Online Parameter Identification for Traffic Simulation via Lagrangian Sensing

This paper deals with parameter identification for traffic models. We present a method to estimate model parameters based on Lagrangian measurement data that can be used for online traffic simulation. In the context of Lagrangian sensing the sensor placement is reduced to the optimum choice of measurement vehicles currently moving with the traffic flow. Identifiability is investigated using the Fisher Information Matrix (FIM). Simulation results are presented and validated via simulation based on a car-following-model. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

COMPARISON OF TWO MODELS AND TWO ESTIMATION METHODS FOR CATCH AND EFFORT DATA

We consider the problem of estimating the optimal steady effort level from a time series of catch and effort data, taking account of errors in the observation of the “effective effort” as well as randomness in the stock-production function. The “total least squares” method ignores the time series nature of the data, while the “approximate likelihood” method takes it into account. We compare estimation schemes based upon these two methods by applying them to artificial data for which the “correct” parameters are known. We use a similar procedure to compare the effectiveness of a “power model” for stock and production with the “Ricker model.” We apply these estimation methods to some sets of real data, and obtain an interval estimate of the optimal effort.     

一种基于收费数据的断面流量估算方法

高速公路的流量是反映高速公路交通状态的一个重要参数.目前,针对高速公路断面流量的采集主要依靠人工方式或者通过安装一些车流检测设备来获得,成本较高.结合高速公路收费系统的特点,通过对收费数据的统计分析,提出一种基于收费数据估算高速公路断面流量的方法.首先,对收费数据进行初步的处理,从中计算出行驶时间;然后,根据收费数据中的车辆信息统计出各个OD的交通流量;最后,结合各个收费站出入口的位置信息、路段行驶速度信息以及OD流量来估算断面的交通流量.     

Estimating freeway traffic measures from mobile phone location data

The worldwide propagation of mobile phone and the rapid development of location technologies have provided the chance to monitor freeway traffic conditions without requiring extra infrastructure investment. Over the past decade, a number of research studies and operational tests have attempted to investigate the methods to estimate traffic measures using information from mobile phone. However, most of these works ignored the fact that each vehicle has more than one phone due to the rapid popularity of mobile phone. This paper considered the circumstance of multi-phones and proposed a relatively simplistic clustering technique to identify whether phones travel in the same vehicle. By using this technique, mobile phone data can be used to determine not only speed, but also vehicle counts by type, and therefore density. A complex simulation covering different traffic condition and location accuracy of mobile phone has been developed to evaluate the proposed approach. Simulation results indicate that location accuracy of mobile phone is a crucial factor to estimate accurate traffic measures in case of a given location frequency and the number of continuous location data. In addition, traffic demand and clustering method have a certain effect on the accuracy of traffic measures.     

A Note on a Traffic Counting Distribution

In this paper the author formulates and derives a discrete traffic counting distribution and a passing rule which lead to bunches of zero or one “fast” vehicle behind a “slow” vehicle. “Slow” vehicles are distributed in a Poisson fashion and, except for those instants at which passing occurs, the constrained or “fast” vehicles follow immediately behind a “slow” one. The counting distribution is a simple example of one in which successive inter-vehicle spacings have the Markov property.     

Chaos in a dynamic model of urban transportation network flow based on user equilibrium states

In this study, we investigate the dynamical behavior of network traffic flow. We first build a two-stage mathematical model to analyze the complex behavior of network flow, a dynamical model, which is based on the dynamical gravity model proposed by Dendrinos and Sonis [Dendrinos DS, Sonis M. Chaos and social-spatial dynamic. Berlin: Springer-Verlag; 1990] is used to estimate the number of trips. Considering the fact that the Origin–Destination (O–D) trip cost in the traffic network is hard to express as a functional form, in the second stage, the user equilibrium network assignment model was used to estimate the trip cost, which is the minimum cost of used path when user equilibrium (UE) conditions are satisfied. It is important to use UE to estimate the O–D cost, since a connection is built among link flow, path flow, and O–D flow. The dynamical model describes the variations of O–D flows over discrete time periods, such as each day and each week. It is shown that even in a system with dimensions equal to two, chaos phenomenon still exists. A “Chaos Propagation” phenomenon is found in the given model.     

Investigation of a traffic modelling inspired two dimensional map

Commencing with a class of differential equation based car following models for traffic flow a simplified discrete time model on a closed loop is found. A two dimensional map, containing two free parameters, and corresponding to the behaviour of two vehicles on the loop is investigated. Bounds on the prisoner set of the map are determined, and a number of strange attractors are presented. The variation of largest Lyapunov exponent over a range of parameter space is considered. The resulting behaviour of the map is considered within a traffic flow context.     

Locating Active Sensors on Traffic Networks

Sensors are used to monitor traffic in networks. For example, in transportation networks, they may be used to measure traffic volumes on given arcs and paths of the network. This paper refers to an active sensor when it reads identifications of vehicles, including their routes in the network, that the vehicles actively provide when they use the network. On the other hand, the conventional inductance loop detectors are passive sensors that mostly count vehicles at points in a network to obtain traffic volumes (e.g., vehicles per hour) on a lane or road of the network.This paper introduces a new set of network location problems that determine where to locate active sensors in order to monitor or manage particular classes of identified traffic streams. In particular, it focuses on the development of two generic locational decision models for active sensors, which seek to answer these questions: (1) “How many and where should such sensors be located to obtain sufficient information on flow volumes on specified paths?”, and (2) “Given that the traffic management planners have already located count detectors on some network arcs, how many and where should active sensors be located to get the maximum information on flow volumes on specified paths?”The problem is formulated and analyzed for three different scenarios depending on whether there are already count detectors on arcs and if so, whether all the arcs or a fraction of them have them. Location of an active sensor results in a set of linear equations in path flow variables, whose solution provide the path flows. The general problem, which is related to the set-covering problem, is shown to be NP-Hard, but special cases are devised, where an arc may carry only two routes, that are shown to be polynomially solvable. New graph theoretic models and theorems are obtained for the latter cases, including the introduction of the generalized edge-covering by nodes problem on the path intersection graph for these special cases. An exact algorithm for the special cases and an approximate one for the general case are presented.     

Instability of cooperative adaptive cruise control traffic flow: A macroscopic approach

This paper proposes a macroscopic model to describe the operations of cooperative adaptive cruise control (CACC) traffic flow, which is an extension of adaptive cruise control (ACC) traffic flow. In CACC traffic flow a vehicle can exchange information with many preceding vehicles through wireless communication. Due to such communication the CACC vehicle can follow its leader at a closer distance than the ACC vehicle. The stability diagrams are constructed from the developed model based on the linear and nonlinear stability method for a certain model parameter set. It is found analytically that CACC vehicles enhance the stabilization of traffic flow with respect to both small and large perturbations compared to ACC vehicles. Numerical simulation is carried out to support our analytical findings. Based on the nonlinear stability analysis, we will show analytically and numerically that the CACC system better improves the dynamic equilibrium capacity over the ACC system. We have argued that in parallel to microscopic models for CACC traffic flow, the newly developed macroscopic will provide a complete insight into the dynamics of intelligent traffic flow.     

A dynamic network loading model for mesosimulation in transportation systems

Flow propagation models can be divided into static and dynamic network loading models. Different approaches to dynamic network loading problem formulated in the literature point out models that can be classified as disaggregate or aggregate.Applying aggregate models, it is possible to trace implicitly or explicitly vehicles movements. The second case concerns mesoscopic models. These models consider the traffic as a sequence of “packets” of vehicles. Two approaches can be followed:

• (a)continuous packets, where vehicles are distributed inside each packet, defined by the head and the tail points;
• (b)discrete packets, where all users belonging to a packet are grouped and represented by a single point, for instance the head.

In this paper, a mesoscopic model based on discrete packets has been developed, taking into account the vehicles acceleration. The proposed model, assuming discrete packets and uniformly accelerated movement, appears lifelike in the representation of outflow dynamics and quite easy to calculate.     

Quantile regression metamodeling: Toward improved responsiveness in the high-tech electronics manufacturing industry

Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input–output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment “on demand” to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.     

基于系统动力学的新能源汽车“绿色悖论”效应研究

面对日益严峻的环境问题, 为减少机动车排放污染气体, 我国各地政府纷纷出台相应鼓励发展新能源汽车的政策。新能源汽车主要是采用电作为推动力, 能够有效降低空气污染程度。本文运用系统动力学方法建立模型, 从系统的角度出发, 采用VENSIM软件进行仿真模拟, 探究产生“绿色悖论”的原因。动态仿真结果显示, 政策对新能源汽车的影响实施阶段有显著效果, 但是随着时间的推移, 政策的边际效用会逐渐降低; 新能源汽车总量的上升直接吞噬甚至超出了其节约的资源, 而且带来了停车位紧张、道路拥挤度上升的不良后果。     

Problems with the estimation of stochastic differential equations using structural equations models

The present paper deals with the identification and maximum likelihood estimation of systems of linear stochastic differential equations using panel data. So we only have a sample of discrete observations over time of the relevant variables for each individual. A popular approach in the social sciences advocates the estimation of the “exact discrete model” after a reparameterization with LISREL or similar programs for structural equations models. The “exact discrete model” corresponds to the continuous time model in the sense that observations at equidistant points in time that are generated by the latter system also satisfy the former. In the LISREL approach the reparameterized discrete time model is estimated first without taking into account the nonlinear mapping from the continuous to the discrete time parameters. In a second step, using the inverse mapping, the fundamental system parameters of the continuous time system in which we are interested, are inferred. However, some severe problems arise with this “indirect approach”. First, an identification problem may arise in multiple equation systems, since the matrix exponential function denning some of the new parameters is in general not one‐to‐one, and hence the inverse mapping mentioned above does not exist. Second, usually some sort of approximation of the time paths of the exogenous variables is necessary before the structural parameters of the system can be estimated with discrete data. Two simple approximation methods are discussed. In both approximation methods the resulting new discrete time parameters are connected in a complicated way. So estimating the reparameterized discrete model by OLS without restrictions does not yield maximum likelihood estimates of the desired continuous time parameters as claimed by some authors. Third, a further limitation of estimating the reparameterized model with programs for structural equations models is that even simple restrictions on the original fundamental parameters of the continuous time system cannot be dealt with. This issue is also discussed in some detail. For these reasons the “indirect method” cannot be recommended. In many cases the approach leads to misleading inferences. We strongly advocate the direct estimation of the continuous time parameters. This approach is more involved, because the exact discrete model is nonlinear in the original parameters. A computer program by Hermann Singer that provides appropriate maximum likelihood estimates is described.     

Parametric modeling of protein–DNA binding kinetics: A discrete event based simulation approach

To understand the stochastic behavior of biological systems, we adopt an “in silico” stochastic event based simulation methodology that can determine the temporal dynamics of different molecules. The main requirement for this technique is the event execution time models for the different biological functions of the system. This paper presents a parametric model to determine the execution time of one such biological function: protein–DNA binding. This biological function is modeled as a combination of microlevel biological events using a coarse-grained probability measure to estimate the stochastic parameters of the function. Our model considers the actual binding mechanism along with some approximated protein and DNA structural information. We use a collision theory based approach to transform the thermal and concentration gradients of this biological function into their corresponding probability measure. This information theoretic approach significantly removes the complexity of the classical protein sliding along the DNA model, improves the speed of computation and can bypass the speed-stability paradox. This model can produce acceptable estimates of DNA–protein binding time necessary for our event based stochastic system simulator where the higher order (more than second order statistics) uncertainties can be ignored. The results show good correspondence with available experimental estimates. The model depends very little on experimentally generated rate constants and brings the important biological parameters and functions into consideration. We also present some “in silico” results showing the effects of protein–DNA binding on gene expression in prokaryotic cells.     

带时空相关性分析的行车时间估计模型

基于流体动力学方程的行车时间估计模型不能很好地反映真实的行车时间,需要对其进行一定的改进.在对交通流进行流体动力学建模的基础之上,引入对高速公路路网中不同路段之间的行车时间相关性和同一路段不同季节、不同时段的行车时间相关性分析,建立了带时空相关性分析的时间估计模型,使用统计学的方法消除动力学模型的误差.     

简单闭环路网交通流定常解

基于在分岔路口满足用户均衡原理的假定,研究了由三条路段和两个交叉路口组成的简单闭环路网的交通流定常解问题,发现定常解参数及其性态依赖于路网上的车流总数:当车流总数不大于第一个临界值,或不小于第二个临界值时,定常解在每一条路段上均为密度取常数的平凡解;否则,在瓶颈路口(上游最大流量大于下游最大流量的路口)的上游路段将产生...     

Airport management: taxi planning

The Taxi Planning studies the aircraft routing and scheduling on the airport ground. This is a dynamic problem, which must be updated almost every time that a new aircraft enters or exits the system. Taxi Planning has been modelled using a linear multicommodity flow network model with side constraints and binary variables. The flow capacity constraints are used to represent the conflicts and competence between aircrafts using a given airport capacity. The “Branch and Bound” and “Fix and Relax” methodologies have been used. The computational tests have been run at the Madrid-Barajas airport, using actual data from the airport traffic.     

A fuzzy set-based approach to origin–destination matrix estimation in urban traffic networks with imprecise data

An important issue in the management of urban traffic networks is the estimation of origin–destination (O–D) matrices whose entries represent the travel demands of network users. We discuss the challenges of O–D matrix estimation with incomplete, imprecise data. We propose a fuzzy set-based approach that utilises successive linear approximation. The fuzzy sets used have triangular membership functions that are easy to interpret and enable straightforward calibration of the parameters that weight the discrepancy between observed data and those predicted by the proposed approach. The method is potentially useful when prior O–D matrix entry estimates are unavailable or scarce, requiring trip generation information on origin departures and/or destination arrivals, leading to multiple modelling alternatives. The method may also be useful when there is no O–D matrix that can be user-optimally assigned to the network to reproduce observed link counts exactly. The method has been tested on some numerical examples from the literature and the results compare favourably with the results of earlier methods. It has also been successfully used to estimate O–D matrices for a practical urban traffic network in Brazil.     

A CUSUM test for detecting change in the transfer functions of open loop stochastic systems

A cumulative sum test for detecting change in the transfer function of open loop stochastic systems is proposed. The test is based on the logarithmic transformation of the “evolutionary gain-spectra” estimate. A design relation between the band width parameters of the evolutionary spectral density estimate and the average run length of the cumulative sum test is obtained. This cumulative sum technique has been applied to four simulated systems, one of which is completely time invariant and the other remaining three had change points at different time points.     

Fuzzy variant of a statistical test point Kalman filter

In this paper, we propose the conceptual use of fuzzy clustering techniques as iterative spatial methods to estimate a posteriori statistics in place of the weighted averaging scheme of the Unscented Kalman filter. Specifically, instead of a linearization methodology involving the statistical linear regression of the process and measurement functions through some deterministically chosen set of test points (sigma points) contained within the “uncertainty region” around the state estimate, we present a variant of the Unscented transformation involving fuzzy clustering techniques which will be applied to the test points yielding “degrees of membership” in which Gaussian shapes can be “fit” using a least squares scheme. Implementation into the Kalman methodology will be shown along with simple state and parameter estimation examples.