异构旅行者共享无人驾驶和人车系统的动态系统优化性能

IF 1.8 4区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Mathematical and Computer Modelling of Dynamical Systems Pub Date : 2020-07-16 DOI:10.1080/13873954.2020.1792509

Yao Li

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引用次数: 0

摘要

自动驾驶汽车（AV）可以解决传统单向车辆共享系统面临的车辆迁移问题。本文探讨了混合共享车辆（SAV）和人-车（SHV）系统的确定性时变系统优化，为混合车辆流的情况提供了基准。在这样的系统中，系统规划器确定交通网络中的车辆-旅行者分配和最佳车辆路线，以满足不同旅行者的预定旅行需求。由于涉及大量车辆，出行时间被认为是拥堵的内生因素。利用链路传输模型（LTM）作为交通流模型，将确定性时变系统最优解公式化为线性规划模型，以最小化综合成本，包括出行时间成本、等待时间成本和空车重新定位时间成本。通过算例验证了系统的性能和模型的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Dynamic system optimal performances of shared autonomous and human vehicle system for heterogeneous travellers

ABSTRACT Autonomous vehicles (AV) can solve vehicle relocation problems faced by traditional one-way vehicle-sharing systems. This paper explores the deterministic time-dependent system optimum of mixed shared AVs (SAV) and human vehicles (SHV) system to provide the benchmark for the situation of mixed vehicle flows. In such a system, the system planner determines vehicle-traveller assignment and optimal vehicle routing in transportation networks to serve predetermined travel demand of heterogeneous travellers. Due to large number of vehicles involved, travel time is considered endogenous with congestion. Using link transmission model (LTM) as a traffic flow model, the deterministic time-dependent system optimum is formulated as linear programming (LP) model to minimize the comprehensive cost including travellers’ travel time cost, waiting time cost and empty vehicle repositioning time cost. Numerical examples are conducted to show system performances and model effectiveness.

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来源期刊

Mathematical and Computer Modelling of Dynamical Systems 数学-计算机：跨学科应用

CiteScore

3.80

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.