A real time forecasting tool for dynamic travel time from clustered time series

This paper addresses the problem of dynamic travel time (DTT) forecasting within highway traffic networks using speed measurements. Definitions, computational details and properties in the construction of DTT are provided. DTT is dynamically clustered using a K-means algorithm and then information on the level and the trend of the centroid of the clusters is used to devise a predictor computationally simple to be implemented. To take into account the lack of information in the cluster assignment for the new predicted values, a weighted average fusion based on a similarity measurement is proposed to combine the predictions of each model. The algorithm is deployed in a real time application and the performance is evaluated using real traffic data from the South Ring of the Grenoble city in France.     

Using survival models to estimate bus travel times and associated uncertainties

Transit agencies often provide travelers with point estimates of bus travel times to downstream stops to improve the perceived reliability of bus transit systems. Prediction models that can estimate both point estimates and the level of uncertainty associated with these estimates (e.g., travel time variance) might help to further improve reliability by tempering user expectations. In this paper, accelerated failure time survival models are proposed to provide such simultaneous predictions. Data from a headway-based bus route serving the Pennsylvania State University-University Park campus were used to estimate bus travel times using the proposed survival model and traditional linear regression frameworks for comparison. Overall, the accuracy of point estimates from the two approaches, measured using the root-mean-squared errors (RMSEs) and mean absolute errors (MAEs), was similar. This suggests that both methods predict travel times equally well. However, the survival models were found to more accurately describe the uncertainty associated with the predictions. Furthermore, survival model estimates were found to have smaller uncertainties on average, especially when predicted travel times were small. Tests for transferability over time suggested that the models did not over-fit the dataset and validated the predictive ability of models established with historical data. Overall, the survival model approach appears to be a promising method to predict both expected bus travel times and the uncertainty associated with these travel times.     

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.     

道路工程的用地困难问题与其“虚属性”的迫切性要求分析

现有工程建设大多面临着直接、机械式的制约后续工程建设用地、后续工程建设持续发展困难的问题。文中从虚工作既不占用时间、也不占用社会生活环节的资源，但它具有时间指标、时间属性虚资源这个角度．提出采用无机动时差的双代号网络进度计划图所实现的“虚工作”的虚特性，解决占地、后续工程持续发展困难等问题的方法。它从虚工作（具有时间属性）的维系客观背景所带来的新的工程客观条件和合乎社会的时代步伐、节拍基础上，解决了工程占地、资源重复利用方面的困扰。它能够满足地区或国家建设领域的稳定、安全发展需要。     

Spectral and time‐frequency analyses of freeway traffic flow

This paper uses spectral and time‐frequency analyses to treat three macroscopic traffic characteristics, namely, time mean speed, volume and occupancy as stochastic processes. Spectral and time‐frequency analyses are performed to characterize power spectral density (PSD), cross‐PSD, autocorrelation and cross‐correlation of these characteristics using TransGuide traffic data collected from four different freeways. It is found that low‐frequency components dominate the PSDs of speed, volume and occupancy at all times. The magnitude of PSDs decreases dramatically as frequency increases and remains almost at a constant level in high‐frequency regimes. A power law is found to exist, which describes the relationship between the frequency and the PSD of traffic characteristics. It is also found that speed can be properly modeled by a narrowband low‐pass stochastic process in a low‐frequency regime and by a nonzero mean white noise in a high‐frequency regime. Strong periodicities and synchronization are both shown in traffic flow. A variety of frequencies can be excited by congestion, and there is no dominant frequency found in stop‐and‐go traffic. Copyright © 2013 John Wiley & Sons, Ltd.     

Future electricity demand from battery‐powered vehicles

An important decision faced by airline schedulers is how to adapt the flight schedule and aircraft assignment to unforeseen perturbations in an established schedule. In the face of unforeseen aircraft delays, schedulers have to decide which flights to delay, and when delays become excessive, which to cancel. Current scheduling models deal with simple decision problems of delay or cancellation, but not with both simultaneously. But in practice the optimal decision may involve results from the integration of both flight cancellations and delays. In Part I of this paper, a quadratic programming model for the integration decision problem is given. The model can formulate the integration of flight cancellations and delays as well as some special cases, such as the ferrying of surplus aircraft and the possibility of swapping different types of aircraft. In this paper, based on the special structure of the model, an effective algorithm is presented, sufficient computational experiments are conducted and some results are reported. These show that we can expect to obtain a sufficiently good solution in terms of reasonable CPU time.     

Bi-objective routing problem with asymmetrical travel time distributions

Recent studies have confirmed that travelers consider travel time reliability in addition to average travel time when making route choice decisions. In this study, we develop a bi-objective routing model that seeks to simultaneously optimize the average travel time and travel time reliability. The semi-standard deviation (SSD) is chosen as the reliability measure because it reflects travelers' concerns over longer travel time better than the commonly used standard deviation. The Pareto-optimal solutions to the bi-objective model are found by using an improved strength Pareto evolutionary algorithm. Tests on a real-world urban network with field measured travel time data have demonstrated good performance of the algorithm in the aspects, such as computational efficiency, quick convergence, and closeness to the global Pareto-optimal. Overall, the bi-objective routing model generates reasonable path recommendations. The SSD-based model is sensitive to the asymmetry of travel time distribution and tends to avoid paths with excessively long delays. This would be particularly helpful to those users placing high values on travel time reliability.     

隧道微差爆破的延时计算及电子雷管减振应用

为实现复杂环境下隧道爆破减振,根据微差爆破延时计算理论,综合考虑围岩的物理力学参数及隧道爆破施工参数,提出采用电子雷管进行微差爆破的延时计算方法。研究起爆药量对爆破地震波振速和主频的影响,并将研究成果应用到成渝客专新红岩隧道爆破工程中。结果表明： 1）微差爆破的合理延时需要考虑岩石破碎及沿裂缝扩展到掌子面并抛掷足够距离的时间; 2）采用微差爆破延时计算方法,可合理设置电子雷管延时,实现单孔间隔微差爆破,有效降低峰值振速、提高振动主频; 3）在新红岩隧道爆破工程中,采用电子雷管爆破相比毫秒非电雷管爆破振速减小60%以上,证明电子雷管微差爆破延时计算方法的准确性和合理性。     

顶部张紧式立管干涉分析

顶部张紧式立管（TTR）是油气开发必不可少的立管类型。研究了南海1500米水深半潜式干树深水平台TTR的概念设计,使用ORCAFLEX软件建立TTR的干涉分析非线性时域分析模型,基于Huse尾流模型,分析了浪流方向为0°,45°,90°,135°和180°的100年一遇环境载荷下的立管间距。结果表明,浪流方向为0°时立管最小间距小于TTR最大外径之和,发生干涉,其他方向不发生干涉。TTR为阵列式设计的立管群,应当从多方面因素考虑以避免干涉发生的危险,合理安排立管间距、调整TTF等措施可以有效减小干涉发生的可能。