基于高速铁路常旅客计划的全流程智慧行李服务方案构建

随着"铁路畅行"和"客运提质计划"的提出,提供高质量、个性化的旅客服务成为高速铁路客运发展的关键方向。为打造全新旅客行李服务概念,基于"铁路畅行"会员常旅客计划,构建全流程、门到门智慧行李服务方案,从旅客需求角度出发,采取"人货分离"模式,突破传统站内行李服务的模式,拓宽业务场景,延长商业链,形成一套系统完整的方案。全流程智慧行李服务作为一项新的服务模式,为培育旅客需求,提高顾客粘合度,挖掘高铁行李服务的潜在市场,优化铁路盈利结构,加快构建铁路客运服务体系,提升铁路在运输市场的竞争力提供支持。     

Price effects of airlines frequent flyer programs: The case of the dominant firm in Chile

Frequent flyer programs create a switching cost for the consumer and allow firms to obtain rents, for example, by exploiting the principal agent problem existing between the employee who travel and purchases the ticket and the employer paying for that ticket. In Chile LAN is the dominant airline in domestic markets and the only one that has a frequent flyer program (FFP); it faces some competition from two small carriers. Using a unique dataset for Chile, collected by ourselves from airlines websites in 2011 and 2012, we estimate the impact of the dominant airline FFP. For this purpose, we compare for each route the fares between airlines and between weekday trips (that accumulate full miles and are mainly for business purposes) and weekend trips (that accumulate less than full miles and are mainly for leisure purposes). The results show that the differential premium LAN is able to charge for weekday trips due to the FFP is around 35%. Three particularities of the Chilean market help the econometric identification: there is only one hub for all airlines (the capital city of Santiago), there is no business class in domestic flights, and none of the airlines is a low-cost carrier.     

A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction

With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.     

高架地铁车站抗震分析

以某轨道交通工程高架地铁车站为背景,建立有限元计算模型,分析地震作用下高架车站墩柱结构的地震反应。结果表明:在多遇地震作用下,该高架车站墩柱强度满足规范要求;在罕遇地震作用下,该高架车站墩柱非线性位移延性比满足规范要求。计算结果已为该高架车站的抗震设计提供依据,分析方法可为同类结构提供参考。