公路隧道交通量的预测及分析

公路隧道交通量的预测对隧道通风系统的节能以及降低隧道运营成本有很重要的意义。分别利用多元统计分析法和BP神经网络两种方法对公路隧道交通量进行了预测,并对两类预测数据进行分析比较,得出了多元统计分析法适用于车流量少而且稳定的公路隧道的预测,而BP神经网络法则适用于车流量大而且不稳定的公路隧道的预测的结论。     

Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development

In this paper, we aim to quantify uncertainty in short-term traffic volume prediction by enhancing a hybrid machine learning model based on Particle Swarm Optimization (PSO) and Extreme Learning Machine (ELM) neural network. Different from the previous studies, the PSO-ELM models require no statistical inference nor distribution assumption of the model parameters, but rather focus on generating the prediction intervals (PIs) that can minimize a multi-objective function which considers two criteria, reliability and interval sharpness. The improved PSO-ELM models are developed for an hourly border crossing traffic dataset and compared to: (1) the original PSO-ELMs; (2) two state of the art models proposed by Zhang et al. (2014) and Guo et al. (2014) separately; and (3) the traditional ARMA and Kalman filter models. The results show that the improved PSO-ELM can always keep the mean PI length the lowest, and guarantee that the PI coverage probability is higher than the corresponding PI nominal confidence, regardless of the confidence level assumed. The study also probes the reasons that led to a few points being not covered by the PIs of PSO-ELMs. Finally, the study proposes a comprehensive optimization framework to make staffing plans for border crossing authority based on bounds of PIs and point predictions. The results show that for holidays, the staffing plans based on PI upper bounds generated much lower total system costs, and that those plans derived from PI upper bounds of the improved PSO-ELM models, are capable of producing the lowest average waiting times at the border. For a weekday or a typical Monday, the workforce plans based on point predictions from Zhang et al. (2014) and Guo et al. (2014) models generated the smallest system costs with low border crossing delays. Moreover, for both holiday and normal Monday scenarios, if the border crossing authority lacked the required staff to implement the plans based on PI upper bounds or point predictions, the staffing plans based on PI lower bounds from the improved PSO-ELMs performed the best, with an acceptable level of service and total system costs close to the point prediction plans.     

Experienced travel time prediction for congested freeways

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.     

一种计及姿态变化的船舶阻力预报方法

对于航行于水面的舰船,船体姿态会随着航速的增加在航行中发生明显变化,这种姿态变化后的阻力较通常计算的以约束模型为基准的阻力预报是有明显差别的。采用CFD方法实现了以KCS船为模型进行模型自由状态下的阻力预报,并和相关实验数据进行了比较。结果表明,该方法避免了由于船体运动造成流场网格扭曲与计算发散等问题,具有良好的稳定性。另外,该方法充分考虑了船体姿态的影响,计算结果令人满意。     

非等时距GM(1,1)灰色模型在路基沉降预测中的应用

灰色理论现已普遍运用于路基沉降的预测中,以某高速公路为例,运用灰色理论建立路基沉降的非等时距GM(1,1)预测模型,并采用后验差法进行精度检验。以工程实测沉降数据为基础,对模型的预测结果进行了分析。结果表明,预测值与实测值之间误差较小,从而证实该方法的可行性。     

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.     

等里程间隔GM(1,1)模型在关角隧道6号斜井涌水量预测中的应用

在原始灰色GM(1,1)模型的基础上,通过运用等间距里程序列的分析方法建立模型,并对关角隧道6号斜井的涌水量进行了模拟预测。经检验,预测结果精度较高,对隧道工程涌水量的短期预测具有较大的实用价值。     

预测和确定汽车气动特性的新理论模型

本文以汽车空气动力学的“升力面理论”为出发点，提出一种预测和确定汽车空气动力学特性的新理论模型，并就其在评价空气动力学因素对汽车性能的影响，在研究开发气动阻力最小且同时满足汽车工程各方面要求的近地形体及车身优化设计等方面的应用价值进行了探讨。     

金属冷锻破裂的预测

系统地分析了冷锻件破裂的原因，提出了提高冷锻件质量的方案，研究了不同热处理状态下镦粗的极限应变。考虑到静水压应力分量具有抑制材料韧性破坏的重要特性，从能量观点出发提出了冷锻材料破裂准则。试验验证结果表明，该准则可用于金属冷锻成型的破裂预测，并可指导冷锻成型工艺的编制及模具的调试。     

实船航速预报技术研究

本文通过对船模试验和实船试航资料的整理、分析和研究,提供了各类相关因素及图表,可较准确地预报船航速。文中还提出了一种新型的航速预报方法－－（ＣＡＤ,φｎ）法,可改善航速预报精度。