A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models

Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates, it is difficult to place any level of trust to the model predictions, and operational strategies relying on overconfident predictions can lead to worsening traffic conditions. In this study, we propose a Bayesian recurrent neural network framework for uncertainty quantification in traffic prediction with higher generalizability by introducing spectral normalization to its hidden layers. In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting to the training data. This, in turn, helps improve the generalization performance of the model on out-of-distribution datasets. Results demonstrate that spectral normalization improves uncertainty estimates and significantly outperforms both the layer normalization and model without normalization in single-step prediction horizons. This improved performance can be attributed to the ability of spectral normalization to better localize the feature space of the data under perturbations. Our findings are especially relevant to traffic management applications, where predicting traffic conditions across multiple locations is the goal, but the availability of training data from multiple locations is limited. Spectral normalization, therefore, provides a more generalizable approach that can effectively capture the underlying patterns in traffic data without requiring location-specific models

Hybrid hidden Markov LSTM for short-term traffic flow prediction

Deep learning (DL) methods have outperformed parametric models such as historical average, ARIMA and variants in predicting traffic variables into short and near-short future, that are critical for traffic management. Specifically, recurrent neural network (RNN) and its variants (e.g. long short-term memory) are designed to retain long-term temporal correlations and therefore are suitable for modeling sequences. However, multi-regime models assume the traffic system to evolve through multiple states (say, free-flow, congestion in traffic) with distinct characteristics, and hence, separate models are trained to characterize the traffic dynamics within each regime. For instance, Markov-switching models with a hidden Markov model (HMM) for regime identification is capable of capturing complex dynamic patterns and non-stationarity. Interestingly, both HMM and LSTM can be used for modeling an observation sequence from a set of latent or, hidden state variables. In LSTM, the latent variable is computed in a deterministic manner from the current observation and the previous latent variable, while, in HMM, the set of latent variables is a Markov chain. Inspired by research in natural language processing, a hybrid hidden Markov-LSTM model that is capable of learning complementary features in traffic data is proposed for traffic flow prediction. Results indicate significant performance gains in using hybrid architecture compared to conventional methods such as Markov switching ARIMA and LSTM

Traffic incident duration prediction via a deep learning framework for text description encoding

Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents. This paper proposes a new fusion framework for predicting the incident duration from limited information by using an integration of machine learning with traffic flow/speed and incident description as features, encoded via several Deep Learning methods (ANN autoencoder and character-level LSTM-ANN sentiment classifier). The paper constructs a cross-disciplinary modelling approach in transport and data science. The approach improves the incident duration prediction accuracy over the top-performing ML models applied to baseline incident reports. Results show that our proposed method can improve the accuracy by $60\%$ when compared to standard linear or support vector regression models, and a further $7\%$ improvement with respect to the hybrid deep learning auto-encoded GBDT model which seems to outperform all other models. The application area is the city of San Francisco, rich in both traffic incident logs (Countrywide Traffic Accident Data set) and past historical traffic congestion information (5-minute precision measurements from Caltrans Performance Measurement System)

A Bidirectional Context-Aware and Multi-Scale Fusion Hybrid Network for Short-Term Traffic Flow Prediction

Short-term traffic flow prediction is to automatically predict the traffic flow changes in a period of future time based on the extraction of the spatiotemporal features in the road network. For governments, timely and accurate traffic flow prediction is crucial to plan road manage-ment and improve traffic efficiency. Recent advances in deep learning have shown their dominance on short-term traffic flow prediction. However, previous methods based on deep learning are mainly limited to temporal features and have so far failed to predict the bidirectional con-textual spatiotemporal relationship correctly. Besides, the precision and the practicality are limited by the road network scale and the single time scale. To remedy these issues, a Bidirectional Context-aware and Multi-scale fusion hybrid Network (BCM-Net) is proposed, which is a novel short-term traffic flow prediction framework to predict timely and accurate traffic flow changes. In BCM-Net, the Bidirectional Context-aware (BCM) block is added to the feature extraction structure to effective-ly integrate spatiotemporal features. The Interpolation Back Propagation sub-network is used to merge multi-scale information, which further improves the robustness of the model. Experiment results on diverse datasets demonstrated that the proposed method outperformed the state-of-the-art methods

Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system

Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). AOA makes use of the distribution properties of mathematics’ primary arithmetic operators, including multiplication, division, addition, and subtraction. AOA is mathematically modeled and implemented to optimize processes across a broad range of search spaces. The performance of AOA is evaluated against 29 benchmark functions, and several real-world engineering design problems are to demonstrate AOA’s applicability. The hyper-parameter tuning framework consists of a set of Lorenz chaotic system datasets, hybrid DNN architecture, and AOA that works automatically. As a result, AOA produced the highest accuracy in the test dataset with a combination of optimized hyper-parameters for DNN architecture. The boxplot analysis also produced the ten AOA particles that are the most accurately chosen. Hence, AOA with ten particles had the smallest size of boxplot for all hyper-parameters, which concluded the best solution. In particular, the result for the proposed system is outperformed compared to the architecture tested with particle swarm optimization

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

Information entropy-based intention prediction of aerial targets under uncertain and incomplete information

© 2020 by authors. To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-makin