Double Loop Detectors Research Articles

Methodology to Backcalculate Individual Speed Data Originally Aggregated by Road Detectors

Highway and traffic engineers collect vehicular speed data with detectors based on a variety of fixed and mobile device technologies, to support analysis and design activities. Most acquisition units aggregate speed data into speed classes for ease of management and storage. An unfortunate result of this practice is a significant loss of content associated with individual speed data. Moreover, the use of individual speeds is often necessary to support road safety analysis and speed management decisions. For bridging of this gap, this paper introduces an algorithm that disaggregates speed data collected with automatic road detectors that can measure speed frequency only in intervals. The objective is to obtain backcalculated individual speeds that operate with continuous distribution functions rather than discrete ones. This information allows the derivation of more robust, basic descriptive measures (average, variance, and percentiles) according to normal, lognormal, and gamma probability distribution functions. Therefore, the information produced is more useful than that calculated from standard aggregated speed reports. In this investigation, individual speed data collected from video cameras were used to derive reference distributions and descriptive measures on the same road sections where inductive double-loop detectors were installed. Comparisons of the backcalculated individual speeds and those collected from video cameras support the validity of the proposed algorithm.

A simple algorithm for the estimation of road traffic space mean speeds from data available to most management centres

The control of the evolution of road traffic streams is highly related to productivity, safety, sustainability and, even, comfort. Although, nowadays, the findings from research efforts and the development of new technologies enable accurate traffic forecasts in almost any conditions, these calculations are usually limited by the data and the equipment available. Most traffic management centres depend on the data provided, at best, by double-loop detectors. These loops supply time means over different aggregation periods, which are indiscriminately used as the bases for subsequent estimations. Since space mean speeds are those needed in most applications (note the fundamental relationship between flow and density in traffic flow theory), most current practice begins with an error. This paper introduces a simple algorithm that the allows estimation of space mean speeds from the data provided by the loops without the need for any additional financial outlay, as long as the traffic in each time interval of aggregation is stationary and its speed distribution is log-normal. Specifically, it is focused on the calculation of the variance of the speeds with regard to the time mean, thus making possible to use the relationship between time mean speeds and space mean speeds defined by Rakha (2005). The results obtained with real data show that the algorithm behaves well if the calculation conditions help fulfil the initial hypotheses. The primary difficulties arise with transient traffic and, in this case, other specific methodologies should be used. Data fusion seems promising in this regard. Nevertheless, it cannot be denied that the improvement provided by the algorithm turns out to be highly beneficial both when used alone in the case of stationarity or as a part of a fusion.

Estimation of traffic stream space-mean speed from time agregations of double loop detector data

In one of the very first papers on traffic flow theory back in 1952, Wardrop presented the difference between the space mean speed (SMS) and the time mean speed (TMS) of a group of traveling vehicles, and derived a relationship suitable for estimating TMS, given SMS and the speed variance over SMS. As time goes by, traffic practitioners have tended towards computing TMS instead of SMS, mainly when using double loop detectors, and nowadays this is the usual practice in traffic management centers. Therefore, the useful relationship between TMS and SMS should go the other way around in relation to Wardrop’s. Recently, the complementary relationship, suitable for estimating SMS from TMS and the speed variance over TMS, has been proved. However this is not enough, as speed variance is usually not available. The present paper develops a probabilistic method to estimate SMS from TMS without the previous knowledge of speed variance and only using the usual time aggregations of double loop detector data. The main assumption of the method – the normality of vehicle speed distribution – is discussed and a formulation to obtain the expected error of the estimation is derived. The results obtained with test data from the AP-7 highway, near Barcelona in Spain, show that the developed methodology is able to estimate SMS with an average relative error as low as 0.5%.

Estimation of traffic stream space mean speed from time aggregations of double loop detector data

In one of the very first papers on traffic flow theory back in 1952, Wardrop presented the difference between the space mean speed (SMS) and the time mean speed (TMS) of a group of traveling vehicles, and derived a relationship suitable for estimating TMS, given SMS and the speed variance over SMS. As time goes by, traffic practitioners have tended towards computing TMS instead of SMS, mainly when using double loop detectors, and nowadays this is the usual practice in traffic management centers. Therefore, the useful relationship between TMS and SMS should go the other way around in relation to Wardrop’s. Recently, the complementary relationship, suitable for estimating SMS from TMS and the speed variance over TMS, has been proved. However this is not enough, as speed variance is usually not available. The present paper develops a probabilistic method to estimate SMS from TMS without the previous knowledge of speed variance and only using the usual time aggregations of double loop detector data. The main assumption of the method – the normality of vehicle speed distribution – is discussed and a formulation to obtain the expected error of the estimation is derived. The results obtained with test data from the AP-7 highway, near Barcelona in Spain, show that the developed methodology is able to estimate SMS with an average relative error as low as 0.5%.

Model for Accurate Speed Measurement Using Double-Loop Detectors

Vehicle speed is an important parameter in measurements of road traffic. At present, double-loop detectors are generally used for vehicular speed measurement. However, these detectors incur errors caused by scanning time, spacing between double loops, irregular vehicle trajectories, and the presence of multiple vehicles in the detection zone. This paper suggests a new model that uses an error-filtering algorithm to improve the accuracy of speed measurements. In the field tests, all percent errors of the vehicular speeds measured by the proposed model were within the error tolerance limit (/spl plusmn/5%). Furthermore, the variance of percent errors was reduced. Therefore, it can be concluded that the proposed model significantly improves vehicle-speed-measuring accuracy.

Calibration and Validation of Microscopic Models of Traffic Flow

Because microscopic models are heavily used in applications, the appropriate calibration and validation of these models have been a recent concern. The contribution of this paper is to compare some of these models by calibrating and validating them with data from double loop detectors on a multilane freeway. To simplify this task, the models were tested by simplifying the multilane reality to a simulation of only a single lane. The results show that by simulating the multilane road with single-lane models, calibration errors (Theil's U-value, or the root mean square error) of 14% to 16% were obtained. A validation of the models, which was done by taking the calibrated parameters of one data set to reproduce the other data sets, gives additional errors of about 0.5 to 2.5 percentage points. This is in good agreement with other calibration and validation approaches performed recently.

Calibration and Validation of Microscopic Models of Traffic Flow

Because microscopic models are heavily used in applications, the appropriate calibration and validation of these models have been a recent concern. The contribution of this paper is to compare some of these models by calibrating and validating them with data from double loop detectors on a multilane freeway. To simplify this task, the models were tested by simplifying the multilane reality to a simulation of only a single lane. The results show that by simulating the multilane road with single-lane models, calibration errors (Theil's U-value, or the root mean square error) of 14% to 16% were obtained. A validation of the models, which was done by taking the calibrated parameters of one data set to reproduce the other data sets, gives additional errors of about 0.5 to 2.5 percentage points. This is in good agreement with other calibration and validation approaches performed recently.

A Simple and Effective Method for Predicting Travel Times on Freeways

We present a method to predict the time that will be needed to traverse a given section of a freeway when the departure is at a given time in the future. The prediction is done on the basis of the current traffic situation in combination with historical data. We argue that, for our purposes, the current traffic situation of a section of a freeway is well summarized by the current status travel time. This is the travel time that would result if one were to depart immediately and no significant changes in traffic would occur. This current status travel time can be estimated from single- or double-loop detectors, video data, probe vehicles, or any other means. Our prediction method arises from the empirical observation that there exists a linear relationship between any future travel time and the current status travel time. The slope and intercept of this relationship may change subject to the time of day and the time until departure, but linearity persists. This observation leads to a prediction scheme by means of linear regression with time-varying coefficients.

Short-term travel time prediction

Effective prediction of travel times is central to many advanced traveler information and transportation management systems. In this paper we propose a method to predict freeway travel times using a linear model in which the coefficients vary as smooth functions of the departure time. The method is straightforward to implement, computationally efficient and applicable to widely available freeway sensor data. We demonstrate the effectiveness of the proposed method by applying the method to two real-life loop detector data sets. The first data set––on I-880––is relatively small in scale, but very high in quality, containing information from probe vehicles and double loop detectors. On this data set the prediction error ranges from 5% for a trip leaving immediately to 10% for a trip leaving 30 min or more in the future. Having obtained encouraging results from the small data set, we move on to apply the method to a data set on a much larger spatial scale, from Caltrans District 12 in Los Angeles. On this data set, our errors range from about 8% at zero lag to 13% at a time lag of 30 min or more. We also investigate several extensions to the original method in the context of this larger data set.