Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

The rapid development of connected automatic vehicle (CAV) technology makes vehicular ad hoc networks (VANETs) an urgently needed research field. It includes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) message flows. A roadside unit (RSU) is an important infrastructure for V2I communication and provides roadside information services for CAVs. However, an unoptimal RSU deployment may result in RSUs failing to improve the efficiency of VANETs and compromising the capability of service to most vehicles. Motivated by this observation, this study focuses on balancing the two objectives of efficiency and coverage and establishing an RSU deployment strategy based on traffic demand. In detail, this model optimizes both the average data delivery delay in VANETs and the number of vehicles covered by RSUs. The effectiveness of the method is verified by simulation in a 4 km ${\times }4$ km virtual road network. We also found that: 1) if 25% of the road segments in the road network are covered by RSUs, most vehicles can be served, and the delay of VANETs can be reduced; 2) compared with the road network with low traffic demand, more RSUs need to be deployed in the road network with high traffic demand to achieve the same effect; and 3) early RSU investment is more cost effective. Our method can provide a reference for the areas where RSU investments should be made and the priority of the areas.

Recently, short-term traffic prediction under conditions with corrupted or missing data has become a popular topic. Since a road section has predictive power regarding the adjacent roads at a specific location, this paper proposes a novel hybrid convolutional long short-term memory neural network model based on critical road sections (CRS-ConvLSTM NN) to predict the traffic evolution of global networks. The critical road sections that have the most powerful impact on the subnetwork are identified by a spatiotemporal correlation algorithm. Subsequently, the traffic speed of the critical road sections is used as the input to the ConvLSTM to predict the future traffic states of the entire network. The experimental results from a Beijing traffic network indicate that the CRS-ConvLSTM outperforms prevailing deep learning (DL) approaches for cases that consider critical road sections and the results validate the capability and generalizability of the model when predicting with different numbers of critical road sections.

Predicting the future trajectories of dynamic traffic actors is the Gordian knot for autonomous vehicles to achieve collision-free driving. Most existing works suffer from a gap in characterizing the evolving interactions of scenario components and ensuring the physical feasibility of predictions, particularly in highly heterogeneous scenarios. Therefore, we propose an Enhanced Multi-Stream Interaction Network (EMSIN), which is devoted to providing accurate trajectory predictions. EMSIN highlights several threads of high-level time-varying interactions, including agent-traffic semantic, self-trend, and agentagent dependencies. A novelly-designed trend-aware mechanism is developed to capture the self-trend interactions from different representation subspaces sufficiently. To model the spatial information of traffic agents and extract their evolutions, we present a dynamic adaptive graph convolutional network that extends previously predefined graph paradigms. The adaptive and dynamic graphs in EMSIN are created using learnable node embeddings, allowing the model to discern interaction strengths without additional attention modules. Finally, all highlevel feature spaces elaborating multi-stream interactions are fused to generate possible agent actions with corresponding confidence values. Comprehensive experiments conducted on both L5kit and nuScenes datasets demonstrate that EMSIN surpasses its counterparts, boasting smaller prediction errors and faster inference times. This study also introduces a fuzzy-based metric to probe the physical feasibility of predicted trajectories, providing valuable insights into appraising the performance of various prediction models from the perspective of fuzziness.

Vehicular platooning is emerging as a promising method that can enhance road utilization, alleviate traffic congestion and even decrease energy expenditure by shortening the distance between vehicles in the platoon. In platooning, a platoon leader (PL) is required to communicate with platoon members (PMs) to issue instructions. In this way, the PMs are simply expected to follow the command and enjoy their free time. However, when the same instruction is sent to different PMs in the form of single-hop unicast, the ciphertext will go up as the quantity of PMs in the platoon resulting in a dramatic increase in transmission time. Moreover, transmission instructions without security and authentication guarantees are easily intercepted, forged or deleted. Therefore, we propose BSPC, a broadcast signcryption scheme for platoon communication based on identity. In our BSPC, with only one signcryption, the PL can generate a public verifiable ciphertext with fixed-length for PMs, while employing broadcast functionality to deliver the ciphertext to multiple PMs at once. In such a manner, the confidentiality, integrity and authentication of transmitted commands are ensured, such that malicious vehicles cannot manipulate data and unauthorized vehicles have no way to access it. Besides, the instructions' transmit time is greatly reduced. Our BSPC proposal is proved to be secure and unforgeable through rigorous analysis. Furthermore, simulation results demonstrate our BSPC is practical and efficient for platoon communication.