Speed plays a significant role in evaluating the evolution of traffic status, and predicting speed is one of the fundamental tasks for the intelligent transportation system. There exists a large number of works on speed forecast; however, the problem of long-term prediction for the next day is still not well addressed. In this paper, we propose a multiscale spatio-temporal feature learning network (MSTFLN) as the model to handle the challenging task of long-term traffic speed prediction for elevated highways. Raw traffic speed data collected from loop detectors every 5 min are transformed into spatial-temporal matrices; each matrix represents the one-day speed information, rows of the matrix indicate the numbers of loop detectors, and time intervals are denoted by columns. To predict the traffic speed of a certain day, nine speed matrices of three historical days with three different time scales are served as the input of MSTFLN. The proposed MSTFLN model consists of convolutional long short-term memories and convolutional neural networks. Experiments are evaluated using the data of three main elevated highways in Shanghai, China. The presented results demonstrate that our approach outperforms the state-of-the-art work and it can effectively predict the long-term speed information.

A vehicle’s license plate is the unique feature by which to identify each individual vehicle. As an important research area of an intelligent transportation system, the recognition of vehicle license plates has been investigated for some decades. An approach based on a visual attention model and deep learning is proposed to handle the problem of Chinese car license plate recognition for traffic videos. We first use a modified visual attention model to locate the license plate, and then the license plate is segmented into seven blocks using a projection method. Two classifiers, which combine the advantages of convolutional neural network-based feature learning and support vector machine for multichannel processing, are designed to recognize Chinese characters, numbers, and alphabet letters, respectively. Experimental results demonstrate that the presented method can achieve high recognition accuracy and works robustly even under the conditions of illumination change and noise contamination.

Traffic flow prediction is quite crucial for estimating the future traffic states, efficient and accurate prediction models greatly contribute to the smooth traffic of road networks. However, existing methods mainly concentrate on the short-term prediction. The challenging task of long-term flow prediction for the next day, as the important reference of traffic management, is still not well solved. In this paper, we present a residual deconvolution based deep generative network (RDBDGN) to handle the problem of long-term traffic flow prediction. The proposed method consists of a generator and a discriminator. The generator is composed of multi-channel residual deconvolutional neural networks, and the discriminator contains a convolutional neural network which aims to optimize the adversarial training process. Experiments are evaluated based on the traffic flow data of elevated highways, presented results demonstrate that our approach outperforms the state-of-the-art works.

Deep learning models for short-term travel speed prediction on urban expressways, such as the convolutional neural network (CNN), still present several limitations in multiscale spatiotemporal feature extraction. Hence, in this paper, three hybrid CNN models are proposed to improve the basic CNN model with regard to three target aspects for short-term (i.e., 5 min) travel speed prediction on urban expressways. More specifically, long short-term memory (LSTM), AutoEncoder (AE), and Inception module are incorporated into the basic CNN model to capture multiscale spatiotemporal features of travel speed data effectively and improve the accuracy and robustness of the basic CNN model. Based on loop detector data collected on the Yan'an expressway in Shanghai, the proposed hybrid CNN models are trained and tuned. To validate the improvements on the target aspects, a comprehensive comparison is conducted using a classical statistical model (i.e., autoregressive integrated moving average), a typical shallow neural network model (i.e., artificial neural network), and two basic deep learning models (i.e., recurrent neural network and CNN). Results show that the prediction accuracies of all the proposed hybrid CNN models exceed 96% and the mean absolute errors are less than 2.5 km/h, which are superior to other models. In terms of target improving aspects, two new metrics were introduced, and the proposed models, especially the AE-CNN model, showed better robustness under various input data structures and traffic states. The LSTM-CNN model outperformed the other models in learning time-series features, and the Inception-CNN model is superior in reproducing the dynamics of traffic congestion patterns on urban expressways.

Accurate performance evaluation of discrete event systems needs a huge number of simulation replications and is thus time-consuming and costly. Hence, efficiency is always a big concern when simulations are conducted. To drastically reduce its cost when conducting them, ordinal optimization emerges. To further enhance the efficiency of ordinal optimization, optimal computing budget allocation (OCBA) is proposed to decide the best design accurately and quickly. Its variants have been introduced to achieve goals with distinct assumptions, such as to identify the optimal subset of designs. They are restricted in selecting the best design or optimal subset of designs. However, a highly challenging issue, i.e., subset ranking, remains unaddressed. It goes beyond best design and optimal subset problems. This work develops a new OCBA-based approach to address the issue and establishes its theoretical foundation. The numerical testing results show that, with proper parameters, it can indeed enhance the simulation efficiency and outperform other existing methods in terms of the probability of correct subset ranking and computational efficiency.