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Parallel shuttle bus service design for planned mass rapid transit shutdown: The Singapore experience

机译:计划的大规模快速公交关闭的并行穿梭巴士服务设计:新加坡的经验

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摘要

19 MRT stations in Singapore experienced a two full-day shutdown as planned in order to upgrade the infrastructure of aging mass rapid transit (MRT) lines. A large bus fleet, running entirely parallel to MRT lines, was deployed to ferry passengers between affected stations, which however results in prolonged congestion and queue of buses near terminals. Rail transit systems in many metropolitan areas are threatened because of aging infrastructure. This study, motivated by the Singapore case, proposes a novel parallel shuttle bus service design (PSBSD) problem that addresses route design, terminal selection, berth allocation, and bus deployment to minimize inconvenience caused to passengers, which is seldom addressed in previous studies. We further develop a mixed-integer nonlinear programming (MINLP) model for the PSBSD problem. In order to solve the non-convex MINLP model optimally, we put forward an effective decomposition method, which comprises (i) the generation of candidate shuttle bus routes through a brute-force search for semi-route and a terminal selection procedure and (ii) the deployment of buses and assignment of terminal berths for selected shuttle bus routes by using an optimization-based approach. Numerical experiments were conducted using data sets from the MRT closure incident in Singapore. The results demonstrate that the decomposition method is capable of finding optimal solutions. Useful insights are also obtained from testing the models under different scenarios.
机译:新加坡的19个捷运站计划按计划关闭两天,以升级老化的捷运(MRT)线路的基础设施。部署了一个完全平行于捷运线的大型公交车队,将乘客运送到受影响的车站之间,但是这导致了码头附近公交车的拥挤和排队时间延长。由于基础设施老化,许多大都市地区的铁路运输系统受到威胁。这项以新加坡为例的研究提出了一种新颖的并行穿梭巴士服务设计(PSBSD)问题,该问题解决了路线设计,航站楼选择,泊位分配和公交车部署,以最大程度地减少对乘客造成的不便,这在以前的研究中很少解决。我们针对PSBSD问题进一步开发了混合整数非线性规划(MINLP)模型。为了优化求解非凸MINLP模型,我们提出了一种有效的分解方法,该方法包括(i)通过蛮力搜索半路线和终端选择程序生成候选穿梭巴士路线,以及(ii )使用基于优化的方法,为选定的穿梭巴士路线部署公交车并分配码头泊位。使用来自新加坡地铁关闭事件的数据集进行了数值实验。结果表明,该分解方法能够找到最优解。通过在不同情况下测试模型也可以获得有用的见解。

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