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Statistical Procedures To Determine Seasonal Factors For Traffic Volume Monitoring In West Virginia


Statistical Procedures To Determine Seasonal Factors For Traffic Volume Monitoring In West Virginia
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Statistical Procedures To Determine Seasonal Factors For Traffic Volume Monitoring In West Virginia


Statistical Procedures To Determine Seasonal Factors For Traffic Volume Monitoring In West Virginia
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Author :
language : en
Publisher:
Release Date : 1997

Statistical Procedures To Determine Seasonal Factors For Traffic Volume Monitoring In West Virginia written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Cluster analysis categories.




Procedures For Traffic Volume Forecasting And Seasonal Adjustment Factors


Procedures For Traffic Volume Forecasting And Seasonal Adjustment Factors
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Author : Traci L. Thomas Quarrick
language : en
Publisher:
Release Date : 1998

Procedures For Traffic Volume Forecasting And Seasonal Adjustment Factors written by Traci L. Thomas Quarrick and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Traffic engineering categories.




Use Of Permanent Traffic Recorder Data To Develop Factors For Traffic And Truck Variations


Use Of Permanent Traffic Recorder Data To Develop Factors For Traffic And Truck Variations
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Author : L. James French
language : en
Publisher:
Release Date : 2002

Use Of Permanent Traffic Recorder Data To Develop Factors For Traffic And Truck Variations written by L. James French and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Traffic flow categories.




Determination Of Seasonal Adjustment Factors And Assignment Of Short Term Counts To Factor Groupings


Determination Of Seasonal Adjustment Factors And Assignment Of Short Term Counts To Factor Groupings
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Author : Ioannis Tsapakis
language : en
Publisher:
Release Date : 2009

Determination Of Seasonal Adjustment Factors And Assignment Of Short Term Counts To Factor Groupings written by Ioannis Tsapakis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Averaging method (Differential equations) categories.


The traffic volume of a roadway segment is of significant importance for several public and private sections of the industry. This volume is represented by the Annual Average Daily Traffic (AADT). The AADT expresses the average number of vehicles that travel daily on this particular roadway section within a year. The traditional method of estimating AADT is examined along with new methods in order to improve the accuracy of the predictions. The literature review conducted at the beginning of this study comprises the theoretical background to develop the research methodology. The study data are provided from 2002 to 2007 by the Ohio Department of Transportation (ODOT). The first type of data is obtained from traffic counters that perform continuously throughout a year. The second type of data is generated by portable counters that record traffic volumes for a short-period of time. The prediction of the AADT is based on the combination of both types of data using several mathematical methods and newly developed statistical approaches. The determination of seasonal adjustment factors (SAF) is the first step of the AADT estimation. Seven SAFs and five approaches of estimating the AADT are examined for thirteen individual vehicle classes and groups of classes. The most effective SAFs are selected based on the mean absolute error (MAE) and the standard deviation (SD) of the predictions. Two analyses are conducted for each step of the study: the first is based on SAFs estimated from the sum of the two directional volumes of a roadway, and; the second on SAFs calculated for each direction of the traffic. The continuous counters are grouped together using eight different combinations of traditional grouping techniques and cluster analysis. The k-means algorithm, a non-hierarchical clustering method, is used to group the continuous counters based on their monthly SAFs. Furthermore, a statistical-based method for determining the optimal number of clusters was developed. The results are consistent over time and show a significant improvement in the accuracy of the AADT when clustering is used. Based on the performance, the applicability and the practicality of the examined methods, geographical classification and cluster analysis were selected to generate the final factor groupings. The assignment of short-term counts to counter groups includes the investigation of three methods: the traditional method; discriminant analysis, and; a new approach based on statistical similarities of traffic and temporal characteristics between a short-period count and factor groups. In total, fifty six assignment models were developed and compared. The analysis based on directional SAFs is more effective than the total volume analysis by 15% to 40%. The final results indicate that the statistical approach developed in this study results in a MAE and SD improvement over the traditional method by 51.75% and 67.73% correspondingly. In addition to the traditional method, regression and Bayesian negative binomial techniques are examined to predict AADT. In total twelve models are developed with a training data set and the results are compared using a validation data set. Parameters of significance include the HPMS roadway functional classification, population density, spatial location and the average daily traffic. The results show a full Bayesian negative binomial model with a coefficient offset was the most efficient model framework for all four seasons of the year. This model was able to describe between 87% and 92% of the variability within the data set.



Annual Report


Annual Report
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Author : Pennsylvania. Dept. of Transportation
language : en
Publisher:
Release Date : 2001

Annual Report written by Pennsylvania. Dept. of Transportation and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.




Report Summaries


Report Summaries
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Author : United States. Environmental Protection Agency
language : en
Publisher:
Release Date : 1983

Report Summaries written by United States. Environmental Protection Agency and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with categories.




Traffic Monitoring Guide


Traffic Monitoring Guide
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Author : United States. Federal Highway Administration. Office of Highway Information Management
language : en
Publisher:
Release Date : 1992

Traffic Monitoring Guide written by United States. Federal Highway Administration. Office of Highway Information Management and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Traffic congestion categories.




Statistical Methods In Water Resources


Statistical Methods In Water Resources
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Author : D.R. Helsel
language : en
Publisher: Elsevier
Release Date : 1993-03-03

Statistical Methods In Water Resources written by D.R. Helsel and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-03-03 with Science categories.


Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources. The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies. The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.



Traffic Data Collection Analysis And Forecasting For Mechanistic Pavement Design


Traffic Data Collection Analysis And Forecasting For Mechanistic Pavement Design
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Author : Cambridge Systematics
language : en
Publisher: Transportation Research Board
Release Date : 2005

Traffic Data Collection Analysis And Forecasting For Mechanistic Pavement Design written by Cambridge Systematics and has been published by Transportation Research Board this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Pavements categories.




Emission Estimation Based On Traffic Models And Measurements


Emission Estimation Based On Traffic Models And Measurements
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Author : Nikolaos Tsanakas
language : en
Publisher: Linköping University Electronic Press
Release Date : 2019-04-24

Emission Estimation Based On Traffic Models And Measurements written by Nikolaos Tsanakas and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-24 with categories.


Traffic congestion increases travel times, but also results in higher energy usage and vehicular emissions. To evaluate the impact of traffic emissions on environment and human health, the accurate estimation of their rates and location is required. Traffic emission models can be used for estimating emissions, providing emission factors in grams per vehicle and kilometre. Emission factors are defined for specific traffic situations, and traffic data is necessary in order to determine these traffic situations along a traffic network. The required traffic data, which consists of average speed and flow, can be obtained either from traffic models or sensor measurements. In large urban areas, the collection of cross-sectional data from stationary sensors is a costefficient method of deriving traffic data for emission modelling. However, the traditional approaches of extrapolating this data in time and space may not accurately capture the variations of the traffic variables when congestion is high, affecting the emission estimation. Static transportation planning models, commonly used for the evaluation of infrastructure investments and policy changes, constitute an alternative efficient method of estimating the traffic data. Nevertheless, their static nature may result in an inaccurate estimation of dynamic traffic variables, such as the location of congestion, having a direct impact on emission estimation. Congestion is strongly correlated with increased emission rates, and since emissions have location specific effects, the location of congestion becomes a crucial aspect. Therefore, the derivation of traffic data for emission modelling usually relies on the simplified, traditional approaches. The aim of this thesis is to identify, quantify and finally reduce the potential errors that these traditional approaches introduce in an emission estimation analysis. According to our main findings, traditional approaches may be sufficient for analysing pollutants with global effects such as CO2, or for large-scale emission modelling applications such as emission inventories. However, for more temporally and spatially sensitive applications, such as dispersion and exposure modelling, a more detailed approach is needed. In case of cross-sectional measurements, we suggest and evaluate the use of a more detailed, but computationally more expensive, data extrapolation approach. Additionally, considering the inabilities of static models, we propose and evaluate the post-processing of their results, by applying quasi-dynamic network loading.