Short-term traffic flow prediction plays a critical role in Intelligent Transportation System (ITS), and has attracted continuous attention. Previous studies have focused on improving the prediction accuracy of mean traffic flow. Due to the dynamics and propagation of traffic system, reliable traffic control and induction measures have been considered to be dependent on prediction intervals of short-term traffic flows. The current parametric models used to quantify uncertainty in traffic flow prediction cannot well capture the nonlinear patterns of traffic flow series, and may not apply to situations without long-term continuous observations. This paper proposes a hybrid framework combining long short-term memory neural network (LSTM NN) and Bayesian neural network (BNN) for real-time traffic flow prediction and uncertainty quantification based on sequence data. Caltrans Performance Measurement System (PeMS) traffic flow data for 6 freeways in Sacramento city is aggregated at 15-min intervals to evaluate the proposed model. Compared to the SARIMA-GARCH model, the proposed LSTM-BNN model outperforms in predicting both the mean and interval of the traffic flow. Especially, the experiments show that the LSTM-BNN model is superior during the daytime and under non-seasonal traffic conditions. The proposed LSTM-BNN model can be utilized in ITS for making reliable management decisions.

Real-time and accurate queue length information is crucial to developing effective queue management applications in modern traffic control systems to alleviate traffic congestion. A Random Forests (RF) based real-time queue length estimation method is proposed using the vehicle Global Position System (GPS) trajectory and License Plate Recognition (LPR) Data. The RF model is trained to predict the vehicle stop locations provided by the GPS data by features of traffic flow characteristics extracted from the LRP data. The predicted stop locations are further used to estimate the cyclic maximum queue length for each approach lane. The proposed method has been implemented on sixteen lanes of eight links from both major and minor arterials in Kunshan City, China. Key findings and conclusions include: 1) By feature selection, the travel time has the most significant impact on the prediction accuracy of the vehicle stop location, and the number of departed vehicles is the secondary informative feature. 2) The RF model achieves a satisfying accuracy for the stop location prediction and cyclic maximum queue length estimation, which has the best performance with a larger sample size in the training data. 3) Comparative analysis also shows the superiorities of the proposed model to have more accurate results by incorporating comprehensive features and a machine learning process.

Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose an efficient and robust low-rank model for precise spatiotemporal traffic speed state estimation (TSE) using low-penetration vehicle trajectory data. Leveraging traffic wave priors, an oblique grid-based matrix is first designed to transform the inherent dependencies of spatiotemporal traffic states into the algebraic low-rankness of a matrix. Then, with the enhanced traffic state low-rankness in the oblique matrix, a low-rank matrix completion method is tailored to explicitly capture spatiotemporal traffic propagation characteristics and precisely reconstruct traffic states. In addition, an anomaly-tolerant module based on a sparse matrix is developed to accommodate corrupted data input and thereby improve the TSE model robustness. Notably, driven by the understanding of traffic waves, the computational complexity of the proposed efficient method is only correlated with the problem size itself, not with dataset size and hyperparameter selection prevalent in existing studies. Extensive experiments demonstrate the effectiveness, robustness, and efficiency of the proposed model. The performance of the proposed method achieves up to a 12 $\%$ improvement in Root Mean Squared Error (RMSE) in the TSE scenarios and an 18 $\%$ improvement in RMSE in the robust TSE scenarios, and it runs more than 20 times faster than the state-of-the-art (SOTA) methods.

Rapid advancements in traffic monitoring and sensing technologies have permitted the multiplex and democratized gathering of numerous traffic data (e.g. speed, volume), depicting identical traffic dynamics from various but complementary views. Incomplete values are ubiquitous in these data, which undermines their utility in subsequent applications. In order to manage and enhance traffic data quality, most existing methods recover single traffic variable data independently based on intra-view spatiotemporal correlations, while the inter-view complementarities are ignored. In this paper, we leverage both intra-view and inter-view correlations for multiple traffic variable data simultaneous recovery. To explore the inter-view relationships, a multi-view subspace consistency learning module is developed to bridge connections and activate complementarities among multi-view traffic data. Specifically, the latent subspace features of each data view are extracted and organized as a multi-view subspace tensor with low-rank regularization. The multi-view low-rank tensor captures the consistent subspace structure across multiple data views while reserving unique features within each data view. To characterize the intra-view dependencies, a tensor-based low-rank representation is presented to explore the distinct spatiotemporal patterns within single-view traffic data. For model validation, we additionally design a nonrandom missing pattern to simulate sensor permanent failure cases in practice. Extensive experiments implemented on three real-world multi-view traffic datasets demonstrate the effectiveness and robustness of the proposed model.

Accurate and reliable traffic state identification is crucial to developing responsive and proactive traffic management applications. In this study, the problem of vehicle discharge state identification at signalized intersections is investigated, which focuses on the vehicle discharge process during the green interval. Instead of using detailed vehicle trajectory data and treating the observations of vehicles independently, this study formulates the vehicle discharge process in a Hidden Markov Model (HMM) framework using sequential observations of vehicle travel time and discharge headway as inputs. Three vehicle discharge states (i.e., overflow, single stop, and free arrival) are encoded as latent states, and a restricted left-to-right state transition matrix is imposed to respect the nature of the vehicle discharge process in the real world. The standard HMM is further extended to incorporate two informative covariates to parameterize the probabilities of the initial states and state transitions. The proposed models have been validated on the Next Generation Simulation (NGSIM) dataset. Compared to a benchmark model, the proposed models show their strength in correctly inferring the vehicle discharge state and are more reliable to use in presence of random missing observations. The effectiveness of covariate incorporation is also investigated, and several extended applications of the proposed models are discussed.

The acquisition of real-time and accurate traffic arrival information is of vital importance for proactive traffic control systems, especially in partially connected vehicle environments. License plate recognition (LPR) data that record both vehicle departures and identities are proven to be desirable in reconstructing lane-based arrival curves in previous works. Existing LPR data-based methods are predominantly designed for reconstructing historical arrival curves. For real-time reconstruction of multi-lane urban roads, it is pivotal to determine the lane choice of real-time link-based arrivals, which has not been exploited in previous studies. In this study, we propose a Bayesian deep learning approach for real-time lane-based arrival curve reconstruction, in which the lane choice patterns and uncertainties of link-based arrivals are both characterized. Specifically, the learning process is designed to effectively capture the relationship between partially observed link-based arrivals and lane-based arrivals, which can be physically interpreted as lane choice proportion. Moreover, the lane choice uncertainties are characterized using Bayesian parameter inference techniques, minimizing arrival curve reconstruction uncertainties, especially in low LPR data matching rate conditions. Real-world experiment results conducted in multiple matching rate scenarios demonstrate the superiority and necessity of lane choice modeling in reconstructing arrival curves.