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.

Introduction

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

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 …
Last updated on 1 min read

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 …
Last updated on 3 min read

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 …
Last updated on 3 min read

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 …
Last updated on 3 min read

Our Group Members Attended the First INCLUSION FinTech Conference in Shanghai

The First INCLUSION Fintech Conference held by Alibaba’s fintech arm Ant Group kicked off on September 24, 2020, in Shanghai. The …
Last updated on 1 min read
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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

routingproject

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

translearningproject

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

Selected Projects

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All Autonomy Systems Environment Logistics

Air Quality project - Toxicity Burden with Ambient PM2.5

Delay Prediction project - Spatiotemporal Models for Predicting Delays in Transportation Networks

MoST project - Vehicle Autonomy and Network Optimisation

Royal Society project - Urban Traffic Management and Control

TOD project - Transit-Oriented-Development Framework for Hangzhou

Meet the team

Principle Investigator

Avatar Simon
Hu

Assistant Professor

Researchers

Avatar Zhengqi
Chen

Master Student

Reinforcement learning

Avatar Jianghan
Hu

Master Student

Traffic monitoring

Avatar Qinru
Hu

PhD Student

Vehicle Routing and Planning

Avatar Xinlong
Huang

Master Student

Intelligent Connected Vehicle

Avatar Junyi
Li

PhD Student

Deep Learning in Traffic Data Analysis

Avatar Song
Lian

Master Student

Computer Vision

Avatar Siqi
Shu

PhD Student

Smart Mobility

Avatar Hanyu
Xin

Master Student

Traffic Control

Avatar Yuntao
Yang

Master Student

Transport and Environment

Avatar Yuanbo
Yang

Master Student

Computer Vision

Avatar Anke
Ye

PhD Student

Urban Transportation Systems

Avatar Qianqian
Ye

Master Student

Traffic Data Analysis

Avatar Zhengyang
Zhang

Master Student

Deep Learning

Avatar Bin
Zhou

PhD Student

Reinforcement Learning

Avatar Qishen
Zhou

PhD Student

Traffic data analysis

Avatar Bing
Zhu

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
(2018). IQGA: A route selection method based on quantum genetic algorithm- toward urban traffic management under big data environment. World Wide Web. PDF DOI
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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.
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