集成交通规划和交通影响分析进行ITS的项目评价--以IDAS为例

本文通过介绍美国交通规划后处理的ITS项目评价软件--KDAS(ITS Deployment Analysis Software),给出了集成交通规划和交通影响分析进行ITS项目评价的评价层次,并分析了这类项目评价的模块构成和评价流程.文章同时提出,结合交通规划和交通分析混合建模将是ITS评价今后的发展方向,并指出了同类评价过程中涉及的几个关键技术问题.     

城市道路交通管理规划及交通需求预测法分析

介绍了我国城市道路交通管理规划的现状，阐述了交通管理规划的内容及方法，论述了交通需求预测的分析方法，阐明了其相应交通需求模型在交通管理规划中的应用。     

A data-centric bottom-up model for generation of stochastic internal load profiles based on space-use type

There is currently no established methodology for the generation of synthetic stochastic internal load profiles for input into building energy simulation. In this paper, a Functional Data Analysis approach is used to propose a new data-centric bottom-up model of plug loads based on hourly data monitored at a high spatial resolution and by space-use type for a case-study building. The model comprises a set of fundamental Principal Components (PCs) that describe the structure of all data samples in terms of amplitude and phase. Scores (or weightings) for each daily demand profile express the contribution of each PC to the demand. Together the principal components and the scores constitute a structure-based model potentially applicable beyond the building considered. The results show good agreement between samples generated using the model and monitored data for key parameters of interest including the timing of the daily peak demand.     

Methodology for spatio‐temporal predictions of traffic counts across an urban road network and generation of an on‐road greenhouse gas emission inventory

On‐road emission inventories in urban areas have typically been developed using traffic data derived from travel demand models. These approaches tend to underestimate emissions because they often only incorporate data on household travel, not including commercial vehicle movements, taxis, ride hailing services, and other trips typically underreported within travel surveys. In contrast, traffic counts embed all types of on‐road vehicles; however, they are only conducted at selected locations in an urban area. Traffic counts are typically spatially correlated, which enables the development of methods that can interpolate traffic data at selected monitoring stations across an urban road network and in turn develop emission estimates. This paper presents a new and universal methodology designed to use traffic count data for the prediction of periodic and annual volumes as well as greenhouse gas emissions at the level of each individual roadway and for multiple years across a large road network. The methodology relies on the data collected and the spatio‐temporal relationships between traffic counts at various stations; it recognizes patterns in the data and identifies locations with similar trends. Traffic volumes and emissions prediction can be made even on roads where no count data exist. Data from the City of Toronto traffic count program were used to validate the output of various algorithms, indicating robust model performance, even in areas with limited data.     

自来水用户用水模式的确定方法研究

针对管网建模过程中用水量变化规律的复杂性,提出了根据实际用水量数据进行聚类分析,合理地对用户进行分类的方法,并通过主成分综合模型确定了每一类用户典型的用水模式曲线.该方法借助电磁水表实时远传的水量数据,能够及时更新用户的用水模式曲线,为提高建模的精确性打下了坚实的基础.     

Data–Driven Methodology for Signal Timing Plan Development: A Computational Approach

Traffic signal systems serve as one of the most powerful control tools available to improve the efficiency of surface transportation travel. A large number of signal systems currently operate using the time–of–day (TOD) approach. In TOD systems, a day is segmented into a number of intervals in which a different timing plan is used. Thus, the challenge in operating a TOD system effectively is to (1) identify appropriate TOD intervals, and (2) develop optimal timing plans for each interval. The existing procedures used by traffic engineers to address these challenges are time consuming and use relatively small sets of data. This research effort developed a new timing plan development methodology that takes advantage of the large sets of archived traffic data (volume and occupancy) that modern systems are equipped to compile. Based upon statistical cluster analysis, this methodology (1) automates the identification of TOD intervals using a high–resolution definition of system state, and (2) provides representative volumes for plan optimization based on the set of archived data. The results of a case study reported in this paper demonstrate that the methodology supports the development of a TOD system that provides benefits when considering performance measures such as delay, when compared to currently used techniques.     

A COMPARISON OF ACCIDENT RATES USING THE LIKELIHOOD RATIO TESTING TECHNIQUE

Comparing transportation facilities (i.e., intersections and road sections) in terms of traffic accident occurrences is among the interests of most traffic safety analysts. This paper uses statistical techniques as appropriate tools for performing such analyses. Traditionally, traffic accidents are represented as occurrences of events per certain unit, such as time and vehicle miles; this representation is consistent with Poisson nature. In this paper, Poisson distribution is used to describe the distribution of traffic accident occurrences. The objective is to develop a test statistic in order to enable traffic analysts to compare traffic accident rates in various transportation facilities. The obtained test statistic is simple and requires minimal data to perform the comparison analysis.     

Sediment oxygen demand techniques: A review and comparison of laboratory and in situ systems

Sediment oxygen demand (SOD) measurement techniques are reviewed and suggestions offered for improving their performance, including an experimental approach toward standardizing the SOD methodology. Five principal analytical systems are used to evaluate SOD. These are conveniently classified as: (1) batch (laboratory and in situ); (2) continuous flow; (3) manometric; (4) electrolytic and (5) dehydrogenase activity. Comparison of SOD methods requires establishing certain criteria which should be met for a given method to be considered acceptable. These criteria are: (1) consistency; (2) reproducibility and (3) efficiency. Laboratory techniques can meet the acceptability criteria more easily than in situ techniques. A SOD reaction chamber used in our laboratory provided reproducible data for replicate analyses of stream and lakes sediments and met our acceptability criteria.     

Seismic performance assessment of highway bridge networks considering post-disaster traffic demand of a transportation system in emergency conditions

Highway bridge networks (HBN) are one of the critical components of a transportation system that supports the operation of modern society. The traffic-carrying capacity of a transportation system is essential for emergency responses in a catastrophic seismic hazard. In this article, a methodology is proposed to assess the regional seismic performance of HBN considering the post-disaster traffic demand of a transportation system in emergency conditions. First, the framework of the methodology is introduced. Then, topological properties and vulnerability analyses are conducted to understand the connectivity of the HBN and the traffic-carrying capacity of the damaged bridge networks. In addition, traffic flow analysis is employed to consider the traffic situation in an emergency condition. Then, the entropy-right method is adopted to integrate these factors and to assess the importance of each bridge link in an emergency condition. Last, the seismic performance of HBN is also evaluated considering the decrease in the traffic-carrying capacity and the increase in traffic cost after earthquake events. The methodology is demonstrated using a HBN located in San Jose, California.     

Assessing the Reliable Outputs of Groundwater Sources

The described methodology for the assessment of the reliable outputs of groundwater sources was developed during a recent contract for UK Water Industry Research. It is relatively simple and flexible so that it can be applied to the wide range of groundwater source types and aquifers encountered in the UK. It can be used for all water level conditions, including that of drought, and for various demand conditions. There are two approaches, one based upon operational data and the second upon the analysis and extrapolation of pumping test data. The operational approach has several options of increasing refinement – the choice of option depending on data availability and user requirements. The application of the methodology to real groundwater sources is described using three examples.     

The “effect procargo” on technical and scale efficiency at airports: The case of Spanish airports (2009–2011)

This paper studies the “effect procargo” (or effect of the proportion of cargo traffic relative to total traffic) on technical and scale efficiency at airports. To this end, using Data Envelopment Analysis (DEA) methodology, a comparative technical efficiency analysis is developed for 35 Spanish airports over the 2009 to 2011 period. In a second stage, using Tobit regression, we analysed the effects of airport size, low-cost carrier (LCC) presence, and cargo traffic on efficiency. The results suggest that cargo traffic has a positive impact on the technical and scale efficiency of Spanish airport operations. Airports with a higher share of cargo traffic are expected to have higher overall technical efficiency, pure technical efficiency, and scale efficiency, in comparison to airports with a lower share.     

Simulation model based on Monte Carlo method for traffic assignment in local area road network

For a local area road network, the available traffic data of traveling are the flow volumes in the key intersections, not the complete OD matrix. Considering the circumstance characteristic and the data availability of a local area road network, a new model for traffic assignment based on Monte Carlo simulation of intersection turning movement is provided in this paper. For good stability in temporal sequence, turning ratio is adopted as the important parameter of this model. The formulation for local area road network assignment problems is proposed on the assumption of random turning behavior. The traffic assignment model based on the Monte Carlo method has been used in traffic analysis for an actual urban road network. The results comparing surveying traffic flow data and determining flow data by the previous model verify the applicability and validity of the proposed methodology.     

Probabilistic climate projections with dynamic building simulation: Predicting overheating in dwellings

This study, as part of the Low Carbon Futures project, proposes a methodology to incorporate probabilistic climate projections into dynamic building simulation analyses of overheating in dwellings. Using a large climate projection database, suitable building software and statistical techniques (focussing on principal component analysis), output is presented that demonstrates the future overheating risk of a building in the form of a probability curve. Such output could be used by building engineers and architects to design a building to an acceptable future overheating risk level, i.e. providing evidence that the building, with specific adaptation measures to prevent overheating, should achieve thermal comfort for the majority of future climate projections. This methodology is overviewed and the use of the algorithm proposed in relation to existing building practices. While the methodology is being applied to a range of buildings and scenarios, this study concentrates on night-time overheating in UK dwellings with simple and achievable adaptation measures investigated.     

Innovation in artificial neural network learning: Learn-On-Demand methodology

The artificial neural networks represent the state of the art tool for forecasting and prediction. However, the technique relies heavily on the availability of adequate data for its training. There have been many attempts to overcome the problems associated with the acquisition of learning data. These include the use of simulation techniques, which prepare the data for pre-processing prior to learning. Nevertheless, these methods tend to undermine the specific nature of the application that is reflected in its data. Furthermore, it is evident that, in certain circumstances, the current learning methods, grouped under on-line and off-line, do not provide an effective learning solution and their advantages are mutually exclusive. With these problems in mind, this research proposes a method for rectifying these shortcomings. The solution focuses on the learning processes rather than data. The work offers a new learning mechanism, namely the “Learn-On-Demand” (LOD) methodology, which enables the ANN to learn where the lack of knowledge is evident. The proposed LOD methodology integrates into ANN's learning process. Having produced the algorithm for its implementation, the paper then produces the mathematical representation of the Learn-On-Demand methodology by integrating the new algorithm into existing methodologies. The need for this solution emerged out of a research in the field of construction, where Structured Systems Analysis and Design was sued as a platform for integrating a hybrid of AI techniques in order to develop an enhanced method of client briefing.     

公路隧道通风计算中车密度系数的取值方法研究

隧道通风需风量确定是公路隧道通风中的关键技术问题。对《公路隧道通风照明设计规范 JTJ 026.1—1999》中的需风量计算进行了讨论,从交通工程的基本理论角度分析了车密度系数概念的不合理性和取值的不科学性。分析表明:单位时间内交通量是唯一决定需风量的变量,《规范》引入车密度系数是表象,其本质作用是修正设计交通量。因此,研究车密度系数的取值的本质是研究如何修正不同工况下交通量,也即研究不同速度下的交通量的取值。在分析交通流中流量—密度—速度关系的基础上,提出了一种基于交通流基本图模型的改进的隧道通风计算中车密度系数的取值办法,并以Greenshields线性速度—密度模型为例说明了在实际需风量计算中如何合理正确计算车密度系数。     

Evaluating the system reliability of corroding pipelines based on inspection data

This paper presents a methodology for evaluating the time-dependent system reliability of a pressurised gas pipeline segment containing multiple active metal-loss corrosion defects. The methodology incorporates three distinctive failure modes of the pipe segment due to corrosion, namely small leak, large leak and rupture. The growth of the depth of individual corrosion defect is assumed to follow a power-law function of time. The Bayesian updating and Markov Chain Monte Carlo (MCMC) simulation techniques are used to quantify the parameters of the power-law growth model based on data obtained from multiple inspections carried out at different times. The simple Monte Carlo and MCMC techniques are combined to evaluate the system reliability. A numerical example involving an in-service gas pipeline located in Alberta, Canada, is used to illustrate the proposed methodology. Results of the sensitivity analysis suggest that the use of a defect-specific or segment-specific growth model for the defect depth has a marked impact on the evaluated system reliability. The proposed methodology can be incorporated in reliability-based pipeline corrosion management programmes to assist integrity engineers in making informed decisions about defect repair and mitigation.     

The dynamic effects of the Asian financial crisis on construction demand and tender price levels in Singapore

The paper aims to demonstrate the usefulness of applying a sophisticated technique to examine, by measuring, the dynamic effects of an external shock on the construction industry via two macro-level indicators. The time series for `value of contracts awarded' (or contractor's new orders), a proxy for demand for construction, and the `tender price index' are used for the analysis, with the Asian financial crisis as the intervention event. The methodology comprises five main stages to produce appropriate ARIMA models that describe the characteristic of the underlying process. The main advantage of the intervention analysis, as noted, is that the estimates of the effect are based on the entire series for `value of contracts awarded' or `tender price index', and not on a simple comparison of a few quarters' data.     

Clustering a building stock towards representative buildings in the context of air-conditioning electricity demand flexibility

Energy modelling for the prediction of energy use in buildings, especially under novel energy management strategies, is of great importance. In buildings there are several flexible electrical loads which can be shifted in time such as thermostatically controllable loads. The main novelty of this paper is to apply an aggregation method to effectively characterize the electrical energy demand of air-conditioning (AC) systems in residential buildings under flexible operation during demand response and demand shaping programs. The method is based on clustering techniques to aggregate a large and diverse building stock of residential buildings to a smaller, representative ensemble of buildings. The methodology is tested against a detailed simulation model of building stocks in Houston, New York and Los Angeles. Results show good agreement between the energy demand predicted by the aggregated model and by the full model during normal operation (normalized mean absolute error, NMAE, below 10%), even with a small number of clusters (sample size of 1%). During flexible operation, the NMAE rises (around 20%) and a higher number of representative buildings become necessary (sample size at least 10%). Multiple cases for the input data series were considered, namely by varying the time resolution of the input data and the type of input data. These characteristics of the input time series data are shown to play a crucial role in the aggregation performance. The aggregated model showed lower NMAE compared to the original model when clustering is based on a hybrid signal resolved at 60-minute time intervals, which is a combination of the electricity demand profile and AC modulation level.     

商务居住项目交通影响评价研究

从商务居住项目交通需求特点出发,提出商务居住项目交通影响评价原则,结合西安某商务居住区交通影响评价进行了论述,并对周边公交线路服务水平、停车设施利用状况进行了评价,从而为进行定量评估以及制定交通组织方案提供了指导。     

Risk-based importance factors for bridge networks under highway traffic loads

This paper proposes a risk analysis framework for the quantification of the importance to a highway network of bridges subjected to overweight traffic loads. First, the level of risk to the network is quantified taking into consideration current bridge safety levels and the consequences of their failures. A risk mitigation strategy is proposed through the calibration of importance factors that can be used during bridge design and rating processes to induce different levels of risk reduction in the network. The applicability of the method is illustrated through the analysis and the calibration of importance factors for bridges of a network composed by interstate highways and principal state roads in New York State (NYS) which includes 1,315 typical bridges. Analysis results show that traffic delays constitute the major consequence of bridge failure representing 61% of the total risk while the second major risk component is the maintenance of bridges that accounts for about 21%. It is also observed that the relationship between bridge importance factors and risk is well represented by a power law equation.