Bus network plays a critical role in urban transportation affecting the use of private vehicles, traffic congestion, and urban accessibility. The accurate prediction of bus passenger flow is key to improving transit passenger experience and increasing the efficiency of bus network operations. In line with recent advances in deep learning for passenger flow prediction, graph neural networks (GNNs) have become increasingly popular due to their ability to account for the network structure between stops. Existing GNN-based models for bus passenger flow prediction, however, face several limitations. First, they do not take into account some distinctive characteristics of bus networks, such as their coexistence with vehicular traffic and their high sensitivity to urban traffic conditions. Moreover, sequence prediction models that have been widely applied to multistep passenger flow prediction suffer from a critical issue, called “exposure bias.” This results in the propagation and accumulation of errors through prediction steps while making predictions for farther time horizons. To address these issues, this study presents the Traffic-Aware multistep Graph Neural Network (TMS-GNN) model with Scheduled Sampling, a graph-based deep-learning framework designed to forecast multistep bus passenger flows at individual stops across a bus network. The model takes into account factors such as bus stop connectivity, urban traffic impacts, and multi-dimensional temporal patterns; and addresses exposure bias by employing a curriculum learning strategy called Scheduled Sampling. The comparison between the proposed model and other popular baseline models on two real-world networks with different geographical and urban patterns in Canada and USA shows that TMS-GNN outperforms the baselines in both the overall network-wide task, as well as multistep prediction. Also, to verify the contribution of the proposed components of the model, an ablation study is conducted. The results of the ablation study validate the design choices as well.

The integration of automated vehicles (AVs) into transportation systems presents an unprecedented opportunity to enhance road safety and efficiency. However, understanding the interactions between AVs and human-driven vehicles (HVs) at intersections remains an open research question. This study aims to bridge this gap by examining behavioral differences and adaptations of AVs and HVs at unsignalized intersections by utilizing two large-scale AV datasets from Waymo and Lyft. By using a systematic methodology, the research identifies and analyzes merging and crossing conflicts by calculating key safety and efficiency metrics, including time to collision, post-encroachment time, maximum required deceleration, time advantage, and speed and acceleration profiles. Through this approach, the study assesses the safety and efficiency implications of these behavioral differences and adaptations for mixed-autonomy traffic. The findings reveal a paradox: while AVs maintain larger safety margins, their conservative behavior can lead to unexpected situations for human drivers, potentially causing unsafe conditions. From a performance point of view, human drivers tend to exhibit more consistent behavior when interacting with AVs versus other HVs, suggesting AVs may contribute to harmonizing traffic flow patterns. Moreover, notable differences were observed between Waymo and Lyft vehicles, which highlights the importance of considering manufacturer-specific AV behaviors in traffic modeling and management strategies for the safe integration of AVs. The processed dataset, as well as the developed algorithms and scripts, are openly published to foster research on AV–HV interactions.

Ethical dilemmas are a common challenge in everyday driving, requiring human drivers to balance competing priorities such as safety, efficiency, and rule compliance. However, much of the existing research in automated vehicles (AVs) has focused on high-stakes "trolley problems,"which involve extreme and rare situations. Such scenarios, though rich in ethical implications, are rarely applicable in real-world AV decision-making. In practice, when AVs confront everyday ethical dilemmas, they often appear to prioritise strict adherence to traffic rules. By contrast, human drivers may bend the rules in context-specific situations, using judgement informed by practical concerns such as safety and efficiency. According to the concept of meaningful human control, AVs should respond to human reasons, including those of drivers, vulnerable road users, and policymakers. This work introduces a novel human reasons-based supervision framework that detects when AV behaviour misaligns with expected human reasons to trigger trajectory reconsideration. The framework integrates with motion planning and control systems to support real-time adaptation, enabling decisions that better reflect safety, efficiency, and regulatory considerations. Simulation results demonstrate that this approach could help AVs respond more effectively to ethical challenges in dynamic driving environments by prompting replanning when the current trajectory fails to align with human reasons. These findings suggest that our approach offers a path toward more adaptable, human-centered decision-making in AVs.

Predicting short-term passenger flows in bus networks is crucial to improving the overall performance of such systems and increasing their attractiveness. This study develops a graph neural network-based framework for multi-step passenger flow prediction specifically designed for bus networks to capture their unique characteristics. We propose the Multi-step Multi-component Graph Convolutional Long Short-Term Memory (Multi-GCN-LSTM) model, which uses 1) a proximity matrix in addition to an adjacency matrix to consider the effects of vehicular traffic and link-level distances; 2) Scheduled Sampling for multi-step prediction, which prevents error propagation across prediction steps; and 3) a novel fusion mechanism for considering time-varying spatial and temporal correlations among passenger flow data based on recent, daily, and weekly travel patterns. This model is validated using real-world data collected from the Laval bus network. Also, benchmarking the established model against state-of-the-art baselines indicated its competency.

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domain-specific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNN-based frameworks.

Intersections are critical bottlenecks within urban transportation networks. Current models for simulating two-dimensional (2D) vehicular movements at intersections are met with limitations in accurately representing complex interactions and capturing vehicle dynamics. Accordingly, this paper proposes a novel microsimulation framework for trajectory planning and vehicular control at intersections. The model considers vehicle dynamics and control variables, such as acceleration and steering angle, and releases the popular assumption that there is full knowledge sharing or cooperation among vehicles at intersections. These features make the proposed framework more realistic compared to previous microsimulation attempts and applicable to traffic flow and environmental impact assessment studies. In addition, it efficiently operates in realtime for multiple vehicles, overcoming the limitations of offline methods. Moreover, the model is capable of accounting for driver/vehicle detection range, reaction time, and perception and prediction inaccuracies, which enhances its suitability for safety assessments. The evaluation in several scenarios indicates the ability of the proposed framework in realtime planning and following realistic and consistent 2D paths while avoiding collisions with other vehicles.