客运交通需求分析

文章针对客运交通的特点,分析了客运交通需求的几个主要影响因素,提出稳定需求和活动需求概念,并探讨了这两种需求的需求特征、时间函数、增长特征及其最优定价规律。在此基础上探讨了客运交通系统需求函数的一般表达式并给出了近年来我国客运交通系统需求函数的实证形式。     

铁路客运高峰定价模型研究

铁路客运存在高低峰运输的差别，在高峰期，铁路在各个方面都要承受巨大的负荷；而到了低峰期，高峰期的额外投入又将出现闲置，造成浪费。铁路部门作为公共事业，在努力降低高峰时段与低峰时段之间客流量落差的同时，还要力求社会福利最大化。针对这些实际问题，以边际成本定价为依据，结合消费者剩余情况，对铁路客运高峰定价问题进行了分析，建立了数学模型，并进行了仿真。该仿真为研究铁路客运高峰定价提供了一个实用的办法。     

重庆市农村客运发展战略研究

运用TOWS法分析重庆农村客运系统面临的机遇与挑战,构建农村客运发展的战略目标体系,提出重庆市农村客运发展战略目标、任务与基本政策,为重庆市城乡统筹发展大计提供理论依据与支持。     

基于Dijkstra-PD-ACO算法的大城市公交线路优化与评价方法研究

城市公交线路的布设受到诸多影响因素的作用,单纯的追求线路单一指标的"最优"在实际公交线路优化时往往难以取得满意的效果。首先针对传统公交线路优化算法在求解线路优化问题中存在的不足,基于蚁群优化算法的寻优特性,结合Dijkstra算法在局部路径寻优中的优点,提出了Dijkstra蚁群混合优化算法。其次对于线路优化所得到的可行备选方案集,基于分层聚类主成分分析评价法进行优化效能评价。最后以合肥市一环内公交线路优化为实例进行验证。结果表明, 本文提出的方法在兼顾客流密度最大、出行路径最短的同时给出了有效公交线路备选方案,优化结果也符合合肥市的实际情况。同时,该方法对我国大中城市公交线网优化问题的研究具有一定的借鉴意义。     

运输产品配置模型研究

通过效用函数模型解析旅客选择运输服务的机理,利用AHP模型研究运输需求主体所属类别及其选择具体运输产品整个过程。在此基础上,结合客运需求不同类别生成率及数量,并按照经济均衡原则配置运输产品。以西安-延安铁路运输产品配置为例,检验了该配置模型的科学性。     

成渝客运市场博弈分析

运用博弈模型对成渝容运市场竞争状况进行了实证分析。采用斯塔伯格竞争模型研究结论认为,在高速公路进入前,成渝客运市场已达到纳什均衡。价格博弈模型表明,铁路与高速公路竞争中,避免价格战是双方最优主导战略。旅客间的ＰＵＶ博弈结果证明,成渝客运市场中旅客在不同运输方式中的分布是对其总效用博弈的结果。     

高速铁路对区域运输通道系统结构的影响分析

为了促进区域运输通道内各种运输方式的合理分工和协调发展,在对长三角地区旅客出行意向调查的基础上,运用运输通道理论从运输结构配置的角度分析了高速铁路对区域运输通道系统结构的影响。分析结果表明：高速铁路对不同区域运输通道系统结构的影响不同,但仍有一定规律性。高速铁路的比较竞争优势集中在出行时间为2-5小时交通圈内的密集班次,在中距离运输市场与公路和航空竞争,使公路和航空分别向短途和长途运输市场分化。区域运输通道系统在经过一定调整期后,将形成新的各种交通方式竞合关系平衡的有序结构。     

中国民航客运机票定价策略研究

通过分析目前中国民航客运市场的现状 ,针对统一限价政策存在的许多问题和中国加入WTO形势下的新问题 ,对价格体系的科学性和合理性进行探讨 ,并借鉴国外航空公司收益管理的先进理论与方法 ,提出建立适合中国民航客运的多级票价策略     

Measuring the Benefit of Offering Auxiliary Services: Do Bag‐Checkers Differ in Their Sensitivities to Airline Itinerary Attributes?

When firms evaluate their service system design choices, there is typically more uncertainty surrounding the value that a particular auxiliary service provides than there is on the cost of providing that service. To help inform this decision, we propose an approach where we compare the relative value of the segment of passengers who use an auxiliary service to the relative value of the segment that does not use it. We demonstrate this approach for a typical auxiliary service common to the airline industry. In 2008, most US airlines implemented checked baggage fee policies to decrease their costs by reducing the number of customer service agents needed in the check‐in and baggage handling processes. The success of this change has led to a current debate at many of these airlines on whether to make further staffing cuts in these areas, essentially making it even less attractive for passengers to check their baggage. Our proposed methodology helps answer whether passengers who continue to check bags in today's baggage‐fee era are more or less valuable than passengers who do not check bags. We explore this question empirically by examining, through a stated preference survey, if a history of checking or not checking bags can be used to segment passengers based on how their itinerary choices are influenced by common airline service attributes (on‐time performance, itinerary time, number of connections, airfare, and schedule delay). Contrary to the opinions of some top airline executives, we find that the passengers who continue to check bags at airlines that charge baggage fees are generally less sensitive to differences in three of these important service attributes and are less likely to switch airlines when a competing airline improves its offerings along these dimensions. Thus, airlines that charge for checked bags should consider improving the customer experience for their bag‐checking passengers, as they represent a potentially more valuable segment class to the airline.