The Leader-Follower Cooperative Driving Automation (LF-CDA) system, crucial for applications such as truck platooning and off-road vehicle convoys, relies on automation and communication technologies to virtually link multiple vehicles and has become a core focus in the automated vehicle industry. Accurate relative positioning is critical for LF-CDA operations, yet GNSS can be unreliable in challenging environments. Asymmetric architecture is common in many LF-CDA systems, making direct application of localization models either infeasible or both computationally and communication intensive. This manuscript presents a lightweight LiDAR-based cooperative localization model that leverages the unique characteristics of asymmetric LF-CDA systems, specifically the property of “asynchronous view repetition.” In this context, the follower vehicle, operating in vehicle-following mode, consistently receives similar visual and spatial information as the leader vehicle, though with a time delay. To capitalize on such system characteristics, an asynchronous view repetition-based graph optimization model is formulated to minimize the positional errors of both leader and follower vehicles. To provide input to and solve the graph optimization model, a lightweight cooperative localization framework with multiple submodules is established, allowing the system to function independently of environmental constraints. A comprehensive set of experiments was conducted in the CARLA simulation environment, using CT-ICP and KISS-ICP as benchmarks, given their strong performance in single-vehicle settings. The results indicate that, under the LF-CDA scenario, our proposed model demonstrates greater suitability by achieving higher localization accuracy while maintaining comparable or even superior computational efficiency.

The core of the freeway merging control problem lies in dynamic queue propagation and dissipation linked to merging vehicle behavior. Traditionally, queueing is modeled through demand-supply interactions with time-varying demand and fixed capacity. However, field observations indicate that flow rates drop during freeway congestion at merges due to the impact of intersecting traffic—a factor often overlooked in fundamental diagrams. This manuscript introduces an analytical approach to characterize and control the dynamic multi-stage merging of autonomous vehicles, prioritizing traffic efficiency and safety. For the first time, the effective discharge rate at the merging point is analytically derived in a closed-form, accounting for the reduction caused by the dynamic multi-stage merging process. Leveraging this expression, performance metrics such as queue length and traffic delay are derived as the first optimization objective. Additionally, a crash risk function is formulated to quantitatively assess potential collisions during the merging process, serving as the second objective. Finally, the problem is formulated as a dynamic programming model to jointly minimize delay and crash risk, with the merging location and speed as decision variables. Given the terminal state, the ramp vehicle merging task is formulated as a recursive optimization problem, employing backward induction to find the minimum-cost solution. Numerical experiments using the NGSIM dataset validate the derived effective discharge rate. The results indicate that the proposed model outperforms two benchmark algorithms, leading to a more efficient and safer merging process.

Digital twin (DT) technology integrates the physical world with its digitalized counterpart and suggests significant potential for intelligent transportation system development, such as CAV test and development. In the foreseeable near future, human-driven vehicles (HDVs) will continue to predominate, and a digital replica of the transportation system should reflect their behavioral patterns for enhanced simulation realism purposes. As such, stochastic driver behavior and vehicle dynamics should be respected. The observations serving as DT input, often captured at discrete moments (e.g., the roadside units and cameras are only installed at certain locations), result in discontinuously captured vehicle trajectories. The stochastic generation of behaviorally consistent vehicle trajectories conditional on such incomplete information becomes important. Current conditional approaches include modified Brownian bridge (MBB) and guided proposal bridge (GPB) may not be able to output realistic results. To fill this gap, we propose conditional generation methods of behaviorally consistent trajectories, employing the stochastic bridge approach for the first time. First, a vehicular dynamics model that encapsulates the stochasticity of the human–vehicle system is employed, and then we prove that MBB and GPB fail to generate satisfactory results. Then, a forward–backward method is proposed based on the backward Markov process, which takes the vehicular dynamics model as behavioral input. The proposed method is validated against real-world data and mainstream simulation platforms, showing that the forward–backward generation method provides consistent and realistic results. Its time consumption has also been proven to be promising for real-time DT applications.

Nowadays, Connected Automated Vehicles (CAVs) have emerged as powerful infrastructures for the next-generation Intelligent Transportation System (ITS) as the rapid technological advancements of communication networks and vehicular intelligence. While prospective platoon-based techniques in CAVs, the heterogeneous traffic condition poses a challenge for platoon control in the self-organized traffic bottleneck, thus making an urgent need for a practical sustainable transportation architecture. To address this problem, we propose a software defined architecture that leverages multi-agent techniques to mobile-edge computing networks for multi-vehicle adaptive platoon, which is called SD-M3ASP. The architecture supports centralized and decentralized management of vehicular edge communication resources between mobile vehicles and edge devices, and underpins sustainable vehicular platooning capabilities. Then, we propose cluster-based kinematic models by grouping vehicles into multi-vehicle clusters (MVCs) to facilitate efficient platoon control with collision avoidance. Furthermore, we propose three-stage platoon control algorithms to adaptively balance the size of MVCs and form stable platoons in heterogeneous traffic flows. The intra-platoon and inter-platoon convergence are analyzed by using the Routh stability criterion and Lyapunov technique. A CAV simulation software is developed for demonstration purposes which is available online athttps://qgailab.com/cav-sim. Extensive numerical simulation results have shown the superiority of the proposed method, which can greatly eliminate the self-organized congestion caused by heterogeneous traffic flow.

With the rapid development of wireless communication and localization technologies, the easier collection of trajectory data can bring potential data-driven value. Recently, there has been an increasing interest in how to publish trajectory dataset without revealing personal information. However, since the large-scale and real-world sequential trajectory dataset presents a heterogeneous regional distribution, the existing study ignores the relationship between privacy budget allocation and spatial characteristics, resulting in unreasonable continuity and mapping distortion, and thus lowering the utility of the synthetic dataset. To address this problem, we propose a probability distribution model named Adaptive grid-based Weighted Differential Privacy (AWDP). First, trajectories are adaptively discretized into the multi-resolution grid structures to make trajectories more uniformly distributed and less disturbed by the noise. Second, we allocate different weighted budgets for different grids according to density-based regional characteristics. Third, a spatio-temporal continuity maintenance method is designed to solve unrealistic direction- and density-based continuity deviations of synthetic trajectories. An application system is developed for demonstration purposes which is available online athttp://qgailab.com/awdp/. The extensive experiments on three datasets demonstrate that AWDP performs significantly better than the state-of-the-art model in preserving the density distribution of the original trajectories with differential privacy guarantee and high utility.

Recent developments in multiagent consensus problems have heightened the role of network topology when the agent number increases largely. The existing works assume that the convergence evolution typically proceeds over a peer-to-peer architecture where agents are treated equally and communicate directly with perceived one-hop neighbors, thus resulting in slower convergence speed. In this article, we first extract the backbone network topology to provide a hierarchical organization over the original multiagent system (MAS). Second, we introduce a geometric convergence method based on the constraint set (CS) under periodically extracted switching-backbone topologies. Finally, we derive a fully decentralized framework named hierarchical switching-backbone MAS (HSBMAS) that is designed to conduct agents converge to a common stable equilibrium. Provable connectivity and convergence guarantees of the framework are provided when the initial topology is connected. Extensive simulation results on different-type and varying-density topologies have shown the superiority of the proposed framework.

Trajectory datasets have been widely used in transportation research, but the risk of privacy breach comes with data sharing. Privacy budget allocation is a key step of the differential privacy (DP)-based privacy-preserving data publishing (PPDP) algorithm development, as it directly impacts the data utility of the released dataset. Most prior research used simple logic to allocate privacy budgets, such as evenly distributing them among different tree levels, without theoretical support to reach optimality. This manuscript presents the development of an optimal privacy budget allocation algorithm for transit smart card data, with the goal of publishing non-interactive sanitized trajectory data under a differential privacy definition. To this end, the smart card trajectory data are first stored in a prefix tree structure, and a query probability model is developed to quantitatively measure the probability of a trajectory location pair being queried. Next, the privacy budget is optimized for each prefix tree node to minimize the query error, while satisfying the differential privacy definition. The Lagrangian relaxation method is adopted to derive the optimal privacy budget values, and several propositions on the solution property are proposed and proved. Real-life metro smart card data from Shenzhen, China that include a total of 2.8 million individual travelers and over 220 million records are used in the case study section. The developed algorithm is demonstrated to output a sanitized dataset with the highest utilities when compared with three benchmark algorithms. Sensitivity analysis shows that the resulting data utility remains stable when the privacy budget changes. The runtime of the proposed algorithm is less than 160 seconds in all experiments, exhibiting good computational efficiency.

Accurate prediction of asphalt pavement condition is important to guide pavement maintenance practices. The existing models for pavement condition predictions are predominantly based on linear regressions or simple machine learning techniques. However, additional work on these models is needed to improve their basic assumptions, training efficiency, and interpretability. To this end, a new modeling approach is proposed in this manuscript, which includes a ThunderGBM-based ensemble learning model, coupled with the Shapley Additive Explanation (SHAP) method, to predict the International Roughness Index (IRI) of asphalt pavements. The SHAP method was applied to interpret the underlying influencing factors and their interactions. Twenty features were initially identified as the model inputs, and 2,699 observations were extracted from the Long-Term Pavement Performance (LTPP) database. Three benchmark models, namely the Mechanistic-Empirical Pavement Design Guide (MEPDG) model, the ANN model and the RF model, were used for comparison. The results showed that the developed model achieved a satisfactory result with a R-squared ($\mathbf {R}^{2}$) value of 0.88 and Root Mean Square Error (RMSE) of 0.08, both better than three benchmark models. It ran 86 times and 2.3 times faster than the ANN and RF model, respectively. Feature interpretation was performed to identify the top influencing factors of IRI. The 20-feature model was further simplified based on the analysis result. The simplified model only required six features to efficiently and effectively predict IRI using the proposed ThunderGBM-based approach, which can reduce the workload in data collection and management for pavement engineers.

The existence of a macroscopic fundamental diagram (MFD) in a network/subnetwork allows one to formulate hierarchical traffic management strategies. In order to achieve this, a robust and efficient network partitioning algorithm is needed. This research aims to create such an algorithm, where distinct MFD properties exist for each respective partition. The proposed four-step network partition approach utilizes the concept of lambda-connectedness and the technique of region growing and, unlike prior studies, can work with partial traffic data. This research brings forth the following contributions: 1) an algorithmic approach that allows for incomplete traffic datasets as an input and 2) an approach that does not require the user to arbitrarily pre-determine the number of necessary subnetworks. The proposed algorithmic approach can intuitively decide on the number of partitions based on the network connectivity and traffic congestion patterns. The proposed approach was implemented and tested on the regional planning network of Tucson/Pima County Arizona, USA. The MFD related statistics for each subnetwork are presented and discussed. Numerical analysis on lambda choice and algorithm sensitivity regarding different data missing ratios were also performed and elaborated.

This paper presents a two-stage model to calibrate the time-dependent, dynamic origin-destination (O-D) demand under congested traffic conditions. The first-stage model estimates O-D trip rates by minimizing link demand deviation with a one-norm formulation approach, so that over the calibration time period, the traffic demand on calibration links matches with the link demand from the field data. Due to its linear model structure, the first-stage model is more computationally effective and solvable on large real-life networks compared with the commonly seen least-square formulation. Then, a time-dependent user equilibrium traffic assignment model is formulated at the second stage to adjust the departure time profile iteratively, aiming to match the calibrated result with the field observed dynamic traffic condition, i.e., time-dependent speed profile. The second-stage model starts from the concept of a demand-capacity-volume relationship at a congested road segment, where demand exceeds supply, and utilizes shockwave theory to capture the differences between true demand and volume output, together with the idea of using travel time propagation between origin and bottleneck locations to infer real demand at the origin location. The two-stage model was implemented and tested in a case study in Tucson, AZ, USA, as an experimental proof of concept, which demonstrated the effectiveness of the proposed calibration framework and method under circumstances, in which the departure time profile was systematically distorted and initial demand solutions deviated from the true O-D matrices.