Advanced Air Mobility (AAM) is a crucial component of future intelligent transportation systems, where Uncrewed Aerial Vehicles (UAVs) undertake tasks such as inspection, monitoring, rescue, and logistics. However, in the early stages of development, it is essential to coordinate with other ground transportation vehicles to address issues such as battery life, multi-UAV conflicts, and computational load, thereby enhancing system reliability. Consequently, this paper investigates an urban emergency response paradigm, termed UBT, where UAVs routinely ride on the roofs of buses for charging and inspection, and collaborate with surrounding taxis and those near emergency sites to respond to unpredictable random incidents in urban areas. Within the UBT paradigm, a multi-modal emergency response process model for a single UAV is established, considering the mobility of buses and the spatiotemporal characteristics of taxis. Building on this, a multi-UAV emergency dispatch model is developed, incorporating data-driven spatiotemporal prediction models for taxis, and considering emergency response delays, UAV hovering time, and relay costs, to maximize urban spatiotemporal coverage benefits. Finally, the performance of UBT is comprehensively evaluated using a large-scale real-world vehicle trajectory dataset. The results indicate that, compared to baseline methods, under the premise of 95% spatiotemporal coverage, the UBT scheme increases UAV hovering time by 65.3%, reduces infrastructure costs by 99.6%, and increases emergency response delays by only 9.36%.

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.

In the planning of public charging facilities and the charging activity network of users, there is a decision-making conflict among three stakeholders: the government, charging station enterprises, and electric vehicle users. Previous studies have described the tripartite game relationship in a relatively simplistic manner, and when designing charging facility planning schemes, they did not consider scenarios where users’ choice preferences undergo continuous random changes. In order to reduce the impacts of queuing phenomenon and resource idleness on the three participants, we introduce a bilateral matching algorithm combined with the dynamic Huff model as a strategy for EV charging selection in the passenger flow problem based on the three-dimensional activity network of time–space–energy of users. Meanwhile, the Dirichlet distribution is utilized to control the selection preferences on the user side, constructing uncertain scenarios for the choice of user charging activities. In this study, we establish a bilevel programming model that takes into account the uncertainty in social responsibility and user charging selection behavior. Solutions for the activity network and facility planning schemes can be derived based on the collaborative relationships among the three parties. The model employs a robust optimization method to collaboratively design the charging activity network and facility planning scheme. For this mixed-integer nonlinear multi-objective multi-constraint optimization problem, the model is solved by the NSGA-II algorithm, and the optimal compromise scheme is determined by using the EWM-TOPSIS comprehensive evaluation method for the Pareto solution set. Finally, the efficacy of the model and the solution algorithm is illustrated by a simulation example in a real urban space.

Signalized intersections are among the most crash-prone elements of urban traffic networks. Their complex layouts and heterogeneous road users pose substantial challenges for accurate trajectory prediction. Although high-definition (HD) maps are widely used to boost prediction performance, their high construction and maintenance costs and limited update frequency hinder practical deployment. Meanwhile, existing map-free models still struggle to reach the accuracy required in such complex settings. To address these issues, we propose a map-free intersection graph network (MFIG-Net), a map-free, multiagent trajectory prediction framework tailored to signalized intersections. MFIG-Net comprises three components. First, a multisource feature encoder (MSFE) builds per-agent node embeddings by jointly encoding motion histories and static attributes together with traffic-signal phase semantics; the latter are represented using remaining-time sinusoidal positional encodings passed through a multilayer perceptron (MLP) to capture phase transitions. Second, the interaction layer combines a conflict-aware graph attention (CAGA) module—modeling dense spatial interactions via physically grounded, directed edge features and enhanced multihead attention—with a Mamba-based temporal block that captures long-range dependencies through linear-time selective state-space dynamics. Third, a Laplace mixture density network (MDN) decoder produces $K$ multimodal future trajectories per agent with per-timestep scale parameters and mode probabilities, yielding accurate and calibrated uncertainty. Experiments on the SinD dataset from two-phase signalized intersections in Tianjin and Xi’an validate the effectiveness of the proposed model. Additional evaluations on the Argoverse dataset further demonstrate its applicability across diverse urban scenarios, where it outperforms most existing map-free approaches.