Featured Publications

To model the long-term dependency of future actions on historical driving situations, we developed a long-sequence car-following trajectory prediction model based on the attention-based Transformer model. The encoder takes historical speed and spacing data as inputs and forms a mixed representation of historical driving context using multi-head self-attention. The decoder takes the future LV speed profile as input and outputs the predicted future FV speed profile in a generative way (instead of an auto-regressive way, avoiding compounding errors). Through cross-attention between encoder and decoder, the decoder learns to build a connection between historical driving and future LV speed, based on which a prediction of future FV speed can be obtained.
Arxiv preprint. ASA TSIG Student Paper Award., 2022.

This work supported the Federal Highway Administration (FHWA) in presenting state and metropolitan area vehicle occupancy information in compliance with Title 23 of the US Code of Federal Regulations, Part 490 National Performance Measures. The basic goal is to provide and introduce a statistically effective and realistic approach to approximate bus occupancy rates for each US state and Washington, DC. Bus occupancies were calculated separately for transit buses, school buses, and motorcoaches.
Journal of Transportation Engineering, Part A: Systems, Vol. 147, Issue 6, 2021.

This study proposes an adaptive multi-input and multi-output traffic signal control method that not only can improve network-wide traffic operations in terms of reduced traffic delay and energy consumption, but also is more computationally feasible than existing centralized signal control methods. Considering intersection interactions, a linear dynamic traffic system model was built and adaptively updated to reflect how the signal control input of each intersection affects network-wide vehicle travel delay. Based on the system model, an adaptive linear-quadratic regulator (LQR) was designed to minimize both traffic delay and incremental changes in the control input.
IEEE Transactions on Intelligent Transportation Systems, Vol. 23, Issue 1., 2020.

This study aimed to investigate the impact of Forward Collision Warning (FCW) systems on drivers’ car-following behaviors. Five data collecting vehicles are equipped with Mobileye® systems, which include an FCW function. Participants drive the instrumented vehicles for two months, with the Mobileye® system not activated for the first month, but activated for the second month. The results of this study show that (1) drivers tended to maintain a longer headway when FCW activated; and (2) the FCW resulted in a 0.13s decrease of reaction time in daytime driving, and a 0.09s decrease when a following vehicle had higher speed than the lead vehicle. Moreover, this study further confirms that the reaction time is affected by relative distance, lead vehicle acceleration, and traffic density.
Transportation Research Part C: Emerging Technologies 111, 226-244, 2020.

This study proposed a system for monitoring real-time public transit passenger ridership flow and O-D information based on customized Wi-Fi and Bluetooth sensing device. By combining the consideration of the assumed overlapping feature spaces of passenger and nonpassenger media access control address data, a three-step data-driven algorithm framework for estimating transit ridership flow and O-D information is proposed.
IEEE Internet of Things Journal 8 (1), 474-486, 2020.

A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). A total of 1,341 car-following events extracted from the public Next Generation Simulation (NGSIM) dataset were used to train the model. Results show that the model demonstrated the capability of safe, efficient, and comfortable velocity control in that it 1) has small percentages (8%) of dangerous minimum time to collision values (< 5s) than human drivers in the NGSIM data (35%); 2) can maintain efficient and safe headways in the range of 1s to 2s; and 3) can follow the lead vehicle comfortably with smooth acceleration.
Transportation Research Part C: Emerging Technologies 117, 102662, 2020.

This study uses the technique of deep reinforcement learning to model drivers’ car following behavior. Two thousand car-following periods extracted from the Shanghai Naturalistic Driving Study were used to train the proposed model and compare its performance with that of four traditional car-following models. The proposed model can reproduce human-like car-following behavior with significantly higher accuracy than traditional car-following models, especially in terms of speed replicating. Moreover, the model demonstrates good capability of generalization to different driving situations and can adapt to different drivers by continuously learning.
Transportation Research Part C: Emerging Technologies 97, 348-368, 2018.

Five representative car-following models were calibrated and evaluated for Shanghai drivers, using 2100 urban-expressway car-following periods extracted from the 161,055 km of driving data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The results show that the intelligent driver model (IDM) has good transferability to model traffic situations not presented in calibration, and it performs best among the evaluated models. Compared to the Wiedemann 99 model used by VISSIM®
Transportation Research Part C: Emerging Technologies 93, 425-445, 2018.

A simulator is used to test drivers’ collision avoidance behaviors under different initial headways and different lead vehicle deceleration rates. As situational urgency increase, drivers release the accelerator faster, brake to full braking with less time and brake harder. Transition time between initial throttle release and brake initiation is not affected by initial headway or LV deceleration rate. At low situational urgency, multi-stage braking behavior leads to longer delays from brake initiation to full braking.
Transportation Research Part C: Emerging Technologies, Vol. 71, pp. 419-433, 2016.

A total of 111 brake-only non-collision events were presented in the Tongji University Driving Simulator and drivers’ braking behaviors were used to model their Expected Response Decelerations (ERDs). We found ERDs depended on the interaction of LV deceleration and relative speed. In response to this finding, a non-linear function with an interaction term was combined with a linear function into a piecewise function that accommodated both higher and lower LV deceleration conditions. The applicable domain of the warning onset range was then computed for a wide range of kinematic conditions. Results showed the piecewise function to be a better predictor of ERD than the linear function, and to result in fewer driver rejections of the forward collision warnings.
IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 9, pp. 2583-2591, 2016.


Shanghai Naturalistic Driving Study

The first large-scale naturalistic driving study in China. A total of 161,055 km of real-world detailed driving data were collected from 60 Chinese drivers, providing an unprecedented opportunity for investigating driving behavior in China.

Driving Behavior Research for Intelligent Collision Avoidance Technology

A driving simulator study jointly conducted by Tongji University and China First Automobile Work Corporation. Tongji University’s eight-degree-freedom driving simulator (its fidelity ranking top 3 in the world) was used. Its primary purpose is to improve the understanding of drivers’ collision avoidance behavior under different rear-end scenarios and to develop an effective forward collision warning strategy.

Autonomous Car Following by Deep Reinforcement Learning

This on-going project aims to develop autonomous car-following strategies based on deep reinforcement learning.The demo animation shows the car-following learning process of the RL agents (cars). In early training episodes, cars become red frequently, indicating penalties caused by bad performances in car following, e.g., rear-end crashes. As training converges, the RL agents maintain steady car-following headways and receive fewer penalties.

Human-Like Driving by Inverse Reinforcement Learning

This on-going project is inspired by Levine et al. (2012). It aims to imitate human driving patterns. The assumption is that drivers act based on a utility (reward) function, which represents the preference of the driver and elicits the driving behavior. The role of inverse reinforcement learning is to infer or discover the latent utility function from driver (expert) demonstrations and thus generalize the driving policy to unobserved situations.

Traffic Parameter Analysis Platform based on Unmanned Aerial Vehicle

This project aimed to extract real-time traffic flow parameters, including volume, density, and intersection delay, etc., based on videos captured by an unmanned aerial vehicle. The featured animation demonstrates the extraction of traffic-flow parameters by setting virtual loop detectors.

Traffic Simulation based on Cellular Automaton Method

This project aimed to model the keep-right-except-to-pass rule using Cellular Automaton. The cellular automaton system is a discrete dynamic system consisting of a regular grid of cells, each in one of a finite number of states. And the state of each cell is updated every single discrete time step according to the updating rules, which are determined by the current state of the cell and the states of its neighbor cells.

Optimized Design for Combined Road Alignment

Evaluating the safety performance of combined horizontal and vertical alignments in mountainous freeways, to guide the design of safer mountainous freeways.


Slected Awards and Scholarships

  • Transportation Statistics Interest Group (TSIG) Student Paper Award. Jan 2022
  • 2nd Place, Transportation Forecasting Competition (TRANSFOR 22), Transportation Research Board (TRB) Committee on Artificial Intelligence and Advanced Computing Applications (AED50), Jan 2022
  • Graduate Student Travel Award, PacTrans. 2020, 2022
  • Most Cited Paper, Transportation Research Part C: Emerging Technologies. 2020
  • Wining Award, 2021 Digital China Innovation Contest. Smart Transportation-Collision Detection based on Big Data of Internet of Vehicles. 2021
  • 2nd Place, Poster Competition of 2020 PacTrans Student Transportation Conference. 2020
  • National Graduate Scholarship (twice), Ministry of Education, China. Oct 2017, Oct 2016
  • Outstanding Student Award, Tongji University. Oct 2016
  • China Post-Graduate Mathematical Contest in Modeling, Second Prize. Sep 2016
  • “Inspirational Star”, Tongji University. Jun 2015
  • Volvo Group Scholarship, Volvo Group. Dec 2014
  • Excellent Student Scholarship, Tongji University. Nov 2014
  • National Competition of Transport Science and Technology for Students, Second Prize. May 2014
  • Mathematical Contest in Modeling, Honorable Mention. Jan 2014
  • National Endeavor Fellowship (twice), Ministry of Education, China. Nov 2012, Nov 2013
  • The Second Prize of East China Mathematical Modeling Contest. Oct 2013
  • China Undergraduate Mathematical Contest in Modeling, Second Prize. Sep 2013
  • The Grand Prize of Tongji University Mathematical Modeling Contest. Jun 2013
  • China National Undergraduate Physics Contest, Shanghai Division, Third Prize. Dec 2012
  • First Class Excellent Student Scholarship, Tongji University. Nov 2012


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  • Department of Civil and Environmental Engineering, University of Washington More Hall 101, Box 352700, Seattle, WA 98195

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