Hard shoulder running (HSR) has emerged as a sustainable and cost-effective strategy for improving expressway capacity. To address the limitations of existing approaches in capturing short-term traffic fluctuations and the coarse granularity of conventional sensor data, a short-term congestion prediction–driven, scenario-adaptive dynamic HSR (D-HSR) control framework based on multi-segment expressway video surveillance data is proposed. Specifically, a YOLOv8-DeepSORT pipeline is employed to extract real-time traffic flow parameters from video streams. Acongestion warning model is then developed to define dynamic control thresholds for HSR activation. Two distinct traffic scenarios are considered: recurrent and incident-induced congestion. The corresponding HSR activation decisions are formulated as time series forecasting (TSF) and traffic condition assessment (TCA) tasks, respectively. To enhance temporal modeling performance, S-Mamba is introduced as a high-capacity deep sequence model that enables more responsive and accurate traffic state predictions. The proposed strategy is implemented and evaluated on a calibrated simulation of the G25 section of the Changchun to Shenzhen expressway. Compared with the next-best model and rule-based baselines, the proposed method achieves a 1.95% and 31.13% reduction in average fuel consumption and a 4.06% and 58.53% reduction in average travel time, respectively. The results validate the effectiveness of the proposed strategy in facilitating intelligent and D-HSR operations for congestion mitigation.

The difficulty of vehicle trajectory prediction mainly lies in the shared spatial-temporal relationships among vehicles. To address this task effectively, extracting the spatial-temporal details that affect the inter-vehicle motion, such as motion intentions and interactions, is crucial. This paper proposes a Mamba-driven differential Transformer model with an intention coupling decoder (ICMDT). Differential Transformer determines final attention scores by calculating the difference between two independent softmax attention maps, effectively suppressing the noise in spatial-temporal features. ICMDT integrates the strengths of differential Transformer and Mamba, concentrating on encoding spatial-temporal features while eliminating redundant information. It also enhances global information aggregation and spatial interaction modeling. Additionally, an intention coupling decoder is proposed to align motion intentions with spatial-temporal features, achieving connections between features and intention query instances. This decoder facilitates accurate multi-modal trajectory predictions. ICMDT has demonstrated superior performance across four real-world datasets, surpassing multiple metrics. For instance, improvements of 45.92% to 57.89% in long-term prediction (3∼5s) are achieved using the RMSE metric, which is quite promising. Notably, our experiments reveal that the intention coupling decoder consistently enhances the prediction accuracy of several leading prediction models, providing new insights for the future development of vehicle trajectory prediction algorithms.

Accurate trajectory prediction plays a crucial role in optimizing the performance of Internet of Vehicles (IoV) systems, reducing data transmission overhead, and enhancing communication network security. However, the expanding sensing range in IoV has led to increasingly complex spatial-temporal interactions, posing significant challenges for future trajectory prediction endeavors. Currently, predominant approaches involve constructing spatial-temporal interactions through various attention mechanisms. Nevertheless, these methods often yield numerous redundant interactions, potentially resulting in unstable predictions and diffuse interactions. Consequently, there is a pressing need to enhance the application of these methods in trajectory prediction within IoV contexts. Motivated by these challenges, our work introduces a sparse spatial-temporal Transformer (SSTT) to predict vehicle trajectories. SSTT consists of two main Transformer modules: the sparse spatial Transformer and the local-global temporal Transformer. We integrate a learnable sparse plugin into the former to minimize extraneous information in spatial interactions. This plugin enables SSTT to focus more effectively on critical interactive neighbor vehicles by optimizing attention weight distribution, thereby enhancing optimization convergence and prediction accuracy. For the latter, local time windows are employed to capture temporal local correlations and extend the attentional receptive field. Experimental results conducted on three real-world datasets demonstrate that SSTT achieves state-of-the-art performance, and even when only 15% of the training data is used, it can still outperform SOAT baselines. This study presents novel ideas and methodologies for advancing trajectory prediction techniques within the IoV paradigm. The code and our model will be available at GitHub.

Vehicle trajectory prediction (VTP) poses unique spatial-temporal modeling challenges, as a human-like prediction requires considering fine-grained interactions. Prior models have often used various attention mechanisms to extract key spatial-temporal interactions from the foreground, thus missing background information processing. However, this may result in the model’s insufficient generalization ability in a specific modality. Here this paper presents VCIFormer, a velocity-aware complementary interaction Transformer designed to enhance vehicle trajectory prediction by capturing complex spatial-temporal interactions between foreground and background with context awareness. VCIFormer combines inverse attention with traditional spatial-temporal attention as a complementary mechanism, applying bidirectional optimization to capture foreground and background attention flows. An adaptive visual mask is also developed to align attention allocation with human visual patterns at varying velocities. It enables the model to prioritize critical regions analogous to human driving behavior. Moreover, a context-aware encoder, consisting of a surround-aware module and a motion-enhancement module, is incorporated to provide additional interaction cues and spatial information. VCIFormer is evaluated on six real-world datasets (NGSIM, HighD, RounD, ExiD, Argoverse, and nuScenes) and attains state-of-the-art performance in critical metrics. For example, in comparison to baseline models, there are significant improvements in ADE and FDE by 2.84-18.18% and 9.88-13.33% on the NGSIM, HighD, RounD, and ExiD datasets, respectively. In sum, compared with previous architectures, VCIFormer presents a more effective combination of spatial-temporal interaction layers and context awareness for VTP.

Generative Adversarial Networks (GAN) have been widely used in traffic data imputation to improve the accuracy of data imputation. However, existing GAN-based models often suffer from mode collapse and cannot fully reflect the complex characteristics of real-world traffic, which affects the quality of data imputation. To address these challenges, we incorporate the Diffusion Model (DM) into the GAN framework, integrating the traffic dynamics modeling process within the Diffusion-GAN network. Based on this, we propose a Diffusion-TGAN speed data imputation model to generate individual vehicle speeds. Combined with the generated vehicle speed, the group trajectory reconstruction result is further given. The model uses the forward process of DM to generate condition vectors to guide the training of GAN generator. Subsequently, the discriminator of GAN takes the traffic dynamics constraints into account during adversarial training. Traffic dynamics modeling aims to make the generated speed data consistent with the real traffic characteristics. Experiments on multiple data sets show that the proposed model effectively imputes in the spatio-temporal speed data, and reduces the RMSE of the speed considering the position by 23.4% compared with the common GAN model, and reduces the RMSE by 39.7% in the trajectory reconstruction respectively. The code and our model are available at GitHub.

The upgrading and updating of vehicles have accelerated in the past decades. Out of the need for environmental friendliness and intelligence, electric vehicles (EVs) and connected and automated vehicles (CAVs) have become new components of transportation systems. This paper develops a reinforcement learning framework to implement adaptive control for an electric platoon composed of CAVs and human-driven vehicles (HDVs) at a signalized intersection. Firstly, a Markov Decision Process (MDP) model is proposed to describe the decision process of the mixed platoon. Novel state representation and reward function are designed for the model to consider the behavior of the whole platoon. Secondly, in order to deal with the delayed reward, an Augmented Random Search (ARS) algorithm is proposed. The control policy learned by the agent can guide the longitudinal motion of the CAV, which serves as the leader of the platoon. Finally, a series of simulations are carried out in simulation suite SUMO. Compared with several traditional reinforcement learning approaches, the proposed method can obtain a higher reward. Meanwhile, the simulation results demonstrate the effectiveness of the delay reward, which is designed to outperform distributed reward mechanism. Compared with some state-of-the-art optimization-based frameworks, the simulation analysis reveals that more energy can be saved for different sizes of platoon. Sensitivity analysis is also conducted by adjusting the relative importance of the optimization goal to show the flexibility of ARS and delay reward setting. On the premise that travel delay is not sacrificed, the proposed control method can save up to 53.64% electric energy.

Recent advances in deep reinforcement learning have shown promising results in solving sophisticated control problems with high dimensional states and action space. Inspired by this, we use the latest deep reinforcement learning (DRL) methods to improve freeway traffic mobility and alleviate recurring bottlenecks and congestion. More specifically, this paper proposes a centralized traffic control system that can coordinate multiple ramp metering (RM) and variable speed limit (VSL) traffic controllers on freeways to minimize the total travel time. The system uses a novel double-layer structure to synchronize different traffic controllers and introduces the actor-critic-based DRL methods to learn joint actions in a high-dimensional traffic environment. The reward function takes into account the waiting time of vehicles, the average speed of different road sections, and the on-ramp queuing limit to improve traffic mobility. We also proposed an integrated feedback controller as a benchmark. The simulation results show that the actor-critic-based methods are superior to other methods and can save more than 20% of the total travel time. We also analyzed the curse of dimensionality problem by comparing the performance of two scenarios in the simulation: one is a single-ramp interweaving area scenario; the other is a large freeway corridor with multiple on-ramps and off-ramps. The results show that our system can effectively handle these two situations without significant performance degradation, which means that the centralized control system can effectively control freeway corridors by directly guiding various traffic controllers. This also leads to the conclusion that we can use a centralized actor-critic-based control unit to manage medium-scale freeway traffic to save computing resources instead of using complex collaboration strategies.

The study proposes a cooperative lane changing strategy to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). The cooperative strategy was implemented by the coordination of behaviors between the diverging vehicle and its cooperative vehicle on the target lane. The Minimizing Overall Braking Induced by Lane Changes Model (MOBIL) and Intelligent Driver Model (IDM) were modified to develop a simulation platform for a CAV environment. The optimal cooperative lane changing zones were firstly calculated by a heuristic algorithm, and then were applied in the simulation platform to implement the cooperative strategy. Various metrics were considered to evaluate the proposed strategy, including: total travel time, surrogate safety measures and traffic waves in the system. The experimental results showed that the length of the optimal cooperative zones obtained in our strategy were smaller than the fixed zone required in modified MOBIL strategy. Moreover, the results indicated that the cooperative strategy with the optimal zones, could improve traffic operation, traffic safety and traffic oscillation as compared to the modified MOBIL strategy with the fixed zone. The cooperative strategy can be potentially implemented nearby highway off-ramps by vehicle-based control, with the applications of the aforementioned cooperative zones.

In reality, readings of sensors on highways are usually missing at various unexpected moments due to some sensor or communication errors. These missing values do not only influence the real-time traffic monitoring but also prevent further traffic data mining. In this paper, we propose a multi-view learning method to estimate the missing values for traffic-related time series data. The model combines data-driven algorithms (long-short term memory and support vector regression) and collaborative filtering techniques. It can consider the local and global variation in temporal and spatial views to capture more information from the existing data. The estimations of missing values from four views are aggregated to obtain a final value with a kernel function. Data from a highway network are used to evaluate the performance of the proposed model in terms of accuracy, precision, and agreement. The results indicate that our proposed model outperforms other baselines, especially for block missing pattern with a high missing ratio. Furthermore, the sensitivity of the parameters is analyzed. We can conclude that combining different views can improve the performance of the imputation.

Low-rank tensor completion methods have been advanced recently for modeling sparsely observed data with a multimode structure. However, low-rank priors may fail to interpret the model factors of general tensor objects. The most common method to address this drawback is to use regularizations together with the low-rank priors. However, due to the complex nature and diverse characteristics of real-world multiway data, the use of a single or a few regularizations remains far from efficient, and there are limited systematic experimental reports on the advantages of these regularizations for tensor completion. To fill these gaps, we propose a modified CP tensor factorization framework that fuses the l2norm constraint, sparseness (l1norm), manifold, and smooth information simultaneously. The factorization problem is addressed through a combination of Nesterov's optimal gradient descent method and block coordinate descent. Here, we construct a smooth approximation to the l1norm and TV norm regularizations, and then, the tensor factor is updated using the projected gradient method, where the step size is determined by the Lipschitz constant. Extensive experiments on simulation data, visual data completion, intelligent transportation systems, and GPS data of user involvement are conducted, and the efficiency of our method is confirmed by the results. Moreover, the obtained results reveal the characteristics of these commonly used regularizations for tensor completion in a certain sense and give experimental guidance concerning how to use them.

As the vital node of a passenger transportation network, the transportation hub is the connection between multiple travel modes and the important port for the massive passenger flow to enter into or exit from a city area. Transportation operators need to understand the passenger flow pattern for hub management, transportation planning, and so on. However, it is difficult to use traditional methods, such as video detection, to provide such information. With the increasing number of mobile phone users, mobile phone data have shown remarkable potential in detecting the transportation information with high sampling coverage and low cost. This paper utilizes the mobile phone data to characterize the passenger flow of the Hongqiao transportation hub located in Shanghai, China. First, a temporal-spatial clustering method is proposed to identify the passenger active area of the Hongqiao hub in the wireless communication space. Second, a classification process is presented to extract different types of passengers in this transportation hub. Subsequently, the access characteristics of passengers in the city are studied for various time intervals. The results further verify the potential of using mobile phone data to monitor and characterize passenger flow related to the transportation hubs.

Orthogonal Frequency Division Multiplexing (OFDM) has been considered as a strong candidate for next generation high speed aerial vehicle communication systems. However, OFDM systems suffers severe performance degradation due to inter-carrier interference (ICI) in high mobility channel, if no ICI cancellation is performed. Traditionally, training symbols have been employed in one packet to help the OFDM receiver to estimate the multi-path channel and the carrier frequency offset (CFO) between the transmitter local oscillator and the receiver local oscillator. However, in aerial vehicle communication, the relative transmitter-receiver speed changes so rapidly that it is unreasonable to assume a constant speed (and CFO) during the entire packet transmission. Hence, to accurately estimate the CFO, training symbols need to be transmitted for every OFDM symbol. Obviously, this significantly reduces OFDM throughput while adding complexity due to repeated CFO estimation. In this paper, we extend our previous work to propose a joint channel/CFO estimation and ICI cancellation algorithm. Specifically, in our previous work, we have proposed a total ICI cancellation algorithm using parallel processing for OFDM system which offers the excellent ICI cancellation and BER performance. However, in this work, perfect channel information was assumed. In this paper, we combine the channel estimation with the ICI cancellation together. The proposed general total ICI cancellation algorithm has the ability to jointly estimate the carrier frequency offset and channel information, and improve the performance significantly. Meanwhile, a serial processing is proposed to reduce the computation complexity. Simulation results in different scenarios confirm the performance of the proposed scheme in multipath fading channels for high speed aerial vehicle communication.

This paper proposes a novel multi-carrier CDMA scheme, namely FD-CI/MC-CDMA (frequency division carrier interferometry multi-carrier code division multiple access), to combine the benefits of previously developed FD-MC-CDMA with CI/MC-CDMA together to provide higher BER performance in multipath fading channels. FD-MC-CDMA is capable of exploiting the available frequency diversity benefits in multipath fading channels while reducing MAI. Specifically, instead of transmitting all users' information bits over all carriers, FD-MC-CDMA employs a subset of non-contiguous carriers to support a subset of users (while maintaining the same overall system capacity and throughput as in MC-CDMA). However, since the length of Hadamard-Walsh codes only exists for certain integers, FD-MC-CDMA lacks the flexibility to accurately divide all subcarriers into subsets according to the channel condition. On the other hand, CI/MC-CDMA offers good BER performance while providing any length of orthogonal spreading codes. By combining these two systems together, the proposed FD-CI/MC-CDMA system significantly outperforms FD-MC-CDMA (as well as MC-CDMA) in multipath fading channel at similar complexity.