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.     

The effect of railway local irregularities on ground vibration

The environmental effects of ground-borne vibrations generated due to localised railway defects is a growing concern in urban areas. Frequency domain modelling approaches are well suited for predicting vibration levels on standard railway lines due to track periodicity. However, when considering individual, non-periodic, localised defects (e.g. a rail joint), frequency domain modelling becomes challenging. Therefore in this study, a previously validated, time domain, three-dimensional ground vibration prediction model is modified to analyse such defects. A range of different local (discontinuous) rail and wheel irregularity are mathematically modelled, including: rail joints, switches, crossings and wheel flats. Each is investigated using a sensitivity analysis, where defect size and vehicle speed is varied. To quantify the effect on railroad ground-borne vibration levels, a variety of exposure–response relationships are analysed, including: peak particle velocity, maximum weighted time-averaged velocity and weighted decibel velocity. It is shown that local irregularities cause a significant increase in vibration in comparison to a smooth track, and that the vibrations can propagate to greater distances from the line. Furthermore, the results show that step-down joints generate the highest levels of vibration, whereas wheel flats generate much lower levels. It is also found that defect size influences vibration levels, and larger defects cause greater vibration. Lastly, it is shown that for different defect types, train speed effects are complex, and may cause either an increase or decrease in vibration levels.     

Are railways really that bad? An evaluation of rail systems performance in Europe with a focus on passenger rail

With a large number of railway development projects in Europe and worldwide, which once completed will be serving rail passengers of the future, this paper aims to take a step back and evaluate current railway systems performance. The objectives are to compare statistical data on various passenger-related parameters of the railway system in a number of selected European countries and draw conclusions on the level of their performance when compared to the European average.Analyses of publically available statistical data, extracted from the Eurostat service at a European level will allow for a comparison of various indicators which influence the performance of the railway systems from an infrastructure and operational perspectives. The analyses will also allow identifying key performance indicators for the accurate assessment of the rail systems.The paper will highlight case studies for various parameters which are important to stakeholders of the railways, including infrastructure managers, rail operators, policy makers and the end users. This knowledge will be to the benefit of today’s railway industry as well as the rail systems of the future, as it will show trends drew upon existing data which might continue in the future.     

模块化产业链视角的城市轨道交通设计行业知识整合体系探讨

首先从模块化产业链的视角分析了城市轨道交通设计行业总线模块化结构；描述了知识结构和知识分工的方式，进而对城市轨道交通设计行业的知识整合模式进行了剖析；探讨了知识整合模式运作的知识竞争、知识创新、组织结构等因素；形成了较为全面的城市轨道交通设计行业知识整合思路。     

Long-term impacts of transport infrastructure networks on land-use change: an international review of empirical studies

Improvements in geographical information systems, the wider availability of high-resolution digital data and more sophisticated econometric techniques have all contributed to increasing academic interest and activity in long-term impacts of transport infrastructure networks (TINs) on land use (LU). This paper provides a systematic review of recent empirical evidence from the USA, Europe and East Asia, classified regarding the type of transport infrastructure (road or rail), LU indicator (land cover, population or employment density, development type) and outcome (significance, relationship’s direction) as well as influential exogenous factors. Proximity to the rail network is generally associated with population growth (particularly soon after the development of railway infrastructure), conversion to residential uses and the development of higher residential densities. Meanwhile, proximity to the road network is frequently associated with increases in employment densities as well as the conversion of land to a variety of urban uses including commercial and industrial development. Compared with road infrastructure, the impact of rail infrastructure is often less significant for land cover or population and employment density change. The extent of TINs’ impact on LU over time can be explained by the saturation in TIN-related accessibility and LU development.     

Effective arterial road incident detection: A Bayesian network based algorithm

Timely and accurate incident detection is an essential part of any successful advanced traffic management system. The complex nature of arterial road traffic makes automated incident detection a real challenge. Stable performance and strong transferability remain major issues concerning the existing incident detection algorithms. A new arterial road incident detection algorithm TSC_ar is presented in this paper. In this algorithm, Bayesian networks are used to quantitatively model the causal dependencies between traffic events (e.g. incident) and traffic parameters. Using real time traffic data as evidence, the Bayesian networks update the incident probability at each detection interval through two-way inference. An incident alarm is issued when the estimated incident probability exceeds the predefined decision threshold. The Bayesian networks allow us to subjectively build existing traffic knowledge into their conditional probability tables, which makes the knowledge base for incident detection robust and dynamic. Meanwhile, we incorporate intersection traffic signals into traffic data processing. A total of 40 different types of arterial road incidents are simulated to test the performance of the algorithm. The high detection rate of 88% is obtained while the false alarm rate of the algorithm is maintained as low as 0.62%. Most importantly, it is found that both the detection rate and false alarm rate are not sensitive to the incident decision thresholds. This is the unique feature of the TSC_ar algorithm, which suggests that the Bayesian network approach is advanced in enabling effective arterial road incident detection.     

A fuzzy-logic-based system for freeway bottleneck severity diagnosis in a sensor network

It is essential for local traffic jurisdictions to systematically spot freeway bottlenecks and proactively deploy appropriate congestion mitigation strategies. However, diagnostic results may be influenced by unreliable measurements, analysts’ subjective knowledge and day-to-day traffic pattern variations. In order to suitably address these uncertainties and imprecise data, this study proposes a fuzzy-logic-based approach for bottleneck severity diagnosis in urban sensor networks. A dynamic bottleneck identification model is first proposed to identify bottleneck locations, and a fuzzy inference approach is then proposed to systematically diagnose the severities of the identified recurring and non-recurring bottlenecks by incorporating expert knowledge of local traffic conditions. Sample data over a 1-month period on an urban freeway in Northern Virginia was used as a case study for the analysis. The results reveal that the proposed approach can reasonably determine bottleneck severities and critical links, accounting for both spatial and temporal factors in a sensor network.     

A generalized sensor location model for the estimation of network origin–destination matrices

This study examined the network sensor location problem by using heterogeneous sensor information to estimate link-based network origin–destination (O–D) demands. The proposed generalized sensor location model enables different sensors’ traffic monitoring capabilities to be used efficiently and the optimal number and deployment locations of both passive- and active-type sensors to be determined simultaneously without path enumeration. The proposed sensor location model was applied to solve the network O–D demand estimation problem. One unique aspect of the proposed model and solution algorithms is that they provide satisfactory network O–D demand estimates without requiring unreasonable assumptions of known prior information on O–D demands, turning proportions, or route choice probabilities. Therefore, the proposed model and solution algorithms can be practically used in numerous offline transportation planning and online traffic operation applications.     

Optimal placement of omnidirectional sensors in a transportation network for effective emergency response and crash characterization

Rapid motor vehicle crash detection and characterization is possible through the use of Intelligent Transportation Systems (ITS) and sensors are an integral part of any ITS system. The major focus of this paper is on developing optimal placement of accident detecting omnidirectional sensors to maximize incident detection capabilities and provide ample opportunities for data fusion and crash characterization. Both omnidirectional sensors (placed in suitable infrastructure locations) and mobile sensors are part of our analysis. The surrogates used are acoustic sensors (omnidirectional) and Advanced Automated Crash Notification (AACN) sensors (mobile). This data fusion rich placement is achieved through a hybrid optimization model comprising of an explicit–implicit coverage model followed by an evaluation and local search optimization using simulation. The compound explicit–implicit model delivers good initial solutions and improves the detection and data fusion capabilities compared to the explicit model alone. The results of the studies conducted quantify the use of a data fusion capable environment in crash detection scenarios, and the simulation tool developed helps a decision maker evaluate sensor placement strategy.     

Impact of Everyday Weather on Individual Daily Travel Behaviours in Perspective: A Literature Review

Despite the hundreds of billions of dollars being spent on infrastructure development -- from roads, rail and airports to energy extraction and power networks to the Internet -- surprisingly little reliable knowledge exists about the performance of these investments in terms of actual costs, benefits and risks. This paper presents results from the first statistically significant study of cost performance in transport infrastructure projects. The sample used is the largest of its kind, covering 258 projects in 20 nations worth approximately US$90 billion (constant 1995 prices). The paper shows with overwhelming statistical significance that in terms of costs transport infrastructure projects do not perform as promised. The conclusion is tested for different project types, different geographical regions and different historical periods. Substantial cost escalation is the rule rather than the exception. For rail, average cost escalation is 45% (SD=38), for fixed links (tunnels and bridges) it is 34% (62) and for roads 20% (30). Cost escalation appears a global phenomenon, existing across 20 nations on five continents. Cost estimates have not improved and cost escalation not decreased over the past 70 years. Cost estimates used in decision-making for transport infrastructure development are highly, systematically and significantly misleading. Large cost escalations combined with large standard deviations translate into large financial risks. However, such risks are typically ignored or underplayed in decision-making, to the detriment of social and economic welfare.     

轨道交通ATS仿真运行框架的设计与实现

在分析轨道交通ATS系统特性的基础上,选用面向对象的仿真建模框架和逻辑控制框架,结合城市轨道交通的运行特点,对ATS仿真系统的时间控制和对象控制进行明确定义,克服传统仿真控制逻辑发散于描述模型过程代码中的缺点,介绍了一种面向对象的ATS仿真系统运行框架的设计和实现方法,为ATS仿真系统提供新的思路和方案。     

Model uncertainty and the management of a system of infrastructure facilities

The network-level infrastructure management problem involves selecting and scheduling maintenance, repair, and rehabilitation (MR&R) activities on networks of infrastructure facilities so as to maintain the level of service provided by the network in a cost-effective manner. This problem is frequently formulated as a Markov decision problem (MDP) solved via linear programming (LP). The conditions of facilities are represented by elements of discrete condition rating sets, and transition probabilities are employed to describe deterioration processes. Epistemic and parametric uncertainties not considered within the standard MDP/LP framework are associated with the transition probabilities used in infrastructure management optimization routines. This paper contrasts the expected costs incurred when model uncertainty is ignored with those incurred when this uncertainty is explicitly considered using robust optimization. A case study involving a network-level pavement management MDP/LP problem demonstrates how explicitly considering uncertainty may limit worst-case MR&R expenditures. The methods and results can also be used to identify the costs of uncertainty in transition probability matrices used in infrastructure management systems.     

Discrete dynamic models for freeway incident detection systems

This paper considers the problem of freeway incident detection within the general framework of computer‐based freeway surveillance and control. A new approach to the detection of freeway traffic incidents is presented based on a discrete‐time stochastic model of the form ARIMA (0, 1, 3) that describes the dynamics of traffic occupancy observations. This approach utilizes real‐time estimates of the variability in traffic occupancies as detection thresholds, thus eliminating the need for threshold calibration and lessening the problem of false‐alarms. Because the moving average parameters of the ARIMA (0, 1, 3) model change over time, these parameters can be updated occasionally. The performance of the developed detection algorithm has been evaluated in terms of detection rate, false‐alarm rate, and average time‐lag to detection, using a total of 1692 minutes of occupancy observations recorded during 50 representative traffic incidents.     

Generating lane-based intersection maps from crowdsourcing big trace data

Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMs). The objective of our study is to generate LIMs automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data.     

连续刚构桥沥青砼桥面铺装病害调查与分析

文章结合连续刚构桥梁自身的受力特点,通过定量分析桥面铺装的病害种类和分布情况,得出裂缝、变形与防水粘结层失效是其铺装层破坏的主要形式,并分析了病害产生的原因,提出以铺装层顶面的横向拉应力和底面剪应力为铺装结构分析的力学控制指标。     

Impacts of rail transit access on land and housing values in China: a quantitative synthesis

ABSTRACT

Rail transit investment has been viewed as a prominent policy instrument for local and regional development. However, little is known about to what extent the theorised changes in land and housing values arising from rail transit access can be substantiated by evidence in a large developing country context. This paper presents a quantitative review of empirical studies that analysed the impacts of rail transit access on land and housing values in China. We review empirical analyses in 67 studies from 1997 to 2018 for which we encode quantitative results along with a range of theoretically combinations of spatially contextual characteristics, data and methodological-design characteristics. The results show that there are significant variations in the size estimates of effects of rail transit access across studies. Such variations are associated with rail project types, data and methodological designs. Our study provides the insights on what has already been known and what needs to be known on evaluating real estate consequences of rail transit improvements in developing countries.     

A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures

In this paper, we propose a methodology to use the communication network infrastructure, in particular WiFi traces, to detect the sequence of activity episodes visited by pedestrians. Due to the poor quality of WiFi localization, a probabilistic method is proposed that infers activity-episode locations based on WiFi traces and calculates the likelihood of observing these traces in the pedestrian network, taking into account prior knowledge. The output of the method consists of candidates of activity-episodes sequences associated with the likelihood to be the true one. The methodology is validated on traces generated by a known sequence of activities, while the performance being evaluated on a set of anonymous users. Results show that it is possible to predict the number of episodes and the activity-episodes locations and durations, by merging information about the activity locations on the map, WiFi measurements and prior information about schedules and the attractivity in pedestrian infrastructure. The ambiguity of each activity episode in the sequence is explicitly measured.     

Modelling mode choice for freight transport using advanced choice experiments

In this paper we use advanced choice modelling techniques to analyse demand for freight transport in a context of modal choice. To this end, a stated preference (SP) survey was conducted in order to estimate freight shipper preferences for the main attributes that define the service offered by the different transport modes. From a methodological point of view, we focus on two critical issues in the construction of efficient choice experiments. Firstly, in obtaining good quality prior information about the parameters; and secondly, in the improved quality of the experimental data by tailoring a specific efficient design for every respondent in the sample.With these data, different mixed logit models incorporating panel correlation effects and accounting for systematic and random taste heterogeneity are estimated. For the best model specification we obtain the willingness to pay for improving the level of service and the elasticity of the choice probabilities for the different attributes. Our model provide interesting results that can be used to analyse the potential diversion of traffic from road (the current option) to alternative modes, rail or maritime, as well as to help in the obtaining of the modal distribution of commercial traffic between Spain and the European Union, currently passing through the Pyrenees.