More robust and better: a multiple kernel support vector machine ensemble approach for traffic incident detection

This paper presents a multiple kernel support vector machine (MKL‐SVM) ensemble algorithm to detect traffic incidents. It uses resampling technology to generate training set, test set, and training subset firstly; then uses different training subsets to train individual MKL‐SVM classifiers; and finally introduces ensemble methods to construct MKL‐SVM ensemble to detect traffic incidents. Extensive experiments have been performed to evaluate the performances of the four algorithms: standard SVM, SVM ensemble, MKL‐SVM, and the proposed algorithm (MKL‐SVM ensemble). The experimental results show that the proposed algorithm has the best comprehensive performances in traffic incidents detection. To achieve better performances, the proposed algorithm needs less individual classifiers to construct the ensemble than SVM ensemble algorithm. Thus, compared with SVM ensemble algorithm, the complexity of the ensemble classifier of the proposed algorithm is reduced greatly. Conveniently, the proposed algorithm also avoids the burden of selecting the appropriate kernel function and parameters. Copyright © 2013 John Wiley & Sons, Ltd.     

Condition monitoring of wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are to large extent based on visual analysis. In this paper a machine vision based approach has been considered to emulate the visual abilities of the human operator to enable automation of the process. Digital images from either ends (left and right) of the sleepers have been acquired. A pattern recognition approach has been adopted to classify the condition of the sleeper into classes (good or bad) and thereby achieve automation. Appropriate image analysis techniques were applied and relevant features such as the number of cracks on a sleeper, average length and width of the crack and the condition of the metal plate were determined. Feature fusion has been proposed in order to integrate the features obtained from each end for the classification task which follows. The effect of using classifiers like multi-layer perceptron and support vector machines has been tested and compared. Results obtained from the experiments show that multi-layer perceptron and support vector machines have achieved encouraging results, with a classification accuracy of 90%; thereby exhibiting a competitive performance when compared to a human operator.     

高铁枢纽建设与城市功能空间协同发展效率评价

随着我国高铁迅速发展,高铁建设如何与城市功能空间协同发展的问题引起广泛关注。为了评价高铁与城市协同发展的效率,本文从城市协同性、高铁枢纽建设、站城距离等角度,构建高铁枢纽建设与城市功能空间协同发展效率评价指标体系。并引入DEA模型进行分析计算其效率匹配度,为城市既有高铁站与城市协同发展程度提供依据。最后以京沪高铁为案例分析了24个车站与所在城市的协同关系效率值,结果表明,北京南站、上海虹桥站等高铁枢纽发展较好,常州北站、苏州北站、天津南站等较差,尤其是天津南站的通过能力、客流量等产出指标还需要进一步加强。     

A decision support approach for condition-based maintenance of rails based on big data analysis

In this paper, a decision support approach is proposed for condition-based maintenance of rails relying on expert-based systems. The methodology takes into account both the actual conditions of the rails (using axle box acceleration measurements and rail video images) and the prior knowledge of the railway track. The approach provides an integrated estimation of the rail health conditions to support the maintenance decisions for a given time period. An expert-based system is defined to analyse interdependency between the prior knowledge of the track (defined by influential factors) and the surface defect measurements over the rail. When the rail health conditions is computed, the different track segments are prioritized, in order to facilitate grinding planning of those segments of rail that are prone to critical conditions. In this paper, real-life rail conditions measurements from the track Amersfoort-Weert in the Dutch railway network are used to show the benefits of the proposed methodology. The results support infrastructure managers to analyse the problems in their rail infrastructure and to efficiently perform a condition-based maintenance decision making.     

内压载荷下X80管道裂纹应力分布规律研究

为了研究内压作用下管道裂纹应力场分布规律,以含有表面裂纹的X80管道为研究对象,对不同形状、不同方向、不同内压、不同尺寸的含裂纹管道进行仿真分析和实验验证。结果表明:裂纹尖端处应力远大于裂纹中心应力。裂纹形状对应力影响作用较小,随着裂纹方向与管道轴向夹角增大,裂纹尖端应力先增大后减小,随着管道内压、裂纹深度、裂纹长度的增大,裂纹尖端处应力随之线性增大。其中,裂纹长度对裂纹尖端应力的影响小于管道内压和裂纹深度。     

Transportation system in the future

The paper begins with a review of the major features of the transport policy of the European Community. It continues with an assessment of the likely characteristics of future transport supply, identifying the particular competing developments in road and air transport which would affect the success of high speed rail investments such as that proposed between Amsterdam, Groningen and Hamburg.     

水泥稳定碎石层裂纹控制探讨

水泥稳定碎石层具有强度高、水稳定性好、耐冲刷等力学性能,但由于其脆性较大,容易产生裂纹。文章阐述了水泥稳定碎石层裂纹产生的机理,并结合工程实例,分析了裂纹的成因,提出了裂纹的防范措施。     

A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul

This paper provides a hierarchical customer satisfaction framework to measure rail transit lines’ performances in Istanbul. The problems related to rail transit line systems are addressed via customer satisfaction surveys. Then, a framework is proposed combining statistical analysis, fuzzy analytic hierarchy process, trapezoidal fuzzy sets and Choquet integral to evaluate customer satisfaction levels. Next, the criteria need to be improved are determined and specific recommendations to enhance the operation for specific lines are suggested. The proposed framework provides directions for the future investments and it also can be used at a more macroscopic level to determine the operational deficiencies. Furthermore, it can be generalized and applied to complex decision making problems that include uncertain and subjective data or vague information.     

新建桥梁桥台裂缝成因探讨

文章结合全州县湘源大桥桥台施工实例,对新建桥梁桥台裂缝成因进行探讨,并对带裂缝的拱座进行安全性及耐久性评估,提出了裂缝处理及预防裂缝出现的措施,为同类型桥梁桥台裂缝成因分析及处治提供参考。     

New routes on old railways: increasing rail’s mode share within the constraints of the existing railway network

This paper describes an integrated methodology for identifying potential ‘quick wins’ for mode shift from road to passenger rail transport. Firstly, a procedure for analysing rail’s relative competitiveness in the market for passenger transport between large urban areas is developed and then applied to a UK case study. The purpose of such analysis is to allow the identification of flows where rail is currently relatively uncompetitive (in terms of journey time in particular) and to assess the reasons for this poor performance, so that the issues which suppress rail use may be addressed. In parallel, a framework, methodology and tool for the assessment of existing and potential capacity (trains, seats, TEUs, etc.) is developed for both passenger and freight traffic, to identify and address network constraints. An illustrative example of the use of these demand and capacity assessment tools is then presented, with the tools used to identify and evaluate flows where rail demand is suppressed by poor service quality and where spare capacity exists which would allow the passenger rail service to be improved without requiring significant investments in infrastructure. The effects of such improvements on demand are predicted, and the cost implications of operating such additional services are discussed. The analysis suggests that there may be significant potential for increasing rail’s mode share by providing additional inter-urban services where rail currently offers an inferior service.     

Identification of occupant posture using a Bayesian classification methodology to reduce the risk of injury in a collision

Recent studies have shown that smart restraint systems, which will recognize and then adapt to a specific collision and occupant combination, have a strong opportunity to significantly reduce occupant injuries during a traffic accident. As a step toward the development of these adaptive restraint systems, this study proposes a novel methodology for the classification of pre-crash occupant posture. Various occupant postures were simulated with a human model and the corresponding data was recorded using sensor models implemented in a mid-size car interior. The sensor data was then used to train two Bayesian classifiers which categorized an unknown occupant posture as one of nine predefined classes. The posture classifiers and a look-up table which contained optimized restraint laws for each class were combined to form catalog controllers for the restraint systems. The benefit of these restraint systems with catalog controllers vs. a restraint system optimized at a nominal posture was estimated by analyzing crash simulations with the occupant in 200 different postures. While the minimum error rate classifier showed the highest correct classification rate (90%), the Bayesian minimum risk classifier estimated the highest average injury reduction (21%). As expected, the highest injury reduction (up to 45%) was recorded for the posture classes closest to the windshield, whereas the lowest injury reduction was found for the classes closest to the nominal position. While the proposed restraint system with a catalog controller requires considerable “offline” computational effort, it is more versatile in terms of using complex human models and injury criteria and is much faster during the brief decision window available than recent “online” controllers proposed previously in literature.     

Short-term traffic flow rate forecasting based on identifying similar traffic patterns

The ability to timely and accurately forecast the evolution of traffic is very important in traffic management and control applications. This paper proposes a non-parametric and data-driven methodology for short-term traffic forecasting based on identifying similar traffic patterns using an enhanced K-nearest neighbor (K-NN) algorithm. Weighted Euclidean distance, which gives more weight to recent measurements, is used as a similarity measure for K-NN. Moreover, winsorization of the neighbors is implemented to dampen the effects of dominant candidates, and rank exponent is used to aggregate the candidate values. Robustness of the proposed method is demonstrated by implementing it on large datasets collected from different regions and by comparing it with advanced time series models, such as SARIMA and adaptive Kalman Filter models proposed by others. It is demonstrated that the proposed method reduces the mean absolute percent error by more than 25%. In addition, the effectiveness of the proposed enhanced K-NN algorithm is evaluated for multiple forecast steps and also its performance is tested under data with missing values. This research provides strong evidence suggesting that the proposed non-parametric and data-driven approach for short-term traffic forecasting provides promising results. Given the simplicity, accuracy, and robustness of the proposed approach, it can be easily incorporated with real-time traffic control for proactive freeway traffic management.     

Demand for rail travel to and from airports

Rail access to airports is becoming increasingly important for both train operators and the airports themselves. This paper reports analysis of inter-urban rail demand to and from Manchester and Stansted Airports and the sensitivity of this market segment to growth in air traffic and the cost and service quality of rail services. The estimated demand parameters vary in an expected manner between outward and inward air travellers as well as between airport users and general rail travellers. These parameters can be entered into the demand forecasting framework widely used in the rail industry in Great Britain to provide an appropriate means of forecasting for this otherwise neglected market segment. The novel features of this research, at least in the British context, are that it provides the first detailed analysis of aggregate rail flows to and from airports, it has disaggregated the traditional generalised time measure of rail service quality in order to estimate separate elasticities to journey time, service headway and interchange, and it has successfully explored departures from the conventional constant elasticity position.     

Towards data-driven car-following models

Car following models have been studied with many diverse approaches for decades. Nowadays, technological advances have significantly improved our traffic data collection capabilities. Conventional car following models rely on mathematical formulas and are derived from traffic flow theory; a property that often makes them more restrictive. On the other hand, data-driven approaches are more flexible and allow the incorporation of additional information to the model; however, they may not provide as much insight into traffic flow theory as the traditional models. In this research, an innovative methodological framework based on a data-driven approach is proposed for the estimation of car-following models, suitable for incorporation into microscopic traffic simulation models. An existing technique, i.e. locally weighted regression (loess), is defined through an optimization problem and is employed in a novel way. The proposed methodology is demonstrated using data collected from a sequence of instrumented vehicles in Naples, Italy. Gipps’ model, one of the most extensively used car-following models, is calibrated against the same data and used as a reference benchmark. Optimization issues are raised in both cases. The obtained results suggest that data-driven car-following models could be a promising research direction.     

European passenger rail reforms: A comparative assessment of the emerging models

This paper provides a comprehensive framework for analysing and comparing the various institutional models and regulatory arrangements that have recently emerged from the restructuring of European passenger rail industries. The framework identifies seven key dimensions that can be used to describe these models and arrangements. It shows how these dimensions are connected with one another in a complex web of interactions. Empirical material gathered on the passenger rail industries of five EU Member States, i.e. Belgium, France, Germany, the UK, and Sweden is used, as well as extrapolation to highlight the costs and benefits (incentive properties) associated with alternative arrangements and contract features and to show how certain dimensions can be traded-off against one another to optimize the industry's performance.     

A three-phase QFD-based framework for identifying key passenger needs to improve satisfaction with the seat of high-speed rail in China

This study aims to suggest a three-phased methodological framework based on the operational approach of quality function deployment (QFD) to improve the service quality and passenger satisfaction with China’s high-speed rail (HSR) by identifying the key passenger needs with regard the HSR seats. For the first phase, the collection of the voice of the customers/passengers (VOC), the processing of the collected VOC into need items and further into passenger needs of QFD would be explained in terms of knowledge management. For the second phase, a reference comparison-based fuzzy best–worst method is developed for determining the relative importance of passenger needs, with a particular purpose of coping with the uncertainty and ambiguity associated with qualitative assessment of respondents. For the third phase, the importance-performance analysis is performed to determine the improvement priorities for meeting passenger needs. Findings showed that Body-friendly seat structure and Reasonable layout of the seat are the two most important needs demanded by the passengers of second-class cabins, with the former being the top priority. The current study provides useful references for service operators of HSR to formulate development strategies for improving the seat comfortability, which subsequently contributes to improving HSR’s service quality and passenger satisfaction. Moreover, the proposed methodological framework for identifying the important passenger needs can be appropriately adjusted and expanded to similar transportation infrastructures and facilities.

     

Equity-oriented skip-stopping schedule optimization in an oversaturated urban rail transit network

In this study, we focus on improving system-wide equity performance in an oversaturated urban rail transit network based on multi-commodity flow formulation. From the system perspective, an urban rail transit network is a distributed system, where a set of resources (i.e., train capacity) is shared by a number of users (i.e., passengers), and equitable individuals and groups should receive equal shares of resources. However, when oversaturation occurs in an urban rail transit network during peak hours, passengers waiting at different stations may receive varying shares of train capacity leading to the inequity problem under train all-stopping pattern. Train skip-stopping pattern is an effective operational approach, which holds back some passengers at stations and re-routes their journeys in the time dimension based on the available capacity of each train. In this study, the inequity problem in an oversaturated urban rail transit network is analyzed using a multi-commodity flow modeling framework. In detail, first, discretized states, corresponding to the number of missed trains for passengers, are constructed in a space-time-state three-dimensional network, so that the system-wide equity performance can be viewed as a distribution of all passengers in different states. Different from existing flow-based optimization models, we formulate individual passenger and train stopping pattern as commodity and network structure in the multi-commodity flow-modeling framework, respectively. Then, we aim to find an optimal commodity flow and well-designed network structure through the proposed multi-commodity flow model and simultaneously achieve the equitable distribution of all passengers and the optimal train skip-stopping pattern. To quickly solve the proposed model and find an optimal train skip-stopping pattern with preferable system-wide equity performance, the proposed linear programming model can be effectively decomposed to a least-cost sub-problem with positive arc costs for each individual passenger and a least-cost sub-problem with negative arc costs for each individual train under a Lagrangian relaxation framework. For application and implementation, the proposed train skip-stopping optimization model is applied to a simple case and a real-world case based on Batong Line in the Beijing Subway Network. The simple case demonstrates that our proposed Lagrangian relaxation framework can obtain the approximate optimal solution with a small-gap lower bound and a lot of computing time saved compared with CPLEX solver. The real-world case based on Batong Line in the Beijing Subway Network compares the equity and efficiency indices under the operational approach of train skip-stopping pattern with those under the train all-stopping pattern to state the advantage of the train skip-stopping operational approach.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.     

圆管外表面轴向椭圆裂纹应力强度因子的有限元研究

采用1/4节点奇异单元,建立了求解圆筒外表面轴向椭圆裂纹应力强度因子的断裂力学分析模型,分析了应力强度因子随圆筒外径与内径比k=ro/ri、椭圆率a/b及裂纹的相对深度a/t的变化规律,并结合VB编制出了求解圆筒外表面椭圆裂纹应力强度因子的自动分析软件。