关于我国现代物流发展的思考

在全球经济一体化和信息化的推动下.现代物流业已从原来单纯的运输业发展为以物 流企业为主体、涉及生产、流通和消费过程的现代物流系统.随着经济的发展.作为国民经济动 脉的现代物流业将越来越为各国所重视.本文在分析我国物流业发展历史和现状的荃础上.提 出了加快我国现代物流发展的几点思考.     

快速路可变限速与匝道控制协同优化策略

可变限速控制和匝道控制是快速路交通控制的主要手段,本文对两者的协同优化策略进行了研究.借助智能车路协同系统强大的信息感知能力,通过引入微观交通流信息,对经典METANET模型进行了改造,构建了可变限速控制影响下的微观METANET模型,实现了一种新的可变限速控制策略,同时,采用ALINEA算法,对入口匝道进行了优化控制,实现了两者的协同优化.最后,基于实际道路和交通流数据搭建了仿真平台,对微观METANET模型和协同优化策略的有效性进行了验证.仿真结果表明,微观METANET模型具有良好的交通流预测效果,协同优化策略能有效地改善快速路交通流状态.     

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。     

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.     

Quantifying the environmental impacts of increasing high occupancy vehicle lanes in the United States

To what extent will increasing High Occupancy Vehicle (HOV) lane-kilometer incentivize carpooling and reduce emissions of air pollutants and greenhouse gases? To answer these questions, we develop a multiple regression model relating HOV lanes and other socioeconomic factors to carpooling propensity in all 50 U.S. states and the District of Columbia, then calculate the extent to which increasing HOV lane-kilometers would lead to reductions in carbon dioxide equivalent (CO₂e) and major air pollutants across the U.S., by state. Increasing HOV lane extent has the greatest potential to reduce annual CO₂e in the District of Columbia, followed by Hawaii and New York. The smallest potential is found in states with the lowest population density, led by North Dakota. We then explore the extent to which recommendations made at one level of data aggregation (that of individual states) may be valid for another level, such as individual counties. The only state with sufficient data available to disaggregate the model to the county level is California, where we found a lower potential for state-wide CO₂e emission reductions under the county-level model as compared to the state-level model (0.69% as compared to 1.08%, under the same hypothetical scenario), albeit with significant differences in emission reduction potential between counties with higher vs. lower population densities. This analysis demonstrates the potential to generate generalizable insight into the magnitude of vehicle emission reductions that might be achieved through expanding HOV lanes, and highlights the importance of data disaggregation in identifying the optimal locations for potential reductions.     

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.     

A meta-analysis of Western European external costs estimates

This paper analyses the external transport cost estimates that have been found in various European studies. These estimates seem at first glance to be highly dispersed, a fact which raises questions about their reliability. The article offers a list of the possible reasons for discrepancies between the various studies; differences in the specific situations studied, in the type of cost calculated, in the physical laws relating to emissions, in the measurement of values of external costs. It assesses the relative magnitude of each of these causes through a meta-analysis. It turns out that the main differences come from the specificity of the situation under review and the type of cost calculated. In comparison, differences in the laws and in the values methods have a lower importance. The conclusion is that the estimates of external costs are reliable for decision-making policies.     

航运电子市场精确定价

根据航运市场中影响定价的主要因素，提出了精确定价是在当今信息网络化和经济全球化的浪潮下，承运人最合适的定价形态，分析了精确定价及其条件．指出了在航运电子市场巾，承运人应从托运人的偏好、差异化的航运服务、单独付帐和避免托运人套利等4个方而对航运交易加以控制，从理论和实例2方面阐述了采用价格差异(精确定价)策略对承运人所带来的收益，在得出航运企业利润最大化条件的基础上，提出了承运人制定精确定价的5个步骤。从而为我国航运企业制订信息化及电子商务发展战略提供有益的参考。     

一种基于价格和效用的帧聚合策略

MAC层帧聚合是改善无线网络信道利用率、提高频谱效率的有效方法之一。将定价策略和排队论模型引入到IEEE 802.11系列MAC协议的帧聚合策略中,以平均时延作为主要QoS(Quality of Service)指标,在满足业务流最大时延约束基础上,从用户效用和网络效用两个方面研究价格及网络效用的优化问题。此外,还分析了包平均到达率与平均时延的关系。仿真结果表明:所提出的基于定价和效用的帧聚合策略是有效的,可以提高系统平均吞吐量,减少端到端平均时延。