397 citations · 988 across the 17 of their papers we have counts for
17 papers
AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model
Lening Wang, Yilong Ren, Han Jiang +9
Traffic accidents, being a significant contributor to both human casualties and property damage, have long been a focal point of research for many scholars in the field of traffic…
Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting
Meng-Ju Tsai, Zhiyong Cui, Hao Yang +3
With accurate and timely traffic forecasting, the impacted traffic conditions can be predicted in advance to guide agencies and residents to respond to changes in traffic patterns…
Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications
Ruimin Ke, Zhiyong Cui, Yanlong Chen +3
Traffic near-crash events serve as critical data sources for various smart transportation applications, such as being surrogate safety measures for traffic safety research and corn…
Traffic Performance Score for Measuring the Impact of COVID-19 on Urban Mobility
Zhiyong Cui, Meixin Zhu, Shuo Wang +5
Measuring traffic performance is critical for public agencies who manage traffic and individuals who plan trips, especially when special events happen. The COVID-19 pandemic has si…
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values
Zhiyong Cui, Ruimin Ke, Ziyuan Pu +1
Short-term traffic forecasting based on deep learning methods, especially recurrent neural networks (RNN), has received much attention in recent years. However, the potential of RN…
Graph Markov Network for Traffic Forecasting with Missing Data
Zhiyong Cui, Longfei Lin, Ziyuan Pu +1
Traffic forecasting is a classical task for traffic management and it plays an important role in intelligent transportation systems. However, since traffic data are mostly collecte…
Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit
Jinlei Zhang, Feng Chen, Zhiyong Cui +2
Short-term passenger flow forecasting is an essential component in urban rail transit operation. Emerging deep learning models provide good insight into improving prediction precis…
Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data
Ziyuan Pu, Shuo Wang, Chenglong Liu +2
Road surface friction significantly impacts traffic safety and mobility. A precise road surface friction prediction model can help to alleviate the influence of inclement road cond…
Mining Public Transit Ridership Flow and Origin-Destination Information from Wi-Fi and Bluetooth Sensing Data
Ziyuan Pu, Meixin Zhu, Zhiyong Cui +1
Transit ridership flow and origin-destination (O-D) information is essential for enhancing transit network design, optimizing transit route and improving service. The effectiveness…
Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data
Ziyuan Pu, Zhiyong Cui, Shuo Wang +2
An accurate road surface friction prediction algorithm can enable intelligent transportation systems to share timely road surface condition to the public for increasing the safety…
Traffic Sign Detection and Recognition for Autonomous Driving in Virtual Simulation Environment
Meixin Zhu, Jingyun Hu, Ziyuan Pu +3
This study developed a traffic sign detection and recognition algorithm based on the RetinaNet. Two main aspects were revised to improve the detection of traffic signs: image cropp…
Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact
Ruimin Ke, Wan Li, Zhiyong Cui +1
Traffic speed prediction is a critically important component of intelligent transportation systems (ITS). Recently, with the rapid development of deep learning and transportation d…
Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models
Xiaolei Ma, Yi Li, Zhiyong Cui +1
Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies o…
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
Zhiyong Cui, Kristian Henrickson, Ruimin Ke +2
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on ro…
A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation
Yunyi Liang, Zhiyong Cui, Yu Tian +2
This study proposes a deep generative adversarial architecture (GAA) for network-wide spatial-temporal traffic state estimation. The GAA is able to combine traffic flow theory with…
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction
Zhiyong Cui, Ruimin Ke, Ziyuan Pu +1
Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the p…
Perspectives on stability and mobility of transit passenger's travel behaviour through smart card data
Zhiyong Cui, Ying Long
Existing studies have extensively used spatiotemporal data to discover the mobility patterns of various types of travellers. Smart card data (SCD) collected by the automated fare c…