A fundamental challenge in car-following (CF) modeling lies in accurately representing the multiscale complexity of driving behaviors, particularly the intradriver heterogeneity where a single driver's actions fluctuate dynamically under varying conditions. While existing models, both conventional and data-driven, address behavioral heterogeneity to some extent, they often emphasize interdriver heterogeneity or rely on simplified assumptions, limiting their ability to capture the dynamic heterogeneity of a single driver under different driving conditions. To address this gap, we propose a novel data-driven CF framework that systematically embeds discrete driving regimes (e.g., steady-state following, acceleration, cruising) into vehicular motion predictions. Leveraging high-resolution traffic trajectory datasets, the proposed hybrid deep learning architecture combines gated recurrent units (GRUs) for discrete driving regime classification with long short-term memory networks (LSTMs) for continuous kinematic prediction, unifying discrete decision-making processes and continuous vehicular dynamics to comprehensively represent interdriver and intradriver heterogeneity. Driving regimes are identified using a bottom-up segmentation algorithm and dynamic time warping (DTW), ensuring robust characterization of behavioral states across diverse traffic scenarios. Comparative analyses demonstrate that the framework significantly reduces prediction errors for multiple metrics while reproducing critical traffic phenomena, such as stop-and-go wave propagation and oscillatory dynamics.

Vehicular crowdsensing could enhance the traffic accident detection performance on freeways to the existing infrastructures by global task allocation. However, the uncertainty of traffic accidents and unknown of individual users bring difficulties for the task allocation. To address the problem, a global optimization method of task allocation with non-deterministic tasks and unknown users (GOTA-NU) is proposed. In the method, to reduce the influence of accident uncertainty, the accident risk is used to represent the sensing tasks of traffic accidents, and sensing task representation model is constructed by estimating the intrinsic temporal-spatial distribution of accident risk according to the wavelet transform and optimal transport theory. Meanwhile, to determine the users in future, a user estimation model is established according to macro statistical characteristics of traffic flow. Then the task allocation problem is transformed into a coverage problem for accident risk. A task allocation model is constructed by minimizing the user incentive cost with coverage level constraints of accident risk. And a greedy algorithm is proposed to solve it. To validate the proposed method, sensitivity experiments, robustness experiments, and comparison experiments are carried out on an open data source. The results show that the proposed method is efficient and reliable under different traffic conditions. The proposed method provides a reference for the long-term task allocation with non-deterministic tasks and unknown users.

Vehicular crowdsensing provides a new approach for freeway traffic accident detection. However, the uncertainty on traffic accidents and Mobile Users (MUs) brings great challenges for participant recruitment in constructing the deterministic representation of sensing tasks and estimating the participants. To address the challenges, a participant recruitment method for freeway traffic accident detection is proposed. In the method, to deal with the non-deterministic sensing tasks and MUs, the temporal-spatial distribution of accident risk is estimated by optimal transport theory to represent sensing tasks, and the probability distributions of MUs' trip distance and requested rewards are used to estimate MUs. Then the participant recruitment problem is converted into an optimal coverage problem for accident risk under the macro statistical characteristics of MUs. The participant recruitment model is established to determine the participants by maximizing the coverage rate of accident risk with the budget constraint. And a greedy heuristic strategy is used to solve the model. Simulation experiments are carried out to validate the proposed method. The results show the proposed method is effective and reliable in freeway traffic accident detection.

Cell tracking is critical for the modeling of plant cell growth patterns. A local graph matching algorithm is proposed to track cells by exploiting the tight spatial topology of cells. However, the local graph matching approach lacks robustness in the unregistered images because the feature descriptors are handcrafted. In this paper, we propose a Deep Local Patch Matching Network (DLPM-Net) to track cells robustly, by exploiting local patches' deep similarity information and cells' spatial-temporal contextual information. Furthermore, to reduce the time consumption during the matching process and enhance tracking accuracy, we take two steps to realize the tracking of non-division cells and the detection of cell divisions. In the first step, the DLPM-Net is employed to match the non-division cells by exploiting the cell pair candidates' local patch contextual information, then the non-matched cells are recorded as the cell division candidates. In the second step, the DLPM-Net is used to detect cell divisions from these non-matched cells, by exploiting the local patch contextual similarity between the mother cell's local patch and daughter cells' local patch. Compared with the existing local graph matching method, the experimental results show that the proposed method gains 29.1% improvement in the tracking accuracy.