regression Splines Approach for Short-Term Freeway Traffic Volume Prediction,” The 16th International IEEE Conference on Intelligent Transportation Systems (ITSC), Hague, Netherlands, 2013. [pdf]

22. Yanyan Xu, Qing-Jie Kong, and Yuncai Liu, “Short-Term Traffic Volume Prediction Using Classification and Regression Trees,” The 2013 IEEE Intelligent Vehicles Symposium (IV’13), Gold Coast, Australia, 2013, pp. 493-498. [pdf]

23. Yanyan Xu, Xi Zhai, Qing-Jie Kong, and Yuncai Liu, “Short-term Freeway Traffic Flow Prediction Approach,” Journal of Traffic and Transportation Engineering (Chinese), vol.13, no. 2, pp. 114-119, 2013.

24. Yanyan Xu, Qing-Jie Kong, and Yuncai Liu “Comparison of Urban Traffic Prediction Methods between UTN-Based Spatial Model and Time Series Models,” The 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), Anchorage, AK, USA, 2012, pp. 814-819. [pdf]

25. Yanyan Xu, Qing-jie Kong, Shu Lin, and Yuncai Liu, “Urban Traffic Flow Prediction Based on Road Network Model,” The 9th IEEE International Conference on Networking, Sensing and Control (ICNSC), Beijing, China, 2012, pp. 334-339. [pdf]

26. Yanyan Xu, Hui Chen, Reinhard Klette, Jiaju Liu, and Tobi Vaudrey, “Belief Propagation Implementation using CUDA on an NVIDIA GTX 280,” Proceeding AI’09: The 22nd Australasian Joint Conference on Artificial Intelligence, Melbourne, Australia. LNAI 5866, 2009, pp. 180-189. [pdf]

27. Jiaju Liu, Hui Chen, Yanyan Xu, Reinhard Klette, and Tobi Vaudrey, “Disparity Map Computation On a Cell Processor,” RTA2009, IASTED, Beijing, China, 2009, pp. 661-669. [pdf]

28. Wen Rong, Hui Chen, Jiaju Liu, Yanyan Xu, and Ralf Haeusler, “Mosaicing of Microscope Images based on SURF,” Image and Vision Computing New Zealand (IVCNZ), Wellington, New Zealand, 2009, pp. 271-275. [pdf]

Reachability Index for Facility Spatial Distribution in cities

Understanding facility distribution in cities is of a paramount importance of civil planers and decision makers, as it relates to the design quality of cities and the well-being of citizens. Several studies have looked at the optimal spatial distribution of facilities and their scaling low function of population counts, however, very little is said about the actual reachability guaranteed by such optimal solutions. We propose to use the shortest travel time to the nearest facility as a proxy to score the reachability of facilities within a city. We collect data about different types of facilities from Foursquare and data about population counts from the gridded population dataset. We use OpenStreetMap to collect and build road networks.

Travel demand management for collective benefits during mega events

Information technologies today can inform each of us about the route with the shortest time, but they do not contain incentives to manage travelers such that we all get collective benefits in travel times. To that end we need travel demand estimates and target strategies to reduce the traffic volume from the congested roads during peak hours in a feasible way. During large events the traffic inconveniences in large cities are unusually high, yet temporary, and the entire population may be more willing to adopt collective recommendations for collective benefits in traffic. In this paper, we integrate, for the first time, big data resources to estimate the impact of events on traffic and propose target strategies for collective good at the urban scale.

The travel time visualization plateform: http://www.flows-rio2016.com/

Yanyan Xu, and Marta C. Gonzalez, “Collective benefits in traffic during mega events via the use of information technologies,” J. Roy. Soc. Interface, 14(129), 2017. [pdf]

Clearer Skies in Beijing: High Density Spatio-temporal Data for Air Quality Assessment

Urban air pollution is one of the largest environmental health risks worldwide, and is expected to worsen over the coming decades as cities expand. Detailed, quantitative monitoring of urban air quality at high spatial and temporal resolution will be critical to assessing risks and mitigating impacts. Although most existing monitoring networks lack the requisite spatial and temporal resolution, sensitive and inexpensive new technologies now enable the deployment of distributed air quality (AQ) networks to capture the full range of pollutant variability in an urban area. However, no proven techniques for analyzing large AQ datasets for metrics of interest—pollution sources, distributions and exposures—are yet well established.

Yanyan Xu, Ruiqi Li, Shan Jiang, Jiang Zhang, and Marta C. Gonzalez, “Clearer skies in Beijing – revealing the impacts of traffic on the modeling of air quality,” Proc. of the 96rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2017. [pdf]

Short-term traffic flow prediction

Current research on traffic flow prediction mainly concentrates on generating accurate prediction results based on intelligent or combined algorithms but ignores the interpretability of the prediction model. In practice, however, the interpretability of the model is equally important for traffic managers to realize which road segment in the road network will affect the future traffic state of the target segment in a specific time interval and when such an influence is expected to happen. In this paper, an interpretable and adaptable spatiotemporal Bayesian multivariate adaptive-regression splines (ST-BMARS) model is developed to predict short-term freeway traffic flow accurately.

Yanyan Xu, Qing-Jie Kong, Reinhard Klette, and Yuncai Liu, “Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 6, pp. 2457-2469, 2014. [pdf]