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Minimising average passenger waiting time in personal rapid transit systems

机译:缩短个人快速公交系统中的平均乘客等待时间

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Personal Rapid Transit (PRT) is an emerging urban transport mode. A PRT system operates much like a conventional hackney taxi system, except that the vehicles are driven by computer (no human driver) between stations in a dedicated network of guideways. The world's first two PRT systems began operating in 2010 and 2011. In both PRT and taxi systems, passengers request immediate service; they do not book ahead. Perfect information about future requests is therefore not available, but statistical information about future requests is available from historical data. If the system does not use this statistical information to position empty vehicles in anticipation of future requests, long passenger waiting times result, which makes the system less attractive to passengers, but using it gives rise to a difficult stochastic optimisation problem. This paper develops three lower bounds on achievable mean passenger waiting time, one based on queuing theory, one based on the static problem, in which it is assumed that perfect information is available, and one based on a Markov Decision Process model. An evaluation of these lower bounds, together with a practical heuristic developed previously, in simulation shows that these lower bounds can often be nearly attained, particularly when the fleet size is large. The results also show that low waiting times and high utilisation can be simultaneously obtained when the fleet size is large, which suggests important economies of scale.
机译:个人快速公交(PRT)是一种新兴的城市交通方式。 PRT系统的运行方式与传统的哈克尼出租车系统非常相似,不同之处在于,车辆是由计算机(没有人工驾驶)在专用导轨网络中的站点之间驱动的。全球首批两个PRT系统于2010年和2011年投入运营。在PRT和出租车系统中,乘客都要求立即服务;他们没有提前预订。因此,没有关于未来请求的完美信息,但是可以从历史数据中获得关于未来请求的统计信息。如果系统不使用此统计信息来定位空车以预期将来的需求,则会导致较长的乘客等待时间,这会使系统对乘客的吸引力降低,但使用它会引起困难的随机优化问题。本文针对可达到的平均乘客等待时间提出了三个下界,一个基于排队论,一个基于静态问题(假设可以得到完美的信息),另一个基于马尔可夫决策过程模型。对这些下限的评估以及先前开发的实用启发式算法在仿真中的评估表明,这些下限通常可以接近实现,尤其是在机队规模较大时。结果还显示,当机队规模较大时,可以同时获得低等待时间和高利用率,这表明重要的规模经济。

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