马尔柯夫灰色残差模型在剐构桥控制中的应用

文章结合罗天乐特大桥的马尔柯夫灰色残差模型预测实例，介绍马尔柯夫灰色残差模型在线性控制中的原理、计算及应用过程，并根据实际监控经验提出了建议。罗天乐特大桥的具体监控应用实践表明，马尔柯夫灰色残差模型预测精度高，能较好地应用于大跨连续刚构桥的施工线形控制。     

基于灰色理论的隧道围岩收敛预测

文章根据隧道围岩收敛监测信息,应用灰色系统原理和方法,建立了GM(1,1)模型并进行了预测。预测结果表明,灰色模型预测值与实测值吻合程度较好,能为隧道的动态设计和信息化施工提供决策依据。     

城市燃气长期负荷预测模型的灰色方法

提出利用灰色系统理论 ,建立动态等维灰数递补灰色燃气长期负荷预测模型 ,解决了我国城市燃气长期负荷可用历史数据较少 ,序列的完整性及数据资料的可靠性较低 ,规划设计中往往又需要对未来数年后的用气负荷作出预测的问题。实例预测计算表明 :动态等维灰数递补灰色燃气长期负荷预测模型和方法对城市燃气长期用气负荷的预测具有较好的适应性和较高的精度。     

基于缓冲算子修正的新维无偏灰色模型的洞室围岩位移预测

洞室围岩位移预测是保证施工安全以及工程质量的重要措施。文章针对洞室围岩变形具有时间序列的单调增长性和随机动态性等特征,基于无偏灰色GM(1,1)预测模型的构建理论,引入指数型弱化缓冲算子来消除围岩位移原始数据序列所受的随机干扰,同时结合新信息优先的原理,提出了一种基于缓冲算子修正的新维无偏灰色GM(1,1)模型对围岩位移进行预测并将该模型应用于旁多水利枢纽工程洞室围岩位移时序预测中。结果表明,该模型具有精度高、科学可靠的特点,为洞室围岩位移预测提供了新的途径。     

灰色BP网络串联式组合模型在铁路客货运输量预测中的应用

针对铁路客货运输量发展趋势的研究,建立一种基于灰色理论和BP神经网络的串联式组合预测模型。该模型首先用同一组数据序列建立不同参数的灰色方程,然后用各灰色方程分别预测,最后将各灰色方程预测的结果进行BP神经网络非线性组合,形成串联式组合预测模型。对湖南省铁路客货运量进行分析预测,结果表明:该组合模型预测的准确性高于单独使用灰色模型的准确性,是一种可靠有效的预测方法。     

天然气消费量的灰色模型预测

介绍了利用灰色理论预测天然气消费量的模型,包括GM(1,1)模型和动态等维灰数递补灰色预测模型,对模型进行了求解,介绍了模型精度的检验方法,并用实例加以验证,同时与实际消费量进行了对比.计算结果表明:此方法预测天然气消费量既简单又准确,有较好的适应性和较高的精度,对天然气输配管网的优化运行和统一调度管理具有重要的参考意义.     

灰色预测模型在城市道路交通事故中的应用

详细介绍了灰色模型的原理和特点,根据交通事故的发生特点,探讨了灰色模型在道路交通事故预测中的具体应用,并利用此模型对青岛市某地区的交通事故进行预测,建立了灰色预测模型,根据实际事故数据与预测值进行了比较,灰色预测模型的精度比较好.     

基于NNBR模型的纵向地表沉降动态预测

针对现有预测地表沉降方法的不足,结合土压平衡式盾构一个掘进周期中不同阶段引起地表沉降的状况不同,提出了应用最近邻抽样回归模型(NNBR模型)对纵向地表沉降进行动态预测的方法。文章初步总结出土舱压力和平均掘进速度、注浆压力和平均掘进速度、监测时间可分别作为开挖面前方变形、盾尾空隙沉降、固结沉降三个阶段的主要预测因子,并列出了相应阶段的相似计算公式。利用该方法对某一工程实例的固结沉降阶段进行了预测,在样本数较少的情况下,预测结果最大相对误差只有14.04%,从而论证了基于NNBR模型的动态预测方法的准确性和可行性。     

隧道变形预测的灰色与回归模型对比分析

模型预测法是目前常用的隧道围岩变形预测的方法之一。文章结合广梧高速公路茶林顶隧道工程实例,建立GM（1,1）灰色模型、GM（2,1）灰色模型和双曲函数回归模型分别对隧道围岩变形进行预测,并对各模型的预测情况进行对比分析。结果表明,不论是从短期还是从长期看,GM（1,1）灰色模型都体现了优越的模拟和预测效果,且建立预测模型时不需要大量的统计数据,可应用于工程实际。     

灰色系统理论在原油管道结蜡规律预测模型的应用

原油结蜡是影响管道安全、经济和高效运行的一个重要因素。为了对输油管道的结蜡状况进行预测,掌握输油管道结蜡的基本规律,应用灰色系统理论中的模型对输油管道结蜡速度和结蜡厚度等指标的实际统计数据进行了灰色动态拟合,建立了相应的灰色微分方程和时间响应函数。结果表明：残差小于2%,模型精度满足工程实际需要。     

Dynamic network loading: A stochastic differentiable model that derives link state distributions

We present a dynamic network loading model that yields queue length distributions, accounts for spillbacks, and maintains a differentiable mapping from the dynamic demand on the dynamic queue lengths. The model also captures the spatial correlation of all queues adjacent to a node, and derives their joint distribution. The approach builds upon an existing stationary queueing network model that is based on finite capacity queueing theory. The original model is specified in terms of a set of differentiable equations, which in the new model are carried over to a set of equally smooth difference equations. The physical correctness of the new model is experimentally confirmed in several congestion regimes. A comparison with results predicted by the kinematic wave model (KWM) shows that the new model correctly represents the dynamic build-up, spillback and dissipation of queues. It goes beyond the KWM in that it captures queue lengths and spillbacks probabilistically, which allows for a richer analysis than the deterministic predictions of the KWM. The new model also generates a plausible fundamental diagram, which demonstrates that it captures well the stationary flow/density relationships in both congested and uncongested conditions.     

New insights on random regret minimization models

This paper develops new methodological insights on Random Regret Minimization (RRM) models. It starts by showing that the classical RRM model is not scale-invariant, and that – as a result – the degree of regret minimization behavior imposed by the classical RRM model depends crucially on the sizes of the estimated taste parameters in combination with the distribution of attribute-values in the data. Motivated by this insight, this paper makes three methodological contributions: (1) it clarifies how the estimated taste parameters and the decision rule are related to one another; (2) it introduces the notion of “profundity of regret”, and presents a formal measure of this concept; and (3) it proposes two new family members of random regret minimization models: the μRRM model, and the Pure-RRM model. These new methodological insights are illustrated by re-analyzing 10 datasets which have been used to compare linear-additive RUM and classical RRM models in recently published papers. Our re-analyses reveal that the degree of regret minimizing behavior imposed by the classical RRM model is generally very limited. This insight explains the small differences in model fit that have previously been reported in the literature between the classical RRM model and the linear-additive RUM model. Furthermore, we find that on 4 out of 10 datasets the μRRM model improves model fit very substantially as compared to the RUM and the classical RRM model.     

A simultaneous route-level transit patronage model: demand,supply, and inter-route relationship

In this paper we present a route-level patronage model that incorporates transit demand, supply and inter-route effects in a simultaneous system. The model is estimated at the route-segment level by time of day and direction. The results show strong simultaneity among transit demand, supply and competing routes. Transit ridership is affected by the level of service, which in turn is determined by current demand and ridership in the previous year. The model demonstrates that a service improvement has a twofold impact on ridership; it increases ridership on the route with service changes, but it also reduces the ridership on competing routes so that the net ridership change is small. The model is thus useful for both system-level analysis and route-level service planning.     

A parameterized consideration set model for airport choice: an application to the San Francisco Bay Area

Airport choice is an important air travel-related decision in multiple airport regions. This paper proposes the use of a probabilistic choice set multinomial logit (PCMNL) model for airport choice that generalizes the multinomial logit model used in all earlier airport choice studies. The paper discusses the properties of the PCMNL model, and applies it to examine airport choice of business travelers residing in the San Francisco Bay Area. Substantive policy implications of the results are discussed. Overall, the results indicate that it is important to analyze the choice (consideration) set formation of travelers. Failure to recognize consideration effects of air travelers can lead to biased model parameters, misleading evaluation of the effects of policy action, and a diminished data fit.     

Study on mean-standard deviation shortest path problem in stochastic and time-dependent networks: A stochastic dominance based approach

This paper studies a mean-standard deviation shortest path model, also called travel time budget (TTB) model. A route’s TTB is defined as this route’s mean travel time plus a travel time margin, which is the route travel time’s standard deviation multiplied with a factor. The TTB model violates the Bellman’s Principle of Optimality (BPO), making it difficult to solve it in any large stochastic and time-dependent network. Moreover, it is found that if path travel time distributions are skewed, the conventional TTB model cannot reflect travelers’ heterogeneous risk-taking behavior in route choice. This paper proposes to use the upper or lower semi-standard deviation to replace the standard deviation in the conventional TTB model (the new models are called derived TTB models), because these derived TTB models can well capture such heterogeneous risk-taking behavior when the path travel time distributions are skewed. More importantly, this paper shows that the optimal solutions of these two derived TTB models must be non-dominated paths under some specific stochastic dominance (SD) rules. These finding opens the door to solve these derived TTB models efficiently in large stochastic and time-dependent networks. Numerical examples are presented to illustrate these findings.     

LUTI operational models review based on the proposition of an a priori ALUTI conceptual model

ABSTRACT

This paper focuses on understanding to what extent the components of LUTI models and their mutual interactions are conceptually represented by eight operational LUTI models. This is important for the understanding of LUTI models’ mechanisms, firstly because it may reduce communication barriers between planning communities, secondly because it may help us understand the models’ applicability, and thirdly it may highlight the models’ shortcomings and point for future research. We present a discussion about what subsystems should be considered for LUTI modelling, from which we derived an “a priori” conceptual ALUTI model (incorporating Activities, besides Land Use and Transport). By comparing the rationale behind each model with this conceptual model, we establish the basis for our review, focussing on whether these models incorporate the ALUTI components, its inner workings and the relationships between these components. Results indicate three main limitations of the reviewed models. First, models not always adequately include all the components of the a priori ALUTI model. Second, the ALUTI subsystems’ internal functions are not explicitly modelled in several of the models reviewed, making it difficult to evaluate how planning decisions affect the subsystem. Third, only few models recognise all mutual interactions, especially in respect to the Activity subsystem.     

Mitigation of disruptions in public transit by Bee Colony Optimization

Dispatchers in many public transit companies face the daily problem of assigning available buses to bus routes under conditions of bus shortages. In addition to this, weather conditions, crew absenteeism, traffic accidents, traffic congestion and other factors lead to disturbances of the planned schedule. We propose the Bee Colony Optimization (BCO) algorithm for mitigation of bus schedule disturbances. The developed model takes care of interests of the transit operator and passengers. The model reassigns available buses to bus routes and, if it is allowed, the model simultaneously changes the transportation network topology (it shortens some of the planned bus routes) and reassigns available buses to a new set of bus routes. The model is tested on the network of Rivera (Uruguay). Results obtained show that the proposed algorithm can significantly mitigate disruptions.     

A theoretical travel-time model for flexible-route buses

A theoretical model for estimating the expectation and variance of buses' running times under a flexibly-routed mode of service is proposed. The model is based on a probabilistic concept that adequately accommodates the usual randomness in the number and location of passengers served during successive vehicle trips. A few simplifications are embodied in the model but it can serve as a basis for a more refined model such as a computer simulation model that can be used in designing real bus systems.The physical setting assumed in the model is a rectangular grid road network where all houses face the side streets as in some suburban regions. Because it oversimplifies real-life situations, this assumption would need to be relaxed to make the model applicable to more general cases.Both the partially flexible-route service where some passengers are captive to fixed-route service and others are served at their doorsteps, and the fully flexible-route service where all passengers are served at their doorsteps, are studied. In each case, a very simple routing convention that can be conveniently executed by the bus drivers is assumed. The proposed travel time model confirms the intuitively correct phenomenon that when the concentration of passenger trip-ends is very high, the vehicle-route degenerates into a fixed-route in which the buses visit all possible loading points within the service area.     

A probability analysis of some spatial interaction models

This paper derives several well-known spatial models in a framework based upon the laws of conditional probability analysis. In particular, it relates the structure of some existing models of trip distribution, elementary residential location and residential location with capacity constraints, to either the multinomial or hypergeometric probability distributions. The major changes from traditional methods for developing these models deal with the derivation and form of the objective function for each interaction model. This alternative analysis reaches a wider audience than that only familiar with entropy methods and leads to several improvements in generality. Further, when population constraints were imposed on residential location models, it was found that the model which developed naturally from the approach taken in the paper contained as a special case the model proposed by Dacey and Norcliffe and not the Wilson model.     

Validation of an extended discrete first-order model with variable speed limits

This paper validates the prediction model embedded in a model predictive controller (MPC) of variable speed limits (VSLs). The MPC controller was designed based on an extended discrete first-order model with a triangular fundamental diagram. In our previous work, the extended discrete first-order model was designed to reproduce the capacity drop and the propagation of jam waves, and it was validated with reasonable accuracy without the presence of VSLs. As VSLs influence traffic dynamics, the dynamics including VSLs needs to be validated, before it can be applied as a prediction model in MPC. For conceptual illustrations, we use two synthetic examples to show how the model reproduces the key mechanisms of VSLs that are applied by existing VSL control approaches. Furthermore, the model is calibrated by use of real traffic data from Dutch freeway A12, where the field test of a speed limit control algorithm (SPECIALIST) was conducted. In the calibration, the original model is extended by using a quadrangular fundamental diagram which keeps the linear feature of the model and represents traffic states at the under-critical branch more accurately. The resulting model is validated using various traffic data sets. The accuracy of the model is compared with a second-order traffic flow model. The performance of two models is comparable: both models reproduce accurate results matching with real data. Flow errors of the calibration and validation are around 10%. The extended discrete first-order model-based MPC controller has been demonstrated to resolve freeway jam waves efficiently by synthetic cases. It has a higher computation speed comparing to the second-order model-based MPC.