城市轨道交通首期开通客流预测方法研究

发布时间：2018-11-16 14:09

【摘要】：我国轨道建设虽然起步较晚,但近十年发展迅猛,自1969年,北京地铁1号线开通运营后,上海、广州、武汉、南京、深圳、沈阳等城市先后开通了城市第一条地铁线路。随着开通地铁城市增多,预测客流与实际客流差距较大的问题越发明显。首期开通客流预测是地铁运营单位制定合理运营模式、市场策略的重要依据,不同城市市民因社会经济水平、文化、气候以及出行习惯等因素的差异,在出行选择时,对时间费用的敏感程度不同。首期开通客流预测中,票制票价、行车间隔等因素的变化与客流量的波动关系,可以为运营单位制定合理高效的运营模式提供科学的依据。所以首期开通客流预测结果的对轨道运营管理有着重要意义。目前国内外专家主要研究城市土地规划、人口规模达到城市规划目标,各交通方式协调配合的乐观假设前提下的远期客流预测。针对轨道建设初期,特别是城市中仅有一条轨道开通运营情况下的近期客流预测研究较少。城市中仅开通一条轨道,其吸引范围有限,吸引强度偏低,系统服务水平低,市民对其没有依赖性,若仍沿用远期客流预测的方法,可能由于过于乐观估计轨道交通的竞争能力,使预测值偏高造成误差。对此,本文提出首期开通客流预测方式是：以轨道沿线土地利用调查、个人出行调查和现状公交调查数据为基础,研究分析轨道相关域中市民的出行方式。通过分析确定影响开通客流因素：票制票价、发车间隔和站点衔接规划实施情况的方案,利用情境设计组合方案。利用随机效用理论,模拟设计在不同情境下,出行者对不同出行目的,权衡原有交通方式与轨道交通效用,确定出行方式的过程,建立其他交通方式向轨道交通客流转移预测模型,预测轨道交通出行量。通过公交分配软件计算得到预测年轨道交通的客运量。将预测方法运用到成都地铁1号线开通客流的预测,通过预测值与地铁1号线实际运营的客流量的比较,验证方法的可行性。
[Abstract]:Although the track construction of our country started relatively late, it has developed rapidly in the past ten years. Since 1969, after the opening of Beijing Metro Line 1, Shanghai, Guangzhou, Wuhan, Nanjing, Shenzhen, Shenyang and other cities have successively opened the first subway line of the city. With the increasing number of subway cities, the gap between forecasting passenger flow and actual passenger flow becomes more and more obvious. The first opening of passenger flow forecast is an important basis for subway operating units to formulate reasonable operation mode and market strategy. Because of the differences in social and economic level, culture, climate, travel habits and other factors, different urban residents choose to travel. Sensitivity to time costs varies. In the forecast of passenger flow in the first period, the change of ticket price, travel interval and the fluctuation of passenger flow can provide a scientific basis for the operation unit to establish a reasonable and efficient operation model. Therefore, the first opening of passenger flow forecast results of rail operation management has an important significance. At present, experts at home and abroad mainly study urban land planning, the population scale reaches the goal of urban planning, and the optimistic assumption of coordination of traffic modes is the long-term passenger flow prediction. In the early stage of track construction, especially under the condition that only one track is opened and operated in the city, there is little research on the forecast of recent passenger flow. There is only one track in the city, which has a limited range of attraction, low attraction intensity, low service level of the system, and the citizens do not depend on it. If the method of long-term passenger flow prediction is still used, It is possible that the over-optimistic estimation of the competitive power of rail transit results in the error caused by the high prediction value. In view of this, this paper puts forward the first mode of passenger flow prediction: based on the land use survey along the track, personal travel survey and current public transport survey data, this paper studies and analyzes the travel mode of citizens in the track related areas. By analyzing and determining the factors that influence the opening of passenger flow: ticket price, departure interval and station connection planning and implementation plan, using the situation design combination scheme. Using the theory of stochastic utility, the simulation design is designed to determine the process of travel mode under different circumstances, and the travelers weigh the original mode of transportation and the utility of rail transit for different travel purposes. A prediction model of passenger flow transfer from other modes to rail transit is established to predict the travel volume of rail transit. The passenger traffic volume of rail transit is predicted by the calculation of bus distribution software. The prediction method is applied to the passenger flow prediction of Chengdu Metro Line 1. The feasibility of the method is verified by comparing the forecast value with the actual operating passenger flow of Metro Line 1.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：U293.13;U293.5

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