Binary logistic regression has been used to estimate the probability of lane change (LC) in Cell Transmission Model (CTM). These models remain rigid, as flexibility predict LC for different cell size configurations not accounted for. This paper introduces a relaxation method refine conventional binary model using an event-tree approach. The increasing and length was estimated by expanding pre-d...

The lane-change maneuver prediction system for human drivers is valuable for advanced driver assistance systems (ADAS) in terms of avoiding unnecessary maneuver efforts or unsafe merging, as well as encouraging lane-change behaviors that could increase travel efficiency. Learning the decision-making process of an intended lane changing is essential to model semi/full autonomous vehicles control...

Recently released Autonomous Vehicle (AV) trajectory datasets can potentially catalyze research progress on AV-oriented traffic flow analysis. This paper aims to comprehensively and systematically process assess one of the open datasets, i.e., Waymo Open Dataset, with a focus car following paired trajectories. First, original dataset has been processed into user-friendly format which contains a...

1 The traffic flow heterogeneity caused by the different car-following dynamics among the different types 2 of vehicles has drawn increasing attention recently. This paper explores the characteristics of the four 3 types of car-truck car-following combinations, car-following-car (CC), car-following-truck (CT), truck4 following-car (TC) and truck-following-truck (TT), and their impacts on traffi...

We present a physics-informed deep learning (PIDL) approach to tackle the challenge of data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens (DL) neural network with knowledge flow theory accurately estimate conditions. The ‘physics’—a priori information system—acts as regularization agent during training. illustrate implementation propos...

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning autonomous vehicles. This paper proposes graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions all using past trajectories. tackles spatial interactions graph (GCN), captures temporal features with neural (CNN). The are encoded decoded by ga...

In the context of high-speed mixed traffic and intricate multi-vehicle interaction, existing driving intention recognition models for research vehicles inadequately address crucial factors, such as style vehicle-vehicle interaction information. This paper introduces a novel model based on an enhanced bidirectional long- short-term memory network (Bi LSTM). The proposed leverages trajectory sequ...

background: the trajectory of marital quality over the life course assumes a curvilinear pattern and declines over time. however, most studies to date have been conducted in developed societies, leaving the generalizability of their findings open to skepticism. in this study, we aimed to delineate the trajectory of marital satisfaction in iran. methods: using cluster-sampling method, representa...