Stochastic approximation to the effects of headways on knock-on delays of trains
Stochastic approximation to the effects of headways on knock-on delays of trains
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DOI：
10.1016/0191-2615(94)90001-9
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发表时间：
1994-08
影响因子：
6.8
通讯作者：
M. Carey;A. Kwieciński
中科院分区：
文献类型：
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作者：
M. Carey;A. Kwieciński
在列车规划和调度中，每个线路上的行程时间被假定为取决于列车类型和线路特性，但通常被视为与列车之间的时间间隔（车头时距）无关。然而，在实践中，列车会因各种原因而延误，并且由于通常不允许它们在链路上相互通过，因此一列列车的任何延误都可能导致后续列车的“连锁”延误。英国和欧洲的高密度双轨和多轨铁路尤其如此。列车之间的计划间隔越短，预期的连锁延误就越大，因此后续列车的预期行程时间也就越长。我们开发了简单的随机近似这些敲门列车延误。为了测试和校准的近似，我们进行详细的随机模拟列车之间的相互作用，因为他们穿越路段的链接。我们推导出的列车间隔时间和撞击延误之间的近似关系可以用于，例如，（a）为列车规划、调度或控制的其他随机或确定性模型提供校正因子;（B）调整列车时刻表，目前制定的时刻表没有明确考虑预期的连锁效应，以及（c）通过减少或消除对模拟每个链路内的行为的需要，使得进行更大规模的列车网络模拟成为可能。
In train planning and timetabling, the trip time on each link is assumed to depend on the type of train and characteristics of the link, but is usually treated as independent of the time interval (headway) between trains. However, in practice trains are subject to delays from a variety of causes, and since normally they are not allowed to pass each other on a link, any delay to one train may cause “knock-on” delays to following trains. This is especially true of the high density double and multiple track railways in Britain and Europe. The shorter the scheduled headway between trains, the greater is the expected knock-on delay and hence the greater the expected trip times of following trains. We develop simple stochastic approximations to these knock-on train delays. To test and calibrate the approximations, we conduct detailed stochastic simulation of the interaction between trains as they traverse sections of the link. The approximate relationships that we derive between scheduled headways and knock-on delays can be used, for example, (a) to provide correction factors for other stochastic or deterministic models of train planning, dispatching, or control; (b) to adjust train timetables, which are currently produced without explicitly considering the expected knock-on effects, and (c) to make it feasible to conduct larger scale simulations of train networks, by reducing or removing the need to simulate behaviour within each link.