中国经济增长的波动分析

对国民经济增长的波动进行计算分析。应用Mexican Hat小波对国内生产总值（GDP）的增长率进行多时间尺度分析，再利用宏观经济学的理论从政府宏观调控政策、投资、消费等方面分析引起波动的原因。在不同时间尺度下，经济波动受到政策、投资和消费及重大政治事件的影响。通过对经济增长波动的因素及规律的分析，可以提高对经济发展的预见性，促进经济更好的增长。     

任意随机变量序列关于广义随机选择系统的若干极限性质

采用鞅方法研究了对任意随机变量序列关于广义随机选择普遍成立的一类强极限定理,并作为推论得到了m阶马氏过程,鞅序列,鞅差序列,独立随机变量序列的一类强极限定理.把赌博系统的随机变换概念推广到任意随机变量序列,得到了任意随机变量序列随机选择与公平比的若干极限定理.     

Structural equation approach to investigate trip-chaining and mode choice relationships in the context of developing countries

This paper investigates empirical relationships between trip chain type and mode class choice for developing countries. To formulate these two sets of decisions, four empirical models are developed using structural equation modeling (SEM). Those models are calibrated using one-month travel diary data collected in Dhaka city. SEM correlates the observed variables and identifies their relationship with trip-chaining type utility and mode class choice utility. The fitted models are selected based on statistical results and similarity with the real-life situation. Direct relationships between trip-chaining and mode choice utilities are found insignificant. However, several socio-demographic factors influence both simultaneously. Consequently, it is essential to consider mode class choice concurrently for modeling trip chains. This study also investigates the influencing factors for work-based and non-work-based trip chains separately and effects of road users’ heterogeneity. The research results can be utilized to perceive trip chain-mode choice patterns for developing countries.     

Urban spatial location advantage: The dual of the transportation problem and its implications for land-use and transport planning

Numerous recent studies have investigated the relationship between the location of jobs and housing in urban areas and how this relates to urban commuting patterns. Few have utilised the dual of the transportation problem of linear programming (TPLP) to provide insights into these relationships Accordingly, this analysis utilises the TPLP to determine dual variable values (shadow prices) for a study area in Dublin, Ireland. The approach determines the pattern of relative location advantage for the peak and off-peak travel periods and for public and private transport for 1991 and 2001. The results are set against the expected results for hypothetical urban structures. The results show that the pattern of relative location advantage has altered sharply over the study period for off-peak trip-making but has remained more or less the same for trip-making in the peak period. For the off-peak period, the pattern of relative location advantage has shifted from the central area to the periphery specifically for private transport trips; for public transport, the pattern has remained focused on the city centre. This indicates that private transport users can react more quickly to changes in the distribution of land-use activities than their public transport counterparts due to the relatively fixed nature of the latter mode. This implies that the public transport network needs to be reorganized to better reflect the revised pattern of trip-making specifically for the off-peak period. The results demonstrate the value of using the approach for providing information about the spatial organisation of land uses within cities and where future development may be targeted.     

Separation of car-dependent commuters from normal-choice riders in mode-choice analysis

The ever-increasing use of cars is a big problem in metropolitan areas. To manage the traffic stream and alleviate air pollution, most metropolitan governments are attempting to discourage the use of cars. Nevertheless, the results have not been satisfactory. It is well known that normal-choice riders choose their travel mode based on utility, which is determined by mode-specific impedances and individual characteristics. On the other hand, this study focuses on identifying car-dependent commuters who tend to keep driving cars regardless of the circumstances they are confronted with. For this study, psychometric factors characterizing car-dependent commuters were investigated. However, the performance of the mode-choice model was not sufficiently enhanced despite incorporation of the psychometric factors. The performance improved considerably when the car-dependent commuters were excluded. Based on psychometric factors, the support vector machine successfully separated the car-dependent commuters from normal-choice riders.

基于Authorware的计算器的实现

分析了利用Authorware强大功能的系统函数结合变量,开发出优秀的交互多媒体作品,讨论了使用这些函数和变量编写计算器应用软件的方法,并给出了开发的流程及其具体的实现.     

An investigation on the performances of mode shift models in transit ridership forecasting

This paper aims at investigating the over-prediction of public transit ridership by traditional mode choice models estimated using revealed preference data. Five different types of models are estimated and analysed, namely a traditional Revealed Preference (RP) data-based mode choice model, a hybrid mode choice model with a latent variable, a Stated Preference (SP) data-based mode switching model, a joint RP/SP mode switching model, and a hybrid mode switching model with a latent variable. A comparison of the RP data-based mode choice model with the mode choice models including a latent variable showed that the inclusion of behavioural factors (especially habit formation) significantly improved the models. The SP data-based mode switching models elucidated the reasons why traditional models tend to over-predict transit ridership by revealing the role played by different transit level-of-service attributes and their relative importance to mode switching decisions. The results showed that traditional attributes (e.g. travel cost and time) are of lower importance to mode switching behaviour than behavioural factors (e.g. habit formation towards car driving) and other transit service design attributes (e.g. crowding level, number of transfers, and schedule delays). The findings of this study provide general guidelines for developing a variety of transit ridership forecasting models depending on the availability of data and the experience of the planner.     

A comprehensive dwelling unit choice model accommodating psychological constructs within a search strategy for consideration set formation

This study adopts a dwelling unit level of analysis and considers a probabilistic choice set generation approach for residential choice modeling. In doing so, we accommodate the fact that housing choices involve both characteristics of the dwelling unit and its location, while also mimicking the search process that underlies housing decisions. In particular, we model a complete range of dwelling unit choices that include tenure type (rent or own), housing type (single family detached, single family attached, or apartment complex), number of bedrooms, number of bathrooms, number of storeys (one or multiple), square footage of the house, lot size, housing costs, density of residential neighborhood, and commute distance. Bhat’s (2015) generalized heterogeneous data model (GHDM) system is used to accommodate the different types of dependent outcomes associated with housing choices, while capturing jointness caused by unobserved factors. The proposed analytic framework is applied to study housing choices using data derived from the 2009 American Housing Survey (AHS), sponsored by the Department of Housing and Urban Development (HUD) and conducted by the U.S. Census Bureau. The results confirm the jointness in housing choices, and indicate the superiority of a choice set formation model relative to a model that assumes the availability of all dwelling unit alternatives in the choice set.     

Is car drivers’ response to congestion charging schemes based on the correct perception of price signals?

This paper deals with the question of whether the capability of car drivers to estimate the cost of a new hypothetical, highly differentiated congestion charge influences their decision to change travel behaviour. The analysis makes use of an integrated choice and latent variable model (ICLV) which merges classic choice models with the structural equation approach (SEM) for latent variables. This hybrid model improves the explanatory power considerably compared with a conventional discrete choice model. The results suggest that charge complexity decreases the resistance in considering behavioural changes. Car drivers tend to avoid a travel option where the price is not known beforehand, a phenomenon known as ambiguity avoidance.     

Synopsis of bicycle demand in the City of Toronto: Investigating the effects of perception,consciousness and comfortability on the purpose of biking and bike ownership

This paper provides an empirical basis for the evaluation of policies and programs that can increase the usage of bikes for different purposes as well as bike ownership. It uses an integrated econometric model of latent variable connecting multiple discrete choices. Empirical models are estimated by using a bicycle demand survey conducted in the City of Toronto in 2009. Empirical investigations reveal that latent perceptions of ‘bikeability’ and ‘safety consciousness’ directly influence the choice of biking. It is also found that the choice of the level of bike ownership (number of bikes) is directly influenced by latent ‘comfortability of biking’. The number of bikes owned moreover has a strong influence on the choices of biking for different purposes. It is clear that bike users in the City of Toronto are highly safety conscious. Increasing on-street and separate bike lanes proved to have the maximum effects on attracting more people to biking by increasing the perception of bikeability in the city, comfortability of biking in the city and increasing bike users’ sense of safety. In terms of individuals’ characteristics, older males are found to be the most conformable and younger females are the least comfortable group of cyclists in Toronto.