| 1. |
- Chi, Pengnan, et al.
(författare)
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Difforecast : Image Generation Based Highway Traffic Forecasting with Diffusion Model
- 2023
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Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 608-615
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Konferensbidrag (refereegranskat)abstract
- Monitoring and forecasting of road traffic conditions is a common practice for real traffic information system, and is of vital importance to traffic management and control. While dynamic traffic patterns can be intuitively represented by space-time diagrams, this study proposes a new concept of space-time image (ST-image) to incorporate physical meanings of traffic state variables. We therefore transform the forecasting problem for time-series traffic states into a conditional image generation problem. We explore the inherent properties of the ST images from the perspectives of physical meaning and traffic dynamics. An innovative deep learning based architecture is designed to process the ST-image, and a diffusion model is trained to obtain traffic forecasts by generating the future ST-images based on the historical patterns.
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| 2. |
- Chi, Pengnan, et al.
(författare)
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Short-Term Traffic Prediction on Swedish Highways: A Deep Learning Approach with Knowledge Representation
- 2023
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Konferensbidrag (refereegranskat)abstract
- Accurate prediction of highway traffic is of vital importance to proactive traffic monitoring, operation and controls. In the data mining of highway traffic, abstracting temporal knowledge is often prioritized than exploring topological relationship. In this study, we propose a deep learning model, called Knowledge-Sequence-to-Sequence (K-Seq2Seq), to solve the short-term highway traffic prediction problem in two stages: representing temporal knowledge and predicting future traffic. Through computational experiment in a road section of a Swedish motorway, we show that our model outperforms the conventional Seq2Seq model significantly, more than 20% when predicting information of longer time step.
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| 3. |
- Ji, Qingyuan, et al.
(författare)
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GraphPro : A Graph-based Proactive Prediction Approach for Link Speeds on Signalized Urban Traffic Network
- 2022
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Ingår i: Conference Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 339-346
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Konferensbidrag (refereegranskat)abstract
- This paper proposes GraphPro, a short-term link speed prediction framework for signalized urban traffic networks. Different from other traditional approaches that adopt only reactive inputs (i.e., surrounding traffic data), GraphPro also accepts proactive inputs (i.e., traffic signal timing). This allows GraphPro to predict link speed more accurately, depending on whether or not there is a contextual change in traffic signal timing. A Wasserstein generative adversarial network (WGAN) structure, including a generator (prediction model) and a discriminator, is employed to incorporate unprecedented network traffic states and ensures a high level of generalizability for the prediction model. A hybrid graph block, comprised of a reactive cell and a proactive cell, is implemented into each neural layer of the generator. In order to jointly capture spatio-temporal influences and signal contextual information on traffic links, the two cells adopt several key neural network-based components, including graph convolutional network, recurrent neural architecture, and self-attention mechanism. The double-cell structure ensures GraphPro learns from proactive input only when required. The effectiveness and efficiency of GraphPro are tested on a short-term link speed prediction task using real-world traffic data. Due to the capabilities of learning from real data distribution and generating unseen samples, GraphPro offers a more reliable and robust prediction when compared with state-of-the-art data-driven models.
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| 4. |
- Jin, J., et al.
(författare)
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A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
- 2022
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:9, s. 16185-16196
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Tidskriftsartikel (refereegranskat)abstract
- Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a large-scale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks.
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| 5. |
- Jin, Junchen, et al.
(författare)
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PRECOM : A Parallel Recommendation Engine for Control, Operations, and Management on Congested Urban Traffic Networks
- 2021
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; , s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a parallel recommendation engine, PRECOM, for traffic control operations to mitigate congestion of road traffic in the metropolitan area. The recommendation engine can provide, in real-time, effective and optimal control plans to traffic engineers, who are responsible for manually calibrating traffic signal plans especially when a road network suffers from heavy congestion due to disruptive events. With the idea of incorporating expert knowledge in the operation loop, the PRECOM system is designed to include three conceptual components: an artificial system model, a computational experiment module, and a parallel execution module. Meanwhile, three essential algorithmic steps are implemented in the recommendation engine: a candidate generator based on a graph model, a spatiotemporal ranker, and a context-aware re-ranker. The PRECOM system has been deployed in the city of Hangzhou, China, through both offline and online evaluation. The experimental results are promising, and prove that the recommendation system can provide effective support to the current human-in-the-loop control scheme in the practice of traffic control, operations, and management.
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| 6. |
- Lensink, Marc F., et al.
(författare)
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Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment
- 2023
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Ingår i: Proteins. - : WILEY. - 0887-3585 .- 1097-0134.
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Tidskriftsartikel (refereegranskat)abstract
- We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average similar to 70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.
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| 7. |
- Liang, Xinyue, et al.
(författare)
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AVIATOR: fAst Visual Perception and Analytics for Drone-Based Traffic Operations
- 2023
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Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2959-2964
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Konferensbidrag (refereegranskat)abstract
- Drone-based system is an emerging technology for advanced applications in Intelligent Transport Systems (ITS). This paper presents our latest developments of a visual perception and analysis system, called AVIATOR, for drone-based road traffic management. The system advances from the previous SeeFar system in several aspects. For visual perception, deep-learning based computer vision models still play the central role but the current system development focuses on fast and efficient detection and tracking performance during real-time image processing. To achieve that, YOLOv7 and ByteTrack models have replaced the previous perception modules to gain better computational performance. Meanwhile, a lane-based traffic steam detection module is added for recognizing detailed traffic flow per lane, enabling more detailed estimation of traffic flow patterns. The traffic analytics module has been modified to estimate traffic states using lane-based data collection. This includes detailed lane-based traffic flow counting as well as traffic density estimation according to vehicle arrival patterns per lane.
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| 8. |
- Ma, Xiaoliang, Docent, et al.
(författare)
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DigiWays: A Digitalisation Testbed for Sustainable Traffic Management on Swedish Motorways
- 2023
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Ingår i: 2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023. - : Institute of Electrical and Electronics Engineers Inc..
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Konferensbidrag (refereegranskat)abstract
- Motorway traffic management system plays important roles for modern Intelligent Transport Systems (ITS). The Swedish motorways near large cities such as Stockholm are equipped with a large number of sensors for traffic monitoring and advanced traffic management purposes. This paper introduces our recent experiments of digitalising motorway traffic system using vehicle-to-everything (V2X) communication and other sensors deployed for measuring road traffic and road-side air pollutants. In addition to the deployment of V2X testbed, a Cyber-Physical system (CPS) framework is presented to integrate the deployed sensors with the computational models for estimation and prediction of traffic and road-side environmental conditions. A digital twin of motorway traffic flow is established using traffic flow models of different levels. The computation in experiment of the cyber space shows that traffic states can be estimated using V2X sensing data by applying the model-based estimation approach.
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| 9. |
- Ma, Xiaoliang, Docent, et al.
(författare)
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METRIC : Toward a Drone-based Cyber-Physical Traffic Management System
- 2022
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Ingår i: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3324-3329
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Konferensbidrag (refereegranskat)abstract
- Drone-based system has a big potential to be applied for traffic monitoring and other advanced applications in Intelligent Transport Systems (ITS). This paper introduces our latest efforts of digitalising road traffic by various types of sensing systems, among which visual detection by drones provides a promising technical solution. A platform, called METRIC, is under recent development to carry out real-time traffic measurement and prediction using drone-based data collection. The current system is designed as a cyber-physical system (CPS) with essential functions aiming for visual traffic detection and analysis, real-time traffic estimation and prediction as well as decision supports based on simulation. In addition to the computer vision functions developed in the earlier stage, this paper also presents the CPS system architecture and the current implementation of the drone front-end system and a simulation-based system being used for further drone operations.
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| 10. |
- Ning, Mang, et al.
(författare)
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SeeFar : Vehicle Speed Estimation and Flow Analysis from a Moving UAV
- 2022
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Ingår i: Image Analysis and Processing, ICIAP 2022, PT III. - Cham : Springer Nature. ; , s. 278-289
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Konferensbidrag (refereegranskat)abstract
- Visual perception from drones has been largely investigated for Intelligent Traffic Monitoring System (ITMS) recently. In this paper, we introduce SeeFar to achieve vehicle speed estimation and traffic flow analysis based on YOLOv5 and DeepSORT from a moving drone. SeeFar differs from previous works in three key ways: the speed estimation and flow analysis components are integrated into a unified framework; our method of predicting car speed has the least constraints while maintaining a high accuracy; our flow analysor is direction-aware and outlier-aware. Specifically, we design the speed estimator only using the camera imaging geometry, where the transformation between world space and image space is completed by the variable Ground Sampling Distance. Besides, previous papers do not evaluate their speed estimators at scale due to the difficulty of obtaining the ground truth, we therefore propose a simple yet efficient approach to estimate the true speeds of vehicles via the prior size of the road signs. We evaluate SeeFar on our ten videos that contain 929 vehicle samples. Experiments on these sequences demonstrate the effectiveness of SeeFar by achieving 98.0% accuracy of speed estimation and 99.1% accuracy of traffic volume prediction, respectively.
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