基于铁路客票分析的序列模式挖掘

就数据挖掘中的两种模式:关联规则和序列模式的概念和作用进行了探讨,对序列模式挖掘用程序流程的方式加以说明;并基于铁路客票分析,讨论了实现效果.     

论汉语的八大句类

由于思维定势的影响和理论背景的制约,汉语语法学界一直将现代汉语的句类模式定格在陈述、疑问、祈使、感叹的四分系统之中,句类划分理论上裹足不前、无法突破;操作上削足适履、无视事实。本文拟借鉴言语行为理论和认知心理学的研究成果,在整理分析大量汉语句子语料的基础上提出句子功能类型八分的模式,并进而分析八大句类的语用条件,从而构拟出一个全新的汉语句类系统。     

城市轨道交通与城市空间形态模式选择

从土地使用功能、开发强度、沿线土地价值、城市用地布局及地区经济活力5个方面阐述了轨道交通对土地利用的影响。分析了轨道交通对城市空间形态的影响,包括引导城市空间结构调整、促进城市发展轴形成、带动城市中心区和副中心区发展3个方面。探讨了基于轨道交通的城市空间形态发展模式,主要有轴向、组团式、主轴-网络状3种。最后,指出大城市空间形态模式的选择应充分考虑轨道交通的特征,借鉴主轴-网络状模式。     

城市交通走廊分流措施与设计模式

针对我国大城市存在不同性质交通流对走廊核心干路过度依赖的问题,提出路网分流和断面分流两种交通走廊功能保障措施,分别以上海市杨高路交通走廊和库里蒂巴西南交通走廊为例,剖析以这两种保障措施为核心的交通走廊设计要点。对城市发展轴交通走廊提出三种设计模式,对一般性交通走廊提出两种设计模式,分别探讨了每种模式的适用条件、设置要素、优缺点和实施保障要求。     

Indicators of reliability and vulnerability: Similarities and differences in ranking links of a complex road system

This article investigates two performance attributes of road networks, reliability and vulnerability, analyzing their similarities as well as the differences that justify distinct definitions, based on consolidation of recent studies. We also discuss the indicators found in the literature for these two performance attributes. Since various authors treat vulnerability as an aspect of reliability instead of a specific attribute, we carried out an application to a complex road network representative of the city of Rio de Janeiro to check the suitability of this approach. The results show that the vulnerability indicators are more strongly affected by the characteristics of alternative routes while the reliability metrics are more sensitive to the congestion level. The conclusion is that reliability and vulnerability should be treated distinctly for evaluating the performance of road network links.     

Forecasting travel behavior using Markov Chains-based approaches

Recent advances in agent-based micro-simulation modeling have further highlighted the importance of a thorough full synthetic population procedure for guaranteeing the correct characterization of real-world populations and underlying travel demands. In this regard, we propose an integrated approach including Markov Chain Monte Carlo (MCMC) simulation and profiling-based methods to capture the behavioral complexity and the great heterogeneity of agents of the true population through representative micro-samples. The population synthesis method is capable of building the joint distribution of a given population with its corresponding marginal distributions using either full or partial conditional probabilities or both of them simultaneously. In particular, the estimation of socio-demographic or transport-related variables and the characterization of daily activity-travel patterns are included within the framework. The fully probabilistic structure based on Markov Chains characterizing this framework makes it innovative compared to standard activity-based models. Moreover, data stemming from the 2010 Belgian Household Daily Travel Survey (BELDAM) are used to calibrate the modeling framework. We illustrate that this framework effectively captures the behavioral heterogeneity of travelers. Furthermore, we demonstrate that the proposed framework is adequately adapted to meeting the demand for large-scale micro-simulation scenarios of transportation and urban systems.     

Activity patterns,time use,and travel of millennials: a generation in transition?

Millennials, defined in this study as those born between 1979 and 2000, became the largest population segment in the United States in 2015. Compared to recent previous generations, they have been found to travel less, own fewer cars, have lower driver’s licensure rates, and use alternative modes more. But to what extent will these differences in behaviour persist as millennials move through various phases of the lifecycle? To address this question, this paper presents the results of a longitudinal analysis of the 2003–2013 American Time Use Survey data series. In early adulthood, younger millennials (born 1988–1994) are found to spend significantly more time in-home than older millennials (born 1979–1985), which indicates that there are substantial differences in activity-time use patterns across generations in early adulthood. Older millennials are, however, showing activity-time use patterns similar to their prior generation counterparts as they age, although some differences – particularly in time spent as a car driver – persist. Millennials appear to exhibit a lag in adopting the activity patterns of predecessor generations due to delayed lifecycle milestones (e.g. completing their education, getting jobs, marrying, and having children) and lingering effects of the economic recession, suggesting that travel demand will resume growth in the future.     

Airline delays,congestion internalization and non-price spillover effects of low cost carrier entry

This paper develops an econometric model of flight delays to investigate the influence of competition and dominance on the incentives of carriers to maintain on-time performance. We consider both the route and the airport levels to inspect the local and global effects of competition, with a unifying framework to test the hypotheses of 1. airport congestion internalization and 2. the market competition-quality relationship in a single econometric model. In particular, we examine the impacts of the entry of low cost carriers (LCC) on the flight delays of incumbent full service carriers in the Brazilian airline industry. The main results indicate a highly significant effect of airport congestion self-internalization in parallel with route-level quality competition. Additionally, the potential competition caused by LCC presence provokes a global effect that suggests the existence of non-price spillovers of the LCC entry to non-entered routes.     

Emergence of cooperation in congested road networks using ICT and future and emerging technologies: A game-based review

Information and communications technologies (ICT) and future and emerging technologies (FET) are expected to revolutionize transportation in the next generation. Travelers’ behavioral adaptation is a key to their success. We discuss the notion of managing traffic congestion by enhancing cooperation in road networks enabled with ICT and FET. Cooperation is an emergent social state related to the dynamics and complexity of road traffic and reinforced learning. Game theory and research in behavioral economics show that cooperation can be leveraged to efficiently solve social dilemmas similar to traffic congestion. We review the applicability of behavioral economics and game theory concepts to route, mode and departure time choice problems. Beyond advancing theory, research on cooperation in the context of transportation is still in its infancy. We discuss state-of-the-art methodologies and their weaknesses and review the unexplored opportunities inherent in game-based methodologies. A behavioral-technological research agenda for FET is also discussed.     

Including congestion effects in urban road traffic CO2 emissions modelling: Do Local Government Authorities have the right options?

Tailpipe emissions from vehicles on urban road networks have damaging impacts, with the problem exacerbated by the common occurrence of congestion. This article focuses on carbon dioxide because it is the largest constituent of road traffic greenhouse gas emissions. Local Government Authorities (LGAs) are typically responsible for facilitating mitigation of these emissions, and critical to this task is the ability to assess the impact of transport interventions on road traffic emissions for a whole network.This article presents a contemporary review of literature concerning road traffic data and its use by LGAs in emissions models (EMs). Emphasis on the practicalities of using data readily available to LGAs to estimate network level emissions and inform effective policy is a relatively new research area, and this article summarises achievements so far. Results of the literature review indicate that readily available data are aggregated at traffic level rather than disaggregated at individual vehicle level. Hence, a hypothesis is put forward that optimal EM complexity is one using traffic variables as inputs, allowing LGAs to capture the influence of congestion whilst avoiding the complexity of detailed EMs that estimate emissions at vehicle level.Existing methodologies for estimating network emissions based on traffic variables typically have limitations. Conclusions are that LGAs do not necessarily have the right options, and that more research in this domain is required, both to quantify accuracy and to further develop EMs that explicitly include congestion, whilst remaining within LGA resource constraints.