停车换乘需求分析与定价方法——以上海市轨道交通停车换乘为例

如何充分利用已建停车换乘设施的能力,又不会由于需求太大在换乘点产生拥堵是政策制定者和P＋R停车场（库）运营者的一个挑战。选取上海市地铁1号线两个车站和9号线1个车站进行停车换乘需求行为调查,分析P＋R潜在使用者的行为特性,得出换乘点停车费用是影响出行者选择停车换乘的一个重要因素,可利用价格杠杆引导停车换乘选择行为。将换乘点停车费用作为变量构建停车换乘选择Logit模型,得到停车换乘分担率与换乘点停车收费的函数关系。以上海市地铁莘庄站为例,将相关数据和目标分担率代入模型,反推得到其停车收费标准为5元·次^-1。     

弹性需求下多类型用户拥挤收费模型研究

单一类型用户拥挤收费模型存在两大缺点：拥挤收费对所有用户影响相同和不能真实反映用户实际出行;在此基础上提出了弹性需求下多类型用户拥挤收费模型,并对其进行求解。以实例证明了多类型用户收费模型相对单一类型用户收费模型更能反映用户的实际交通出行,更能真实地反映收费后用户的出行选择,实现整个系统效益的最大化。     

拥挤定价理论发展及对我国城市交通管理的启示

拥挤定价作为城市交通需求管理的重要组成部分,是交通经济学的核心内容之一。本文综述了拥挤定价理论的发展历程和最新进展,提出了该领域的研究方向,分析了拥挤定价理论在实践中的应用效果,给出了我国推行该政策的建议,拥挤定价的实践表明该政策在控制交通流方面效果显著,该政策是否具有可操作性取决于公众的态度,这将是我国大中城市推行该政策的基本出发点和难点。     

城市道路拥挤收费福利影响研究

城市道路拥挤收费虽然使总的社会福利增加,但对不同时间价值出行者的福利影响却不尽相同。以福利经济学为理论基础,以出行的货币成本和时间成本为影响因素,量化分析收费后不同出行者的福利得失。研究发现,在确定拥挤方案后如果能得到交通量的变化量,就可以得到不同选择方案出行者的福利得失。时间价值较低的出行者只有选择公共交通时才能从拥挤收费中受益,而时间价值较高的出行者,选择付费出行使他们获益最大。     

高速铁路动态定价与票额分配协同优化研究

目前我国高速铁路票价制定还比较缺乏灵活的市场化机制,严重影响铁路运输企业收益和服务水平的提高。基于高速铁路旅客出行规律和收益管理策略,研究高速铁路动态定价与票额分配协同优化方法。在分析旅客出行价格需求弹性的基础上,构建基于Logit模型的客流弹性函数,考虑临近发车客票不涨价和运输能力等约束,建立以期望客票总收入最大化为目标的优化模型,设计一种基于超分方程的分步算法,求解不同时段下不同列车的最优票价与票额分配方案。以京沪高速铁路为例进行计算和分析,与固定费率票价相比,动态票价策略能保证服务水平,系统总客流需求下降0.73%,企业客票总收益提高4.95%。     

基于单箱总质量的集装箱班轮二部定价模型

为了解决集装箱班轮运输中因定价方式不合理而导致的运力利用不当及舱位利用率较低的问题, 基于经典的二部定价理论, 借助样本分析所得的相关统计学特征, 构建了非线性定价收益优化模型, 通过MATLAB编程对算例进行了一系列的数学试验, 求得了相关影响因素在不同变化规律下的模型优化结果。分析结果表明: 在特定条件下, 该模型可使班轮运输企业的收益水平较原先提高近20%, 箱位利用率可提升至100%, 模型正确有效。     

拥挤道路使用收费的理论构架

拥挤道路使用收费作为现代城市交通需求管理的有效措施在理论上和实践上都具有十分重要的意义。综述了拥挤道路使用收费的理论构架, 包括静态拥挤道路使用收费的涵义、分类、理论基础及动态收费理论, 这对进一步研究道路使用收费有一定的借鉴意义     

Distance-dependent congestion pricing for downtown zones

A growing literature exploits macroscopic theories of traffic to model congestion pricing policies in downtown zones. This study introduces trip length heterogeneity into this analysis and proposes a usage-based, time-varying congestion toll that alleviates congestion while prioritizing shorter trips. Unlike conventional trip-based tolls the scheme is intended to align the fees paid by drivers with the actual congestion damage they do, and to increase the toll’s benefits as a result.The scheme is intended to maximize the number of people that finish their trips close to their desired times. The usage-based toll is compared to a traditional, trip-based toll which neglects trip length. It is found that, like trip-based tolls, properly designed usage-based tolls alleviate congestion. But they reduce schedule delay more than trip-based tolls and do so with much smaller user fees. As a result usage-based tolls leave most of those who pay with a large welfare gain. This may increase the tolls’ political acceptability.     

Robust optimization of distance-based tolls in a network considering stochastic day to day dynamics

This paper investigates the nonlinear distance-based congestion pricing in a network considering stochastic day-to-day dynamics. After an implementation/adjustment of a congestion pricing scheme, the network flows in a certain period of days are not on an equilibrium state, thus it is problematic to take the equilibrium-based indexes as the pricing objective. Therefore, the concept of robust optimization is taken for the congestion toll determination problem, which takes into account the network performance of each day. First, a minimax model which minimizes the maximum regret on each day is proposed. Taking as a constraint of the minimax model, a path-based day to day dynamics model under stochastic user equilibrium (SUE) constraints is discussed in this paper. It is difficult to solve this minimax model by exact algorithms because of the implicity of the flow map function. Hence, a two-phase artificial bee colony algorithm is developed to solve the proposed minimax regret model, of which the first phase solves the minimal expected total travel cost for each day and the second phase handles the minimax robust optimization problem. Finally, a numerical example is conducted to validate the proposed models and methods.     

Measuring risk aversion to guide transportation policy: Contexts,incentives, and respondents

Road pricing may provide a solution to increasing traffic congestion in metropolitan areas. Route, departure time and travel mode choices depend on risk attitudes as commuters perceive the options as having uncertain effects on travel times. We propose that Experimental Economics methods can deliver data that uses real consequences and where the context can be varied by the researcher. The approach relies on the controlled manipulation of contexts, similar to what is done in the Stated Choice approach, but builds in actual consequences, similar to the Revealed Preference approach. This paper investigates some of the trade-offs between the cost of conducting Experimental Economics studies and the behavioral responses elicited. In particular, we compare responses to traditional lottery choice tasks to the route choice tasks in simulated driving environments. We also compare students (a low cost convenient participant pool) to field participants recruited from the driving population. While we see initial differences across our treatment groups, we find that their risk taking behavior converge with minimal repetition.