西安铁路枢纽客运系统扩能方案研究

西安铁路枢纽将有8个方向、7条铁路线引入,既有西安站已不能满足客流增长的需要。分析西安枢纽内影响客运能力的各项因素,结合西安枢纽的改扩建及西安城市规划,提出新建第二客运站——西安北站和辅助客运站——纺织城站,并对原西安站进行扩建的方案,形成西安枢纽客运系统二主一辅的格局。     

How does demand uncertainty affect food and beverage capacity in the hotel industry?

This study investigates the effect of demand uncertainty on hotels’ food and beverage (F&B) capacity using the operation data of international tourist hotels in Taiwan. The empirical results of this study show that demand uncertainty leads to an increase in F&B capacity. Moreover, the magnitude of this effect increasingly strengthens for hotels with larger F&B scales. Our results together with other studies on room capacity collectively indicate that hotels’ overcapacity problem resulting from demand uncertainty considerably varies with hotel scales and between different hotel sectors.     

A multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach

This paper presents a new multi-objective mathematical model to address a Healthcare Inventory Routing Problem (HIRP) for medicinal drug distribution to healthcare facilities. The first part of objective function minimizes total inventory and transportation costs, while satisfaction is maximized by minimizing forecast error which caused by product shortage and the amount of expired drugs; Greenhouse Gas (GHG) emissions are also minimized. A demand forecast approach has been integrated into the mathematical model to decrease drug shortage risk. A hybridized possibilistic method is applied to cope with uncertainty and an interactive fuzzy approach is considered to solve an auxiliary crisp multi-objective model and find optimized solutions.     

The determinants of inbound tourism to Malaysia: a panel data analysis

Malaysia has experienced a significant increase in tourist arrivals over the past 10 years. The challenge is to sustain this growth and therefore it is important to understand the factors influencing inbound tourism to Malaysia. This paper investigates the economic and non-economic determinants of international tourist flows to Malaysia using the generalized method of moment. The annual panel data set includes the number of arrivals from 33 countries during the period 2000–2012, and the number of possible explanatory variables. It is found that habit persistence (word of mouth), income, hotel room and political stability have a positive impact on tourism demand for Malaysia. Also, results indicate that the estimated coefficients of substitute tourism price in the model are negative. This implies that the five alternative destinations are complementary destinations to Malaysia. In addition, the dummy variable for Visit Malaysia Year in 2007 and severe acute respiratory syndrome in 2003 had positive and negative impacts on tourism demand for Malaysia, respectively.     

坚持发展是第一要务的方针扎实推进铁路多元经营主辅分离辅业改制工作

结合铁路多经实际，以主辅分离为主线，以规范发展为重点，论述了主辅分离辅业改制的重要性和必要性，以及在主辅分离目标要求下加快多经发展的意义、目的和措施，强调了全路多经系统当前推进主辅分离辅业改制应当重点抓好的几项工作。     

铁路运输企业主辅分离理论问题及相关分析模型研究

在系统介绍主辅分离的定义、内涵等相关理论及现阶段主输分离改革的创新与突破的基础上,提出了主辅分离时机成熟的表述式,并通过建立关联/效益模型对辅业进行了4个象限的分类,同时论述了主辅分类的相应措施。     

Driving demand: What can gasoline refueling patterns tell us about planning an alternative fuel network?

Knowing which variables predict gasoline demand can help inform which are useful in determining future demand at an alternative fuel station such as those for bio-fuels, natural gas, hydrogen, or fast-charge electricity. This study explores the spatial distribution of demand by comparing two main classes of variables: those without a displacement component such as population in a census block group, and those that imply a vector or directionality such as vehicle kilometers traveled. The spatial distribution of these variables is compared to the spatial distribution of demand for gasoline using regression. Many models examining the transition from gasoline to an alternative fuel assume a demand pattern for fuel a priori in order to estimate potential demand at a future alternative fuel station. This paper studies not the models themselves but the variables used to predict demand. The results indicate that vehicle kilometers traveled (VKT) is the best variable to pinpoint where demand for fuel will occur. However, travel to the central business district of the metropolitan area does not appear to translate into demand for fuel in proportion to the VKT. While gasoline demand does appear to vary with population as well, the location of demand is much less specific than that predicted by VKT. The results also suggest that the route between home and the nearest freeway entrance may help predict a large portion of refueling and merits further investigation. This possible tendency can be used to create a new variable called “population-traffic” which appears to describe the spatial distribution of demand well. The good performance of this independent variable in regressions suggests that stations sited along the freeway may serve customers needs and provide the necessary concentration of demand for initial alternative fuel stations. A practical application of this work would be to help define refueling demand patterns in a rollout of alternative fueled vehicles in a neighborhood or town.     

Sources of intermittent demand for aircraft spare parts within airline operations

Owing to the sporadic nature of demand for aircraft maintenance repair parts, airline operators perceive difficulties in parts demand forecasting. In this paper we investigate the sources of demand lumpiness, as a function of flying hours, that may affect the parts demand rate. Experimental results of demand lumpiness, measured by the square coefficient of variation (CV²) and the average inter-demand interval (ADI), are examined and clarified through statistical analysis. The general linear model approach is used to explain the variation attributable to the various experimental factors and their interactions. Actual historical data for hard-time and condition-monitoring components from an airlines operator are used. This study shows that aircraft utilization rate can be a major source of lumpiness since it increases and decreases the square coefficient of variation and the average inter-demand interval respectively for the observed demand. This assumes a strictly linear relationship between demand and flying hours/landings.     

Estimating the impact of airport privatization on airline demand: A regression-based event study

This paper develops an empirical model of passenger demand for routes of airports subject to either imminent or recent privatization. We investigate whether the privatization process produces a sequential impact over traffic. By employing a regression-based event methodology and controlling for fixed effects, price endogeneity and sample selection, we perform an econometric analysis of pre-privatization and post-privatization dynamic patterns of demand to infer the demand consequences of the major change in airport governance. We examine recent Brazilian airport privatization experience as a case. The main results suggest that privatization produced an overall increase in airline demand and that the airport notably recognized with the greatest demand potential and with the largest market penetration of a fast-growing low cost newcomer had the highest estimated ceteris paribus effect of privatization on demand.     

Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the “China Optics Valley” in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields ‘better’ configuration in meeting real-world bike riding needs.     

日本高速铁路投融资模式研究

分析日本高速铁路的发展现状、投资规模、资金来源渠道、投融资管理机构,认为日本高速铁路的投融资政策要求政府在新干线建设中起主导作用,同时制定新干线建设的配套政策。     

Global intermodal liner shipping network design

This paper presents a holistic analysis for the network design problem of the intermodal liner shipping system. Existing methods for liner shipping network design mainly deal with port-to-port demand. However, most of the demand has inland origins and/or destinations. Thus, it is necessary to cope with inland origin–destination (OD) pairs involving a change in transport mode from inland transportation to maritime shipping. A method is first proposed to convert inland OD demand to port-to-port demand. Then, a framework for global intermodal liner shipping network design is proposed. Finally, the proposed methodology is applied to and numerically verified by a large-scale network example.     

云南铁路货运量增长问题研究

在分析国内外铁路货运需求增长模型的基础上,提出铁路货运需求增长过程的4阶段划分理论,结合云南铁路发展滞后的现状,建立了消除铁路货运能力限制的货运量增长模型。通过对云南铁路货运量和货运需求量的计算,应用MATLAB仿真技术验证了模型的正确性,并对云南铁路货运量和货运需求量进行了预测。     

A model to forecast airport-level General Aviation demand

General Aviation (GA) demand forecast plays an important role in aviation management, planning and policy making. The objective of this paper is to develop an airport-level GA demand forecast model. The GA demand at an airport is modeled as a function of social-economic and demographic factors, the availability of supply factors, the competition from the commercial aviation, the number of based aircraft, and the presence of a flight school. Our models suggest that the relative fuel price – fuel price compared with personal income – is a significant determinant of airport level GA demand. The elasticity of itinerant and local GA demand with respect to the relative fuel price is −0.43 and −0.52, respectively. Our results are compared with those reported in other studies. Furthermore, we made projections of GA demand for the airports in the Terminal Area Forecast (TAF) using three fuel price scenarios from the Energy Information Administration. Our projections under the “business-as-usual” fuel price scenario are close to those in the TAF. Our models could prove useful, for example, for the Federal Aviation Administration and airport planners to prepare airport-level GA demand forecast.     

Determining flight frequencies on an airline network with demand–supply interactions

This paper determines flight frequencies on an airline network with demand–supply interactions between passenger demand and flight frequencies. The model consists of two submodels, a passenger airline flight choice model and an airline flight frequency programming model. The demand–supply interactions relevant to determining flight frequency on an airline’s network are analyzed by integrating these two submodels. The necessary condition for the convergence of the demand–supply interaction is discussed. An example demonstrates the feasibility of applying the proposed models. The results are more accurate than those obtained without considering demand–supply interactions, and the models provide ways to consider demand–supply interactions well in advance to determine flight frequencies on an airline network.     

Spare parts demand: Linking forecasting to equipment maintenance

Demand for spare parts is typically intermittent and forecasting the relevant requirements constitutes a very challenging exercise. Why is the demand for spare parts intermittent and how can we use models developed in maintenance research to forecast such demand? We attempt to answer these questions; we present a novel idea to forecast demand that relies upon the very sources of the demand generation process and we compare it with a well-known time-series method. We conclude that maintenance driven models are associated with a better performance under certain conditions. We also outline an inter-disciplinary agenda for further research in this area.     

Demand clustering in freight logistics networks

Demand clustering in freight logistics networks is an important strategic decision for carriers. It is used to incorporate new business to their networks, detecting potential economies, optimizing their operation, and developing revenue management strategies. A specific example of demand clustering is truckload combinatorial auctions where carriers bundle lanes of demand and price them taking advantage of economies of scope. This research presents a novel approach to cluster lanes of demand. Community detection is used to cluster the emergent network finding profitable collections of demand. Numerical results show the advantages of this method.     

A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting

Air transport demand forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. Total passengers are used as a proxy for air transport demand. However, the air passenger time series usually has a complex behavior due to their irregularity, high volatility and seasonality. This paper proposes a new hybrid approach, combining singular spectrum analysis (SSA), adaptive-network-based fuzzy inference system (ANFIS) and improved particle swarm optimization (IPSO), for short-term air passenger traffic prediction. The SSA is used for identifying and extracting the trend and seasonality of air transport demand and the artificial intelligence technologies, including ANFIS and IPSO, are utilized to deal with the irregularity and volatility of the demand. The HK air passenger data are collected to establish and validate the forecasting model. Empirical results clearly points to the enormous potential that the proposed approach possesses in air transport demand forecasting and can be considered as a viable alternative.     

Aircraft replacement scheduling: A dynamic programming approach

This study developed a stochastic dynamic programming model to optimize airline decisions regarding purchasing, leasing, or disposing of aircraft over time. Grey topological models with Markov-chain were employed to forecast passenger traffic and capture the randomness of the demand. The results show that severe demand fluctuations would drive the airline to lease rather than to purchase its aircrafts. This would allow greater flexibility in fleet management and allows for matching short-term variations in the demand. The results of this study provide a useful reference for airlines in their replacement decision-making procedure by taking into consideration the fluctuations in the market demand and the status of the aircraft.