Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

Regulation of the informal transport sector in Rio de Janeiro,Brazil: welfare impacts and policy analysis

In Brazil, the explosion of informal transport activity during the past decade has had profound effects on formal public transport systems and is a source of great controversy in the urban transportation sector. A variety of policies have been proposed to manage the growth of the sector. This study seeks to understand how proposed policies will impact the users of these systems. A corridor in Rio de Janeiro with substantial informal activity was used as a case study. Measures of welfare changes in a discrete choice framework were used to estimate proposed policies’ impacts on users. Eleven candidate policies were evaluated, ranging from the eradication of the informal modes and investment in formal modes, to the legalization of the informal modes. Benefits were compared with costs and the distribution of benefits across income classes was explored. Net benefits from some policies were found to be substantial. Legalizing the informal sector was found to benefit users slightly but further investments in the sector are probably inefficient. Users benefited most from improvements in formal mass transit modes, at roughly 100–200 dollars per commuter per year. Finally, policies to foster a competitive environment for the delivery of both informal and formal services were shown to benefit users about 100 dollars per commuter per year. Together, the regulation of the informal sector and investments in the formal sector serve to reinforce the movement towards competitive concessions for services and help reduce the impacts of cartelization and costly in-road competition.

基于灰色二次指数平滑的QAR参数关联度预测

由于QAR参数波动的后果具有潜在性和滞后性,因此分析人员容易忽视其危险性而使其发展为不利事件。针对该问题,提出运用QAR参数关联度分析的方法,建立QAR参数波动与事件之间的对应关系;并构建基于二次指数平滑-灰色关联法的关联度预测模型,进一步明确QAR参数波动与事件之间关系的动态变化趋势。运用实际数据对该模型进行验证,表明结论符合实际,该模型科学合理,可以用于QAR参数分析过程。     

TBM掘进数据标准化预处理方法研究北大核心CSCD

TBM信息化采集了海量数据,对TBM数据的标准化预处理是进行诸多研究的前提。基于此,提出了一种TBM掘进数据标准化处理方法,依托TBM现场施工掘进大数据,以破岩特征为依据选取基本掘进参数(刀盘转速、推进速度、刀盘推力及刀盘扭矩)分析掘进过程TBM数据特点,提出循环掘进过程空推段、上升段、稳定段及下降段起点的判别方法,对稳定段起点提出了标准差法判别方法、均值判别方法、直方图判别方法,满足实时和非实时的数据划分需求。最后对两个TBM工程的数据进行标准化预处理,实现施工大数据的标准化。结果表明,提出的标准化预处理方法可实现循环掘进过程数据的有效划分。研究成果可推广应用于众多TBM工程的数据标准化处理,有效实现机器学习数据库的建立。     

盾构隧道整体道床剥离病害规律研究北大核心CSCD

盾构隧道整体道床的剥离病害已严重影响地铁的安全运营。为研究整体道床剥离病害的规律,文章以国内某运营地铁线路为工程背景,建立整体道床-管片-注浆层数值模型进行分析研究。结果表明:列车轮组作用在道床一侧靠近伸缩缝位置时会引起较大的道床剥离变形,是最不利位置;列车荷载在振动作用下引起的剥离量远大于静载作用时的,且两者间存在函数关系;伸缩缝位置是最易发生剥离的区域,剥离首先发生在伸缩缝两侧边缘,随列车动载作用时间增加,剥离区域扩展至伸缩缝1.5 m范围内;注浆层刚度与接触面黏结强度均是影响剥离量的重要因素,提升注浆层刚度和道床-管片接触面的黏结强度可有效减少剥离量。     

复杂环境超小间距隧道盾构掘进施工技术北大核心CSCD

针对浅埋超小间距隧道盾构施工,文章以特拉维夫红线轻轨工程西标段双线盾构施工为工程背景,从隧道加固施工、盾构掘进控制、监测控制与应急管理等方面进行系统研究。结果表明,加固施工在小间距盾构隧道施工中至关重要,可有效降低隧道施工风险;对未加固的小间距盾构隧道施工,应保持土压平稳,加强土压精细化控制;严格控制出渣量,做好渣土改良,控制土体损失率低于0.3%;在盾壳外部注入膨润土或克泥效,可有效地减少邻近隧道的位移量;应结合实时监测数据,控制回填注浆量及注浆质量;隧道施工过程应加强对邻近既有建筑物的监测。     

Large-scale application of MILATRAS: case study of the Toronto transit network

This paper documents the efforts to operationalize the conceptual framework of MIcrosimulation Learning-based Approach to TRansit Assignment (MILATRAS) and its component models of departure time and path choices. It presents a large-scale real-world application, namely the multi-modal transit network of Toronto which is operated by the Toronto Transit Commission (TTC). This large-scale network is represented by over 500 branches with more than 10,000 stops. About 332,000 passenger-agents are modelled to represent the demand for the TTC in the AM peak period. A learning-based departure time and path choice model was adopted using the concept of mental models for the modelling of the transit assignment problem. The choice model parameters were calibrated such that the entropy of the simulated route loads was optimized with reference to the observed route loads, and validated with individual choices. A Parallel Genetic Algorithm engine was used for the parameter calibration process. The modelled route loads, based on the calibrated parameters, greatly approximate the distribution underlying the observed loads. 75% of the exact sequence of transfer point choices were correctly predicted by the off-stop/on-stop choice mechanism. The model predictability of the exact sequence of route transfers was about 60%. In this application, transit passengers were assumed to plan their transit trip based on their experience with the transportation network; with no prior (or perfect) knowledge of service performance.     

Analysis of the environmental benefits of a motorcycle idling stop policy at urban intersections

In an attempt to reduce CO₂ emissions from motorized transport, the Taiwanese government introduced an idling stop policy for vehicles in early 2007. This paper seeks to quantify the environmental benefits of the policy based on a stated preference analysis. Motorcyclists were surveyed at urban intersections in Taiwan, to identify the amount of time they would be willing to turn off their engines while waiting at traffic lights (the WTO). A contingent valuation framework based on stated preference questions was designed to determine the WTO. Results obtained from the Spike model showed that the average motorcyclist’s WTO is 82 s. In another analysis, in which other variables were taken into consideration, such as the possibility that the policy will be enacted as legislation, the expected WTO increased to 101 s. In both cases, an idling stop policy would have positive environmental effects, reducing gasoline usage by 1021 L per hour and reducing CO₂ emissions by 0.56 metric tons per hour at the intersection studied during peak periods.     

Estimating the value of risk reductions for car drivers when pedestrians are involved: a case study in Spain

We estimated the benefits associated with reducing fatal and severe injuries from traffic accidents using a stated choice experiment where choice situations were generated through a statistically efficient design. Specifically, the risk variables were defined as the expected annual number of vehicle car-users that suffered their death or were severely injured in a traffic accident. In addition, and differing from previous research, the number of pedestrians that died or were severely injured in traffic accidents per year was also included as a risk attribute in the choice experiment, to attempt at measuring drivers’ willingness to pay to reduce the risk of hitting pedestrians in a crash. The empirical setting was a choice of route for a particular trip that a sample of car drivers periodically undertakes in Tenerife, Spain. Models were estimated accounting for random taste heterogeneity and pseudo-panel data correlation. The median of the distribution of simulated parameters was used to obtain a representative measure for the monetary valuation of risk reductions. We found that the ratio between the values of reducing the risk of suffering a serious injury and that of reducing a fatality was approximately 18 %. Further, and quite novel, we also found that the value of reducing a pedestrian fatality was 39 % of the value of reducing a car occupant fatality.     

Are we ready to embrace connected and self-driving vehicles? A case study of Texans

While connected, highly automated, and autonomous vehicles (CAVs) will eventually hit the roads, their success and market penetration rates depend largely on public opinions regarding benefits, concerns, and adoption of these technologies. Additionally, the introduction of these technologies is accompanied by uncertainties in their effects on the carsharing market and land use patterns, and raises the need for tolling policies to appease the travel demand induced due to the increased convenience. To these ends, this study surveyed 1088 respondents across Texas to understand their opinions about smart vehicle technologies and related decisions. The key summary statistics indicate that Texans are willing to pay (WTP) $2910, $4607, $7589, and $127 for Level 2, Level 3, and Level 4 automation and connectivity, respectively, on average. Moreover, affordability and equipment failure are Texans’ top two concerns regarding AVs. This study also estimates interval regression and ordered probit models to understand the multivariate correlation between explanatory variables, such as demographics, built-environment attributes, travel patterns, and crash histories, and response variables, including willingness to pay for CAV technologies, adoption rates of shared AVs at different pricing points, home location shift decisions, adoption timing of automation technologies, and opinions about various tolling policies. The practically significant relationships indicate that more experienced licensed drivers and older people associate lower WTP values with all new vehicle technologies. Such parameter estimates help not only in forecasting long-term adoption of CAV technologies, but also help transportation planners in understanding the characteristics of regions with high or low future-year CAV adoption levels, and subsequently, develop smart strategies in respective regions.