396 research outputs found
Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction
Tracking congestion throughout the network road is a critical component of
Intelligent transportation network management systems. Understanding how the
traffic flows and short-term prediction of congestion occurrence due to
rush-hour or incidents can be beneficial to such systems to effectively manage
and direct the traffic to the most appropriate detours. Many of the current
traffic flow prediction systems are designed by utilizing a central processing
component where the prediction is carried out through aggregation of the
information gathered from all measuring stations. However, centralized systems
are not scalable and fail provide real-time feedback to the system whereas in a
decentralized scheme, each node is responsible to predict its own short-term
congestion based on the local current measurements in neighboring nodes.
We propose a decentralized deep learning-based method where each node
accurately predicts its own congestion state in real-time based on the
congestion state of the neighboring stations. Moreover, historical data from
the deployment site is not required, which makes the proposed method more
suitable for newly installed stations. In order to achieve higher performance,
we introduce a regularized Euclidean loss function that favors high congestion
samples over low congestion samples to avoid the impact of the unbalanced
training dataset. A novel dataset for this purpose is designed based on the
traffic data obtained from traffic control stations in northern California.
Extensive experiments conducted on the designed benchmark reflect a successful
congestion prediction
The Use of Markov Chain Analysis for Rule-Based Power and Energy Management Optimisation in Electric Vehicles
In this paper the development of a Rule-Based Power and Energy Management Strategy as a result of Markov Chain analysis will be shown. Using real-world drive cycle data a Markov Chain Transition matrix is build from which a Bias matrix is developed showing the difference between acceleration and deceleration with respect to the next velocity as an extension to the Markov Chain. From this the parameters for a PEMS are developed, simulated and the results discussed and compared to other strategies
Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
Large language models (LLMs) have been widely used in various applications
but are known to suffer from issues related to untruthfulness and toxicity.
While parameter-efficient modules (PEMs) have demonstrated their effectiveness
in equipping models with new skills, leveraging PEMs for deficiency unlearning
remains underexplored. In this work, we propose a PEMs operation approach,
namely Extraction-before-Subtraction (Ext-Sub), to enhance the truthfulness and
detoxification of LLMs through the integration of ``expert'' PEM and
``anti-expert'' PEM. Remarkably, even anti-expert PEM possess valuable
capabilities due to their proficiency in generating fabricated content, which
necessitates language modeling and logical narrative competence. Rather than
merely negating the parameters, our approach involves extracting and
eliminating solely the deficiency capability within anti-expert PEM while
preserving the general capabilities. To evaluate the effectiveness of our
approach in terms of truthfulness and detoxification, we conduct extensive
experiments on LLMs, encompassing additional abilities such as language
modeling and mathematical reasoning. Our empirical results demonstrate that our
approach effectively improves truthfulness and detoxification, while largely
preserving the fundamental abilities of LLMs
Designing the next generation intelligent transportation sensor system using big data driven machine learning techniques
Accurate traffic data collection is essential for supporting advanced traffic management system operations. This study investigated a large-scale data-driven sequential traffic sensor health monitoring (TSHM) module that can be used to monitor sensor health conditions over large traffic networks. Our proposed module consists of three sequential steps for detecting different types of abnormal sensor issues. The first step detects sensors with abnormally high missing data rates, while the second step uses clustering anomaly detection to detect sensors reporting abnormal records. The final step introduces a novel Bayesian changepoint modeling technique to detect sensors reporting abnormal traffic data fluctuations by assuming a constant vehicle length distribution based on average effective vehicle length (AEVL). Our proposed method is then compared with two benchmark algorithms to show its efficacy. Results obtained by applying our method to the statewide traffic sensor data of Iowa show it can successfully detect different classes of sensor issues. This demonstrates that sequential TSHM modules can help transportation agencies determine traffic sensors’ exact problems, thereby enabling them to take the required corrective steps.
The second research objective will focus on the traffic data imputation after we discard the anomaly/missing data collected from failure traffic sensors. Sufficient high-quality traffic data are a crucial component of various Intelligent Transportation System (ITS) applications and research related to congestion prediction, speed prediction, incident detection, and other traffic operation tasks. Nonetheless, missing traffic data are a common issue in sensor data which is inevitable due to several reasons, such as malfunctioning, poor maintenance or calibration, and intermittent communications. Such missing data issues often make data analysis and decision-making complicated and challenging. In this study, we have developed a generative adversarial network (GAN) based traffic sensor data imputation framework (TSDIGAN) to efficiently reconstruct the missing data by generating realistic synthetic data. In recent years, GANs have shown impressive success in image data generation. However, generating traffic data by taking advantage of GAN based modeling is a challenging task, since traffic data have strong time dependency. To address this problem, we propose a novel time-dependent encoding method called the Gramian Angular Summation Field (GASF) that converts the problem of traffic time-series data generation into that of image generation. We have evaluated and tested our proposed model using the benchmark dataset provided by Caltrans Performance Management Systems (PeMS). This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset. Further, the model achieves reasonably high accuracy in imputation tasks even under a very high missing data rate (\u3e50%), which shows the robustness and efficiency of the proposed model.
Besides the loop and radar sensors, traffic cameras have shown great ability to provide insightful traffic information using the image and video processing techniques. Therefore, the third and final part of this work aimed to introduce an end to end real-time cloud-enabled traffic video analysis (IVA) framework to support the development of the future smart city. As Artificial intelligence (AI) growing rapidly, Computer vision (CV) techniques are expected to significantly improve the development of intelligent transportation systems (ITS), which are anticipated to be a key component of future Smart City (SC) frameworks. Powered by computer vision techniques, the converting of existing traffic cameras into connected ``smart sensors called intelligent video analysis (IVA) systems has shown the great capability of producing insightful data to support ITS applications. However, developing such IVA systems for large-scale, real-time application deserves further study, as the current research efforts are focused more on model effectiveness instead of model efficiency. Therefore, we have introduced a real-time, large-scale, cloud-enabled traffic video analysis framework using NVIDIA DeepStream, which is a streaming analysis toolkit for AI-based video and image analysis. In this study, we have evaluated the technical and economic feasibility of our proposed framework to help traffic agency to build IVA systems more efficiently. Our study shows that the daily operating cost for our proposed framework on Google Cloud Platform (GCP) is less than $0.14 per camera, and that, compared with manual inspections, our framework achieves an average vehicle-counting accuracy of 83.7% on sunny days
STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction
As the development of cities, traffic congestion becomes an increasingly
pressing issue, and traffic prediction is a classic method to relieve that
issue. Traffic prediction is one specific application of spatio-temporal
prediction learning, like taxi scheduling, weather prediction, and ship
trajectory prediction. Against these problems, classical spatio-temporal
prediction learning methods including deep learning, require large amounts of
training data. In reality, some newly developed cities with insufficient
sensors would not hold that assumption, and the data scarcity makes predictive
performance worse. In such situation, the learning method on insufficient data
is known as few-shot learning (FSL), and the FSL of traffic prediction remains
challenges. On the one hand, graph structures' irregularity and dynamic nature
of graphs cannot hold the performance of spatio-temporal learning method. On
the other hand, conventional domain adaptation methods cannot work well on
insufficient training data, when transferring knowledge from different domains
to the intended target domain.To address these challenges, we propose a novel
spatio-temporal domain adaptation (STDA) method that learns transferable
spatio-temporal meta-knowledge from data-sufficient cities in an adversarial
manner. This learned meta-knowledge can improve the prediction performance of
data-scarce cities. Specifically, we train the STDA model using a
Model-Agnostic Meta-Learning (MAML) based episode learning process, which is a
model-agnostic meta-learning framework that enables the model to solve new
learning tasks using only a small number of training samples. We conduct
numerous experiments on four traffic prediction datasets, and our results show
that the prediction performance of our model has improved by 7\% compared to
baseline models on the two metrics of MAE and RMSE
Graph Convolutional Networks for Traffic Forecasting with Missing Values
Traffic forecasting has attracted widespread attention recently. In reality,
traffic data usually contains missing values due to sensor or communication
errors. The Spatio-temporal feature in traffic data brings more challenges for
processing such missing values, for which the classic techniques (e.g., data
imputations) are limited: 1) in temporal axis, the values can be randomly or
consecutively missing; 2) in spatial axis, the missing values can happen on one
single sensor or on multiple sensors simultaneously. Recent models powered by
Graph Neural Networks achieved satisfying performance on traffic forecasting
tasks. However, few of them are applicable to such a complex missing-value
context. To this end, we propose GCN-M, a Graph Convolutional Network model
with the ability to handle the complex missing values in the Spatio-temporal
context. Particularly, we jointly model the missing value processing and
traffic forecasting tasks, considering both local Spatio-temporal features and
global historical patterns in an attention-based memory network. We propose as
well a dynamic graph learning module based on the learned local-global
features. The experimental results on real-life datasets show the reliability
of our proposed method.Comment: To appear in Data Mining and Knowledge Discovery (DMKD), Springe
The Application of Regenerative Braking System to the Commercial Hybrid Vehicles with All-Wheel Drive System
The growing issues of energy shortage and the environmental crisis has resulted in new challenges for the automotive industry. Conventional commercial vehicles, such as refuse trucks and delivery vehicles, consume significantly more energy than other on-road vehicles since they have the characteristic of frequent start/stop with high moment of inertia and drive at low speeds on designated city routes. It is important to make these vehicles more fuel efficient and environmentally friendly. The hybrid commercial vehicle is a promising solution to reduce emissions and to meet the future vehicle emission standard since it is generally equipped with braking energy regeneration systems to recover the kinematic loss from frequent braking. This paper introduces a type of all-wheel drive hybrid concept suggested by Dr. Leo Oriet; the new concept allows commercial vehicles to have a significant improvement in kinetic braking energy recovery without sacrificing braking safety. Without mechanical connection involved to transfer energy within the powertrain, greater powertrain efficiency can be achieved. The research is based on the all-wheel drive with a two-axles regenerative braking strategy and driveline control unit. The vehicle model and driveline control unit were executed using AVL CRUISE to demonstrate its reliable braking energy regeneration system, effective energy management and emission reduction. Finally, the power system and engine operating condition, as well as vehicle driving mode, were analyzed after simulation to ensure the whole powertrain component functions together with high efficiency and significant reliability
Towards better traffic volume estimation: Tackling both underdetermined and non-equilibrium problems via a correlation-adaptive graph convolution network
Traffic volume is an indispensable ingredient to provide fine-grained
information for traffic management and control. However, due to limited
deployment of traffic sensors, obtaining full-scale volume information is far
from easy. Existing works on this topic primarily focus on improving the
overall estimation accuracy of a particular method and ignore the underlying
challenges of volume estimation, thereby having inferior performances on some
critical tasks. This paper studies two key problems with regard to traffic
volume estimation: (1) underdetermined traffic flows caused by undetected
movements, and (2) non-equilibrium traffic flows arise from congestion
propagation. Here we demonstrate a graph-based deep learning method that can
offer a data-driven, model-free and correlation adaptive approach to tackle the
above issues and perform accurate network-wide traffic volume estimation.
Particularly, in order to quantify the dynamic and nonlinear relationships
between traffic speed and volume for the estimation of underdetermined flows, a
speed patternadaptive adjacent matrix based on graph attention is developed and
integrated into the graph convolution process, to capture non-local
correlations between sensors. To measure the impacts of non-equilibrium flows,
a temporal masked and clipped attention combined with a gated temporal
convolution layer is customized to capture time-asynchronous correlations
between upstream and downstream sensors. We then evaluate our model on a
real-world highway traffic volume dataset and compare it with several benchmark
models. It is demonstrated that the proposed model achieves high estimation
accuracy even under 20% sensor coverage rate and outperforms other baselines
significantly, especially on underdetermined and non-equilibrium flow
locations. Furthermore, comprehensive quantitative model analysis are also
carried out to justify the model designs
Tabular Machine Learning Methods for Predicting Gas Turbine Emissions
The work presented here received funding from EPSRC (EP/W522089/1) and Siemens Energy Industrial Turbomachinery Ltd. as part of the iCASE EPSRC PhD studentship ”Predictive Emission Monitoring Systems for Gas Turbines”.Preprin
- …