Missouri Work Zone Capacity: Results of Field Data Analysis, TPF-5(081), 2011

This report presents the results of work zone field data analyzed on interstate highways in Missouri to determine the mean breakdown and queue-discharge flow rates as measures of capacity. Several days of traffic data collected at a work zone near Pacific, Missouri with a speed limit of 50 mph were analyzed in both the eastbound and westbound directions. As a result, a total of eleven breakdown events were identified using average speed profiles. The traffic flows prior to and after the onset of congestion were studied. Breakdown flow rates ranged between 1194 to 1404 vphpl, with an average of 1295 vphpl, and a mean queue discharge rate of 1072 vphpl was determined. Mean queue discharge, as used by the Highway Capacity Manual 2000 (HCM), in terms of pcphpl was found to be 1199, well below the HCM’s average capacity of 1600 pcphpl. This reduced capacity found at the site is attributable mainly to narrower lane width and higher percentage of heavy vehicles, around 25%, in the traffic stream. The difference found between mean breakdown flow (1295 vphpl) and queue-discharge flow (1072 vphpl) has been observed widely, and is due to reduced traffic flow once traffic breaks down and queues start to form. The Missouri DOT currently uses a spreadsheet for work zone planning applications that assumes the same values of breakdown and mean queue discharge flow rates. This study proposes that breakdown flow rates should be used to forecast the onset of congestion, whereas mean queue discharge flow rates should be used to estimate delays under congested conditions. Hence, it is recommended that the spreadsheet be refined accordingly.

Metric Training for the Transportation Industry Module IV - Transportation Planning and Traffic Monitoring, HR-376

"Metric Training For The Highway Industry", HR-376 was designed to produce training materials for the various divisions of the Iowa DOT, local government and the highway construction industry. The project materials were to be used to introduce the highway industry in Iowa to metric measurements in their daily activities. Five modules were developed and used in training over 1,000 DOT, county, city, consultant and contractor staff in the use of metric measurements. The training modules developed deal with the planning through operation areas of highway transportation. The materials and selection of modules were developed with the aid of an advisory personnel from the highway industry. Each module is design as a four hour block of instruction and a stand along module for specific types of personnel. Each module is subdivided into four chapters with chapter one and four covering general topics common to all subjects. Chapters two and three are aimed at hands on experience for a specific group and subject. This module includes: Module 4 - Transportation Planning and Traffic Monitoring. Hands on examples of applications of metric measurements in the development of planning reports and traffic data collection are included in this module.

Evaluation of Different Methods to Calculate Heavy-Truck VMT, Final Report, December 2004

Reliable estimates of heavy-truck volumes are important in a number of transportation applications. Estimates of truck volumes are necessary for pavement design and pavement management. Truck volumes are important in traffic safety. The number of trucks on the road also influences roadway capacity and traffic operations. Additionally, heavy vehicles pollute at higher rates than passenger vehicles. Consequently, reliable estimates of heavy-truck vehicle miles traveled (VMT) are important in creating accurate inventories of on-road emissions. This research evaluated three different methods to calculate heavy-truck annual average daily traffic (AADT) which can subsequently be used to estimate vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa DOT were used to estimate AADT for two different truck groups (single-unit and multi-unit) using the three methods. The first method developed monthly and daily expansion factors for each truck group. The second and third methods created general expansion factors for all vehicles. Accuracy of the three methods was compared using n-fold cross-validation. In n-fold cross-validation, data are split into n partitions, and data from the nth partition are used to validate the remaining data. A comparison of the accuracy of the three methods was made using the estimates of prediction error obtained from cross-validation. The prediction error was determined by averaging the squared error between the estimated AADT and the actual AADT. Overall, the prediction error was the lowest for the method that developed expansion factors separately for the different truck groups for both single- and multi-unit trucks. This indicates that use of expansion factors specific to heavy trucks results in better estimates of AADT, and, subsequently, VMT, than using aggregate expansion factors and applying a percentage of trucks. Monthly, daily, and weekly traffic patterns were also evaluated. Significant variation exists in the temporal and seasonal patterns of heavy trucks as compared to passenger vehicles. This suggests that the use of aggregate expansion factors fails to adequately describe truck travel patterns.

Toolbox to Address Safety and Operations on School Grounds and Public Streets Adjacent to Elementary and Middle Schools in Iowa, August 2006

This study identified transportation safety issues at existing Iowa school sites through on-site observations, traffic data collection, and through interviews with schools, law enforcement, and traffic engineers. Frequently observed problems, such as crossing at unmarked crosswalks, unloading and loading students on the street side, inattentive student safety patrols, and illegal parking, were documented and solutions were recommended for implementation. The results of the study also conclude that regular communications between school officials, traffic engineers, law enforcement, parents, and school transportation personnel are all critical to promoting safe operations within school zones.

Pavement Markings and Safety Final Report, November 2010

Previous research on pavement markings from a safety perspective tackled various issues such as pavement marking retroreflectivity variability, relationship between pavement marking retroreflectivity and driver visibility, or pavement marking improvements and safety. A recent research interest in this area has been to find a correlation between retroreflectivity and crashes, but a significant statistical relationship has not yet been found. This study investigates such a possible statistical relationship by analyzing five years of pavement marking retroreflectivity data collected by the Iowa Department of Transportation (DOT) on all state primary roads and corresponding crash and traffic data. This study developed a spatial-temporal database using measured retroreflectivity data to account for the deterioration of pavement markings over time along with statewide crash data to attempt to quantify a relationship between crash occurrence probability and pavement marking retroreflectivity. First, logistic regression analyses were done for the whole data set to find a statistical relationship between crash occurrence probability and identified variables, which are road type, line type, retroreflectivity, and traffic (vehicle miles traveled). The analysis looked into subsets of the data set such as road type, retroreflectivity measurement source, high crash routes, retroreflectivity range, and line types. Retroreflectivity was found to have a significant effect in crash occurrence probability for four data subsets—interstate, white edge line, yellow edge line, and yellow center line data. For white edge line and yellow center line data, crash occurrence probability was found to increase by decreasing values of retroreflectivity.

Development of Concepts for Pavement Management, MLR-79-04, 1979

Sufficient evidence was not discovered in this brief search to alter the general opinion that the Serviceability (Present Serviceability Index-PSI) - Performance Concepts developed by the AASHO Road Test provides the optimum engineering basis for pavement management. Use of these concepts in Iowa has the additional advantage in that we have a reasonable quantity of historical data over a period of time on the change in pavement condition as measured by PSI's. Some additional benefits would be the ability to better assess our needs with respect to those being recommended to Congress by AASHTO Committees. These concepts have been the basis used for developing policies on dimensions and weight of vehicles and highway needs which the AASHTO Transport Committees have recommended to the United States House Committee on Ways and Means. The first recommendation based on these concepts was made in the mid 1960's. Iowa's participation in the evaluation for this recommendation was under the direction of our present Director of Transportation, Mr. Raymond Kassel. PSI Indexes had to be derived from subjective surface ratings at that time. The most recent recommendation to Congress was made in November of 1977. Based on the rationale expressed above, a pilot study of the major part of the rural interstate system was conducted. The Objective of the study was to measure pavement performance through the use of the Present Serviceability Index (PSI) - Pavement Performance concepts as developed by the AASHO Road Test and to explore the usefulness of this type of data as a pavement management tool. Projects in the vicinity of the major urban centers were not included in this study due to the extra time that would be required to isolate accurate traffic data in these areas. Projects consisting of asphalt surface courses on crushed stone base sections were not included.

Work Zone Simulation Model, 1999

To support the analysis of driver behavior at rural freeway work zone lane closure merge points, Center for Transportation Research and Education staff collected traffic data at merge areas using video image processing technology. The collection of data and the calculation of the capacity of lane closures are reported in a companion report, "Traffic Management Strategies for Merge Areas in Rural Interstate Work Zones". These data are used in the work reported in this document and are used to calibrate a microscopic simulation model of a typical, Iowa rural freeway lane closure. The model developed is a high fidelity computer simulation with an animation interface. It simulates traffic operations at a work zone lane closure. This model enables traffic engineers to visually demonstrate the forecasted delay that is likely to result when freeway reconstruction makes it necessary to close freeway lanes. Further, the model is also sensitive to variations in driver behavior and is used to test the impact of slow moving vehicles and other driver behaviors. This report consists of two parts. The first part describes the development of the work zone simulation model. The simulation analysis is calibrated and verified through data collected at a work zone in Interstate Highway 80 in Scott County, Iowa. The second part is a user's manual for the simulation model, which is provided to assist users with its set up and operation. No prior computer programming skills are required to use the simulation model.

VISSIM Calibration for Urban Freeways, 2015

In urban areas, interchange spacing and the adequacy of design for weaving, merge, and diverge areas can significantly influence available capacity. Traffic microsimulation tools allow detailed analyses of these critical areas in complex locations that often yield results that differ from the generalized approach of the Highway Capacity Manual. In order to obtain valid results, various inputs should be calibrated to local conditions. This project investigated basic calibration factors for the simulation of traffic conditions within an urban freeway merge/diverge environment. By collecting and analyzing urban freeway traffic data from multiple sources, specific Iowa-based calibration factors for use in VISSIM were developed. In particular, a repeatable methodology for collecting standstill distance and headway/time gap data on urban freeways was applied to locations throughout the state of Iowa. This collection process relies on the manual processing of video for standstill distances and individual vehicle data from radar detectors to measure the headways/time gaps. By comparing the data collected from different locations, it was found that standstill distances vary by location and lead-follow vehicle types. Headways and time gaps were found to be consistent within the same driver population and across different driver populations when the conditions were similar. Both standstill distance and headway/time gap were found to follow fairly dispersed and skewed distributions. Therefore, it is recommended that microsimulation models be modified to include the option for standstill distance and headway/time gap to follow distributions as well as be set separately for different vehicle classes. In addition, for the driving behavior parameters that cannot be easily collected, a sensitivity analysis was conducted to examine the impact of these parameters on the capacity of the facility. The sensitivity analysis results can be used as a reference to manually adjust parameters to match the simulation results to the observed traffic conditions. A well-calibrated microsimulation model can enable a higher level of fidelity in modeling traffic behavior and serve to improve decision making in balancing need with investment.

From Empirical Bayes to Full Bayes: Methods for Analyzubg Traffic Safey Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this light, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

Volume of Traffic on the Primary Road System, 2002

The Office of Transportation Data, in cooperation with the Federal Highway Administration, prepares this biennial traffic report. This report is used by federal, state, and local governmental agencies in determining highway needs, construction priorities, route location and environmental impact studies, and the application of appropriate design standards. The general public uses this information in determining the amount of traffic that passes a given area as they make their development plans and propose land use changes. The above reflects only a few of the many technical uses for this data.

Secondary Accident Data Fusion for Assessing Long-Term Performance of Transportation Systems, 2007

Secondary accident statistics can be useful for studying the impact of traffic incident management strategies. An easy-to-implement methodology is presented for classifying secondary accidents using data fusion of a police accident database with intranet incident reports. A current method for classifying secondary accidents uses a static threshold that represents the spatial and temporal region of influence of the primary accident, such as two miles and one hour. An accident is considered secondary if it occurs upstream from the primary accident and is within the duration and queue of the primary accident. However, using the static threshold may result in both false positives and negatives because accident queues are constantly varying. The methodology presented in this report seeks to improve upon this existing method by making the threshold dynamic. An incident progression curve is used to mark the end of the queue throughout the entire incident. Four steps in the development of incident progression curves are described. Step one is the processing of intranet incident reports. Step two is the filling in of incomplete incident reports. Step three is the nonlinear regression of incident progression curves. Step four is the merging of individual incident progression curves into one master curve. To illustrate this methodology, 5,514 accidents from Missouri freeways were analyzed. The results show that secondary accidents identified by dynamic versus static thresholds can differ by more than 30%.

Crash Data Validation: An Iowa Case Study, February, 2007

With the quickening pace of crash reporting, the statistical editing of data on a weekly basis, and the ability to provide working databases to users at CTRE/Iowa Traffic Safety Data Service, the University of Iowa, and the Iowa DOT, databases that would be considered incomplete by past standards of static data files are in “public use” even as the dynamic nature of the central DOT database allows changes to be made to both the aggregate of data and to the individual crashes already reported. Moreover, “definitive” analyses of serious crashes will, by their nature, lag seriously behind the preliminary data files. Even after these analyses, the dynamic nature of the mainframe data file means that crash numbers can continue to change long after the incident year. The Iowa DOT, its Office of Driver Services (the “data owner”), and institutional data users/distributors must establish data use, distribution, and labeling protocols to deal with the new, dynamic nature of data. In order to set these protocols, data must be collected concerning the magnitude of difference between database records and crash narratives and diagrams. This study determines the difference between database records and crash narratives for the Iowa Department of Transportation’s Office of Traffic and Safety crash database and the impacts of this difference.

An Examination of Iowa State Patrol Traffic Stops, October 2000 - March 2002

this report describes traffic law enforcement data collected by the Iowa State Patrol (ISP) related to traffic stops made by Troopers for October 1, 2000 through March 30, 2002. The data contained in this report summarizes the activities of approximately 435 troopers who are assigned to 15 posts throughout the State of Iowa The purpose of this voluntary data collection process was to provide the ISP with the ability to review traffic law enforcement variables in relation to traffic stops. The methodology for this research project was developed and implemented by ISP. Following the data collection period, the Iowa Division of Criminal and Juvenile Justice Planning (CJJP) was asked to assist in the analysis and reporting phase of this ambitious project.

Indianola Origin and Destination Traffic Study,1964

This a survey that determines the total number and type of vehicles entering and leaving Indianola to obtain origin and destination data from representataive samples of those vehicles.

From Empirical Bayes to Full Bayes: Methods for Analysing Traffice Safety Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this slight, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.