共查询到18条相似文献,搜索用时 93 毫秒
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城际铁路建成后,需要经过长时间客流培育。在客流量较小的运营初期采用大小编组的列车开行方案,一方面可以降低运输企业运营成本,另一方面可以缩短行车间隔、提升客运服务水平。以开行跨线列车的城际铁路线网为研究对象,以各运行交路编组方案和发车对数为决策变量,以乘客出行成本和企业运营成本为优化目标,构建列车开行方案优化模型,并使用熵权法开展方案评价,最终得出大小编组模式下城际铁路列车开行方案优化结论。该模型通过输入某都市圈线网OD客流及各交路基本情况等数据,经案例分析得出:大编组列车与小编组列车开行对数保持平均对于乘客出行成本的下降具有显著的优化效果;少量开行小编组列车是平衡乘客出行成本与企业运营成本的最优选择。 相似文献
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《铁道运输与经济》2017,(3)
为合理优化城际列车开行频率,以客流出行特点和规律为依据,对客流日分布数据进行分析,将城际客流分为早平峰、早高峰、午平峰、次高峰、晚高峰、晚平峰6个时段,以此确定不同客流时段的城际列车开行频率。分析旅客出行费用和企业运营成本,在综合考虑旅客候车时间成本、乘车时间成本,以及与列车开行频率相关的列车运行成本、停站成本的基础上,建立以旅客出行费用和企业运营成本最小为目标的列车开行频率优化模型。最后以京津城际铁路为例,采用MATLAB对模型求解,实例表明该模型计算得出的列车开行频率能够更好地满足城际铁路旅客和运营企业双方的利益,为城际列车开行频率优化提供参考。 相似文献
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针对城际铁路开通初期客流时空分布不均衡导致的单一列车编组模式造成运能浪费的问题,从列车运营成本出发,研究多种停站模式的大小编组列车开行方案。以列车运营成本最小化为目标函数,以大小编组列车的开行列数、列车停站方案为决策变量,综合考虑列车区间断面满载率、OD服务频率等约束条件,构建大小编组列车开行方案模型;采用遗传算法求解,得到全日大小编组列车开行方案。以广清城际铁路和广州东环城际铁路为例,与现行列车开行方案相比,多停站模式的大小编组列车开行方案可使列车运营成本减少59%,日断面满载率平均值由0.288 8提高到0.866 5,说明采用多停站模式的大小编组列车开行方案能够有效降低列车运营成本,提高断面满载率。 相似文献
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旅客出行需求不断提高,需要同时考虑旅客出行时段选择与运输企业效益,来优化调整高速铁路列车停站方案。针对一条拥挤的高速铁路客运走廊,分时段确定列车停站计划和开行频率,阐述影响旅客出行时段偏好的2个重要因素——吸引度与可达度,据此构建旅客出行阻抗函数,构建双层规划模型,上层规划是以运营成本最小为目标的整数规划模型,用于确定列车停站方案;下层规划是一个用户平衡模型,用于计算客流在停站方案上的分配结果。根据模型特点设计启发式算法,并通过算例对模型和算法进行验证和分析。研究表明,考虑旅客出行时段偏好优化高速铁路列车停站方案的方法,能更好地匹配旅客需求分布,为旅客提供优质服务。 相似文献
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为实现城市轨道交通的节能降耗,降低运营成本,提出基于平峰时段客流特征和行车组织特点的城市轨道交通平峰节能运行图优化模型。在平峰时段,通过压缩客流量较小车站的停站时间,将富余停站时间用于列车区间运行,在保证整体列车运行周期不变的同时,实现降低列车牵引能耗的目标。建立考虑乘客服务水平的城市轨道交通平峰期节能运行图优化模型,并设计遗传算法对模型进行求解。以深圳地铁7号线上行方向为例,结果表明上行方向区间牵引总能耗整体减少87.35 kW·h,节能效率达到19.44%。研究表明在不影响乘客正常乘降列车的情况下,提出的考虑乘客服务水平的平峰节能运行图模型调整列车区间运营时间,具有显著的节能减排效果。 相似文献
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我国城市群快速发展,城市间人员交流密切频繁,城际铁路成为城际间旅客的重要出行方式。合理的城际铁路列车停站方案可以提高城际铁路的竞争力,提升城际铁路的分担率。分析停站方案相关的运营收益和旅客出行成本,以铁路运营收益最大和旅客出行成本最小为目标函数,以车站服务频率、设备能力、列车停站次数等作为约束条件,建立城际铁路列车停站方案的多目标混合0-1规划模型,运用理想点法和遗传算法求解。算例结果表明,该模型和算法可以优化得到铁路运营收益和旅客出行成本均较优的停站方案。 相似文献
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麦加轻轨是中铁建建设并运营的首条海外铁路,由于服务对象为朝觐客流,其列车运行模式较为独特。通过研究麦加轻轨的5种运行模式(A—E),分析其运营效果,提出麦加轻轨列车运行模式的优化方法。重点针对朝觐期间的运行模式E,以发车间隔、运营成本、车底数为约束,以乘客候车时间最短为目标构建了不同客流分布下的行车间隔优化模型,并针对不同集中度的客流分布计算得出最优方案。研究显示,相比既有的行车间隔保持不变的方案,采用优化后的差异化行车间隔方案可以在不增加运营成本的情况下缩减乘客平均候车时间,客流集中趋势越高,优化效果越好,客流高集中趋势下(标准差19.9%)最多可节省候车时间13.2%。研究结果对降低麦加轻轨运营成本、提高乘客服务水平具有现实意义。 相似文献
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Chikako Keumi Hideki Murakami 《Transportation Research Part E: Logistics and Transportation Review》2012,48(5):1023-1031
We empirically study passenger modal-choice behavior to access an international hub airport, by using stated preference (SP) data and by constructing a binomial logit model. We found that passenger modal choice is affected by the service level of the access modes: travel time, travel cost, waiting time, and delay cost. The results also indicate that if passengers choose access mode in advance they consider service frequency: departure timing from home, and the arrival timing at the airport. Moreover, our results indicate that travelers’ willingness to pay for saving time differs by time of a day. They are apt to pay more in the morning than in the afternoon. These outcomes must contribute to improve the access flight service from local to hub airports to handle the needs of passengers. 相似文献
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China was the first airline market in the world to be hit hard by the COVID-19 pandemic. It has been gradually recovering as the pandemic is largely contained domestically. However, with the global pandemic spread and great uncertainty, there has been a remarkable change in airline passengers’ travel behavior. This paper collected air passenger-level data from TravelSky in the Chinese market. In addition to the analyses on aggregate passenger flow patterns, this paper explores changes in airline passenger travel behavior, such as ticket booking time, age distribution of passengers, refunds and ticket changes, and passenger arrival time at airports. This is one of the first studies to focus on micro-level changes in airline passenger travel behavior by using objective passenger-level data. The pandemic-induced psychological changes in air travelers are explored, providing useful managerial and policymaking implications for the normalization of the pandemic and the recovery of the airline market in the post-pandemic era. 相似文献
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Over the past two decades, smart card data have received increasing interest from transport researchers as a new source of data for travel behaviour investigation. Collected by smart card systems, smart card data surpass traditional travel survey data in providing more comprehensive spatial–temporal information about urban public transport-based (UPT) trips. However, the utility of smart card data has arguably yet to be exploited fully in terms of extracting and exploring the spatial–temporal dynamics of UPT passenger travel behaviour. To advance previous work in this area, this paper demonstrates a multi-step methodology in order to render more insightful spatial–temporal patterns of UPT passenger travel behaviour. Drawing on the Brisbane, Australia, bus network as the case study, a smart card dataset was first processed in combination with General Transit Specification Feed (GTFS) data to reconstruct travel trajectories of bus passengers at bus stop level of spatial granularity. By applying geographical information system-based (GIS) techniques, this dataset was used to create flow-comaps to visualise the aggregate flow patterns at a network level. The flow-comaps uncovered the major pathways of bus passengers and its variations over a one-day period. The differences within the flow-comaps were also quantified to produce weighted flow-comaps that highlighted the major temporal changes of passenger flow patterns along a number of stop-to-stop linkages of the bus network. The proposed methodology visually unveiled the spatial–temporal travel behaviour dynamics of UPT passengers and, in doing so, showed the potential to contribute to a new evidence base with the capacity to inform local public transport policy. 相似文献
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Abrupt airport outages can cause diversions and fuel-critical situations for flights, leading to costly passenger misconnections. We develop a large neighborhood search heuristic to optimize the rerouting of flights bound for a disrupted airport to a hub airport that is not disrupted, with the goal of accommodating passengers on existing flights departing the non-disrupted hub. The objective of the heuristic is to identify and reroute flights to the ad-hoc hub(s) – non-disrupted hub airport(s) – that minimize the sum of passenger travel time and wait time. We minimize the passenger cost as the sum of passenger travel time to the diversion airport and wait time for a connecting flight at the ad-hoc hub airport, subject to on-board fuel and diversion airport capacity constraints. We use the heuristic to determine how a coordinated traffic management strategy could have diverted flights immediately following a real-world airport outage. 相似文献