Applications of Deep Learning Models for Traffic Prediction Problems

Deep learning coupled with existing sensors based multiresolution traffic data and future connected technologies has immense potential to improve traffic operation and management. But to deal with complex transportation problems, we need efficient modeling frameworks for deep learning models. In this study, we propose two different modeling frameworks using Deep Long Short-Term Memory Neural Network (LSTM NN) model to predict future traffic state (speed and signal queue length). In our first problem, we present a modeling framework using deep LSTM NN model to predict traffic speeds in freeways during regular traffic condition as well as under extreme traffic demand, such as a hurricane evacuation. The approach is tested using real-world traffic data collected during hurricane Irma\u27s evacuation for the interstate 75 (I-75), a major evacuation route in Florida. We perform several experiments for predicting speeds for 5 min, 10 min, and 15 min ahead of current time. The results are compared against other traditional prediction models such as K-Nearest Neighbor, Analytic Neural Network (ANN), Auto-Regressive Integrated Moving Average (ARIMA). We find that LSTM-NN performs better than these parametric and non-parametric models. Apart from the improvement in traffic operation, the proposed method can be integrated with evacuation traffic management systems for a better evacuation operation. In our second problem, we develop a data-driven real-time queue length prediction technique using deep LSTM NN model. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync Adaptive Traffic Control System (ATCS) data to train a Long Short-Term Memory Neural Network model capturing time-dependent patterns of a queue of a signal. To select the best combination of hyperparameters, we use sequential model-based optimization (SMBO) technique. Our experiment results show that the proposed modeling framework performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well. Queue length prediction is a crucial part of an ATCS to optimize control parameters and this method can improve the existing signal optimization technique for ATCS

Data Driven Methods for Large Scale Network Level Traffic Modeling

Rapid growth in population along with urban-centric activities impose a massive demand on existing transportation systems, thus increasing traffic congestion and other mobility related challenges. To overcome such challenges, we need network-scale models to accurately predict real-time traffic demand and associated congestion. However, traditional network modeling approaches have shortcomings due to the complexity in traffic flow modeling, limited scope to incorporate real-time data available from emerging data sources and requiring excessive computation time to generate accurate estimation of traffic flows. Advancement in traffic sensing technologies with big data has created a new opportunity to overcome these challenges and implement deployable data-driven models to predict network-level traffic dynamics and congestion propagation in real time. However, existing data-driven approaches are limited in scope: they are developed for small-scale networks; they do not consider the fundamental concept of traffic flow propagation; and they are applied for short-term prediction ( \u3c 1 hour). In this dissertation, we develop graph convolution based neural network architectures for network scale traffic modeling as a solution to overcome these limitations. First, we develop a Graph Convolutional Neural Network (GCNN) Model to solve the traffic assignment problem in a data-driven way; the validation results show that the model can learn the user equilibrium traffic flow well (mean error \u3c 2%). Since the model can instantaneously determine the traffic flows of a large-scale network, this approach can overcome the challenges of deploying mathematical programming or simulation-based traffic assignment solutions for large-scale networks. Second, we scale this approach and develop a Graph Convolutional LSTM (GCN-LSTM) model for traffic movement volume prediction at intersection level. We rigorously tested the model over traffic movement volume data collected from Seminole County\u27s automated signal performance measure (ATSPM) database which show that 90% of cases, absolute error of the predicted values is less than 20. Finally, we develop a Dynamic Graph Convolutional LSTM (DGCN-LSTM) model to predict evacuation traffic flow for interstate network of Florida. The implemented model can be applied to predict evacuation traffic over a longer forecasting horizon (6-hour) with higher accuracy (R^2score 0.95). Hence, it can assist transportation agencies to activate appropriate traffic management strategies to reduce delays for evacuating traffic

Attention-Based Models for Text-Dependent Speaker Verification

Attention-based models have recently shown great performance on a range of tasks, such as speech recognition, machine translation, and image captioning due to their ability to summarize relevant information that expands through the entire length of an input sequence. In this paper, we analyze the usage of attention mechanisms to the problem of sequence summarization in our end-to-end text-dependent speaker recognition system. We explore different topologies and their variants of the attention layer, and compare different pooling methods on the attention weights. Ultimately, we show that attention-based models can improves the Equal Error Rate (EER) of our speaker verification system by relatively 14% compared to our non-attention LSTM baseline model.Comment: Submitted to ICASSP 201

A Cross-Shore Beach Profile Evolution Model

Developing an accurate and reliable time-averaged beach profile evolution model under normal and storm conditions is a challenging task. Over the last few decades, a number of beach deformation models have been developed under limited experimental conditions and uncertainties, and sometimes they required a long computation time. It is quite evident that a large amount of wave, current, sediment and beach profile data is available today. The present study leads to the development of a simple two-dimensional beach profile evolution model with on-offshore sand bar formation under non-storm and storm conditions based on the time-averaged suspended sediment concentration models of Jayaratne & Shibayama [2007] and Jayaratne et al. [2011]. These models were formulated for computing sediment concentration in and outside the surf zone under three different mechanisms: 1) suspension due to turbulent motion over sand ripples, 2) suspension from sheet flow layer and, 3) suspension due to turbulent motion under breaking waves. The suspended load is calculated by the product of time-averaged sediment concentration and undertow velocity from edge of the wave boundary layer to wave trough, and mass transport velocity from wave trough to crest (bore-like wave region). Sediment transport in wave boundary layer is computed from the modified Watanabe [1982] model. Rattanapitikon and Shibayama [1998] wave model is used to calculate the average rate of energy dissipation due to wave breaking. The beach deformation is calculated from the conservation of sediment mass while the avalanching concept of Larson and Kraus [1989] is used to re-distribute the sediment mass in neighbouring grids for a steady solution. Published field-scale experimental and natural beach profiles from 5 high-quality data sources from 1983-2009 [Kajima et al., 1983; Kraus and Larson, 1988; Port and Airport Research Institute, Japan, 2005, 2009; Hasan & Takewaka, 2007, 2009; Ruessink et al., 2007] are used to verify the performance of the proposed numerical model. The key feature in this process-based model is that it takes about a couple of minutes to simulate beach profiles of a 2-3 days storm qualitatively at a fairly satisfactory level using a standard personal computer. It is found that the present numerical predictions are not better than the null hypothesis as the model is in a stage of ongoing development. Therefore, it is believed that the final model is often more value to a practical coastal engineer than a very detailed study of hydrodynamics and sediment transport study, however an incorporation of swash dynamics, more precise evaluation of offshore sand bar formation and continuation to a longer time scale with precise beach deformation is recommended as the next stage of the model

Thermomechanical Properties of Jute/Bamboo Cellulose Composite and Its Hybrid Composites: The Effects of Treatment and Fiber Loading

Jute cellulose composite (JCC), bamboo cellulose composite (BCC), untreated hybrid jute-bamboo fiber composite (UJBC), and jute-bamboo cellulose hybrid biocomposite (JBCC) were fabricated. All cellulose hybrid composites were fabricated with chemical treated jute-bamboo cellulose fiber at 1 : 1 weight ratio and low-density polyethylene (LDPE). The effect of chemical treatment and fiber loading on the thermal, mechanical, and morphological properties of composites was investigated. Treated jute and bamboo cellulose were characterized by Fourier transform infrared spectroscopy (FTIR) to confirm the effectiveness of treatment. All composites were characterized by tensile testing, thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). Additionally, surface morphology and water absorption test was reported. The FTIR results revealed that jute and bamboo cellulose prepared are identical to commercial cellulose. The tensile strength and Young’s modulus of composites are optimum at 10 weight percentage (wt%) fibers loading. All cellulose composites showed high onset decomposition temperature. At 10 wt% fiber loading, JBCC shows highest activation energy followed by BCC and JCC. Significant reduction in crystallinity index was shown by BCC which reduced by 14%. JBCC shows the lowest water absorption up to 43 times lower compared to UJBC. The significant improved mechanical and morphological properties of treated cellulose hybrid composites are further supported by SEM images

EFFECT OF CHEMICAL TREATMENT ON RICE HUSK (RH) REINFORCED POLYETHYLENE (PE) COMPOSITES

In this study rice husk reinforced polyethylene composites and their test specimens were manufactured using a single screw extruder and an injection molding machine, respectively. Raw rice husk was chemically treated with benzene diazonium salt in alkali, acidic, and neutral media, in order to improve in the mechanical properties. The mechanical properties of the composites prepared from alkaline media treated rice husk were found to increase substantially compared to those of acidic media, neutral media, and untreated ones. However, the values for the alkaline media treated rice husk-PE composites at all mixing ratios were found to be higher than those of treated acidic media, treated neutral media, and untreated rice husk composites respectively. The SEM micrographs reveal that interfacial bonding between the treated filler and the matrix has significantly improved, suggesting that better dispersion of the filler into the matrix was achieved upon treatment of rice husk. Based on filler loading, 35% filler reinforced composites had the optimum set of mechanical properties among all composites manufactured