铁路局职称评审管理信息系统的设计与实现

职称评审管理信息系统实现对职称评审工作全过程的计算机管理,使职称评审工作进入信息化的新阶段.介绍系统的总体结构、系统功能、关键技术、实现方法和主要特点.     

基于Linux信息交换安全平台实现及应用模型研究

介绍信息交换平台数据库安全访问代理的需求及实现,对其安全性进行了分析.并在此基础上提出实际生产信息系统隔离与交互的应用模型,为今后的推广应用提供参考框架.     

The impact of real-time information on bus ridership in New York City

In the past few years, numerous mobile applications have made it possible for public transit passengers to find routes and/or learn about the expected arrival time of their transit vehicles. Though these services are widely used, their impact on overall transit ridership remains unclear. The objective of this research is to assess the effect of real-time information provided via web-enabled and mobile devices on public transit ridership. An empirical evaluation is conducted for New York City, which is the setting of a natural experiment in which a real-time bus tracking system was gradually launched on a borough-by-borough basis beginning in 2011. Panel regression techniques are used to evaluate bus ridership over a three year period, while controlling for changes in transit service, fares, local socioeconomic conditions, weather, and other factors. A fixed effects model of average weekday unlinked bus trips per month reveals an increase of approximately 118 trips per route per weekday (median increase of 1.7% of weekday route-level ridership) attributable to providing real-time information. Further refinement of the fixed effects model suggests that this ridership increase may only be occurring on larger routes; specifically, the largest quartile of routes defined by revenue miles of service realized approximately 340 additional trips per route per weekday (median increase of 2.3% per route). Although the increase in weekday route-level ridership may appear modest, on aggregate these increases exert a substantial positive effect on farebox revenue. The implications of this research are critical to decision-makers at the country’s transit operators who face pressure to increase ridership under limited budgets, particularly as they seek to prioritize investments in infrastructure, service offerings, and new technologies.     

Communicating transportation carbon dioxide emissions information: Does gender impact behavioral response?

General concern and knowledge on climate change have been increasingly studied over the past decades. Gender differences have been found for general environmental concern and knowledge, but mixed findings exist with respect to climate change. In transportation, research has examined potential relations between environmental attitudes and transportation behavior, with mixed findings as well. Recently, the use of carbon dioxide (CO₂) emissions information to influence choice has been tested with women being found more willing to pay to reduce their personal impacts, suggesting that women are either more willing to change or that their response to information on climate change is stronger. However, those studies used CO₂ mass and studies that examined understanding of CO₂ information as a mass have found that people struggle to understand it. If concern and knowledge about climate change differ amongst individuals, then, according to theories such as the Transtheoretical Model, the type of information used to motivate choices is likely important. Using a unique data set (n = 236) it is possible to take a first look at how gender might affect concern, knowledge, and action in terms of transportation and climate change. Further, it is also possible to examine behavioral responses to transportation climate change information. Finally, an empirical analysis is conducted of the effect of how the information is presented might differ by gender. Thus, this work aims to investigate whether gender differences might contribute to the explanation of individual behavioral responses (from concern to action) in a transportation climate change context.     

Environmental noise inequity in the city of Barcelona

Environmental noise is a growing concern for urban planners and public health experts. Continuous noise exposure has implications for people’s physical and mental health. Urban planning strategies are also involved in the need for regular noise assessments within urban areas. The objective of this study is to evaluate the exposure to noise of vulnerable population groups in the city of Barcelona, and to determine whether they are affected by an environmental inequity regarding this nuisance. Assessment of noise levels was performed by two methods of analysis—real measures and simulation—in order to build the noise database at block level for the 10 districts of the city. The results obtained by various statistical tests and spatial regression analysis show that children and low-income individuals are not affected by environmental inequity. On the other hand, we found a positive relationship between noise levels and the other groups considered: namely, the unemployed and people over age 65.     

Evaluating the added-value of online bus arrival prediction schemes

Online predictions of bus arrival times have the potential to reduce the uncertainty associated with bus operations. By better anticipating future conditions, online predictions can reduce perceived and actual passenger travel times as well as facilitate more proactive decision making by service providers. Even though considerable research efforts were devoted to the development of computationally expensive bus arrival prediction schemes, real-world real-time information (RTI) systems are typically based on very simple prediction rules. This paper narrows down the gap between the state-of-the-art and the state-of-the-practice in generating RTI for public transport systems by evaluating the added-value of schemes that integrate instantaneous data and dwell time predictions. The evaluation considers static information and a commonly deployed scheme as a benchmark. The RTI generation algorithms were applied and analyzed for a trunk bus network in Stockholm, Sweden. The schemes are assessed and compared based on their accuracy, reliability, robustness and potential waiting time savings. The impact of RTI on passengers waiting times are compared with those attained by service frequency and regularity improvements. A method which incorporates information on downstream travel conditions outperforms the commonly deployed scheme, leading to a 25% reduction in the mean absolute error. Furthermore, the incorporation of instantaneous travel times improves the prediction accuracy and reliability, and contributes to more robust predictions. The potential waiting time gains associated with the prediction scheme are equivalent to the gains expected when introducing a 60% increase in service frequency, and are not attainable by service regularity improvements.     

基于蚁群算法的维修保障资源应急调度研究

在保障资源损毁后,将现有保障资源整合成机动保障小组,按照一定的机动路线巡回实施支援保障,可以最大限度的节省现存保障资源,提高保障效能。实施机动巡回的资源调度,需要确定机动保障小组的数量以及机动的路线,这类问题可归结为带约束条件的组合优化问题。应用蚁群算法建立了机动巡回保障优化数学模型,给出了算法的实现步骤。案例结果表明,该算法可以快速、有效求得巡回保障的优化解,得到巡回保障过程中的较优方案。     

Construction of traffic state vector using mutual information for short-term traffic flow prediction

Short-term traffic flow prediction is an integral part in most of Intelligent Transportation Systems (ITS) research and applications. Many researchers have already developed various methods that predict the future traffic condition from the historical database. Nevertheless, there has not been sufficient effort made to study how to identify and utilize the different factors that affect the traffic flow. In order to improve the performance of short-term traffic flow prediction, it is necessary to consider sufficient information related to the road section to be predicted. In this paper, we propose a method of constructing traffic state vectors by using mutual information (MI). First, the variables with different time delays are generated from the historical traffic time series, and the spatio-temporal correlations between the road sections in urban road network are evaluated by the MI. Then, the variables with the highest correlation related to the target traffic flow are selected by using a greedy search algorithm to construct the traffic state vector. The K-Nearest Neighbor (KNN) model is adapted for the application of the proposed state vector. Experimental results on real-world traffic data show that the proposed method of constructing traffic state vector provides good prediction accuracy in short-term traffic prediction.     

A study of traveler behavior under traffic information-provided conditions in the Beijing area

ABSTRACT

The quality of traffic information has become one of the most important factors that can affect the distribution of urban and highway traffic flow by changing the travel route, transportation mode, and travel time of travelers and trips. Past research has revealed traveler behavior when traffic information is provided. This paper summarizes the related study achievements from a survey conducted in the Beijing area with a specially designed questionnaire considering traffic conditions and the provision of traffic information services. With the survey data, a Logit model is estimated, and the results indicate that travel time can be considered the most significant factor that affects highway travel mode choice between private vehicles and public transit, whereas trip purpose is the least significant factor for private vehicle usage for both urban and highway travel.     

基于交通网络数据集的动态路径诱导系统规划与实现探讨

智能交通是当前公认的有效解决交通拥堵、能源消耗及交通污染等突出问题的根本途径,而动态的交通诱导系统则是智能交通的核心研究领域之一,并一直被学术界和企业界所关注。当前已有诸多成果,但能广泛应用于实际交通环境的不多,通过着重探讨基于交通网络数据集下的动态路径诱导系统的规划设计与实现方式,有利于交通动态路径诱导系统的开发应用。