基于模糊聚类的铁路智能运输系统结构分析方法研究

铁路智能运输系统(RITS)规模大、结构复杂、要素繁多,系统设计难度大.为降低系统设计复杂程度,提出了一种基于模糊聚类的RITS逻辑结构分解方法,通过建立模糊关联强度矩阵描述过程间的关联程度,在此基础上采用模糊聚类将RITS逻辑结构分解为若干相对独立的功能单元,使系统分解过程更为精细和严谨,为系统物理实现及相关研究提供了基础.     

铁路运输企业信息化标准体系框架研究

论述铁路运输企业信息化标准体系建设的基础作用和重要意义,提出统筹发展需求、提供规划指引、强化基础保障、便于日常使用的建设原则.在遵循国家和行业标准化方针和政策的基础上,运用标准化原理分析信息化标准体系建设需求.通过筛选、分类和组合各相关标准,将铁路运输企业信息化标准体系框架设计为二级结构,涉及信息化规划、设计、研发、实...     

Improvement of the volume-delay function by incorporating the impact of trucks on traffic flow

In current transportation modelling, travel time is the most important factor in decisions regarding transport modes, destinations and routes. The calculation of travel time is deployed by volume-delay functions (VDFs), a sub-model of route assignment procedure, using the correlation between increasing numbers of vehicles on a road and the road's restrictive capacity. By investigating existing VDFs, a clear gap is seen, demonstrating that current functions are not suited to reflect the empirically known large impact of trucks on passenger car travel times. This issue becomes crucial when transport models are used to reflect future scenarios where goods transportation is expected to increase greatly, and when transport models combine passenger and commercial traffic. This paper presents a new VDF which successfully includes trucks’ impact on traffic flow in the case of Germany and, with slight deviations, for North America. The function is developed using ideal-type data for German motorways. The differences between German and US data and their implications for VDFs are also discussed.     

Mitigating environmental impacts in advance: Evidence of cost and time savings for transportation projects

The traditional model for mitigating a transportation project’s environmental impacts typically operates project-by-project and delivers the mitigation just-in-time. In contrast, the newer practice of advance mitigation comprehensively assesses and mitigates impacts from one or multiple transportation projects before or during project planning, sometimes long before project construction begins. The practice has gained adherents for its potential to improve ecological outcomes, by better aligning mitigation and conservation goals. Advance mitigation also stands to reduce mitigation costs, an important secondary benefit for transportation agencies with constrained resources. Evidence of cost savings, however, has been piecemeal and anecdotal. This paper advances knowledge of advance mitigation’s financial impacts in two ways. First, it critically assesses the evidence about cost savings realized through advance mitigation, both through avoided up-front costs and reduced project delay. Second, it directly estimates the project time savings that might accrue with advance mitigation of state highway projects in California. Overall, the balance of evidence is encouraging for transportation agencies that would introduce the practice, and general agreement exists on its financial benefits. Considering project delays related only to the environmental process, we estimate advance mitigation could reduce delivery times by 1.3–5.0 months per project. Still, we also identify factors limiting comprehensive analysis. Transportation agencies adopting advance mitigation practices into their operations could use a pilot approach that includes rigorous environmental and mitigation cost accounting; such pilots would build needed empirical evidence of advance mitigation’s financial and ecological outcomes.     

Quantifying the environmental impacts of increasing high occupancy vehicle lanes in the United States

To what extent will increasing High Occupancy Vehicle (HOV) lane-kilometer incentivize carpooling and reduce emissions of air pollutants and greenhouse gases? To answer these questions, we develop a multiple regression model relating HOV lanes and other socioeconomic factors to carpooling propensity in all 50 U.S. states and the District of Columbia, then calculate the extent to which increasing HOV lane-kilometers would lead to reductions in carbon dioxide equivalent (CO₂e) and major air pollutants across the U.S., by state. Increasing HOV lane extent has the greatest potential to reduce annual CO₂e in the District of Columbia, followed by Hawaii and New York. The smallest potential is found in states with the lowest population density, led by North Dakota. We then explore the extent to which recommendations made at one level of data aggregation (that of individual states) may be valid for another level, such as individual counties. The only state with sufficient data available to disaggregate the model to the county level is California, where we found a lower potential for state-wide CO₂e emission reductions under the county-level model as compared to the state-level model (0.69% as compared to 1.08%, under the same hypothetical scenario), albeit with significant differences in emission reduction potential between counties with higher vs. lower population densities. This analysis demonstrates the potential to generate generalizable insight into the magnitude of vehicle emission reductions that might be achieved through expanding HOV lanes, and highlights the importance of data disaggregation in identifying the optimal locations for potential reductions.     

Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling

Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Protégé web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining.     

Innovation strategy in new transportation systems: The case of Crossrail

This article examines how innovation can be strategically incorporated into transportation systems. Large transportation systems generally have a poor record in systematically integrating innovation in their development, construction and operation. Our research setting is Crossrail, a major new railway traversing London, where the creation and implementation of an innovation strategy formalized and systemized its approach to innovation. Based on in-depth, semi-structured interviews with project leaders and participant observation, the paper critically analyses the formulation, implementation and performance of Crossrail’s innovation strategy. Crossrail’s management explicitly uses an ‘open innovation’ strategy that incentivizes partners, contractors, and clients to innovate in the project. Its strategy guides decisions and priorities on innovation and the types and levels of innovation that best match project aims. The paper holds lessons for those developing, operating and studying large transportation systems both now and into the future.     

Air cargo operations: Literature review and comparison with practices

This study reviews the literature on air cargo operations and compares theoretical studies with the practical problems of airlines, freight forwarders, and terminal service providers. In particular, we review studies in which mathematical models were used to identify the essential characteristics of air cargo operations, such as the intrinsic differences from passenger operations, and to explore the service processes in air cargo operations. The typical models used in previous studies are summarized. We then highlight the insightful findings from an industrial interview and present the gaps between previous research and practical realities. We finally discuss the new research opportunities of air cargo operations according to the gaps.     

A new approach for evaluating regional exposure to particulate matter emissions from motor vehicles

Exposure to fine particulate matter from vehicle exhaust is associated with increased health risk. This study develops a new approach for creating spatially detailed regional maps of fine particulate matter concentration from vehicle exhaust using a dispersion model to better evaluate these risks. The spatial extent, diurnal, and seasonal patterns of concentration fields across Los Angeles County, California are evaluated and population exposure and exposure equity by race and income are investigated. The results demonstrate how this modeling approach can create new knowledge about vehicle emissions exposure. This approach also provides a method for proactively screening out regional plans, or specific projects within these plans, that are likely to cause air quality concerns. A proactive and regional air quality assessment can identify potential problems earlier in the planning process and a wider range of solutions, saving time, money and protecting public health. The detailed concentration maps can also be used to improve the siting of regulatory air quality monitors and provide more accurate exposure data for epidemiology studies.