Early estimation of multi-objective traffic flow

Dominik Ascher, Georg Hackenberg
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引用次数: 3

Abstract

Intelligent Transportation Systems (ITS) have come a long way targeting problems such as increasing emissions and growing vehicle numbers. Current approaches address a variety of objectives including congestion management, collision avoidance, energy-efficiency and emission reduction. However, respective solutions typically are designed for and tailored to a predefined set of objectives. Consequently, the effects of drastically changing objectives cannot be assessed easily. To address this situation we present a lightweight approach to estimating multi-objective traffic flow early in systems engineering using non-deterministic models and stochastic optimization techniques. We demonstrate the feasibility of the framework using a basic traffic scenario and conclude with future work.
多目标交通流的早期估计
智能交通系统(ITS)在解决排放增加和车辆数量增加等问题方面取得了长足的进步。目前的方法解决了各种各样的目标,包括拥堵管理、避免碰撞、能源效率和减排。然而,各自的解决方案通常是为一组预定义的目标而设计和定制的。因此,无法轻易评估目标急剧变化的影响。为了解决这种情况,我们提出了一种轻量级的方法,在系统工程的早期使用非确定性模型和随机优化技术来估计多目标交通流。我们使用一个基本的交通场景演示了该框架的可行性,并对未来的工作进行了总结。
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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