Some errors in macroscopic traffic models based on car following models

The macroscopic traffic flow models developed from the car following models of Gazis et al. (1961) are shown to have a flaw in that they do not meet certain of the boundary conditions that researchers have said that they do. This does not affect many existing models but, nevertheless, should be cleared up.     

Derivation of delays based on improved macroscopic traffic models

Recent improved and rigorous mathematical models describing the formation and dissipation of queues at signalized intersections are employed for deriving delay and effective queue size. Analytical and numerical solutions are presented that are applicable for both isolated and coordinated intersections. Comparisons against results obtained from conventional models reveals significant differences. Further, it is demonstrated that the current practice of calculating delays from input-output analysis results in substantial overestimation of delays. Numerical examples supporting this finding are presented.     

Transport demand model estimation from traffic counts

Many problems in transport planning and management tasks require an origindestination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or roadside interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the use of low cost and easily available data is particularly attractive.The need of low-cost methods to estimate current and future O-D matrices is even more valuable in developing countries because of the rapid changes in population, economic activity and land use. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of this is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods.The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Three types of demand models have been used: gravity (GR), opportunity (OP) and gravity-opportunity (GO) models. Three estimation methods have been developed to calibrate these models from traffic counts, namely: non-linear-least-squares (NLLS), weighted-non-linear-least-squares (WNLLS) and maximumlikelihood (ML).The 1978 Ripon (urban vehicle movement) survey was used to test these methods. They were found to perform satisfactorily since each calibrated model reproduced the observed O-D matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and the stochastic method due to Burrell, in determining the routes taken through the network.requests for offprints     

Estimation of origin-destination matrices from link traffic counts on congested networks

Conventional methods for estimating origin-destination (O-D) trip matrices from link traffic counts assume that route choice proportions are given constants. In a network with realistic congestion levels, this assumption does not hold. This paper shows how existing methods such as the generalized least squares technique can be integrated with an equilibrium traffic assignment in the form of a convex bilevel optimization problem. The presence of measurement errors and time variations in the observed link flows are explicitly considered. The feasibility of the model is always guaranteed without a requirement for estimating consistent link flows from counts. A solution algorithm is provided and numerical simulation experiments are implemented in investigating the model's properties. Some related problems concerning O-D matrix estimation are also discussed.     

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.     

Estimating total annual traffic flow from short period counts

The total annual traffic flow at a road site is probably the single most important item of data needed in transport studies. In this article we describe a method of estimating this flow from a traffic count of less than 24 hours duration. The method is extended to enable an assessment to be made of the merits of repeating a count. The application of the method is discussed in relation to the United Kingdom, but we believe this method to be generally applicable to most countries.

     

Short-term traffic flow rate forecasting based on identifying similar traffic patterns

The ability to timely and accurately forecast the evolution of traffic is very important in traffic management and control applications. This paper proposes a non-parametric and data-driven methodology for short-term traffic forecasting based on identifying similar traffic patterns using an enhanced K-nearest neighbor (K-NN) algorithm. Weighted Euclidean distance, which gives more weight to recent measurements, is used as a similarity measure for K-NN. Moreover, winsorization of the neighbors is implemented to dampen the effects of dominant candidates, and rank exponent is used to aggregate the candidate values. Robustness of the proposed method is demonstrated by implementing it on large datasets collected from different regions and by comparing it with advanced time series models, such as SARIMA and adaptive Kalman Filter models proposed by others. It is demonstrated that the proposed method reduces the mean absolute percent error by more than 25%. In addition, the effectiveness of the proposed enhanced K-NN algorithm is evaluated for multiple forecast steps and also its performance is tested under data with missing values. This research provides strong evidence suggesting that the proposed non-parametric and data-driven approach for short-term traffic forecasting provides promising results. Given the simplicity, accuracy, and robustness of the proposed approach, it can be easily incorporated with real-time traffic control for proactive freeway traffic management.     

Network models in traffic management and control

Abstract

The context for network modelling in traffic management and control is described in terms of the current area‐wide nature of traffic management and the range of objectives to which it can contribute. Representation of a road system and traffic management measures in terms of nodes and links and parameters associated with them is described. It is shown that the pattern of traffic has to be represented not only in terms of flows on links of the network but also in terms of numbers of movements per unit time between points of entry to and points of exit from the area being modelled. In modelling so far, these numbers of movements are regarded as given, but the routes taken are estimated by traffic assignment. Models can so far be used for comparison of a range of given schemes and for optimization of traffic control within a scheme. Variation over time is a central feature of the modelling, and this requires the use of time‐dependent queueing theory, and the specification of numbers of movements for a succession of periods of between 10 and 30 minutes. Theoretical approaches to the resulting problems of modelling and optimization are discussed, and the ways in which these are supplemented by heuristic methods in currently available models is described. Some requirements‐for further research are outlined.     

The principles of calibrating traffic microsimulation models

Traffic microsimulation models normally include a large number of parameters that must be calibrated before the model can be used as a tool for prediction. A wave of methodologies for calibrating such models has been recently proposed in the literature, but there have been no attempts to identify general calibration principles based on their collective experience. The current paper attempts to guide traffic analysts through the basic requirements of the calibration of microsimulation models. Among the issues discussed here are underlying assumptions of the calibration process, the scope of the calibration problem, formulation and automation, measuring goodness-of-fit, and the need for repeated model runs.

Macroscopic traffic models for vehicle-follower automated transportation systems

The problem of distributing and routing vehicles in a large automated transportation network may be approached through the design of on-line control algorithms, particularly when the network contains many origin-destination pairs and alternate routes. To develop such algorithms, it is necessary to obtain models that accurately represent the dynamic behavior of vehicles on the guideway network. In this paper, models based on density, flow and average velocity variables are derived for the vehicle-follower longitudinal control scheme. Models suitable for use in analysis and simulation work are developed for links, merges, diverges, and stations. The proposed models are shown to compare favorably with simulation results that use explicit modeling of vehicle dynamic modeling of vehicle dynamic interaction.     

Air traffic management: Optimization models and algorithms

Current air traffic control systems are mainly conceived to ensure the safety of flights by means of tactical interventions, because of the difficulty of accurately foreseeing the traffic evolution. In fact, in real traffic conditions, planes are often penalized since sometimes safety standards are redundant. Today, this management philosophy is no longer valid because of congestion phenomena which often occur in the most important terminal areas. Therefore, as to future control systems it is necessary to introduce not only more automated procedures to keep adequate safety levels, but also planning functions in order to increase the system capacity and to improve system efficiency. In recent years several studies have been carried out, new control concepts have been introduced and some optimization models and algorithms developed to improve air traffic management. In this paper a survey of our early works in this field is reported and a multilevel model of air traffic management is proposed and discussed. The functions corresponding to the on-line control, that is flow control, strategic control of flights and aircraft sequencing in a terminal area, are examined and the optimization models and solution algorithms are illustrated. Finally, relevant problems coped by recent research are mentioned and new trends are indicated.     

A review of Australian traffic system design models

The demands for traffic infrastructure are increasing. Yet over the last decade investment in new infrastructure has decreased at all levels. Traffic systems designers are, therefore, being asked to be more accurate in their prediction of the impacts of changes, to analyse ever more complex situations and to extract more from the existing traffic system. This paper reviews developments in techniques for analysing the impacts of changes in the traffic system. It looks at intersection, route, network, parking lot and public transport design models that have been developed in Australia. Particular emphasis is given to the considerable developments in microcomputers and graphics and the impact these are having on the models. Future developments are also discussed.     

Estimating intersection O-D matrices from observed time-series of traffic counts: The zellner estimator

The problem of estimating intersection O-D matrices from input and output time-series of traffic counts is considered in this paper. Because of possible existence of significant correlation between the error terms across structural equations forming the O-D matrices, the seemingly unrelated estimator (Zellner estimator) was suggested. Estimation results showed evidence of strong correlation between error terms across-equations. Generally, the Zellner estimator produced more efficient estimates than did the ordinary least-squares estimator. Furthermore, the Zellner estimator satisfied all constraints and reproduced turning movements comparable to the actual ones.     

Validation of traffic flow models with respect to the spatiotemporal evolution of congested traffic patterns

We propose a quantitative approach for calibrating and validating key features of traffic instabilities based on speed time series obtained from aggregated data of a series of neighboring stationary detectors. The approach can be used to validate models that are calibrated by other criteria with respect to their collective dynamics. We apply the proposed criteria to historic traffic databases of several freeways in Germany containing about 400 occurrences of congestions thereby providing a reference for model calibration and quality assessment with respect to the spatiotemporal dynamics. First tests with microscopic and macroscopic models indicate that the criteria are both robust and discriminative, i.e., clearly distinguishes between models of higher and lower predictive power.     

Dense network traffic models,Travel time reliability and traffic management. I: General introduction

This paper is the first of a pair of papers discussing two main themes concerning dense network modelling. These themes are: (1) the changing nature of traffic management technology and the underlying objectives behind traffic management practice, and (2) the use of measures of network reliability in models, especially as an element of the evaluation of alternative network configurations. This paper considers the role and function of dense network models, and their relationships with other transport network models on the basis of the hierarchical view of models. It then explores the peculiar characteristics of dense network models and provides examples of typical models. Changing needs for modelling capabilities, in terms of the evolution of traffic management technology and practice are discussed, pointing the way to future model developments. The companion paper develops the second theme through the definition and application of a set of network reliability indices that may be applied to different trip movements.     

基于3DSMAX的交通事故演示程序

利用3DSMAX创建交通事故中所用的三维模型,再现事故发生的场景,把模型导入VC++的窗口程序中来显示,调用OpenGL库函数来控制模型运动,实现交通事故三维再现。     

Verification process and its application to network traffic simulation models

This paper summarizes a standardized verification process for network traffic simulation models. After the general introduction of philosophy of verification, we explain detailed processes of the verification and its application to several well‐known simulation models. “Verification” here means several examination tests of simulation models using virtual data on a simple network so as to confirm their fundamental functions. In the course of model development, the developers have to examine whether the model performance is consistent with the specifications that they intend and also with the well‐authorized traffic engineering theory. Because of several constraints in putting the model specifications into the computer programming such as discretizing of time and space and simplifying vehicle behaviors to some degree, the intended model specifications may not be fully achieved in a computer. Therefore, we strongly recommend the verification before applying the models to a real network.     

Development of network restructuring models for improved air traffic forecasts

Current air traffic forecast methods employed by the United States Federal Aviation Administration function under the assumption that the structure of the network of routes operated by airlines will not change; that is, no new routes will be added nor existing ones removed. However, in reality the competitive nature of the airline industry is such that new routes are routinely added between cities possessing significant passenger demand; city-pairs are also removed. Such phenomena generates a gap between the forecasted and actual state of the US Air Transportation System in the long term, providing insufficient situational awareness to major stakeholders and decision-makers in their consideration of major policy and technology changes. To address this gap, we have developed and compared three algorithms that forecast the likelihood of un-connected city-pairs being connected by service in the future, primarily based on the nodal characteristics of airports in the US network. Validation is performed by feeding historical data to each algorithm and then comparing the accuracy and precision of new city-pairs forecasted using knowledge of actual new city-pairs that developed. While an Artificial Neural Network produces superior precision, fitness function and logistic regression algorithms provide good representation of the distribution of new route types as well as greater flexibility for modeling future scenarios. However, these latter two algorithms face difficulty in resolving differences among the large number of ‘spoke’ airports in the network – additional parameters that may be able to differentiate them are currently under review. These insights gained are valuable stepping stones for exploiting knowledge of restructuring in the service route network to improve overall forecasts that drive policy and technology decision-making.     

Lane‐based optimization of traffic equilibrium settings for area traffic control

Area traffic control is an important element in Intelligent Transportation System (ITS). This paper extends the lane‐based optimization method to a traffic equilibrium network, which improves the operational performance of signal‐controlled network. We formulate a decomposition approach to simultaneously optimize the lane markings and signal settings for a signal‐controlled network that comprises two levels of optimization. At the junction level, the lane markings, control sequence, and other aspects of the signal settings are optimized for individual junctions, whereas at the network level, the group‐based signal settings are optimized to take into account the re‐routing characteristics of travelers and signal coordination effects that are based on a TRANSYT traffic model, which is a well‐known procedure for evaluating the performance of signal‐controlled networks. We use a numerical example to demonstrate the effectiveness of the proposed methodology.