Estimation of the effects of aircraft noise on residential satisfaction

This study assesses the effects of aircraft noise on residential satisfaction, an important indicator of subjective well-being. A structural equation model is specified that estimates the relationships between objective variables, noise annoyance variables and residential satisfaction. Secondary data-analysis is used to estimate the model. The survey was conducted in 1996/1997 among the population living within a 25-km radius of Amsterdam Schiphol, the largest airport in the Netherlands. The effect of aircraft noise annoyance is found to be relatively small. In addition, the objective level of aircraft noise exposure is found to be a better predictor of residential satisfaction than its subjective counterpart. The most important determinants of residential satisfaction are found to be road traffic noise annoyance, age and neighbor noise annoyance.     

Urban travel time reliability at different traffic conditions

The decision making of travelers for route choice and departure time choice depends on the expected travel time and its reliability. A common understanding of reliability is that it is related to several statistical properties of the travel time distribution, especially to the standard deviation of the travel time and also to the skewness. For an important corridor in Changsha (P.R. China) the travel time reliability has been evaluated and a linear model is proposed for the relationship between travel time, standard deviation, skewness, and some other traffic characteristics. Statistical analysis is done for both simulation data from a delay distribution model and for real life data from automated number plate recognition (ANPR) cameras. ANPR data give unbiased travel time data, which is more representative than probe vehicles. The relationship between the mean travel time and its standard deviation is verified with an analytical model for travel time distributions as well as with the ANPR travel times. Average travel time and the standard deviation are linearly correlated for single links as well as corridors. Other influence factors are related to skewness and travel time standard deviations, such as vehicle density and degree of saturation. Skewness appears to be less well to explain from traffic characteristics than the standard deviation is.     

Measurement biases in panel data

The objective of this paper is to examine reporting errors in panel data obtained from multi-day travel diaries. A distinction is made between within and between wave biases. The former leads to an increase in under-reporting associated with the number of days the diary is kept. The latter is related to the number of waves respondents have been participating, so-called panel experience. These biases imply that observed mobility changes between waves are partly due to reporting errors: without controlling for them, changes in mobility can not be inferred from the data. An important cause of these measurement errors is the increase in the number of days on which no trips at all were reported. In addition, shorter trips and less complex chains are more susceptible to underreporting. The methodology used in this paper provides a means of dealing with these problems. Attrition is taken into account by a rather simple measure. The paper concludes with a number of suggestions for sample and survey design.     

基于粗集和神经网络耦合的短时交通流预测

比较分析神经网络和粗糙集在数据处理过程中的各自优缺点,提出一种基于二者强耦合集成方式的短时交通流预测模型。首先利用粗集对获取的交通流数据进行预处理,简化神经网络训练样本数据集并通过粗集属性约简提取决策规则;其次,利用所提取的规则直接确定神经网络的隐层数、隐层节点数及节点的相互关系;最后训练神经网络用于短时交通流预测。通过与单纯利用神经网络预测的结果进行比较,发现该模型降低了网络训练时间,提高了预测精度。     

Biases in response over time in a seven-day travel diary

Data from multi-day travel or activity diaries might be biased if recording inaccuracies and tendencies for respondents to skip certain types of trips or activities increases (or decreases) from day-to-day over the diary period. One objective of the research reported here is to test for such temporal biases in a seven-day travel diary. A second objective is to calculate correction factors which can be applied to the data in the case that biases are found. The analyses were conducted using regression and analysis-of-variance techniques. The variables investigated included total trips per day, total travel time per day, and trips per day by various modes (such as walking, car driver and car passenger). Results showed that most biases per capita statistics are due to increases over time in the percentage of respondents reporting no travel at all for an entire day. However, even after accounting for this bias by measuring statistics in terms of per mobile person, there remains a decrease over time of about 3.5 percent per day in the reporting of walking trips. This appears to be the main factor in the overall bias of about one percent per day in total trips per mobile person per day. No significant differences were found among population segments in terms of the levels of their biases.     

����·�������г�ʱ��Ԥ���о�

本文提出了一种基于状态空间神经网络（SSNN）和拓展卡尔曼滤波（EKF）的混合式行程时间预测模型。以往的研究表明，状态空间神经网络能够较好的处理复杂的非线性时空问题。然而，状态空间神经网络需要大量的历史数据作为离线训练之用。其不足之处在于，首先是需要花费大量的时间和精力去收集、准备数据，以及训练神经网络。其次，输入输出随着时间不断增加，训练过程需要不断的从新重复。为了提高状态空间神经网络的有效性，扩展卡尔曼滤波代替了传统的方法来对神经网络进行训练。荷兰的一条城市道路被选择为模型验证的试验路段。通过与另外两个预测模型之间的对比验证，该模型的预测能力能够达到满意的有效性、准确性和鲁棒性。     

The most likely trip matrix estimated from traffic counts

For a large number of applications conventional methods for estimating an origin destination matrix become too expensive to use. Two models, based on information minimisation and entropy maximisation principles, have been developed by the authors to estimate an O-D matrix from traffic counts. The models assume knowledge of the paths followed by the vehicles over the network. The models then use the traffic counts to estimate the most likely O-D matrix consistent with the link volumes available and any prior information about the trip matrix. Both models can be used to update and improve a previous O-D matrix. An algorithm to find a solution to the model is then described. The models have been tested with artificial data and performed reasonably well. Further research is being carried out to validate the models with real data.     

Implementation and validation of a three degrees of freedom steering-system model in a full vehicle model

This paper describes the coupling between a three degrees of freedom steering-system model and a multi-body truck model. The steering-system model includes the king-pin geometry to provide the correct feedback torque from the road to the steering-system. The steering-system model is combined with a validated tractor semi-trailer model. An instrumented tractor semi-trailer has been tested on a proving ground and the steering-wheel torque, pitman-arm angle, king-pin angles and drag-link force have been measured during steady-state cornering, a step steer input and a sinusoidal steering input. It is shown that the steering-system model is able to accurately predict the steering-wheel torque for all tests and the vehicle model is accurate for vehicle motions up to a frequency where the lateral acceleration gain is minimum. Even though the vehicle response is not accurate above this frequency, the steering-wheel torque is still represented accurately.     

Quantification of motorway capacity variation: influence of day type specific variation and capacity drop

Many road authorities work with static values for road capacities, while it has been proven that capacity is not a fixed quantity. At the same time, there is an increasing need for accurate stochastic input for traffic models, such as the variation in road capacity. In this paper, a methodological framework with a conceptual model for practical stochastic capacity estimation is presented, and a quantification of motorway capacity variation is given for the influence of day‐type specific variations in capacity values. The results of the analysis show that there is a reduction in motorway breakdown capacity of 4% on weekend days in comparison with workdays. Furthermore, a capacity decrease of 8% was found for the discharge capacity in comparison with workdays. The analysis further shows that the breakdown capacity on holidays is not significantly lower than on workdays. Discharge capacity and capacity drops are also derived in each case. The results show that the capacity is significantly different depending on the type of day. A quantification of these differences is given in the form of a Weibull capacity estimation fit for each type‐of‐day scenario. Further consideration of the implications and applications of the framework is also given. Copyright © 2015 John Wiley & Sons, Ltd.     

Cumulative impacts of road developments in floodplains

This paper investigates the impact of differing road development strategies in floodplains for increasing levels of road expansion, focusing on hydraulic characteristics of floods such as water level, velocity and inundation duration and extent. Cumulative impacts are presented in term of both a resistant approach where road transport infrastructure has the secondary purpose of flood dykes and, a resilience approach that maintains the floodplain hydraulics through the use of flow through structures. Each method is examined in the context of road development in the Cambodian Mekong floodplain. Results indicate that resistance approaches necessitate higher levels of road structures, designed to higher technical specifications, whilst resilience approaches maintain the hydraulic character of floodplains but require the inclusion of well-designed flow through structures with localized scour protection.