The urban traffic flow prediction is a significant issue in the intelligent transportation system. In consideration of nonlinear and spatial-temporal features of urban traffic data, we propose a deep hybrid neural network improved by greedy algorithm for urban traffic flow prediction with taxi GPS trace. The proposed deep neural network model first combines the convolutional neural network (CNN), which extracts the spatial features, with the long short term memory (LSTM), which captures the temporal information, to predict urban traffic flow. Then, the proposed model is trained by a greedy policy to short time consumption and improves accuracy when a network goes deeper. Experimental results with real taxis GPS trajectory data from Xi'an city show that the improved deep hybrid CNN-LSTM model can achieve higher prediction accuracy and shorter time consumption compared with existing methods.

License plate recognition (LPR) is an important component of intelligent transportation systems. Compared with letters and numbers, Chinese characters contain more information, making automatic recognition more difficult. Accurate Chinese LPR (CLPR) is determined by three factors: training dataset, feature extractor, and classifier. Most license plates with benchmark dataset contain only letters and numbers; thus, the authors build a large dataset for CLPR. Convolutional neural networks (CNNs) can be used to extract inherent image features, on all levels of abstraction. CNNs can be used for classification if they have a sufficient number of fully connected layers. This implies that CNNs must be trained using gradient descent‐based methods, which often yields sub‐optimal results. Extreme learning machines (ELMs) demonstrate impressive performance on classification, with good generalisation. Therefore, the authors propose a novel deep architecture for CLPR which combines a CNN and an ELM. Firstly, a CNN without fully connected layers, working as a feature extractor, learns deep features associated with characters in written Chinese. Then, a kernel‐based ELM (KELM) classifier, which accepts CNN features as input, is utilised for classification. Compared with CNNs that use Softmax, support vector machines and ELMs, the CNN that uses KELM yields competitive results in a shorter training time.

Predicting city-scale taxi origin-destination (OD) flows takes an important role in understanding passengers’ travel demands as well as managing taxi operation and scheduling. But the complex spatial dependencies and temporal dynamics make this problem challenging. In this paper, a hybrid deep neural network prediction model based on convolutional LSTM (ConvLSTM) is proposed. For improving the prediction accuracy, the implicit correlation between travel time and OD flow is explored and they are combined as inputs of the prediction model. Moreover, in order to realize OD flows prediction at the road network level, and solve the problem that grid-based representation method cannot distinguish traffic flow at different heights, such as in multi-layer overpass areas, this paper presents a grid and road nested method to represent ODs. With the time of day partitioned into time slots, OD flows are extracted and predicted in both spatial and temporal domain. In the experiment, real taxi data are used to verify the proposed model and prediction method fully. And the experimental results show that the proposed model can effectively predict city-wide taxi OD flow, and outperforms the typical time sequence models and existing deep neural network models.

Recently, vehicular ad hoc networks (VANETs) got much popularity and are now being considered as integral parts of the automobile industry. As a subclass of MANETs, the VANETs are being used in the intelligent transport system (ITS) to support passengers, vehicles, and facilities like road protection, including misadventure warnings and driver succor, along with other infotainment services. The advantages and comforts of VANETs are obvious; however, with the continuous progression in autonomous automobile technologies, VANETs are facing numerous security challenges including DoS, Sybil, impersonation, replay, and related attacks. This paper discusses the characteristics and security issues including attacks and threats at different protocol layers of the VANETs architecture. Moreover, the paper also surveys different countermeasures.

An in-vehicle controller area network (CAN) bus is vulnerable because of increased sharing among modern autonomous vehicles and the weak protocol design principle. Spoofing attacks on a CAN bus can be difficult to detect and have the potential to enable devastating attacks. To effectively identify spoofing attacks, we propose the authentication of sender identities using a recurrent neural network with long short-term memory units (RNN-LSTM) based on the features of a fingerprint signal. We also present a way to generate the analog fingerprint signals of electronic control units (ECUs) to train the proposed RNN-LSTM classifier. The proposed RNN-LSTM model is accelerated on embedded Field-Programmable Gate Arrays (FPGA) to allow for real-time detection despite high computational complexity. A comparison of experimental results with the latest studies demonstrates the capability of the proposed RNN-LSTM model and its potential as a solution to in-vehicle CAN bus security.

Efficient carrier separation and suitable band edge position are necessary conditions for photocatalytic water splitting. Based on density functional theory, the electronic properties and optical performance of the GeC/ZnTe vdW heterostructure are systematically explored. The heterostructure with inherent type-II band arrangement is conducive to constantly separate the hole–electron pairs, thus improving the utilization of solar energy. Meanwhile, an excellent optical absorption coefficient has been proved in the heterostructure with large carrier mobility. Within the strain range of –4% to +3%, the heterostructure possesses appropriate band edges for photocatalytic water splitting and the light absorption performance is obviously improved. High solar-to-hydrogen efficiency (26.81%) means that the heterostructure can effectively convert photon energy into water splitting. These findings predict the possibility of GeC/ZnTe heterostructures as photocatalysts for water splitting and other optoelectronic devices.