Indirect Validation of Probe Speed Data on Arterial Corridors

2017 ◽  
Vol 2643 (1) ◽  
pp. 105-111 ◽  
Author(s):  
Sepideh Eshragh ◽  
Stanley E. Young ◽  
Elham Sharifi ◽  
Masoud Hamedi ◽  
Kaveh Farokhi Sadabadi
Keyword(s):  
Linear Models ◽  
Weighted Average ◽  
Access Point ◽  
Functional Class ◽  
Traffic Data ◽  
Strong Correlations ◽  
Signal Density ◽  
Annual Average Daily Traffic ◽  
Speed Error ◽  
Road Characteristics

This study aimed to estimate the accuracy of probe speed data on arterial corridors on the basis of roadway geometric attributes and functional classification. It was assumed that functional class (medium and low) along with other road characteristics (such as weighted average of the annual average daily traffic, average signal density, average access point density, and average speed) were available as correlation factors to estimate the accuracy of probe traffic data. This study tested these factors as predictors of the fidelity of probe traffic data by using the results of an extensive validation exercise. This study showed strong correlations between these geometric attributes and the accuracy of probe data when they were assessed by using average absolute speed error. Linear models were regressed to existing data to estimate appropriate models for medium- and low-type arterial corridors. The proposed models for medium- and low-type arterials were validated further on the basis of the results of a slowdown analysis. These models can be used to predict the accuracy of probe data indirectly in medium and low types of arterial corridors.

scholarly journals Evaluating Annual Average Daily Traffic Calculation Methods with Continuous Truck Traffic Data

2017 ◽  
Vol 2644 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Giuseppe Grande ◽  
Steven Wood ◽  
Auja Ominski ◽  
Jonathan D. Regehr
Keyword(s):  
Count Data ◽  
Weighted Average ◽  
Traffic Monitoring ◽  
Local Context ◽  
Traffic Data ◽  
Data Loss ◽  
Truck Traffic ◽  
Annual Average ◽  
Annual Average Daily Traffic ◽  
Accuracy And Precision

Traffic volume, often measured in relation to annual average daily traffic (AADT), is a fundamental output of traffic monitoring programs. At continuous count sites, unusual events or counter malfunctions periodically cause data loss, which influences AADT accuracy and precision. This paper evaluates five methods used to calculate AADT values from continuous count data, including the use of a simple average, the commonly adopted method developed by AASHTO (the AASHTO method), and methods that incorporate adjustments to the AASHTO method. The evaluation imposes data removal scenarios designed to simulate real-life causes of data loss to quantify the accuracy and precision improvements provided by these adjustments. Truck traffic data are used to reveal issues arising when volumes are low or when they exhibit unusual temporal patterns. Unlike the AASHTO method, which incorporates a weighted average and an hourly base time period, the FHWA method provides the most accurate and precise results in all data removal scenarios, according to the evaluation. Specifically, when up to 15 days of data are randomly removed, application of the FHWA method can be expected to produce errors within approximately é1.4% of the true AADT value, 95% of the time. Results also demonstrate that including a weighted average improves AADT accuracy primarily, whereas the use of hourly rather than daily count data influences precision. If possible, practitioners contemplating the adoption of the FHWA method should assess its relative advantages within their local context.

scholarly journals The Optimal Selection for Restricted Linear Models with Average Estimator

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Qichang Xie ◽  
Meng Du
Keyword(s):  
Model Selection ◽  
Linear Models ◽  
Weighted Average ◽  
Selection Procedure ◽  
Information Criterion ◽  
Optimal Weights ◽  
Model Average ◽  
Squared Error ◽  
Generalized Information Criterion ◽  
Risk Investment

The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing ak-class generalized information criterion (k-GIC), which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.

Urban Bicyclists

2008 ◽  
Vol 2074 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Elizabeth Cahill Delmelle ◽  
Jean-Claude Thill
Keyword(s):  
New York ◽  
The United States ◽  
Demographic Variables ◽  
Functional Class ◽  
Traffic Crash ◽  
Demographic Groups ◽  
Spatial Dependencies ◽  
Model Residuals ◽  
Road Characteristics ◽  
American Cities

As issues related to oil dependency, rising gas prices, and global warming come to the forefront of topics of concern for Americans, the need for alternative modes of transportation has become critical. Urban settings are seemingly ideal for bicycling to become a significant mode, given the greater compactness of destinations. However, in the United States, bicycling is both scarcely used and very dangerous, as bicyclists are 12 times more likely to be killed than automobile drivers. The purpose of this research is to gain greater insights into the geographic dimensions of traffic crash intensity that bicyclists may experience in American cities. Bicycle crashes are studied in Buffalo, New York, for the years 2003 and 2004. The geographic distribution of crashes is determined and compared for both youth and adult bicyclists and factors of crash hazard intensity are statistically identified. Density of development and physical road characteristics such as roadway and intersection functional class are examined, as well as socioeconomic and demographic variables and potential trip attractors. Given the spatial nature of these variables, a spatially weighted regression model is incorporated to account for spatial dependencies of the dependent variables and of their model residuals. The results of the analysis indicate clear distinctions between youth and adult bicycle crashes, both in terms of the neighborhoods where victims reside and in terms of the neighborhoods where these two demographic groups are found to be more frequently involved in a crash with a motorized vehicle.

scholarly journals A self-calibrating model to estimate average speed from AADT

2021 ◽  
Vol 63 (2) ◽  
Author(s):  
M M Bruwer
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Economic Assessment ◽  
Transport Infrastructure ◽  
Heavy Vehicles ◽  
Average Speed ◽  
Annual Average Daily Traffic ◽  
Speed Prediction ◽  
High Level ◽  
Road Characteristics

ABSTRACT Transport practitioners need a universally applicable speed prediction model to estimate average speeds on any road. Average annual speed is a key input to the economic assessment of transport infrastructure where reliable estimates of future average speeds are necessary to calculate economic costs and benefits. The relationship between Annual Average Daily Traffic (AADT) and average annual speed was investigated on higher-order roads across South Africa, revealing a high level of variability in this correlation at different locations. This variation is influenced by road characteristics, such as alignment and cross-section, complicating the formulation of a universal speed prediction model. Two novel speed prediction models are proposed in this article that use AADT to forecast future average annual speed. The speeds of heavy vehicles and light vehicles can be estimated separately, as well as the average speed of all vehicles simultaneously. Both models are self-calibrating, accounting for the variation in the AADT-speed relationship. This calibration step is unique to speed prediction models and increases the reliability of these models to estimate future average speeds considerably. Furthermore, self-calibrating average annual speed prediction models are universally applicable and will simplify economic assessment of transport infrastructure. Keywords: speed prediction, average annual speed, self-calibration, AADT, economic assessment

scholarly journals ANNUAL AVERAGE DAILY TRAFFIC FORECASTING USING DIFFERENT TECHNIQUES

Transport ◽  
2006 ◽  
Vol 21 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Tomas Šliupas
Keyword(s):  
Growth Factor ◽  
Linear Regression ◽  
Multiple Regression ◽  
Traffic Data ◽  
Annual Average ◽  
Traffic Forecasting ◽  
Annual Average Daily Traffic ◽  
Forecasting Method ◽  
The Future

This paper describes annual average daily traffic (AADT) forecasting for the Lithuanian highways using a forecasting method used by Idaho Department for Transportation, growth factor, linear regression and multiple regression. AADT forecasts obtained using these methods are compared with the forecasts evaluated by traffic experts and given in references. The results show that the best Lithuanian traffic data are obtained using Idaho forecasting method. It is assumed that the curve of AADT change should be exponential in the future.

Data-Driven Methodology for Prioritizing Traffic Signal Retiming Operations

2019 ◽  
Vol 2673 (6) ◽  
pp. 104-113
Author(s):  
Michael R. Dunn ◽  
Heidi Westerfield Ross ◽  
Carolina Baumanis ◽  
Jared Wall ◽  
Jonathan Lammert ◽  
...  
Keyword(s):  
Functional Class ◽  
Traffic Signal ◽  
Data Driven ◽  
Probe Vehicle ◽  
Signal Density ◽  
Vehicle Data ◽  
Important Means ◽  
Time Periods ◽  
Transportation Agencies ◽  
The City

Signal retiming is one of the chief responsibilities of municipal transportation agencies, and is an important means of reducing congestion and improving transportation quality and reliability. Many agencies conduct signal retiming and adjustment in a schedule-based manner. However, leveraging a data-driven, need-based approach to signal retiming to prioritize operations could better optimize use of agency resources. Additionally, the growing availability of probe vehicle data has made it an increasingly popular tool for use in roadway performance measurement. This paper presents a methodology for using segment-level probe-based speed data to rank the performance of traffic signal corridors for retiming purposes. This methodology is then demonstrated in an analysis of 79 traffic signal corridors maintained by the City of Austin, Texas. The analysis considers 15-minute speed records for all weekdays in September 2016 and September 2017 to compute metrics and rank corridors based on their relative performance across time periods. The results show that the ranking methodology compares corridors equitably despite differences in road length, functional class, and traffic signal density. Additionally, the results indicate that the corridors prioritized by the ranking methodology represent a much greater potential for improving travel time than the corridors selected under the schedule-based approach.

Spatial Interpolation of Missing Annual Average Daily Traffic Data Using Copula-Based Model

2019 ◽  
Vol 11 (3) ◽  
pp. 158-170 ◽  
Author(s):  
Xiaolei Ma ◽  
Sen Luan ◽  
Chuan Ding ◽  
Haode Liu ◽  
Yunpeng Wang
Keyword(s):  
Spatial Interpolation ◽  
Traffic Data ◽  
Annual Average ◽  
Annual Average Daily Traffic

Annual Average Daily Traffic Prediction Model for County Roads

1998 ◽  
Vol 1617 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Dadang Mohamad ◽  
Kumares C. Sinha ◽  
Thomas Kuczek ◽  
Charles F. Scholer
Keyword(s):  
Prediction Model ◽  
Model Development ◽  
Demographic Variables ◽  
Traffic Prediction ◽  
Traffic Data ◽  
Annual Average ◽  
Independent Variables ◽  
State Highway ◽  
Annual Average Daily Traffic ◽  
Adjustment Factors

A traffic prediction model that incorporates relevant demographic variables for county roads was developed. Field traffic data were collected from 40 out of 92 counties in Indiana. The selection of a county was based on population, state highway mileage, per capita income, and the presence of interstate highways. Three to four automatic traffic counters were installed in each selected county. Most counters installed on the selected road sections were based on the standard 48-hour traffic counts. Then, the obtained average daily traffic was converted to annual average daily traffic by means of adjustment factors. Multiple regression analysis was conducted to develop the model. There were quantitative and qualitative predictor variables used in the model development. To validate the developed model, additional field traffic data were collected from eight randomly selected counties. The accuracy measures of the validation showed the high accuracy of the model. The statistical analyses also found that the independent variables employed in the model were statistically significant. The number of independent variables included in the model was kept to a minimum.

scholarly journals NONPARAMETRIC WEIGHTED AVERAGE QUANTILE DERIVATIVE

2021 ◽  
pp. 1-39
Author(s):  
Ying-Ying Lee
Keyword(s):  
Linear Models ◽  
Weighted Average ◽  
Linear Representation ◽  
Quantile Function ◽  
Structural Effect ◽  
Compact Set ◽  
Conditional Quantile ◽  
Partially Linear ◽  
Conditional Quantile Function ◽  
Quantile Processes

The weighted average quantile derivative (AQD) is the expected value of the partial derivative of the conditional quantile function (CQF) weighted by a function of the covariates. We consider two weighting functions: a known function chosen by researchers and the density function of the covariates that is parallel to the average mean derivative in Powell, Stock, and Stoker (1989, Econometrica 57, 1403–1430). The AQD summarizes the marginal response of the covariates on the CQF and defines a nonparametric quantile regression coefficient. In semiparametric single-index and partially linear models, the AQD identifies the coefficients up to scale. In nonparametric nonseparable structural models, the AQD conveys an average structural effect under certain independence assumptions. Including a stochastic trimming function, the proposed two-step estimator is root-n-consistent for the AQD defined by the entire support of the covariates. To facilitate tractable asymptotic analysis, a key preliminary result is a new Bahadur-type linear representation of the generalized inverse kernel-based CQF estimator uniformly over the covariates in an expanding compact set and over the quantile levels. The weak convergence to Gaussian processes applies to the differentiable nonlinear functionals of the quantile processes.

Sign in / Sign up

Export Citation Format

Share Document