沥青玛蹄脂混合料的高温性能影响因素分析

沥青玛蹄脂混合料作为良好的钢桥面铺装层材料,其高温性能一直是关注的焦点,本文基于室内车辙试验研究了其高温性能影响因素,重点分析了荷载和温度、拌合时间及加铺SMA对沥青玛蹄脂混合料的高温性能指标动稳定度的影响规律,为今后玛蹄脂混合料的路用实践提供了借鉴.     

Assessment of factors associated with travel time reliability and prediction: an empirical analysis using probabilistic reasoning approach

Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence.     

Calibrating a transit assignment model using smart card data in a large-scale multi-modal transit network

This paper describes a practical automated procedure to calibrate and validate a transit assignment model. An optimization method based on particle swarm algorithm is adopted to minimize a defined error term. This error term which is based on the percentage of root mean square error and the mean absolute percent error encompasses deviation of model outputs from observations considering both segment level as well as the mode level and can be applied to a large scale network. This study is based on the frequency-based assignment model using the concept of optimal strategy while any transit assignment model can be used in the proposed methodological framework. Lastly, the model is validated using another weekday data. The proposed methodology uses automatic fare collection (AFC) data to estimate the origin–destination matrix. This study combines data from three sources: the general transit feed specification, AFC, and a strategic transport model from a large-scale multimodal public transport network. The South-East Queensland (SEQ) network in Australia is used as a case study. The AFC system in SEQ has voluminous and high quality data on passenger boardings and alightings across bus, rail and ferry modes. The results indicate that the proposed procedure can successfully develop a multi-modal transit assignment model at a large scale. Higher dispersions are seen for the bus mode, in contrast to rail and ferry modes. Furthermore, a comparison is made between the strategies used by passengers and the generated strategies by the model between each origin and destination to get more insights about the detailed behaviour of the model. Overall, the analysis indicates that the AFC data is a valuable and rich source in calibrating and validating a transit assignment model.

     

Cost and time damping: evidence from aggregate rail direct demand models

There is a significant body of evidence from both disaggregate choice modelling literature and practical travel demand forecasting that the responsiveness to cost and possibly to time diminishes with journey length. This has, in Britain at least, been termed ‘Cost Damping’, and is recognised in guidance issued by the UK Department for Transport. However, the consistency of the effect across modes and data types has not been established. Cost damping, if it exists, affects both the forecasting of demand and our understanding of behaviour. This paper aims to investigate the evidence for cost and time damping in rail demand using aggregate rail ticket sales data. The rail ticket sales data in Britain has, for many years, formed the basis of analysis of a wide range of impacts of rail demand. It records the number of tickets sold between station pairs, and it is generally felt to provide a reasonably accurate reflection of travel demand. However, the consistency of these direct demand models with choice modelling and highway demand model structures has not been investigated. Rail direct demand models estimated by ticket sales data indicate only slight variation in the fare elasticity with distance, as is evidenced in the largest meta-analysis of price elasticities conducted to date (Wardman in J Transp Econ Policy 48(3):367–384, 2014). This study of UK elasticities shows strong variation between urban and inter-urban trips, presumably a segmentation at least in part by purpose, but less remaining variation by trip length. A lack of variation by length supports the hypothesis of cost damping, because constant cost sensitivity would imply that fare elasticity would increase strongly with distance, because of the increasing impact of higher fares at longer distances. In this paper we indicate that rail direct demand models have some consistency of behavioural paradigm with utility based choice models used in highway planning. We go on to use rail demand data to estimate time and fare elasticities in the context of various cost damping functions. Our empirical contribution is to estimate time elasticities on a basis directly comparable with cost elasticities and to show that the phenomenon of cost damping is strongly present in ticket sales data. This finding implies that cost damping should be included in models intended for multimodal analysis, which may otherwise give incorrect predictions.     

Genetics of traffic assignment models for strategic transport planning

This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in capability (genes). We argue that all traffic assignment models can be described by three genes. The first gene determines the spatial capability (unrestricted, capacity restrained, capacity constrained, and capacity and storage constrained) described by four spatial assumptions (shape of the fundamental diagram, capacity constraints, storage constraints, and turn flow restrictions). The second gene determines the temporal capability (static, semi-dynamic, and dynamic) described by three temporal assumptions (wave speeds, vehicle propagation speeds, and residual traffic transfer). The third gene determines the behavioural capability (all-or-nothing, one shot, and equilibrium) described by two behavioural assumptions (decision-making and travel time consideration). This classification provides a deeper understanding of the often implicit assumptions made in traffic assignment models described in the literature. It further allows for comparing different models in terms of functionality, and paves the way for developing novel traffic assignment models.     

Non-local simulation of the stable boundary layer with a third order moments closure model

A new higher order closure model for the stable boundary layer is presented and compared with Large Eddy Simulation data. The model includes numerical solutions for the mean values, second and third order moments equations. A satisfactory agreement is found between the calculated vertical profiles of the turbulent quantities with those provided by the LES. Furthermore the new model results are compared with profiles obtained with a lower order closure model in order to verify the effective importance of including third order dynamical equations in the model.     

An algorithm for oceanic front detection in chlorophyll and SST satellite imagery

An algorithm is described for oceanic front detection in chlorophyll (Chl) and sea surface temperature (SST) satellite imagery. The algorithm is based on a gradient approach: the main novelty is a shape-preserving, scale-sensitive, contextual median filter applied selectively and iteratively until convergence. This filter has been developed specifically for Chl since these fields have spatial patterns such as chlorophyll enhancement at thermohaline fronts and small- and meso-scale chlorophyll blooms that are not present in SST fields. Linear Chl enhancements and localized (point-wise) blooms are modeled as ridges and peaks respectively, whereas conventional fronts in Chl and SST fields are modeled as steps or ramps. Examples are presented of the algorithm performance using modeled (synthetic) images as well as synoptic Chl and SST imagery. After testing, the algorithm was used on > 6000 synoptic images, 1999–2007, to produce climatologies of Chl and SST fronts off the U.S. Northeast.     

On the numerical accuracy of the prediction of resistance coefficients in ship stern flow calculations

This article presents a study on the accuracy of the numerical determination of the friction and pressure resistance coefficients of ship hulls. The investigation was carried out for the KVLCC2 tanker at model- and full-scale Reynolds numbers. Gravity waves were neglected, i.e., we adopted the so-called double-model flow. Single-block grids with H–O topology were adopted for all the calculations. Three eddy viscosity models were employed: the one-equation eddy viscosity and the two-equation models proposed by Menter and the TNT version of the two-equation k-ω model. Verification exercises were performed in sets of nearly geometrically similar grids with different densities in the streamwise, normal, and girthwise directions. The friction and pressure resistance coefficients were calculated for different levels of the iterative error and for computational domains of different size. The results show that on the level of grid refinement used, it is possible to calculate the viscous resistance coefficients in H–O grids that do not match the ship contour with a numerical uncertainty of less than 1%. The differences between the predictions of different turbulence models were larger than the numerical uncertainty; however, these differences tended to decrease with increases in the Reynolds number. The pressure resistance was remarkably sensitive to domain size and far-field boundary conditions. Either a large domain or the application of a viscous–inviscid interaction procedure is needed for reliable results. This work was presented in part at the International Conference on Computational Methods in Marine Engineering—MARINE 2007, Barcelona, June 3–4, 2007.     

Aggregating mobile object trajectories: cumulative time geographic density estimation for GPS data

We present new approaches that expand upon the time geographic density estimation (TGDE) framework previously employed to estimate potential path trees. In the past, TGDE metrics have identified possible locations an individual moving object may have passed between, given known origin and destination points. This paper utilizes a new form of TGDE to investigate taxicab GPS traces over a specified time horizon with position ‘gaps’. To this end, we propose a new extension to the TGDE framework, TGDE-C, which is used to determine the cumulative TGDE values for a group of GPS traces, at a given location. These metrics are applied to multiple taxis and allow for time of day analysis. Additionally, we combine these new extensions with existing TGDE metrics that allow us to determine how accessible individual or groups of vehicles are to urban opportunities.     

Review and meta-analysis of U.K. time elasticities of travel demand

In contrast with reviews of values of time and price elasticities, the literature contains little by way of detailed reviews of travel time based choice and demand elasticities. This paper reports the most extensive meta-analysis of time-based demand elasticities yet undertaken, supplemented with a review of literature not previously in the public domain. The meta-analysis is based upon 427 direct elasticities covering travel time, generalised journey time (GJT) and service headway and drawn from 69 UK studies. The elasticities are found to vary, as expected, across attributes, and quite strong effects have been detected according to distance. We provide interesting insights into the relationship between long and short run elasticities and elasticities obtained from static models and choice models based on actual and hypothetical preferences. Significantly, the results seem to indicate that the duration for the long run demand impact to work through depends upon the periodicity of the model estimated. There is little variation apparent by journey purpose, source of the evidence, nor over time or by region/flow type, whilst travel time elasticities for high speed rail are not materially different from conventional contexts. The findings support some official elasticity recommendations and conventions but challenge others, and can be used to provide time-based elasticities where none exist or to assess new empirical evidence.