Traffic evacuation time under nonhomogeneous conditions

During many manmade and natural crises such as terrorist threats, floods, hazardous chemical and gas leaks, emergency personnel need to estimate the time in which people can evacuate from the affected urban area. Knowing an estimated evacuation time for a given crisis, emergency personnel can plan and prepare accordingly with the understanding that the actual evacuation time will take longer. Given the urban area to be evacuated, street widths exiting the area's perimeter, the area's population density, average vehicle occupancy, transport mode share and crawl speed, an estimation of traffic evacuation time can be derived. Peak-hour traffic data collected at three, midblock, Mumbai sites of varying geometric features and traffic composition were used in calibrating a model that estimates peak-hour traffic flow rates. Model validation revealed a correlation coefficient of +0.98 between observed and predicted peak-hour flow rates. A methodology is developed that estimates traffic evacuation time using the model.     

基于VVA的多分辨率交通仿真建模过程评价框架研究

文章从多分辨率交通仿真的建模过程入手,研究了系统仿真的校核、验证与确认(Verification Validation Accreditation,VVA)过程评价的原理。在此基础上,提出了基于VVA的多分辨率交通仿真建模的过程评价框架。通过研究仿真系统生命周期中的一系列活动来评价研究结果的正确性和有效性,提高了系统的可信性,使仿真系统能够满足实际应用的需要。     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

山西王家岭煤矿底板奥灰水突水危险性评价

我国是受水害最严重的国家,奥灰水突水是我国很多煤矿在安全生产中急待解决的问题。山西王家岭煤业集团公司王家岭煤矿就面临着开采9号煤层底板奥灰水突水威胁的安全问题。结合王家岭煤矿实际钻孔数据,运用突水系数法、多层叠加抗压强度比值系数法分别对9号煤层进行突水危险性预测,并进行相互对比,根据计算所得结果得出9号煤层底板易发生突水区。     

基于粒子群算法下的交叉口信号配时优化

为了合理地优化交叉口信号配时,通过分析交叉口的评价指标,建立了以车辆的平均延误、停车次数最小、交叉口整体通行能力最大、各相位有效绿灯时间和交叉口周期时长作为约束条件的数学模型。并通过改进前人研究基础上的动态加权系数,将多目标的非线性优化问题转化为单一目标的非线性规划问题,为了得出更科学稳定的解,提出了改善粒子群算法系统稳定性的2种方法,并将其与粒子群算法结合起来。然后以Matlab为求解工具,结合临邑市某一交叉口实例进行求解分析。最后的结果表明,在使用改进后的粒子群算法进行优化后交叉口通行能力较之现状提升了9%,延误下降了28%,停车次数下降了9%,且各项优化结果均优于Webster,改进后的算法在程序中运行300代,到216代才开始收敛,而未改进的算法稳定性较差,优化结果和收敛曲线则随着实验次数的变化而变化,最后的结论证明了该算法和模型的可靠性。     

Opportunities to reduce road traffic injury: new insights from the study of urban areas

Abstract

Over the past four decades considerable efforts have been taken to mitigate the growing burden of road injury. With increasing urbanisation along with global mobility that demands not only safe but equitable, efficient and clean (reduced carbon footprint) transport, the responses to dealing with the burgeoning road traffic injury in low- and middle-income countries has become increasingly complex. In this paper, we apply unique methods to identify important strategies that could be implemented to reduce road traffic injury in the Asia-Pacific region; a region comprising large middle-income countries (China and India) that are currently in the throes of rapid motorisation. Using a convolutional neural network approach, we clustered countries containing a total of 1632 cities from around the world into groups based on urban characteristics related to road and public transport infrastructure. We then analysed 20 countries (containing 689 cities) from the Asia-Pacific region and assessed the global burden of disease attributed to road traffic injury and these various urban characteristics. This study demonstrates the utility of employing image recognition methods to discover new insights that afford urban and transport planning opportunities to mitigate road traffic injury at a regional and global scale.     

Impact of Continuous Climb Operations in ATC workload. Case-study Palma airport

This research assesses the impact of the integration of Continuous Climb operations (CCOs) on Air Traffic Control (ATC) workload. The methodology encompasses different modules: CCO, standard departing and arriving trajectories extracted from an external database, an ad-hoc algorithm for detecting and solving conflicts, and an ATC-workload model with the inclusion of CCO-task modifications. Monte Carlo simulations evaluates different combinations of these modules. Then, a sensitivity analysis is performed to evaluate two parameters: the impact of the calibration of the maximum ATC workload and the percentage increase of the CCO tasks on the ATC workload. The methodology is applied to a case study at Palma airport in Spain. Extensive numerical simulations are executed based on the integration of CCOs into the system from 0% to 100%. The integration of CCOs implies the increase of the ATC workload in the Control Tower (TWR) and the reduction in the Approach Control Centre (APP). The sensitivity analysis shows that the decrease in the increase of CCO-task workload barely affects the number of CCOs that can be operated without exceeding the workload limits. Conversely, the reduction of the ATC workload calibration allows the integration of CCOs around 50% in the case of 90% TWR calibration and up to 100% in the case of 80%.     

Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions

To mitigate air traffic demand-capacity imbalances, large European airports implement strategic flight schedules, where flights are assigned arrival/departure slots several months prior to execution. We propose a generic assessment of such strategic schedules using predictions about arrival/departure flight delays and cancellations. We demonstrate our approach for strategic flight schedules in the period 2013–2018 at London Heathrow Airport. Together with the development of dedicated strategic flight schedule optimization models, our proposed approach supports an integrated strategic flight schedule assessment, where schedules are evaluated with respect to flight delays and cancellations.     

Collaborative delay management towards demand-capacity balancing within User Driven Prioritisation Process

The concept of User Driven Prioritisation Process (UDPP) was introduced to give Airspace Users (AUs) more flexibility under demand-capacity imbalance. This paper presents two UDPP-DCB models, built on the UDPP principle, using the Selective Flight Protection (SFP) approach to minimize the total delay cost. AUs are enabled to adapt their operations in a more cost-efficient way in the presence of capacity constraints in airspace, optimizing their flights to keep the priorities track. Then, ATFM integrates the AUs’ decision to reassign the time slots and the preferred rerouting trajectories. Results suggest that the delay cost for AUs can be largely reduced through applying the UDPP-DCB models proposed in this paper, while allowing rerouting proves effective in reducing the system delay cost.     

交通因素对黄土丘陵沟壑区空心村变迁的影响分析

[目的]空心村的形成和发展受到众多因素的影响,包括经济因素、自然因素、社会因素、户籍和社保制度,以及管理和规划因素等。由于我国的自然地理和区域发展差异悬殊,不同地形区空心村发展的主要影响因素各不相同。文章探究在复杂地形条件下,交通因素对丘陵沟壑区空心村的产生和发展所产生的影响。[方法]以陕西省澄城县为例,通过实地调查、走访农户、搜集资料等方法,利用交通便利指数、交通距离指数等开展分析,综合考虑了道路崎岖程度、道路质量状况以及经济发展水平等,对研究区的农村空心化现象进行了研究。[结果]崎岖的道路极大地增加了居民的出行成本和时间,不利于农村的基础设施建设和发展;在当前的社会经济条件下,黄土丘陵沟壑区农村的交通便利程度与村庄空心化程度呈现一定的负相关性,即交通条件越好,村庄的空心化程度越低。[结论]在黄土丘陵沟壑区开展空心村整治工程时,可以优先考虑交通条件较差、相对偏远的地区。