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Optimally combined headway and timetable reliable public transport system
Affiliation:1. Sorbonne Universités, Université Pierre et Marie Curie, Laboratoire LIP6 UMR 7606, 4 place Jussieu, 75005 Paris, France;2. BRT - Center of Excellence, Pontificia Universidad Católica de Chile, Vicuña Mackenna Macul, 4860 Santiago, Chile;3. Universidad Autónoma de Nuevo León, Av. Universidad s/n, San Nicolás de los Garza 66450, Mexico;1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China;2. Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK;1. HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, Montreal, H3T 2A7, Canada;2. Department of Applied Mathematics I, University of Seville, Spain;3. Department of Statistics and Operational Research, University of Seville, Spain;4. Department of Statistics and Operational Research, University of Seville, Spain
Abstract:This paper presents a model-based multiobjective control strategy to reduce bus bunching and hence improve public transport reliability. Our goal is twofold. First, we define a proper model, consisting of multiple static and dynamic components. Bus-following model captures the longitudinal dynamics taking into account the interaction with the surrounding traffic. Furthermore, bus stop operations are modeled to estimate dwell time. Second, a shrinking horizon model predictive controller (MPC) is proposed for solving bus bunching problems. The model is able to predict short time-space behavior of public transport buses enabling constrained, finite horizon, optimal control solution to ensure homogeneity of service both in time and space. In this line, the goal with the selected rolling horizon control scheme is to choose a proper velocity profile for the public transport bus such that it keeps both timetable schedule and a desired headway from the bus in front of it (leading bus). The control strategy predicts the arrival time at a bus stop using a passenger arrival and dwell time model. In this vein, the receding horizon model predictive controller calculates an optimal velocity profile based on its current position and desired arrival time. Four different weighting strategies are proposed to test (i) timetable only, (ii) headway only, (iii) balanced timetable - headway tracking and (iv) adaptive control with varying weights. The controller is tested in a high fidelity traffic simulator with realistic scenarios. The behavior of the system is analyzed by considering extreme disturbances. Finally, the existence of a Pareto front between these two objectives is also demonstrated.
Keywords:Bus bunching  MPC control  Autonomous vehicles  Multiobjective optimization  Timetable reliability
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