基于贝叶斯网络模型的交通状态预测

城市的交通状态是可以预测的.有效的交通状态预测能从很大程度上优化交通状态,减少交通阻塞.贝叶斯网络(Bayesian Networks,BN)是目前不确定知识和推理领域最有效的理论模型之一.提出了一种基于贝叶斯网络模型理论的交通状态预测方法.综合考虑交通阻塞成因的基础上构建网络模型,在已有的交通状态数据的基础上提出基于贝叶斯法则的学习算法,并通过计算变量间的条件概率来计算交通阻塞发生的可能性,达到预测的目的.     

基于交通大数据的动态交通状态预测及全局路径规划研究

为提高动态交通状态预测的准确性,对基于交通大数据的动态交通状态预测及全局路径规划进行研究。以交通大数据为基础,获取指定路段的属性信息与采集信息,通过对样本数据进行描述,得到该路段的试验数据。在具体的预测和分析阶段,构建基于交通大数据的预测模型,实现对动态交通状态的可靠预测。试验结果表明,时间特征值对交通流量的影响较大,某一时间段的交通状态数据可为全局路径规划提供数据源,有利于相关人员作出科学决策,可更好地满足当前智慧交通管理新需求。     

智能算法在短时交通流量预测中的应用

为了深入研究不同智能算法在不同时间尺度下短时交通流量预测中的预测效果,采用历史平均法作为参照,选取小波神经网络、支持向量机回归、非参数回归三种典型的智能算法,对快速路单截面的交通流量进行预测,分别探讨其在1min、5min、15min三种典型预测步长条件下的预测效果。分析了不同时间尺度的波动系数以及道路线形对预测结果的影响,并提出优化思路。     

基于贝叶斯网络模型的交通状态预测

城市的交通状态是可以预测的。有效的交通状态预测能优化交通状态,减少交通阻塞。贝叶斯网络(Bayesian Networks,BN)是目前不确定知识和推理领域最有效的理论模型之一。文章在综合考虑交通阻塞成因的基础上构建网络模型,在已有的交通状态数据的基础上提出基于贝叶斯法则的学习算法,并通过计算变量间的条件概率来计算交通阻塞发生的可能性,达到预测的目的。     

基于HMM的交通运行态势评估及预测方法

文章以西安绕城高速公路为例,应用构建的预测模型,预测路网交通运行态势,并评估预测结果,对比HMM、自回归移动平均模型和灰色马尔可夫模型三种预测方法的准确率和误差,所提出的HMM预测模型不仅能从整体上预测路网交通运行态势的态势值,且准确率更高、误差更小。     

紧邻基坑开挖的PHC桩侧向变形的预测

根据某基坑开挖期间的环境监测的实测数据，运用有限元正交数值试验、回归分析和优化相结合的方法反演了基坑软土的力学参数，研究表明了该方法合理有效；基于反分析的地层参数，预测了紧邻基坑开挖的PHC桩的被动侧向变形，结果表明计算值与实测值较接近。为今后类似工程的预测提供了参考。     

基于岩体指标和掘进参数的TBM净掘进速率预测模型

净掘进速率是TBM施工速度的主要评价指标,与围岩物理力学性质、TBM掘进参数之间存在一定相关性。文章以兰州水源地建设工程输水隧洞双护盾TBM施工为背景,基于现场实测数据,选择岩石单轴抗压强度、抗拉强度、变形模量、泊松比、岩石耐磨性CAI值等岩体指标,以及刀盘推力和刀盘转速等掘进参数,进行TBM净掘进速率与有关影响参数之间的单因素相关性分析,得到相应拟合公式;基于TBM净掘进速率与岩体指标、掘进参数之间的相关性,利用多元非线性回归方法建立了TBM净掘进速率预测模型。通过将兰州水源地建设工程输水隧洞实测TBM净掘进速率和预测结果进行对比,验证了TBM净掘进速率预测模型的合理性。研究结果表明:(1)在复杂的多种地质条件下,TBM净掘进速率与岩石单轴抗压强度、抗拉强度、变形模量、岩石耐磨性CAI值、刀盘推力以及刀盘转速呈负相关关系,与泊松比呈正相关关系;(2)干湿状态对岩石耐磨性CAI值有一定影响,饱和状态下岩石耐磨性CAI值与TBM净掘进速率之间的相关性更显著;(3)建立的多元非线性回归预测模型,预测精度较高,可为相似地质条件下TBM净掘进速率估算提供参考。     

基于随机森林算法的复杂地质条件下盾构掘进消耗研究

为了明确复杂地质条件下地质参数对盾构掘进主要材料消耗量的影响，分别建立随机森林回归算法和随机森林分类算法模型，对油脂、人工、水、电的消耗量和同步注浆系数等参数与地质参数的关系进行模型训练和预测。结果表明：（1）压缩强度是影响人工、水、电和油脂消耗量最重要的地质特征参数，是影响同步注浆系数第二重要的参数；（2）盾构掘进主要材料的消耗量与压缩强度呈现明显的正相关关系；（3）以压缩强度作为单一分级指标，建立复杂地质条件下的盾构掘进定额分级标准，在对主要材料消耗量进行分类统计后发现，在软土地层掘进时的材料消耗量与浙江省定额标准值相近，而在较硬岩段掘进时各项材料消耗量均比标准值高2倍以上；（4）对于人工和水电等非实时记录数据，随机森林回归算法可以获得更加精确的分析结果；而对于油脂消耗量等实时记录数据，随机森林分类算法可以获得更好的预测效果。     

基于AFC数据的城市轨道交通OD客流动态预测

路网客流实时状态是城市轨道交通系统进行日常运营及关键决策的重要基础，针对目前城市轨道交通客流预测中站点层次预测方法较成熟，而客流分布预测较少的情况，提出基于时序神经网络的量测方程OD客流动态预测方法。利用地铁AFC数据，确定时序神经网络预测的最优数据粒度为15mins和最优时间序列阶数为4，以此构建时序神经网络框架，对站点进站量进行预测；对于站点进站客流与OD客流间的时空关联性，主要体现在进站客流的不同去向以及相同去向下不同的到达时间，建立量测方程反应这一联系，将进站客流转化为OD客流，并以成都地铁为例，对路网条件下不同分布特征OD客流进行预测验证，加权相对误差为14.08%，验证了模型的有效性。     

现行沥青路面设计规范存在问题探讨

文章从设计标准、交通荷载、材料参数及设计方法四方面阐述我国现行规范中沥青路面结构设计的原理及存在的问题,并提出了需进一步完善的建议。     

Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections

The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting could be a challenging task. Artificial Neural Network (ANN) could be a good solution to this issue as it is possible to obtain a higher forecasting accuracy within relatively short time through this tool. Traditional methods for traffic flow forecasting generally based on a separated single point. However, it is found that traffic flows from adjacent intersections show a similar trend. It indicates that the vehicle accumulation and dissipation influence the traffic volumes of the adjacent intersections. This paper presents a novel method, which considers the travel flows of the adjacent intersections when forecasting the one of the middle. Computational experiments show that the proposed model is both effective and practical.     

Integrated model for traffic flow forecasting under rainy conditions

Inclement weather, such as heavy rain, significantly affects road traffic flow operation, which may cause severe congestion in road networks in cities. This study investigates the effect of inclement weather, such as rain events, on traffic flow and proposes an integrated model for traffic flow parameter forecasting during such events. First, an analysis of historical observation data indicates that the forecasting error of traffic flow volume has a significant linear correlation with mean precipitation, and thus, forecasting accuracy can be considerably improved by applying this linear correlation to correct forecasting values. An integrated online precipitation‐correction model was proposed for traffic flow volume forecasting based on these findings. We preprocessed precipitation data transformation and used outlier detection techniques to improve the efficiency of the model. Finally, an integrated forecasting model was designed through data fusion methods based on the four basic forecasting models and the proposed online precipitation‐correction model. Results of the model validation with the field data set show that the designed model is better than the other models in terms of overall accuracy throughout the day and under precipitation. However, the designed model is not always ideal under heavy rain conditions. Copyright © 2016 John Wiley & Sons, Ltd.     

Travel demand forecasts improved by using cross-sectional data from multiple time points

Forecasts of travel demand are often based on data from the most recent time point, even when cross-sectional data is available from multiple time points. This is because forecasting models with similar contexts have higher transferability, and the context of the most recent time point is believed to be the most similar to the context of a future time point. In this paper, the author proposes a method for improving the forecasting performance of disaggregate travel demand models by utilising not only the most recent dataset but also an older dataset. The author assumes that the parameters are functions of time, which means that future parameter values can be forecast. These forecast parameters are then used for travel demand forecasting. This paper describes a case study of journeys to work mode choice analysis in Nagoya, Japan, using data collected in 1971, 1981, 1991, and 2001. Behaviours in 2001 are forecast using a model with only the most recent 1991 dataset and models that combine the 1971, 1981, and 1991 datasets. The models proposed by the author using data from three time points can provide better forecasts. This paper also discusses the functional forms for expressing parameter changes and questions the temporal transferability of not only alternative-specific constants but also level-of-service and socio-economic parameters.     

油气田地面集输系统结垢预测模型研究

结垢是油田采出水集输过程中遇到的最严重问题之一,不仅会堵塞管道,而且可能引起垢下腐蚀,带来生产隐患。为了避免结垢造成的不良影响,应该对结垢趋势进行科学而准确的预测。文中对4个常用的预测模型进行分析、总结,并分别针对气田现场实例,将4个模型的预测结果与油气田现场监测结果进行比较,发现Oddo-Tomson饱和指数法预测结果更接近真实情况。将该模型应用在某油田现场,预测结果与现场监测结果一致,对油气田防垢工作有一定的实用价值。     

Temporal transferability: trade-off between data newness and the number of observations for forecasting travel demand

Recent and large amounts of data are crucial for forecasting travel demand. However, in some cases, an older time point may have more data than a more recent time point. A trade-off between older data with a large number of observations and recent data with a smaller number of observations has not been investigated in the context of temporal transferability. In this paper, this trade-off is examined in the context of journey-to-work mode choice behaviours by utilising repeated cross-sectional data collected in Nagoya, Japan. Models estimated utilising different numbers of observations (ranging from 50 to 10,000) obtained at different time points (1971, 1981, and 1991) are applied to the forecasting of behaviours for 2001. Bootstrapping provides insights with statistical meaning. One finding is that the minimum number of observations from a recent time point that is required to produce a forecast statistically significantly better than that produced by older data with a larger number of observations is surprisingly stable, even when the number of observations from the older time point varies considerably. For example, 300–500 stable observations from 1981 produced forecasts that were statistically significantly better than that produced by 500–10,000 wide-ranging observations from 1971. Analysing the trade-off can help determine an efficient survey interval and sample size in an era of declining budgets for travel surveys.     

Forecasting telecommuting

Transportation planners increasingly recognize telecommuting as an important trend. But while they often advocate telecommuting as a transportation demand management strategy, transportation planners have made little progress toward incorporating telecommuting into transportation forecasts, at least partly because of the limited data available. In this paper we explore four alternative methodologies for forecasting telecommuting and discuss the kinds of data that must be collected before these methodologies can be applied. The first approach is trend extrapolation, using curves of technological substitution. Sufficient data are currently available to produce forecasts, albeit highly uncertain forecasts, using this approach. However, even with better data this approach does not address underlying factors and trends that will affect the future of telecommuting. As a result, we explore three additional approaches that should produce more reliable forecasts but which require new data and knowledge about telecommuting: analyzing the characteristics of telecommuters in contrast to nontelecommuters, analyzing factors affecting the individual choice to telecommute, and incorporating telecommuting into traditional transportation forecasting models.     

Adaptive traffic parameter prediction: Effect of number of states and transferability of models

Traffic parameters can show shifts due to factors such as weather, accidents, and driving characteristics. This study develops a model for predicting traffic speeds under these abrupt changes within regime switching framework. The proposed approach utilizes Hidden Markov, Expectation Maximization, Recursive Least Squares Filtering, and ARIMA methods for an adaptive forecasting method. The method is compared with naive and mean updating linear and nonlinear time series models. The model is fitted and tested extensively using 1993 I-880 loop data from California and January 2014 INRIX data from Virginia. Analysis for number of states, impact of number of states on forecasting, prediction scope, and transferability of the model to different locations are investigated. A 5-state model is found to be providing best results. Developed model is tested for 1-step to 45-step forecasts. The accuracy of predictions are improved until 15-step over nonadaptive and mean adaptive models. Except 1-step predictions, the model is found to be transferable to different locations. Even if the developed model is not retrained on different datasets, it is able to provide better or close results with nonadaptive and adaptive models that are retrained on the corresponding dataset.     

The reliability of using the gravity model for forecasting trip distribution

The main purpose of this study is to assess the forecasting capability of the gravity model and to investigate the merit of including K-factors when using the model. Peak hour trip data was obtained for four study year periods 1962, 1971, 1976 and 1981 for the City of Winnipeg. Analysis of the calibration results indicated that the F-factors for the twenty year period were stable within a range of values. In general, however, the K-factors were found to be inconsistent from one prediction period to the next, and when used in forecasting trips they resulted in larger errors than without their use. The validity of using K-factors or the method which has been used to determine them is questionable. It was concluded that while K-factors are very meaningful in theory (as defined), they are not appropriate for use in predicting O-D matrices based on the method by which they are currently estimated (i.e. as a simple ratio). Further study is needed to investigate an alternative method of calibrating the gravity model such as the cell-by-cell regression method.     

Analysis of peak and non‐peak traffic forecasts using combined models

Accurate and timely traffic forecasting is crucial to effective management of intelligent transportation systems (ITS). To predict travel time index (TTI) data, we select six baseline individual predictors as basic combination components. Applying the one‐step‐ahead out‐of‐sample forecasts, the paper proposes several linear combined forecasting techniques. States of traffic situations are classified into peak and non‐peak periods. Based on detailed data analyses, some practical guidance and comments are given in what situation a combined model is better than an individual model or other types of combined models. Indicating which model is more appropriate in each state, persuasive comparisons demonstrate that the combined procedures can significantly reduce forecast error rates. It reveals that the approaches are practically promising in the field. To the best of our knowledge, it is the first time to systematically investigate these approaches in peak and non‐peak traffic forecasts. The studies can provide a reference for optimal forecasting model selection in each period. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets

Fleet operators rely on forecasts of future user requests to reposition empty vehicles and efficiently operate their vehicle fleets. In the context of an on-demand shared-use autonomous vehicle (AV) mobility service (SAMS), this study analyzes the trade-off that arises when selecting a spatio-temporal demand forecast aggregation level to support the operation of a SAMS fleet. In general, when short-term forecasts of user requests are intended for a finer space–time discretization, they tend to become less reliable. However, holding reliability constant, more disaggregate forecasts provide more valuable information to fleet operators. To explore this trade-off, this study presents a flexible methodological framework to evaluate and quantify the impact of spatio-temporal demand forecast aggregation on the operational efficiency of a SAMS fleet. At the core of the methodological framework is an agent-based simulation that requires a demand forecasting method and a SAMS fleet operational strategy. This study employs an offline demand forecasting method, and an online joint AV-user assignment and empty AV repositioning strategy. Using this forecasting method and fleet operational strategy, as well as Manhattan, NY taxi data, this study simulates the operations of a SAMS fleet across various spatio-temporal aggregation levels. Results indicate that as demand forecasts (and subregions) become more spatially disaggregate, fleet performance improves, in terms of user wait time and empty fleet miles. This finding comes despite demand forecast quality decreasing as subregions become more spatially disaggregate. Additionally, results indicate the SAMS fleet significantly benefits from higher quality demand forecasts, especially at more disaggregate levels.