Shangqing Cao

This paper introduces a vehicle-routing formulation of the Urban Air Mobility (UAM) fleet scheduling problem. The proposed model, electric urban air mobility vehicle routing problem with non-linear battery charging time (eUAMVRP-NL), studies the optimal flight scheduling and charging policy for a given fleet of eVToL vehicles to maximize the operating revenue with the assumption of a non-linear charging model for the aircraft batteries. We design a Cluster-First Route-Second (CFRS) solution heuristic that first decomposes the k-vehicle problem into k number of 1-vehicle problems before optimizing the flight scheduling and charging decisions for each aircraft in the fleet. We validate our CFRS algorithm on a small, two-vertiport instance in which an optimal solution can be obtained. We found a loss of optimality less than 2\%. Finally, we demonstrate the use of eUAMVRP-NL in obtaining meaningful metrics for the estimation of capital cost and operating cost by considering the airport access use case of UAM at Los Angeles International Airport (LAX)

The completion of sparse Location-Based Service (LBS) data for modeling urban-scale origin-destination (OD) flow is of great importance to transportation planning applications. Sparse trajectories lack realistic human mobility patterns. Only with completed trajectories one can derive urban-scale OD flow that resembles complete travel diaries as those gathered by surveys or actively collecting phone applications. We present DeepTimeGeo (DTG), a transformer encoder-only model that reconstructs complete trajectories from sparse LBS inputs. We adopt a rank-based representation of locations to preserve individual-level heterogeneity and take a sequence-to-sequence approach to address the issue of gradient back-propagation blockage when it comes to regulating human mobility patterns. We devise human mobility distribution-based loss functions and leverage auxiliary learning to model the dynamics of exploration versus returns in users’ spatial choices. Experimental results show the superiority of DTG in trajectory reconstruction compared to other start-of-the-art generative models for human mobility trajectories. We conducted a case study with LBS data in the city of Coral Gables, Florida. The case study reveals that DTG leads to a reduction of more than 15% (1.35 vs. 1.60), when compared to the state-of-art model, in the cross-entropy loss that measures the deviation from the ground truth departure time distribution. We further demonstrate through SUMO simulation that DTG-generated trip demand captures both morning and evening rush hours, enabled by the more accurate distribution of trip departure time with important implications for traffic estimates.

The long waiting time at airport security has become an emergent issue as demand for air travel continues to grow. Not only does queuing at security cause passengers to miss their flights, but also reduce the amount of time passengers spend at the airport post-security, potentially leading to less revenue for the airport operator. One of the key issues to address to reduce waiting time is the management of arrival priority. As passengers on later flights can arrive before passengers on earlier flights, the security system does not always process passengers in the order of the degree of urgency. In this paper, we propose a chance-constrained optimization model that decides in which time slot passengers should be recommended to arrive. We use chance constraints to obtain solutions that take the uncertainty in passenger non-compliance into account. The experimental results, based on a sample day of flight schedules at the Barcelona airport, show a reduction of 85% in the total waiting time. Compared to the deterministic case, in which passengers are assumed to fully comply with the recommendations, we see a 30% increase in the reduction of the total waiting time. This highlights the importance of considering variation in passenger compliance in the management of airport security queues.

This article reviews the literature on urban air mobility (UAM), examining both the research challenges it presents and the transformative opportunities that make these challenges worth addressing. While UAM has historical precedents, the current iteration is born of novel aircraft technology, primarily electric vertical takeoff and landing (eVTOL) and electric short takeoff and landing (eSTOL) aircraft. These advances raise new questions in aerodynamics, control, and integration with urban infrastructure. We explore several key research areas, including aircraft design, vertiport development, network planning, and air traffic management. We also address the scalability challenges in air traffic management for high-density UAM operations and the potential of autonomous and remotely piloted systems. If new aircraft are to birth a new urbanism, they will do so by integrating aircraft engineering and computational intelligence in control, systems, robotics, and human factors.

Public transit systems are critical to the quality of urban life, and enhancing their efficiency is essential for building cost-effective and sustainable smart cities. Historically, researchers sought reinforcement learning (RL) applications to mitigate bus bunching issues with holding strategies. Nonetheless, these attempts often led to oversimplifications and misalignment with the goal of reducing the total time passengers spent in the system, resulting in less robust or non-optimal solutions. In this study, we introduce a novel setting where each bus, supervised by an RL agent, can appropriately form aggregated policies from three strategies (holding, skipping station, and turning around to serve the opposite direction). It’s difficult to learn them all together, due to learning complexity, we employ domain knowledge and develop a gradually expanding action space curriculum, enabling agents to learn these strategies incrementally. We incorporate Long Short-Term Memory (LSTM) in our model considering the temporal interrelation among these actions. To address the inherent uncertainties of real-world traffic systems, we impose Domain Randomization (DR) on variables such as passenger demand and bus schedules. We conduct extensive numerical experiments with the integration of synthetic and real-world data to evaluate our model. Our methodology proves effective, enhancing bus schedule reliability and reducing total passenger waiting time by over 15%, thereby improving bus operation efficiency and smoothering operations of buses that align with sustainable goals. This work highlights the potential of robust RL combined with curriculum learning for optimizing public transport in smart cities, offering a scalable solution for real-world multi-agent systems.

Realistic simulation of massive human mobility data aids in a range of applications such as traffic management, public transport optimization, and emergency response planning. This task is challenging as most trajectory datasets are sparse. When used for city-level planning, models that merely mimic the sparse input dataset from mobile phones induce errors. In this work, we emphasize the importance of validating simulations with complete trajectories. We present a model for city-wide human mobility generation from sparse data. We first extract spatial, temporal, and activity features from active users' raw trajectory data. With these features, we construct weighted directed graphs to represent individual mobility patterns. We utilize graph convolutional neural networks to learn key parameters in mobility simulation from graphs. With the established parameters, we simulate city-wide mobility with a mechanistic framework. Comparison with baselines demonstrates the efficacy of our method in imitating complete data.