Pei Li

university of wisconsin madison

H指数:16

被引数:925

个人简介

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教育背景

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工作经历

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荣誉奖项

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Real-time lane-level abnormal traffic detection on freeways using sparse telematics data

基于稀疏车联网数据的实时高速公路车道级异常交通检测

Accident Analysis and Prevention

IF6.2

2026-06-010

PRE

Real-time identification of cooperative perception necessity in road traffic scenarios

道路交通场景下合作感知必要性的实时识别

transportation research part c: emerging technologies

IF0

2026-02-030

PRE

Goal-based neural physics vehicle trajectory prediction model

基于目标驱动的神经物理车辆轨迹预测模型

transportation research part c emerging technologies

IF0

2025-07-290

PRE

Knowledge Mapping and global research trends of physical resilience research from 2001 to 2024: A bibliometric analysis based on CiteSpace and VOSviewer

2001年至2024年物理韧性研究领域的知识图谱与全球研究趋势：基于CiteSpace和VOSviewer的文献计量分析

Geriatric Nursing

IF2.4

2025-07-040

PRE

Molecularly imprinted polymer combined with multiple-component covalent organic frameworks for extraction of naringenin enantiomers

分子印迹聚合物与多组分共价有机框架结合用于柚皮素对映体的提取

FOOD CHEMISTRY

IF9.8

2025-05-011

PRE

Photo-promoted one-pot coupling isomerization-codimerization of norbornadiene for the efficient synthesis of liquid fuel with a density higher than 1 g mL-1

SUSTAINABLE ENERGY & FUELS

IF4.1

2025-03-010

PRE

Intelligent Optimization Setting of Froth Velocity by Integrating Multi-Source Flotation Operation Information

通过集成多源浮选操作信息对泡沫速度进行智能优化设置

IEEE Transactions on Automation Science and Engineering

IF6.4

2025-01-010

PRE

Personalized lane departure warning based on non-stationary crossformer and kernel density estimation

ALEXANDRIA ENGINEERING JOURNAL

IF6.8

2024-12-010

OAAI

Diffusion probabilistic model for bike-sharing demand recovery with factual knowledge fusion

NEURAL NETWORKS

IF6.3

2024-11-010

PRE

Analyzing relationships between latent topics in autonomous vehicle crash narratives and crash severity using natural language processing techniques and explainable XGBoost

使用自然语言处理技术和可解释的XGBoost分析自动驾驶汽车碰撞叙述中潜在主题与碰撞严重程度之间的关系

ACCIDENT ANALYSIS AND PREVENTION

IF6.2

2024-08-013

PRE

Spider silk inspired polymer electrolyte with well bonded interface and fast kinetics for solid-state lithium-ion batteries

用于固态锂离子电池的具有良好键合界面和快速动力学的蜘蛛丝启发聚合物电解质

materials today advances

IF22

2024-07-012

PRE

Multiple-component covalent organic frameworks for simultaneous extraction and determination of multitarget pollutants in sea foods

JOURNAL OF HAZARDOUS MATERIALS

IF11.3

2024-07-012

PRE

All-in-one strategy to construct bifunctional covalent triazine-based frameworks for simultaneous extraction of per- and polyfluoroalkyl substances and polychlorinated naphthalenes in foods

JOURNAL OF HAZARDOUS MATERIALS

IF11.3

2024-03-016

PRE

How Does C-V2X Perform in Urban Environments? Results From Real-World Experiments on Urban Arterials

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

IF14.3

2024-01-013

PRE

A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections

用于估计交叉口行人与车辆冲突风险的概率框架

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

IF8.4

2023-12-015

A Deep Learning Approach to Detect Real-Time Vehicle Maneuvers Based on Smartphone Sensors

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

IF8.4

2022-04-0122

PREAI

Using bus critical driving events as surrogate safety measures for pedestrian and bicycle crashes based on GPS trajectory data

使用公共汽车关键驾驶事件作为基于GPS轨迹数据的行人和自行车碰撞的替代安全措施

ACCIDENT ANALYSIS AND PREVENTION

IF6.2

2021-02-0138

PREAI

Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network

ACCIDENT ANALYSIS AND PREVENTION

IF6.2

2020-12-0138

PREAI

This paper has been handled by associate editor Tony Sze.The application of novel connected vehicles emulated data on real-time crash potential prediction for arterials

ACCIDENT ANALYSIS AND PREVENTION

IF6.2

2020-09-0133

PREAI

Real-time crash risk prediction on arterials based on LSTM-CNN

基于lstm-cnn的动脉实时碰撞风险预测

ACCIDENT ANALYSIS AND PREVENTION

IF6.2

2020-02-01237

PREAI

Pei Li

个人简介

教育背景

工作经历

荣誉奖项

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