Transport Systems & Environment Lab

Research at the TSE Lab helps to improve our understanding between the real-world operation of transport systems and their measurable social, economic and environmental impacts.


TSE Lab is led by Dr Simon Hu and is part of the ZJU-UIUC joint Institute at the Zhejiang University. Our areas of interest include Transport Systems, Autonomy, Environment, Logsitics. We have an interdisciplinary team of researchers, we are always keen to recruit new members.

Recent News

Our Team Members Participate in INSTR2023 Conference and Deliver Paper Presentation

  From December 13-14, 2023, the 9th International Symposium on Transportation Network Resilience (INSTR2023) was held in …

Zhejiang University and Alibaba cooperation project on 2022 Asian Games

Recently, a one-year project cooperated with Alibaba on ‘The optimization of reservation-based travel in intelligent transportation …

The Forum "Future Communities with Intelligent Transportation Systems" was Successfully Held

On May 22nd, the forum themed “Future Communities with Intelligent Transportation Systems” was successfully held in the International …

Online Workshop "Mobility and Logistics in the Era of the Coronavirus Pandemic" was Successfully Held

On December 11th, 2020, the online workshop themed “Mobility and Logistics in the Era of the Coronavirus Pandemic” (click …

ZJU Virtual Lab for Computable Digital Transport was Officially Launched

On November 29, 2020, the launch ceremony of the Virtual Lab for Computable Digital Transport (VLCDT), jointly organized by the School …

Research Areas

Automated Taxi Routing and Planning

Our research in automated taxi routing problems includes areas such as multiclass traffic assignment, taxi fleet optimization, trip-vehicle assignment, and vehicle route planning. We have a particular focus on shared automated taxi routing in dynamic transportation networks, aiming to provide door-to-door mobility service for travelers and enable ride-sharing among different requests.

Key Capabilities: - Vehicle routing optimization - Vehicle fleet management - Traffic congestion impacts analysis - System performance evaluation


Trajectory Optimisation

Our research is conducted in the context of the mixed traffic scene on the urban roads (mixed by connected vehicles (CV), autonomous vehicles (AV), manual vehicles (MV) and connected and autonomous vehicles (CAV)), and we explore how to utilize the information provided by the CV and CAV to realize the trajectory optimisation of the CAV and trajectory guidance of CV to achieve the purpose of alleviating traffic congestion and reducing energy consumption and emissions.

Transfer Learning in Traffic Engineering

We devote to designing transferable and self-adaptive models to handle the dynamic changes and data insufficient problems in short-term traffic prediction. Both model and sample based transfer strategies are investigated to reveal the rules of transferable features, and deal with a seires of complex and volatile scenarios in traffic state estimation.

Key Capabilities: - Transfer learning - Domain adaptation - Self-adaptive model


Green Urban Logistics

Our research in green urban logistics includes areas of goods transportation in urban areas with respect to environmental impacts, diverse goods-dependent transport requirements. We have a particular focus on resilience and robust intervention strategies in the face of urban freight traffic regulations and real-time traffic conditions.

Key Capabilities: - Last mile delivery - Green supply chains - Warehousing and distribution - Inventory management - Fleet management - Emission monitoring

logisticsproject Truck fuel economy map in urban networks of London

On-demand transportation system management

We focus on service design in on-demand transportation systems using steady-state models or state-dependent dynamic programming techniques. Our research accounts for unique system features, complex interactions among different stakeholders, and demand and supply balance. We provide frameworks to decision-makers to help them evaluate the performance of the system based on specific regulations or service designs.

Key capabilities: - On-demand meal delivery services - Ride-sourcing services - Market equilibrium - Dynamic pricing - Autonomous vehicles


Meet the team

Principle Investigator

Avatar Simon

Assistant Professor


Avatar Hongxu

Master Student

Machine Learning

Avatar Yuanyi

PhD Student

Dynamic Modeling of CPTNs

Avatar Zhengqi

Master Student

Reinforcement learning

Avatar Jianghan

Master Student

Traffic Digital Twin

Avatar Qinru

PhD Student

Shared mobility

Avatar Xinlong

Master Student

Intelligent Connected Vehicle

Avatar Junyi

PhD Student

Deep Learning in Traffic Data Analysis

Avatar Song

Master Student

Computer Vision

Avatar Siqi

PhD Student

Smart Mobility

Avatar Kang

PhD Student

Traffic data analysis

Avatar Hanyu

Master Student

Traffic Control

Avatar Kaiyao

Master Student

Data analysis

Avatar Yuntao

Master Student

Transport and Environment

Avatar Yuanbo

Master Student

Computer Vision

Avatar Anke

PhD Student

On-demand logistics system

Avatar Qianqian

Master Student

Traffic Data Analysis

Avatar Zhengyang

Master Student

Deep Learning

Avatar Bin

PhD Student

Reinforcement Learning

Avatar Qishen

PhD Student

Traffic data analysis

Avatar Bing

PhD Student

Connected and Autonomous Vehicles

Visit this page for a full list of past and present members.

Recent Publications

(2020). Freeway Traffic Control in Presence of Capacity Drop. IEEE Transactions on Intelligent Transportation Systems. PDF DOI
(2020). Urban Network-Wide Traffic Speed Estimation with Massive Ride-Sourcing GPS Traces. Transportation Research Part C: Emerging Technologies. DOI
(2020). Urban Traffic Route Guidance Method with High Adaptive Learning Ability under Diverse Traffic Scenarios. IEEE Transactions on Intelligent Transportation Systems. DOI
(2019). Exploratory Analysis for Big Social Data Using Deep Network. IEEE Access. DOI

Recent Conferences

(2020). Cluster-based Short-term Demand Forecasting for Bike-sharing System Using Machine Learning Methods. The 99th Annual Meeting of the Transportation Research Record.
(2020). Joint Queue Estimation and Max Pressure Control for Signalized Urban Networks with Connected Vehicles. The 99th Annual Meeting of the Transportation Research Record.
(2020). Prediction of transient particle transport in transient indoor airflow by integrated fast fluid dynamics and Markov chain model. the 16th Conference of the International Society of Indoor Air Quality & Climate.