arXiv subjects

Tianlu Pan

Publications and source records attributed to Tianlu Pan.

4 recordsLinked to original sources

arXiv2026

Preference-Driven Online Adaptation for Personalized Interaction Initiation in Proactive AI Assistants

AI assistants are typically reactive, relying on users to initiate interactions. Proactive assistants go beyond this paradigm by autonomously initiating interactions based on users' activity contexts. However, appropriate interaction timing is user-specific and difficult to determine in advance, while online feedback offers valuable signals for personalization. Direct feedback-driven adaptation is therefore appealing, but remains challenging due to sparse interaction-worthy moments scattered across fine-grained user states. To address the issues, we propose Evidence-driven Online Preference Adaptation (EOPA), which grounds a user's interaction-timing preferences in measurable contextual evidence through two evidence carriers: temporal preference anchors and evidence-bearing activity prototypes. At each polling step, EOPA derives temporal and activity evidence from the carriers through user-prior-smoothed evidence estimation and uncertainty-guided evidence scaling, and adaptively fuses the evidence for interaction-or-silence decisions. When interaction is selected, an LLM uses high-quality historical responses as demonstrations to generate a context-aware response that better reflects user preferences. EOPA updates its evidence carriers and decision parameters from received online feedback without LLM-based reasoning or retraining. Extensive experiments on a ProPerSim-based benchmark show that EOPA improves the interaction-timing F1 score by 19.80 points over the strongest baseline in our experiments, substantially reduces inference latency for both silence and interaction steps, and lowers the average daily adaptation time from 11.41 to 0.39 seconds.

cs.HC↗

arXiv2025

Calibration and uncertainty quantification of macroscopic fundamental diagrams

Traffic congestion occurs as travel demand exceeds network capacity, necessitating a thorough understanding of network capacity for effective traffic control and management. The macroscopic fundamental diagram (MFD) provides an efficient framework for quantifying network capacity. However, empirical MFDs exhibit considerable data scatter and uncertainty. In this paper, we propose a mathematical program that simultaneously calibrates the MFD and quantifies the uncertainty associated with data scatter. We further investigate contributing factors of uncertainties regarding network capacity and traffic resilience. To be specific, we first include two conventional approaches for MFD calibration and uncertainty quantification as special cases. The proposed program is validated using empirical data from two cities in China. Subsequently, we identify how congestion loading and recovery contribute to data scatter. We develop a novel uncertainty quantification approach capable of capturing distinct congestion phases simultaneously. The gap between the upper and lower bounds of the calibrated MFDs, represented by a coefficient parameter, is regarded as the capacity drop induced by traffic congestion. Furthermore, the proposed approach enables further exploration of how macroscopic factors, such as travel demand and traffic control, and microscopic factors, such as driving behavior, influence MFD hysteresis, uncertainty, and traffic resilience. These findings shed light on developing efficient traffic control and demand management strategies to increase network capacity and resilience, while we count on future connected automated vehicular technologies to conquer the influence of various driving behaviors.

physics.soc-ph↗

arXiv2025

Integrated Take-off Management and Trajectory Optimization for Merging Control in Urban Air Mobility Corridors

Urban Air Mobility (UAM) has the potential to revolutionize daily transportation, offering rapid and efficient aerial mobility services. Take-off and merging phases are critical for air corridor operations, requiring the coordination of take-off aircraft and corridor traffic while ensuring safety and seamless transition. This paper proposes an integrated take-off management and trajectory optimization for merging control in UAM corridors. We first introduce a novel take-off airspace design. To our knowledge, this paper is one of the first to propose a structured design for take-off airspace. Based on the take-off airspace design, we devise a hierarchical coordinated take-off and merging management (HCTMM) strategy. To be specific, the take-off airspace design can simplify aircraft dynamics and thus reduce the dimensionality of the trajectory optimization problem whilst mitigating obstacle avoidance complexities. The HCTMM strategy strictly ensures safety and improves the efficiency of take-off and merging operations. At the tactical level, a scheduling algorithm coordinates aircraft take-off times and selects dynamic merging points to reduce conflicts and ensure smooth take-off and merging processes. At the operational level, a trajectory optimization strategy ensures that each aircraft reaches the dynamic merging point efficiently while satisfying safety constraints. Simulation results show that, compared to representative strategies with fixed or dynamic merging points, the HCTMM strategy significantly improves operational efficiency and reduces computational burden, while ensuring safety under various corridor traffic conditions. Further results confirm the scalability of the HCTMM strategy and the computational efficiency enabled by the proposed take-off airspace design.

math.OC↗

arXiv2024

Real-time Traffic Simulation and Management for Large-scale Urban Air Mobility: Integrating Route Guidance and Collision Avoidance

Given the spatial heterogeneity of land use patterns in most cities, large-scale UAM deployments will likely focus on specific areas, such as intertransfer traffic between suburbs and city centers. However, large-scale UAM operations connecting multiple origin-destination pairs raise concerns about air traffic safety and efficiency due to potential conflict movements, particularly at major conflict points analogous to roadway junctions. To meet the safety and efficiency requirements of future UAM operations, this work proposes an air traffic management framework that integrates route guidance and collision avoidance. The route guidance mechanism optimizes aircraft distribution across both spatial and temporal dimensions by regulating their paths (composed of waypoints). Given the optimized paths, the collision avoidance algorithm generates collision-free aircraft trajectories between waypoints in the 3D space. To enable large-scale applications, we develop fast approximation methods for centralized path planning and adopt the velocity obstacle model for distributed collision avoidance. To our knowledge, this work is one of the first to integrate route guidance and collision avoidance for UAM. Simulation results demonstrate that the proposed framework enables efficient and flexible UAM operations, including air traffic assignment, local congestion mitigation, and dynamic no-fly zone management. Compared with a collision-free baseline strategy, the proposed framework achieves considerable improvements in traffic safety and efficiency, with increases in the average minimum separation (+98.2%), the average travel speed (+70.2%), and the trip completion rate (+130%), along with a reduction in the energy consumption (-23.0%). The proposed framework demonstrates its potential for real-time traffic simulation and management in large-scale UAM systems.

eess.SY↗