Wai Yuen Szeto

Express bus services, which use faster routes and make fewer stops than regular bus services, are a potential solution to reduce travel time. This study aims to solve the regular bus network design problem, the express service design problem, and the frequency setting problem simultaneously while considering capacity constraints and incorporating deadheading. A hybrid genetic algorithm is modified to solve this integrated problem. A waypoint-based solution representation is proposed to facilitate the design of both regular and express routes. Numerical experiments are set up to demonstrate the performance of the hybrid genetic algorithm and its components, the effects of minimum frequency for regular routes, the characteristics of express routes, the superior performance of simultaneous optimization of regular routes and the express service compared with optimizing the express service after regular routes design.

This paper develops a bottleneck model in which the capacity of the bottleneck is assumed to be stochastic and follow a general distribution that has a positive upper bound. The user equilibrium principle in terms of mean trip cost is adopted to formulate commuters’ departure time choice in the stochastic bottleneck. We find that there exist five possible equilibrium departure patterns, which depend on both commuters’ unit costs of travel time, schedule delay early and late, and the uncertainty of the stochastic capacity of the bottleneck. All possible equilibrium departure patterns are analytically derived. Both the analytical and numerical results show that increasing the uncertainty of the capacity of the bottleneck leads to an increase of commuters’ individual mean trip cost. In addition, both a time-varying toll scheme and a single-step coarse toll scheme are designed within the proposed stochastic bottleneck model. We provide an analytical method to determine the detailed toll-charging schemes for both toll strategies. The numerical results show that the proposed toll schemes can indeed improve the efficiency of the stochastic bottleneck in terms of decreasing mean total social cost, and the time-varying toll scheme is more efficient than the single-step coarse toll scheme. However, as the uncertainty of the capacity of the bottleneck increases, the efficiency of the time-varying toll scheme decreases, whereas the efficiency of the single-step coarse toll scheme fluctuates slightly.

Improving the level of service and reducing total costs are the most important objectives for the decision support system of home health care companies. But these objectives are often in conflict. In this study, we aim to minimize the total costs and maximize the participants’ preference satisfaction simultaneously. The problem is formulated as a bi‐objective mixed‐integer linear programming model that considers the skill requirements of patients, hard time windows for the service start time, and the working duration of a nurse. Patient preference satisfaction captures the nurse skill level and nurse–patient familiarity, and nurses’ preference satisfaction captures the overtime duration. To solve this problem, a novel hybrid elitist nondominated sorting genetic algorithm (hybrid NSGA‐II) is developed by embedding a local search algorithm into the basic NSGA‐II framework. Computation results on a set of benchmarking instances show that the developed hybrid NSGA‐II can obtain approximate Pareto‐optimal solutions within a shorter computation time when compared with the ‐constraint method for small instances; it can also perform better than the basic NSGA‐II and SPEA‐II with the size increasing for middle and large instances. Using a small instance, this study also analyzes the problem properties and the trade‐off between total costs and participants’ preference satisfaction.

En-route congestion causes delays in air traffic networks and will become more prominent as air traffic demand will continue to increase yet airspace volume cannot grow. However, most existing studies on flight delay modeling do not consider en-route congestion explicitly. In this study, we propose a new flight delay model, Multi-layer Air Traffic Network Delay (MATND) model, to capture the impact of en-route congestion on flight delays over an air traffic network. This model is developed by a data-driven approach, taking aircraft tracking data and flight schedules as inputs to characterize a national air traffic network, as well as a system-level model approach, modeling the delay process based on queueing theory. The two approaches combined make the network delay model a close representation of reality and easy-to-implement for what-if scenario analysis. The proposed MATND model includes 1) a data-driven method to learn a network composed of airports, en-route congestion points, and air corridors from aircraft tracking data, 2) a stochastic and dynamic queuing network model to calculate flight delays and track their propagation at both airports and in en-route congestion areas, in which the delays are computed via a space-time decomposition method. Using one month of historical aircraft tracking data over China's air traffic network, MATND is tested and shows to give an accurate quantification of delays of the national air traffic network. "What-if" scenario analyses are conducted to demonstrate how the proposed model can be used for the evaluation of air traffic network improvement strategies, where the manipulation of reality at such a scale is impossible. Results show that MATND is computationally efficient, well suited for evaluating the impact of policy alternatives on system-wide delay at a macroscopic level.