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.     

Real options and flexibility analysis in design and management of one-way mobility on-demand systems using decision rules

This study explores the concepts of real options and flexibility analysis as an approach to address uncertain demand growth in mobility on-demand (MoD) vehicle-sharing systems, with the goal of improving expected lifecycle performance. As MoD systems are gaining popularity worldwide, they inevitably face significant uncertainty in terms of needs and customer demands. Designing, planning capacity deployment, and operating such system can be challenging, and require significant capital investments for companies and cities. Two distinct real options analysis (ROA) models are developed to evaluate and optimize flexible strategies for these systems, relying on a novel methodological approach to value flexibility based on decision rules. The decision-rule-based approach differs from standard ROA approaches used to quantify the value of flexibility in irreversible investment projects, typically based on dynamic programming. It emulates the decision-making process by capturing mathematically a triggering mechanism that determines when it is best to exercise the flexibilities embedded in the system design. Two prevalent types of MoD systems are studied in this paper as demonstration of the methodological framework: a station-based system where customers must pick up and return the vehicle at specific locations, and a free-floating system, where customers may pick up and drop the car anywhere within a certain area. A simulation-based approach is used to analyze the station-based system, which models the rebalancing operations from a micro-level perspective. The approach consists of a discrete event simulator for performance estimation, and an optimization algorithm for design space exploration that integrates a population-based search algorithm with Optimal Computing Budget Allocation (OCBA). For the free-floating system, an analytical model is developed where the decision rule is formulated into and solved using stochastic mixed integer programming (MIP). The study provides guidance to system operators on potential strategies for deploying MoD systems, considering explicitly uncertainty and flexibility as a value enhancing mechanism.     

Valuation of strategic options in public–private partnerships

This paper investigates the feasibility of and develops an economic valuation model for strategic options in Comprehensive Development Agreements (CDAs). A CDA is a form of public–private partnership whereby the right to price and collect revenues from toll roads is leased to a private entity for a long but finite period of time. In exchange, this provides local and state governments with a quick influx of cash and/or additional infrastructure. Uncertainty associated with such long-term leases is of substantial public concern. This paper examines five different strategic options, namely a buyout option, a conditional buyout option, a revenue-sharing option, and two types of minimum revenue guarantee options. The buyout option in particular could give the public sector additional control over the future use of leased facilities and address potential concerns regarding long-run uncertainty and possible unforeseen windfalls for the private sector. The paper’s contributions include the analysis, feasibility assessment and valuation of several strategic options, sensitivity analysis of the solutions, an economic consumer demand-based revenue model for purposes of cash flow simulation, and analysis of option price sensitivity to “moneyness”. The main conclusion is that strategic options can provide useful risk reduction, but generally have significant value relative to the lease itself. By scaling down payoffs, options could be realistically included in CDAs and other PPPs. For some parameter values, option values to the developer and public authority are offsetting, allowing for costless risk reduction.     

Real option analysis for environmental compliance: LNG and emission control areas

A wide array of technical and operational solutions is available to shipowners in order to comply with existing and upcoming environmental regulation within Emission Control Areas (ECAs). Liquefied Natural Gas (LNG) is a promising alternative since it offers potential cost savings in addition to ensuring compliance with ECA regulation. But investment to retrofit existing vessels to be able to use LNG carries significant upfront costs, and a high degree of uncertainty remains on the differential between the prices of LNG and conventional maritime fuels, as well as on the availability of LNG and the reliability of its supply chain. New technologies such as LNG inherently carry substantial risk and an ill-chosen investment strategy may have irreversible consequences that could jeopardise the future of the shipping company. One important question is whether interested owners should invest in LNG now to comply with ECA rules in 2015 and reap the benefits of lower LNG prices, or whether it would be advisable to wait until some of the uncertainty is resolved.While traditional discounted cash flow techniques are unable to account for the value of managerial flexibility linked, for example, to the possibility of deferring an investment, real option analysis can be used to analyse such cases. The paper discusses the optimal time for investment in LNG retrofit and takes specific account of the value of an investment deferral strategy versus the advantages obtainable from the immediate exploitation of fuel price differentials. Through the use of a real option model the paper shows that there is a trade-off between low fuel prices and capital expenses for investment in LNG retrofit. The development in LNG is critically dependent on its future price as well as the reduction in capital costs and ship retrofitting costs. In this respect, policy makers can play a critical role in providing support to advance technical knowledge, maintain LNG prices at favourable levels and in avoiding ambiguity on regulation.     

车辆碰撞过程的试验分析研究

本文简介了以模拟车对车碰撞事故为目的的实车碰撞试验，采用２１起车对车实车碰撞试验数据对碰撞条件、碰撞形态、碰撞能量消耗分配等碰撞特征参量及其相关关系进行了试验分析研究。     

基于灰色关联分析的房地产价格估算研究

房地产开发价格确定的准确与否直接影响项目开发利润的大小,更会关系到项目的成功与失败.结合房地产开发的特点,在充分考虑房地产价格影响因素的基础上,利用已建类似项目的价格建立灰色关联分析模型,估算出拟建项目房地产价格,并结合实例进行了阐述.     

用EXCEL进行太阳真方位计算

文章介绍了运用EXCEL软件计算太阳真方位及太阳的磁影方位,并给出了计算的具体操作程序和解释。     

浅谈房地产开发设计阶段成本管理

随着我国房地产开发的不断发展,越来越多的房地产商意识到了设计阶段成本管理的重要性.但如何进行设计阶段成本管理,如何改善和提高设计阶段成本管理水平,大多数房地产商还处于摸索阶段.本文通过对一些具体案例的分析,来阐述设计阶段成本管理的一些办法和思路.     

校园房产地理信息系统设计与开发

地理信息系统是一门新兴的边缘综合性学科,同时也是一个具有强大功能的技术系统,它能对房产分布信息进行有效管理.本文对校园房产地理信息系统(Campus Real Estate GIS,CREGIS)的设计与开发从系统总体设计、系统功能结构、系统实现等方面进行了探讨,以重庆交通学院为研究范围进行了初步的探索,并取得了一定成果,为校园房产地理信息系统的建立提供了基础.     

论我国房地产按揭中银行风险的法律防范

随着我国房地产业的发展,房地产按揭这种新的融资购楼方式日渐普遍。但按揭在我国毕竟是新生事物,相关的法律法规并不完善,因而给实际操作带来很大不便。尤其是贷款银行的权益很难得到有效保护。有鉴于此,笔者以此为研究对象,通过对房地产按揭涵义及法律特点的剖析,重点探讨贷款银行风险的法律防范问题。在此基础上,借鉴发达国家的先进经验,具体结合我国当前房地产按揭的现状,进而提出为完善我国贷款银行风险法律防范的建议措施。