Wenbo

Wenbo Zhu

Ph.D. Student

Seattle, WA

wbzhu[at]uw.edu

(206) 209-6432


Languages

Python / R / SQL / PostGIS / HTML / CSS / LATEX

C# / MATLAB / JavaScript

C / Java


Tools

Jupyter / RStudio / Pycharm / MS SQL Server

Visual Studio / Eclipse / ArcMap


Education

University of Washington | Seattle, WA

Sep 2013 - Present

Ph.D. in Transportation Engineering

GPA: 3.76/4.0

M.S. in Statistics

GPA: 3.80/4.0

Tsinghua University | Beijing, China

Sep 2009 - Jul 2013

B.S. in Civil Engineering

GPA: 89.6/100 (Rank: 12/114)

B.S. in Economics

GPA: 89.8/100

Awards

Traffic Bowl ChampionNov 2015

Institute of Transportation Engineers - Oregon


1st Place of ITE Student NightMay 2014

Institute of Transportation Engineers - Washington State


National ScholarshipOct 2012

Ministry of Education of China


Synergistic Activities

PresidentOct 2015 - Oct 2016

Institute of Transportation Engineers - UW Student Chapter


ChairOct 2015 - Oct 2016

PacTrans Annual Student Conference Planning Committee


PresidentJun 2015 - Jun 2016

Chinese Students & Scholars Association (CSSA) at UW



Experience

Research Assistant | University of Washington UW
Sep 2013 - Present
  • Developed optimization/simulation/statistical models for intelligent transportation system applications
  • Implemented data-driven machine learning algorithms to study heterogeneous patterns in traffic network
  • Maintained the Microsoft SQL Server for multi-source transportation big data storage and management
  • Designed and built the research lab website from scratch with HTML, CSS and JavaScript codes

Applied Science Intern | Zillow Group Zillow
Jun 2018 - Sep 2018
  • Estimated nation-wide commute travel time for all homes located in metropolitan statistical areas (MSAs)
  • Improved and tested machine learning models for home price prediction in cluster-computing frameworks
  • Built production-ready python program to integrate geographic features into existing research pipeline

Predoctoral Instructor | University of Washington UW
Jan 2017 - Mar 2017
  • Instructed the graduate-level course: CEE 412/599 Transportation Data Management & Analysis
  • Organized and prepared materials for lectures, labs, and assignments/exams for a class of over 40 students
  • Guided students in writing SQL codes for data definition, query, and manipulation from the course database
  • Adjusted course materials to include the state of the art in database systems and related analytical methods

Research & Projects

Connected Vehicle based Adaptive Routing Algorithm
Apr 2018 - Present
  • Designed a dynamic routing algorithm that allows connected vehicles to change routes based on real-time traffic
  • Developed a Bayesian framework for connected vehicles to learn traffic evolution and predict future traffic states
  • Implemented the algorithm in numerical tests and microscopic traffic simulation to validate the system benefits

Grade Impact on Network Traffic Speed Estimation
Jun 2017 - Present
  • Applied a continuous wavelet transform (CWT) method for roadway alignment classification based on the vertical curve
  • Developed traffic speed prediction models (e.g., SVM, ANN) based on spatiotemporal correlations of speed measurements
  • Evaluated the spatial pattern and distribution of speed estimation errors across different roadway alignment types

Travel Time Reliability Analysis and Data Procedures
Sep 2016 - Jun 2018
  • Drafted a FHWA data guidance for transportation practitioners to implement travel time reliability analysis
  • Designed general pre-processing methods to handle data issues (e.g., sample bias, missing, and multi-collinearity)
  • Proposed travel time reliability metrics for network performance monitoring, reporting, and visualization

Traffic Incident Prediction and Safety Performance Assessment
Jun 2016 - Aug 2017
  • Implemented pattern recognition methods (e.g., KNN, GMM) to learn location and time features of traffic incidents
  • Applied a survival analysis model to predict traffic incident clearance time based on historical incident reports
  • Proposed a generalized nonlinear model (GNM) based multinomial logit (MNL) approach to analyze crash severity

Highway Elevation Data Extraction and Analysis
Apr 2015 - Mar 2016
  • Developed a C# program using Google Earth API to automatically extract altitude information from GE terrain
  • Identified and corrected elevation noise using multiple methods (e.g., threshold, mode decomposition, curve fitting)
  • Computed road grade related fuel consumption using the Comprehensive Modal Emission Model (CMEM)

Cellphone-based Travel Model Classification
Mar 2015 - Aug 2015
  • Implemented machine learning methods (e.g., GMM, RF) to classify GPS trajectories into different travel modes
  • Incorporated the dimension reduction technique (i.e., PCA) to solve high correlations among independent variables
  • Programmed semi-supervised learning procedures to handle unlabeled data from volunteer-participant experiments

Control Optimization for Two-Lane Highway Work Zones
Sep 2013 - Apr 2015
  • Formulated probabilistic road capacity and vehicle delay functions for two-lane highway lane closure work zones
  • Developed microscopic traffic simulation model in VISSIM to validate the accuracy of mathematical functions
  • Optimized work zone length and signal duration to minimize the traveler delay caused by the construction work

Selected Publications & Presentations

Zhu, W., Li, Z., Ash, J., Wang, Y., & Hua, X. (2017). “Capacity modeling and control optimization for two-lane highway lane closure work zones”. ASCE Journal of Transportation Engineering, Part A: Systems, 143(12), 04017059.

Zhu, W., Zeng, Z., Wang, Y., & Pu, Z. (2017). “Predicting incident duration based on spatiotemporal heterogeneous pattern recognition”. The 96th Transportation Research Board Annual Meeting, (No. 17-06787). Washington, DC.

Zhu, W., Ash, J., Li, Z., Wang, Y., & Lowry, M. (2015). “Applying semi-supervised learning method for cellphone-based travel mode classification”. 2015 IEEE First International Smart Cities Conference (ISC2), (pp. 1-6). IEEE.

Chen, X., Li, Z., Wang, Y., Tang, J., Zhu, W., Shi, C., & Wu, H. (2018). Anomaly Detection and Cleaning of Highway Elevation Data from Google Earth Using Ensemble Empirical Mode Decomposition. Journal of Transportation Engineering, Part A: Systems, 144(5), 04018015.

Zeng, Z., Zhu, W., Ke, R., Ash, J., Wang, Y., Xu, J., & Xu, X. (2017). “A generalized nonlinear model-based mixed multinomial logit approach for crash data analysis”. Accident Analysis & Prevention, 99, 51-65.

Pu, Z., Li, Z., Ash, J., Zhu, W., & Wang, Y. (2017). Evaluation of spatial heterogeneity in the sensitivity of on-street parking occupancy to price change. Transportation Research Part C: Emerging Technologies, 77, 67-79.