Βι¶ΉΣ³»­

Hao Xu

Associate Professor
Hao Xu

Prospective graduate students

Dr. Xu has research assistant scholarships for new Ph.D. students. The positions will be involved in the following research topics:

  1. Big data analysis of traffic crashes and intelligent transportation real-time sensor data
  2. Automatic traffic data survey system with videos and 360-degree LIDAR sensors
  3. Data analysis of latest driver behavior and vehicle sensor data from U.S. Federal Highway Administration
  4. Traffic crash prediction modeling

Learn more about our graduate programs 

Research interests

  • Collection and analysis of roadside LiDAR data
  • Vehicle operation cost evaluation
  • Driving behavior analysis with naturalistic driving study data
  • Intelligent transportation systems including connected vehicles
  • Data-driven traffic safety analysis

Grants received

  • PI, Pilot Deployment of Roadside LiDAR in City of Henderson NV, Regional Transportation Commission of Southern Βι¶ΉΣ³»­, $86,071, 2018 - 2019
  • PI, Proof-of-Concept Research of Roadside LiDAR Sensing Multimode Traffic, Βι¶ΉΣ³»­ Department of Transportation, $313,397, 2018 - 2021
  • PI, Pilot Applications of Roadside LiDAR Technologies in Washoe County, Regional Transportation Commission – Washoe County, $250,000, 2018 - 2020
  • PI, Data Collection of Wildlife Animals Crossing I80 in Eastern Βι¶ΉΣ³»­ and USA Parkway, SOLARIS UTC and Βι¶ΉΣ³»­ Department of Transportation, $136,051, 2018-2019
  • PI, Development of a Βι¶ΉΣ³»­ wildlife-fencing GIS dataset, SOLARIS UTC and Βι¶ΉΣ³»­ Department of Transportation, $145,817, 2018-2019
  • PI, University of Βι¶ΉΣ³»­ Support Services to NDOT Traffic Safety Engineering, Βι¶ΉΣ³»­ Department of Transportation, $500,000, 2017-2022

Selected publications

  1. Shanglian Zhou, Hao Xu, Guohui Zhang, Tianwei Ma, and Yin Yang, 2024, DCOR: Dynamic Channel-Wise Outlier Removal to De-Noise LiDAR Data Corrupted by Snow, IEEE Transactions on Intelligent Transportation Systems, Print ISSN: 1524-9050, Online ISSN: 1558-0016, Digital Object Identifier: 10.1109/TITS.2023.3347150.
  2. Zhao, J., Xu, H., Chen, Z. and Liu, H., 2023. Accurate detection of vehicle, pedestrian, cyclist and wheelchair from roadside light detection and ranging sensors. Journal of Intelligent Transportation Systems, pp.1-17, DOI: 10.1080/15472450.2023.2243816.
  3. Chen, Z., Xu, H., Zhao, J. and Liu, H., 2023. A Novel Background Filtering Method with Automatic Parameter Adjustment for Real-Time Roadside LiDAR Sensing System. IEEE Transactions on Instrumentation and Measurement, 72, pp. 1-10, 2023, Art no. 5022310, doi: 10.1109/TIM.2023.3300457.
  4. Guan, F., Xu, H. and Tian, Y., 2023. Evaluation of Roadside LiDAR-Based and Vision-Based Multi-Model All-Traffic Trajectory Data. Sensors, 23(12), p.5377.
  5. Zhao, J., Xu, H., Chen, Z. and Liu, H., 2023. A decoding-based method for fast background filtering of roadside LiDAR data. Advanced Engineering Informatics, 57, p.102043.
  6. Zhou, S., Xu, H., Zhang, G., Ma, T. and Yang, Y., 2023. Deep learning-based pedestrian trajectory prediction and risk assessment at signalized intersections using trajectory data captured through roadside LiDAR. Journal of Intelligent Transportation Systems, pp.1-13.
  7. Zhang, Q., Bhattarai, N., Chen, , Xu, H. and Liu, H., 2023. Vehicle Trajectory Tracking Using Adaptive Kalman Filter from Roadside Lidar. Journal of Transportation Engineering, Part A: Systems, 149(6), p.04023043.
  8. Chen, Z., Xu, H., Zhao, J. and Liu, H., 2023. Curbside Parking Monitoring With Roadside LiDAR. Transportation Research Record, 2677(10), pp.824-838.,
  9. Bhattarai, N., Zhang, Y., Liu, H., Pakzad, Y. and Xu, H., 2023. Proactive Safety Analysis Using Roadside LiDAR Based Vehicle Trajectory Data: A Study of Rear-End Crashes. Transportation Research Record, p.03611981231182704.
  10. Cui, Y., Xu, H. and Gong, K., 2023. A diversion routing optimization model for urban evacuation planning. Natural Hazards, pp.1-18.
  11. Zhou, S., Xu, H., Zhang, G., Ma, T. and Yang, Y., 2022. Leveraging Deep Convolutional Neural Networks Pre-Trained on Autonomous Driving Data for Vehicle Detection From Roadside LiDAR Data. IEEE Transactions on Intelligent Transportation Systems. Volume: 23, Issue: 11, Page(s): 22367-22377.
  12. Zhang, Y., Bhattarai, N., Zhao, J., Liu, H. and Xu, H., An Unsupervised Clustering Method for Processing Roadside LiDAR Data with Improved Computational Efficiency. IEEE Sensors Journal.
  13. Cui, Y., Zou, F., Xu, H., Chen, Z. and Gong, K., 2022. A novel optimization-based method to develop representative driving cycles in various driving conditions. Energy, p.123455.
  14. Chen, Z., Xu, H., Zhao, J. and Liu, H., 2022. Fast-spherical-projection-based point cloud clustering algorithm. Transportation research record, 2676(6), pp.315-329.
  15. Zhao, J., Xu, H., Zhang, Y., Shankar, V. and Liu, H., 2022. Automatic Identification of Vehicle Partial Occlusion in Data Collected by Roadside LiDAR Sensors. Transportation Research Record, p.03611981211069347.
  16. Zhao, J., Xu, H., Tian, Y. and Liu, H., 2022. Towards application of light detection and ranging sensor to traffic detection: an investigation of its built-in features and installation techniques, Journal of Intelligent Transportation Systems, DOI: 10.1080/15472450.2020.1807346
  17. Whitley, T., Tian, Y. and Xu, H., Headway Data Extraction and Highway Capacity Manual Capacity Function Calibration for Roundabouts with Roadside Lidar Data. Transportation Research Record, p.03611981221132853.
  18. Zhao, J., Xu, H., Zhang, Y., Tian, Y. and Liu, H., 2021. Traffic Volume Detection Using Infrastructure-Based LiDAR under Different Levels of Service Conditions. Journal of Transportation Engineering, Part A: Systems, 147(11), p.04021080.
  19. Tian, Y., Xu, H., Guan, F., Toshniwal, S. and Tian, Y., 2021. Projection and integration of connected-infrastructure LiDAR sensing data in a global coordinate. Optics & Laser Technology, 144, p.107421.
  20. Cui, Y., Xu, H., Zou, F., Chen, Z. and Gong, K., 2021. Optimization Based Method to Develop Representative Driving Cycle for Real-World Fuel Consumption Estimation. Energy, p.121434.
  21. Wu, J., Xu, H., Yue, R., Tian, Z., Tian, Y. and Tian, Y., 2021. An automatic skateboarder detection method with roadside LiDAR data. Journal of Transportation Safety & Security, 13(3), pp.298-317.
  22. Wu, J., Xu, H., Sun, R. and Zhuang, P., 2021. Road Boundary-Enhanced Automatic Background Filtering for Roadside Lidar Sensors. IEEE Intelligent Transportation Systems Magazine, 14(4), pp.60-72.
  23. Wu, J., Xu, H., Tian, Y., Zhang, Y., Zhao, J. and Lv, B., 2020. An automatic lane identification method for the roadside light detection and ranging sensor. Journal of Intelligent Transportation Systems, pp.1-13.
  24. Wu, J., Xu, H., Tian, Y., Pi, R. and Yue R., 2020. Vehicle Detection under Adverse Weather from Roadside LiDAR Data, Sensors, 20(12), 3433; https://doi.org/10.3390/s20123433 (registering DOI)
  25. Zhang, Y., Yao, E., Zheng, K. and Xu, H., Metro passenger’s path choice model estimation with travel time correlations derived from smart card data. Transportation Planning and Technology, 43(2), pp.141-157.
  26. Wu, J., Zhang, Y. and Xu, H., A novel skateboarder-related near-crash identification method with roadside LiDAR data. Accident Analysis & Prevention, 137, p.105438.
  27. Zhang, Y., Xu, H. and Wu, J., 2020. An Automatic Background Filtering Method for Detection of Road Users in Heavy Traffics Using Roadside 3-D LiDAR Sensors With Noises, IEEE Sensors Journal, Volume:20, Issue:12, P. 6596-6604, ISSN: 1530-437X/1558-1748, Digital Object Identifier: 10.1109/JSEN.2020.2976663
  28. Yang, G., Wang, Z., Tian, Z., Zhao, L.Y. and Xu, H., Geometric design of metered on-ramps: State-of-the-practice and remaining challenges. Transportation Letters, 12(9), pp.649-658.
  29. Wu, J., Xu, H., Zhang, Y. and Sun, R., 2020. An Improved Vehicle-Pedestrian Near-Crash Identification Method with a Roadside LiDAR Sensor. Journal of Safety Research.
  30. Wu, J. Xu, H., Zhang, Y., Tian, Y. and Song, X., 2020. .
  31. Cui, Y., Xu, H., Wu, J. and Wang, A., 2020. Lane Change Identification and Prediction with Roadside LiDAR Data, Optics and Laser Technology, DOI: 10.1016/j.optlastec.2019.105934.
  32. Wu, J. and Xu, 2020. Automatic Vehicle Detection with Roadside LiDAR Data under Rainy and Snowy Conditions, IEEE Intelligent Transportation Systems Magazine, accepted.
  33. Yue, R., Xu, H., Wu, J., Sun, R. and Yuan, C., 2019. Data registration with ground points for roadside LiDAR sensors. Remote Sensing, 11(11), p.1354.
  34. Zhang, Y., Sun, X., Xu, H., and Yao, E., 2019. Tracking Multi-Vehicles with Reference Points Switches at the Intersection Using a Roadside LiDAR Sensor, IEEE Access, ISSN: 2169-3536, 10.1109/ACCESS.2019.2953747, pp 1-11.
  35. Zhang, Z., Zheng, J., Xu, H., Wang, X., Fan, X. and Chen, R., 2019. Automatic Background Construction and Object Detection Based on Roadside LiDAR. IEEE Transactions on Intelligent Transportation Systems.
  36. Zhao, J., Li, Y., Xu, H. and Liu, H., 2019. Probabilistic Prediction of Pedestrian Crossing Intention Using Roadside LiDAR Data. IEEE Access, 7, pp.93781-93790.
  37. Lv, B., Xu, H., Wu, J., Tian, Y., Zhang, Y., Zheng, Y., Yuan, C. and Tian, S., 2019. LiDAR-enhanced connected infrastructures sensing and broadcasting high-resolution traffic information serving smart cities. IEEE Access, 7, pp.79895-79907.
  38. Cui, Y., Wu, J., Xu, H., Lv, B., Yuan, C., Tian, S. and Tian, Y., 2019. An Automatic Trigged Rectangular Rapid Flashing Beacons (RRFB) System Using the Roadside LiDAR Sensor. IEEE Access, 7, pp.163831-163839.
  39. Wu, J., Xu, H., Yue, R., Tian, Z., Tian, Y. and Tian, Y., 2019. An automatic skateboarder detection method with roadside LiDAR data. Journal of Transportation Safety & Security, pp.1-20.
  40. Lv, B., Xu, H., Wu, J., Tian, Y. and Yuan, C., 2019. Raster-based Background Filtering for Roadside LiDAR Data. IEEE Access.
  41. Chen, J., Xu, H., Wu, J., Yue, R., Yuan, C. and Wang, L., 2019. Deer Crossing Road Detection with Roadside LiDAR Sensor. IEEE Access. 2169-3536, Page 1-3, 2019.
  42. Cui, Y., Xu, H., Wu, J., Sun, Y. and Zhao, J., 2019. Automatic Vehicle Tracking with Roadside LiDAR Data for the Connected-Vehicles System. IEEE Intelligent Systems. page(s): 1-8 Print ISSN: 1541-1672, 2019.
  43. Zhang, Y., Yao, E., Zhang, R. and Xu, H., 2019. Analysis of elderly people's travel behaviors during the morning peak hours in the context of the free bus program in Beijing, China. Journal of Transport Geography, 76, pp.191-199.
  44. Wu, J., Xu, H., Zheng, Y., Zhang, Y., Lv, B. and Tian, Z., 2019. Automatic Vehicle Classification using Roadside LiDAR Data. Transportation Research Record, p.0361198119843857. Volume: 2673 issue: 6, page(s): 153-164, 2019.
  45. Wu, J., Xu, H., Lv, B., Yue, R. and Li, Y., 2019. Automatic Ground Points Identification Method for Roadside LiDAR Data. Transportation Research Record, p.0361198119843869. Volume: 2673 issue: 6, page(s): 140-152, 2019.
  46. Wu, J., Xu, H. and Liu, W., 2019. Points Registration for Roadside LiDAR Sensors. Transportation Research Record, p.0361198119843855.
  47. Wu, J. and Xu, H., 2019. Annual Average Daily Traffic Prediction Model for Minor Roads at Intersections. Journal of Transportation Engineering, Part A: Systems, 145(10), p.04019041..
  48. Wu, J., Tian, Y., Xu, H., Yue, R., Wang, A. and Song, X., 2019. Automatic ground points filtering of roadside LiDAR data using a channel-based filtering algorithm. Optics & Laser Technology, 115, pp.374-383.
  49. Bin, L., Hao, X., Jianqing, W., Yuan, T., Sheng, T. and Feng, S., Revolution and rotation-based method for roadside lidar data integration. Optics and Laser Technology, 119.
  50. Yue, R., Yang, G., Tian, Z., Xu, H., Lin, D. and Wang, A., 2019. Microsimulation Analysis of Traffic Operations at Two Diamond Interchange Types. Journal of Advanced Transportation。 2019, Article ID 6863480, 11 pages, 2019.
  51. Zheng, J., Xu, B., Wang, X., Fan, X., Xu, H. and Sun, G., 2019. A portable roadside vehicle detection system based on multi-sensing fusion. International Journal of Sensor Networks, 29(1), pp.38-47.
  52. Zhao, J., Xu, H., Liu, H., Wu, J., Zheng, Y. and Wu, D., 2019. Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors. Transportation research part C: emerging technologies, 100, pp.68-87.
  53. Wu, J., Xu, H., Zheng, Y. and Tian, Z., 2018. A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data. Accident Analysis & Prevention, 121, pp.238-249.
  54. Wu, J. and Xu, H., 2018. Driver behavior analysis on rural 2-lane, 2-way highways using SHRP 2 NDS data. Traffic injury prevention, 19(8), pp.838-843.
  55. Zhao, J., Xu, H., Wu, J., Zheng, Y. and Liu, H., 2018. Trajectory tracking and prediction of pedestrian's crossing intention using roadside LiDAR. IET Intelligent Transport Systems, 13(5), pp.789-795.
  56. Sun, Y., Xu, H., Wu, J., Zheng, J. and Dietrich, K.M., 2018. 3-D data processing to extract vehicle trajectories from roadside LiDAR data. Transportation research record, 2672(45), pp.14-22.
  57. Wu, J., Xu, H., Sun, Y., Zheng, J. and Yue, R., 2018. Automatic background filtering method for roadside LiDAR data. Transportation Research Record, 2672(45), pp.106-114..
  58. Yang, G., Yue, R., Tian, Z. and Xu, H., 2018. Modeling the Impacts of Traffic Flow Arrival Profiles on Ramp Metering Queues. Transportation Research Record, 2672(15), pp.85-92.
  59. Wu, J., Xu, H. and Zhao, J., 2018. Automatic lane identification using the roadside LiDAR sensors. IEEE Intelligent Transportation Systems Magazine. PP(99):1-1.
  60. Wu, J. and Xu, H., 2018. The influence of road familiarity on distracted driving activities and driving operation using naturalistic driving study data. Transportation research part F: traffic psychology and behaviour, 52, pp.75-85.
  61. Yang, G., Tian, Z., Wang, D. and Xu, H., 2018. Queue length estimation for a metered on-ramp using mesoscopic simulation. Transportation Letters, pp.1-10.
  62. Wu, J. and Xu, H., 2017. Driver behavior analysis for right-turn drivers at signalized intersections using SHRP 2 naturalistic driving study data. Journal of safety research, 63, pp.177-185.
  63. Yang, G., Tian, Z., Xu, H., Wang, Z. and Wang, D., 2018. Impacts of traffic flow arrival pattern on the necessary queue storage space at metered on-ramps. Transportmetrica A: Transport Science, 14(7), pp.543-561.
  64. Sun, Y., Xu, H., Wu, J., Hajj, E.Y. and Geng, X., 2017. Data processing framework for development of driving cycles with data from SHRP 2 Naturalistic Driving Study. Transportation Research Record, 2645(1), pp.50-56.
  65. Wang, Q., Zheng, J., Xu, H., Xu, B. and Chen, R., 2017. Roadside magnetic sensor system for vehicle detection in urban environments. IEEE Transactions on Intelligent Transportation Systems, 19(5), pp.1365-1374.
  66. Yang, G., Wang, Z., Xu, H. and Tian, Z., 2017. Feasibility of using a constant acceleration rate for freeway entrance ramp acceleration lane length design. Journal of Transportation Engineering, Part A: Systems, 144(3), p.06017001.

Selected research projects

  1. PI, Multimodal Traffic Mobility and Safety Data Service with LiDAR 2023-2025, RTC Washoe Βι¶ΉΣ³»­, $230,000, 2023-2025
  2. PI, LiDAR Data Collection and Analysis for Safety Management Plans, Βι¶ΉΣ³»­ Department of Transportation, $150,000, 2023-2024
  3. PI, Extraction of MIRE FDE from NDOT Mobile LiDAR Data, Βι¶ΉΣ³»­ Department of Transportation, $94,456, 2023-2024
  4. PI, Development of a GIS Tool for Rolling-mile Calculation, Βι¶ΉΣ³»­ Department of Transportation, $55,390, 2023-2024
  5. PI, University of Βι¶ΉΣ³»­ Support Services to NDOT Traffic Safety Engineering, Βι¶ΉΣ³»­ Department of Transportation, $600,000, 2023-2027
  6. PI, Multimodal Traffic Mobility and Safety Data Service with LiDAR 2022-2023, RTC Washoe Βι¶ΉΣ³»­, $100,000, 2022-2023
  7. PI, Before-after Study with LiDAR for the Reno Micro-mobility Pilot Program, RTC Washoe Βι¶ΉΣ³»­, $111,650, 2022
  8. PI, Analysis of Advanced Signal Warning Systems on Intersection Safety with LiDAR, $40,000, HDR, 2022
  9. PI, Midtown Virginia St. BRT – Post Construction Traffic Study with LiDAR, RTC Washoe Βι¶ΉΣ³»­, $49,775, 2021-2022
  10. PI, LiDAR Matrix, NSF I-Corps, $50,000, 2021-2022
  11. PI, UNR Support for NDOT Traffic Safety Data (2021-2022), Βι¶ΉΣ³»­ Department of Transportation, $75,277, 2021-2022
  12. PI, Automatic Road Feature Extraction from State-Owned Mobile LiDAR Data for Traffic Safety Analysis and Prediction, U.S. Department of Transportation, $297,538, 2020-2022
  13. PI, Comparison and Evaluation of Roadside Animal Sensing and Driver Warning Systems, Βι¶ΉΣ³»­ Department of Transportation, $299,600, 2020-2023
  14. PI, Street Light Illumination Data Collection and Analysis study, Βι¶ΉΣ³»­ Department of Transportation, $59,068, 2020
  15. PI, Pilot Deployment of Roadside LiDAR in City of Henderson NV, Regional Transportation Commission of Southern Βι¶ΉΣ³»­, $86,071, 2018 - 2020
  16. PI, Proof-of-Concept Research of Roadside LiDAR Sensing Multimode Traffic, Βι¶ΉΣ³»­ Department of Transportation, $313,397, 2018 - 2021
  17. PI, Pilot Applications of Roadside LiDAR Technologies in Washoe County, Regional Transportation Commission – Washoe County, $250,000, 2018 - 2020
  18. PI, Data Collection of Wildlife Animals Crossing I80 in Eastern Βι¶ΉΣ³»­ and USA Parkway, SOLARIS UTC and Βι¶ΉΣ³»­ Department of Transportation, $136,051, 2018-2019
  19. PI, Development of a Βι¶ΉΣ³»­ wildlife-fencing GIS dataset, SOLARIS UTC and Βι¶ΉΣ³»­ Department of Transportation, $145,817, 2018-2019
  20. PI, University of Βι¶ΉΣ³»­ Support Services to NDOT Traffic Safety Engineering, Βι¶ΉΣ³»­ Department of Transportation, $500,000, 2017-2022
  21. PI, Developing a Quality of Signal Timing Performance Measure Methodology for Arterial Operations, Βι¶ΉΣ³»­ Department of Transportation, $272,986, 2017-2020
  22. PI, Development of Βι¶ΉΣ³»­ Safety Performance Functions for Rural Two-Lane, Two-Way Highways, Βι¶ΉΣ³»­ Department of Transportation, $41,745, 2018-2019
  23. PI, High-Resolution Micro Traffic Data from Roadside LiDAR Sensors for Connected-Vehicles and New Traffic Applications, SOLARIS UTC and Βι¶ΉΣ³»­ Department of Transportation, $173,670, 2017-2018
  24. PI, Safety Benefit-Cost Analysis of Roundabouts, Βι¶ΉΣ³»­ Department of Transportation, $52,932, 2017
  25. PI, Before-and-after Data Collection of Complete Streets, Βι¶ΉΣ³»­ Department of Transportation, $40,872, 2017-2018
  26. PI, Correlation Analysis of Βι¶ΉΣ³»­ Crash Data and ITS Sensor Data, SOLARIS UTC and Βι¶ΉΣ³»­ Department of Transportation, $52,447, 2016-2017