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• A systematic distortion of data points caused by the averaging process was uncovered. • An average absolute bias was proposed to objectively quantify the embedded bias. • A practical optimal dataset determination procedure was proposed. • The proposed method was demonstrated using real-world traffic data. Estimating traffic flow models, such as speed-density relationships, using data from multiple sources with different temporal resolutions is a prevalent challenge encountered in real-world scenarios. The resolution incompatibility is often intuitively addressed by averaging the high-resolution (HR) data to synchronize with the low-resolution (LR) data. This paper shows that ignoring the variability of HR data within the LR interval during the averaging process could lead to systematic data point distortions, resulting in biased model estimations. The average absolute biases of models estimated from the average data increase with the lost variability of HR data within the LR intervals. Subsequently, it proves that for any given complete average data dataset, there must exist an optimal dataset that minimizes the average absolute bias in model estimations introduced by the averaging process. A novel procedure for determining the practical optimal dataset is proposed. To test the proposed method, real-world HR data from four sites in Hong Kong and Nanjing, China were collected to mimic situations with multi-resolution data. Results demonstrated that the proposed method can significantly reduce the average absolute biases of models estimated from the determined practical optimal dataset, as compared to models estimated from the complete average dataset. [ABSTRACT FROM AUTHOR]

Electric buses (EBs) are considered a more environmentally friendly mode of public transit. In addition to other practical challenges, including high infrastructure costs and short driving ranges, the operations of EBs are more demanding due to the necessary battery charging activities. Consequently, more sophisticated optimisation models and algorithms are required for effective operations. This paper presents an EB scheduling problem with multiple termini and service routes. Various realistic but complicated factors, such as shared facilities at multiple termini, the flexibility of plugging and unplugging chargers before an EB is fully charged, stochastic travel times, and EB breakdowns, are considered. We propose an integrated learning and mixed-integer linear programming (MILP) framework to overcome the computational difficulties when solving the problem. This framework leverages the strengths of reinforcement learning and MILP for fast computations due to its capability of learning from outcomes of state–action pairs and computational effectiveness guaranteed by the constraints governing the solution feasibility. Q-Learning and Twin Delayed Deep Deterministic Policy Gradient are adopted as our training methods. We conduct numerical experiments on artificial instances and realistic instances of a bus network in Hong Kong to assess the performance of our proposed approach. The results show that our proposed framework outperforms the benchmark optimisation approach, in terms of penalty on missed service trips, average headway, and variance of headway. The benefits of our proposed framework are more significant under a highly stochastic environment. • Electric bus scheduling with multiple termini and service routes. • Shared facilities at multiple termini and the flexibility of plugging and unplugging chargers considered. • Integration of Q-learning and mixed-integer linear programming for solving the problem. • Experiments on artificial instances and a real-world case to demonstrate the high computational performance. • Managerial insights derived from the experiments. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-based commercial services, exemplified by drone delivery, have captured wide interest in tech companies, entrepreneurs, and policymakers. Structured route-based UAV operations have been implemented for traffic management of UAVs in support of commercial delivery services in cities. Yet, its essence, multi-path planning with constraints is not well solved in the existing literature. Centralized planning might result in inefficiencies and unfairness in the allocation of precious urban airspace to individual routes. This paper describes a novel distributed route planning method to support UAV operations in a high-density urban environment. The method allows each origin–destination (OD) pair to compete against other OD pairs for an optimized route (e.g. shortest distance), coordinated by a system-level evaluation, leading to a network design that maximizes the performance of not only the individual routes but also the entire system. The core concept is the introduction of congestion pricing, a soft constraint to coordinate the allocation of airspace. The method is tested in standard 2D scenarios and compared with other state-of-the-art methods. The results show that (1) the method is able to generate routes with short individual distances as well as occupying the least airspace by the route network; (2) in some complex scenarios, the method is able to find a solution in a short period of time while other state-of-the-art method fails. The method has also been applied to a real urban environment (Mong Kok in Hong Kong) to demonstrate its capability. • Presented a multi-path method to design air route networks for urban drone delivery. • Introduced congestion cost as a flexible constraint for airspace allocation. • Employed a distributed approach for resolving conflicts between origin-destination pairs. • Outperformed other leading methods in balancing efficiency and computation speed. • Validated the proposed system through tests in real-world settings. [ABSTRACT FROM AUTHOR]

This paper presents an optimization framework for adaptive scheduling of mixed bus services with flexible fleet size assignment under demand uncertainty. The service scheduling plans are driven by prevailing stochastic passenger demand subject to operational constraints. The optimization problem is formulated as a Markov decision process which aims to minimize passengers' in-vehicle and waiting times, as well as the operator's cost via use of services with different routes, schedules, and fleet sizes. To address the computational challenges, the solutions to the problem are to be calculated with use of reinforcement learning techniques. The proposed framework is implemented and tested using real-world scenarios configured from actual bus service route in Hong Kong. Experiment results reveals the benefits of the proposed bus service scheduling framework with use of flexible routes and fleet sizes in saving passengers' and operator's costs. This study contributes to real time transit operational planning with advanced computing and optimization techniques. • A dynamic bus network optimization model for flexible fleet, route, and schedule assignment. • A novel reinforcement learning based solution framework incorporating operational constraints. • A novel solution framework that can derive operational plans with multiple conflicting objectives. • A case study demonstrating the efficiency and practicality from both passengers' and operators' perspectives. [ABSTRACT FROM AUTHOR]