Showing papers on "Traffic count published in 1993"

Cluster analysis of arizona automatic traffic recorder data

Abstract: Monthly factor data were used as input data for cluster analysis of 28 permanent traffic volume counters installed in Arizona. Monthly factors are the ratio of monthly average daily traffic to annual average daily traffic (AADT). Cluster analysis is a statistical procedure that reveals natural groupings in data. There are two types of clustering methods: hierarchical and nonhierarchical. Hierarchical methods use a successive series of either mergers or division. Nonhierarchical methods group objects into a collection of clusters "K". Monthly factor data for each location collected over 5 years were used in the cluster analysis. The group mean monthly factors of the groups that were determined and the monthly factors of each location were applied to the appropriate randomly selected daily traffic count. These counts were proxy variables for short-term 24-hr counts. Statistical analysis was used to determine the "best" method for deriving monthly factors and also provided the best estimates of AADT. From the results of this analysis, it was determined that the two primary groups derived from using four clusters were the best and the most stable of all the variations used in the analysis. The statistical analysis revealed that the results obtained from using the grouped mean monthly factors of this variation were marginally better than those from the other variations. The two distinct groups that were determined as a result of the analysis are quite stable with respect to time and provide an estimated level of precision that was greater than acceptable.

Overcoming the (apparent) problem of inconsistency in origin-destination matrix estimations

Abstract: Recently a number of models for the estimation of origin-destination trip matrices from traffic counts have been presented. One class of models assumes knowledge of the proportional usage of each link by the interzonal traffic. These models result in an underspecified, and often inconsistent, system of linear equations. Several authors have addressed the problem of inconsistency by, in different ways, changing the traffic count data so as to achieve consistency. We argue that, modeling-wise, this is not a sound approach because inconsistent data is a natural part of any origin-destination matrix estimation problem. By using a stochastic programming approach the inconsistent input data becomes a natural part of the estimation process. This is done by viewing the traffic counts as realizations of some unknown underlying distribution of traffic flows.

Outlier detection and missing value estimation in time series traffic count data: final report of serc project gr/g23180.

Abstract: A serious problem in analysing traffic count data is what to do when missing or extreme values occur, perhaps as a result of a breakdown in automatic counting equipment. The objectives of this current work were to attempt to look at ways of solving this problem by: 1)establishing the applicability of time series and influence function techniques for estimating missing values and detecting outliers in time series traffic data; 2)making a comparative assessment of new techniques with those used by traffic engineers in practice for local, regional or national traffic count systems Two alternative approaches were identified as being potentially useful and these were evaluated and compared with methods currently employed for `cleaning' traffic count series. These were based on evaluating the effect of individual or groups of observations on the estimate of the auto-correlation structure and events influencing a parametric model (ARIMA). These were compared with the existing methods which included visual inspection and smoothing techniques such as the exponentially weighted moving average in which means and variances are updated using observations from the same time and day of week. The results showed advantages and disadvantages for each of the methods. The exponentially weighted moving average method tended to detect unreasonable outliers and also suggested replacements which were consistently larger than could reasonably be expected. Methods based on the autocorrelation structure were reasonably successful in detecting events but the replacement values were suspect particularly when there were groups of values needing replacement. The methods also had problems in the presence of non-stationarity, often detecting outliers which were really a result of the changing level of the data rather than extreme values. In the presence of other events, such as a change in level or seasonality, both the influence function and change in autocorrelation present problems of interpretation since there is no way of distinguishing these events from outliers. It is clear that the outlier problem cannot be separated from that of identifying structural changes as many of the statistics used to identify outliers also respond to structural changes. The ARIMA (1,0,0)(0,1,1)7 was found to describe the vast majority of traffic count series which means that the problem of identifying a starting model can largely be avoided with a high degree of assurance. Unfortunately it is clear that a black-box approach to data validation is prone to error but methods such as those described above lend themselves to an interactive graphics data-validation technique in which outliers and other events are highlighted requiring acceptance or otherwise manually. An adaptive approach to fitting the model may result in something which can be more automatic and this would allow for changes in the underlying model to be accommodated. In conclusion it was found that methods based on the autocorrelation structure are the most computationally efficient but lead to problems of interpretation both between different types of event and in the presence of non-stationarity. Using the residuals from a fitted ARIMA model is the most successful method at finding outliers and distinguishing them from other events, being less expensive than case deletion. The replacement values derived from the ARIMA model were found to be the most accurate.

Urban road traffic surveys

Abstract: This manual provides traffic engineers with a guide to simple, reliable traffic survey techniques, suitable for urban road traffic in developing countries. It covers surveys for urban traffic engineering, but does not cover road safety or transport planning. The manual consists of three parts: in chapter 2 general issues concerning traffic surveys are discussed; chapters 3 to 10 each describe a specific survey, and the appendices contain detailed instructions and survey forms for supervisors and surveyors. While the manual is written for the left-hand rule of the road, it can be used in countries which drive on the right, though the diagrams will need to be transposed and the accompanying text interpreted accordingly. The surveys described in the manual are considered sufficiently robust for use in most urban situations, though it may be necessary for some small modifications to meet specific local needs and conditions. Account must be taken of any existing government guidelines and legal requirements which concern, or could have an impact on, the implementation of traffic surveys. (A)

Modelling outliers and missing values in traffic count data using the arima model

Abstract: This paper considers the application of the methodology to traffic count time series in which both missing values and outliers are present. Intervention analysis and detection using large residuals are shown to he reasonably effective but possible problems that result from non- stationarity in the data are identified. It is shown that despite considerable variabilty in the types of series the model selected from the ARIMA family is surprisingly homogeneous.

Application of Outlier Detection and Missing Value Estimation Techniques to Various Forms of TrafficCount Data

Abstract: This paper reports on the application of suitable techniques for detecting outliers and suggesting estimates for missing values in various forrns of traffic count data. The data used in this study came from three sources. The first set was provided by the Department of Transport's (DOT) regional office in Leeds and consists of automatic hourly traffic counts at four sites. The second set was part of a larger database provided by West Yorkshire Highways, Engineering and Technical Services (HETS). This set consists of automatic half hourly traffic counts on a single site. The third and final set was provided by Nottinghan University and consists of automatic five minute traffic counts at 40 locations, in close proximity to each other, from Leicester. Three suitable techniques emerged from pilot studies of such series conducted by Watson et a1 (1992a) and Redfern et a1 (1992). The three techniques are: a) Maintaining an average and variability measure over time; b) ARIMA modelling with detection of large residuals; C) A point's influence on the correlation structure of the series. A fourth technique, by-eye detection and estimation, provides an intuitive comparison for the first three techniques.

Overcoming the (apparent) problem of inconsistency in O-D matrix estimations

Abstract: Recently a number of models for the estimation of origin-destination trip matrices from traffic counts have been presented. One class of models assumes knowledge of the proportional usage of each link by the interzonal traffic. These models result in an underspecified, and often inconsistent, system of linear equations. Several authors have addressed the problem of inconsistency by, in different ways, changing the traffic count data so as to achieve consistency. We argue that, modeling-wise, this is not a sound approach because inconsistent data is a natural part of any origin-destination matrix estimation problem. By using a stochastic programming approach the inconsistent input data becomes a natural part of the estimation process. This is done by viewing the traffic counts as realizations of some unknown underlying distribution of traffic flows.

Microcomputers in transportation. analysis of vessel and vehicle queues at moveable bridges

Abstract: This paper describes the evaluation of 3 different replacement options for the existing S.E. 17th Street Causeway moveable bridge which crosses the Intracoastal Waterway in Fort Lauderdale, Florida. The options were as follows: a fixed-span bridge with a maximum of 85 feet of vertical clearance; a tunnel; and various height options for a higher level moveable bridge. The reasons for considering a replacement are discussed. The paper describes how microcomputers were used for forecasting vehicle traffic; analyzing vessel survey data and traffic count data; and the interaction between vessels and vehicles at the bascule bridge, and the estimation of queues and delays.

Road traffic and respiratory health of children

Abstract: This letter discusses the article by Wjst et al on the association between road traffic and the respiratory health of children (see IRRD 859240) The point is made that this study failed to ascertain what degree of difference in the outcome measure between the study groups is clinically important Evidence is provided to illustrate that, although the study's results are sometimes significant, they are not necessarily clinically meaningful The results indicate that the highest percentage change per 25,000 cars for any of the pulmonary function tests made was a 071% decrease Since most children lived in areas where the traffic count was 50,000 cars a day or less, this means that even for tests with the worst results the change was less than 15% The author queries whether such a small change is clinically important