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  • Vehicle-width Measurement Technology Development: Phase I Technical Memorandum
    Marinik, Andrew; Trimble, Tammy E.; Baker, Stephanie Ann; Bryson, Jared; Schaudt, William A.; Bowman, Darrell Scott (Virginia Center for Transportation Innovation and Research, 2011-08)
    The Virginia Tech Transportation Institute (VTTI) was tasked with investigating the feasibility of developing a vehicle-width measurement and alert system to reduce over-width violations in Virginia Department of Transportation’s (VDOT) work zones. A two-phase approach was developed to investigate the measurement system feasibility. During Phase I, the focus of the current report, VTTI first generated design criteria with support from stakeholders (i.e., the Virginia Center for Transportation Innovation and Research [VCTIR] and VDOT). Next, researchers and engineers assessed existing vehicle-width measurement systems against these criteria to determine design gaps, and then explored solutions (e.g., new technologies) to these design gaps. Identified potential solutions were then tested on the Virginia Smart Road. In the end, VTTI developed a preliminary system architecture for a vehicle-width measurement system. Based on the findings of this research effort three recommendations are offered to guide future development of a vehicle-width measurement system.
  • Draft Final Report: Development of Hazardous Materials (HM) Shipper Prioritization Program
    Schaudt, William A.; Bowman, Darrell Scott; Marinik, Andrew; Baker, Stephanie Ann; Trimble, Tammy E.; Hanowski, Richard J. (Virginia Tech Transportation Institute, 2009-02-28)
    In the mid-1990s, an attempt was made to develop a performance-based prioritization for Hazardous Materials (HM) shippers. During this attempt it became apparent that there was insufficient performance data to develop such a system. In response, FMCSA developed the HM Package Inspection Program (HMPIP) to focus on inspecting individual shipments of HM at the roadside or on carriers’ docks. Due to the improvements made over the years to the package inspection data collected during HMPIP inspections, HM incident data, and improved departmental data identifying companies involved in shipping HM, FMCSA has begun a second effort to develop a performance-based prioritization of HM shippers. The purpose of the current project was for the Virginia Tech Transportation Institute (VTTI) to review, document, and recommend improvements to FMCSA’s HM Shipper Prioritization Program. This project consisted of six major tasks, all of which were successfully executed by VTTI. After the kick-off meeting and the successful completion of a detailed work plan, a peer review committee was formed. Members of the peer review committee were to participate in two peer review meetings during the course of the project. The purpose of the first peer review meeting was to have the study methodology and data collection techniques reviewed by the committee. The purpose of the second peer review meeting was to review the study findings and conclusions. Another major step in this project was to review and examine the current Hazardous Materials (HM) Shipper Prioritization Program, which included two distinct prioritization algorithms, and to develop software titled the HM Shipper Prioritization Application (HMSPA). HMSPA was then beta tested in states with existing shipper programs. The focus of these onsite tests was usability testing with potential end users. Both subjective and objective data were collected by way of questionnaires and performance tasks. All results were very positive indicating that the beta version, with minor modifications based on user recommendations, should move forward into a fully functioning application for FMCSA.
  • Field Evaluation of Alternative Automated Systems for Reducing Illegal Passing of School Buses, DTNH22-00-07007, Task Order 1
    Hanowski, Richard J.; Spaulding, Jeremy M.; Gaskins, Charla; Schaudt, William A.; Miller, Steven; Holbrook, G. Thomas; Olson, Rebecca Lynn; Dingus, Thomas A.; Hickman, Jeffrey S.; Huey, Richard; Llaneras, Eddy E. (Virginia Tech Transportation Institute, 2007-03-27)
    The overall objective of this research was to develop a prototype system that would automatically detect and record vehicles that illegally pass stopped school buses. There were four primary steps in meeting this objective: (1) determine the feasibility of developing and implementing a prototype system using advanced technology that would automatically document the identity of drivers and their vehicles that illegally pass stopped school buses; (2) if feasible, build a prototype unit; (3) design and conduct a proof-of-concept field test to determine system adequacy, including its accuracy and reliability; and (4) develop a set of recommendations for further development, research, and demonstration of the approach in an operational field setting. The objective of the second part of the research was to refine the initial system that had been developed in the first part.
  • Development of Hazardous Materials (HM) Shipper Prioritization Program: Technical Brief
    Marinik, Andrew; Schaudt, William A.; Daily, Brian; Bowman, Darrell Scott; Hanowski, Richard J. (Virginia Tech Transportation Institute, 2009-02)
    The Federal Motor Carrier Safety Administration (FMCSA) developed the Hazardous Materials Package Inspection Program (HMPIP) to focus on inspecting individual shipments at the roadside or on carriers’ docks. One output of this program is a resulting dataset that can be used to develop a performance-based prioritization of HM shippers for inspection. The Virginia Tech Transportation Institute (VTTI) was tasked to review, document, and recommend improvements to the HM Shipper Prioritization Program. As part of that effort, VTTI developed software titled the HM Shipper Prioritization Application (HMSPA) that was beta tested in a sample of states with existing shipper programs. The purpose of this report is to provide documentation on the development process and the final HMSPA design that was completed by VTTI.
  • Enhanced Rear Signaling (ERS) for Heavy Trucks: Phase III – Development of Field Operational Test
    Schaudt, William A.; Bowman, Darrell Scott; Trimble, Tammy E.; Medina, Alejandra; Bocanegra, Joseph L.; Baker, Stephanie Ann; Marinik, Andrew; Wierwille, Walter W.; Hanowski, Richard J. (Virginia Tech Transportation Institute, 2010-09)
    The Enhanced Rear Signaling (ERS) for Heavy Trucks project was directed at investigating methods to reduce or mitigate those crashes where a heavy truck has been struck in the rear by another vehicle. Prior to the current effort, two phases of work had been completed on this project. The purpose of the current effort, Phase III, focused on exploring the benefits of the countermeasures developed in previous phases, and to develop a plan for a large scale Field Operational Test (FOT). During crash database analyses in the current project it was found that, in 2006, there were approximately 23,500 rear-end crashes involving heavy trucks which resulted in 135 fatalities and 1603 incapacitating injuries. Many different types of ERSs were investigated in this study across both the auditory and visual modalities. Visual warning signals were found to be the most beneficial at signaling following-vehicle drivers (more specifically rear warning-light configurations). The research team recommended that one specific configuration be selected for real-world data collection based on its high performance and the potential success of future design implementation. Overall, the final radar-based cautionary ERS system was robust in real-world driving conditions and is recommended for an FOT.
  • Guidelines for the Operation, Assembly, Repair, Testing and Inspection of Hazardous Material Cargo Tanks
    Bowman, Darrell Scott; Marinik, Andrew; Trimble, Tammy E.; Baker, Stephanie Ann; Selz, Allen (Virginia Tech Transportation Institute, 2009-06-30)
    This document consists of guidelines and recommendations related to operations, assembly, repair, testing and inspection of cargo tanks hauling hazardous materials. This document is the result of research findings from the project titled: Research to Identify the Factors that Affect the Service Life of Cargo Tanks [VTRC # 08-0669-10, FMCSA # TMC75-07-H-00008 Task Order # 2].
  • Light Vehicle-Heavy Vehicle Interactions: A Preliminary Assessment Using Critical Incident Analysis
    Hanowski, Richard J.; Keisler, Aysha S.; Wierwille, Walter W. (United States. Federal Motor Carrier Safety Administration, 2004-05)
    Two recently completed on-road, in situ data collection efforts provided a large data set in which to conduct an examination of near-crashes and crashes (critical incidents) that occurred between light vehicles (LV) and heavy vehicles (HV). Video and other sensor data collected during the two studies were used to characterize critical incidents that were recorded between LV and HV drivers. Across both studies, 210 LV-HV critical incidents were recorded. Of these, 78 percent were initiated by the light vehicle driver. Aggressive driving, on the part of the LV driver, was found to be the primary contributing factor for LV driver initiated incidents. For HV driver initiated incidents, the primary contributing factor was poor driving technique. The results suggest that efforts at addressing LV-HV interaction incidents should focus on aggressive light vehicle drivers. Additionally, it is recommended that HV drivers might benefit from improved driver training that includes instruction on defensive driving.
  • The Impact of Driving, Non-driving Work, and Rest Breaks on Driving Performance in Commercial Vehicle Operations
    Blanco, Myra; Hanowski, Richard J.; Olson, Rebecca Lynn; Morgan, Justin F.; Soccolich, Susan A.; Wu, Shih-Ching (United States. Federal Motor Carrier Safety Administration, 2011-05)
    Current hours-of-service (HOS) regulations prescribe limits to commercial motor vehicle (CMV) drivers' operating hours. Besides assessing activities performed in the 14-hour workday, the relationship between safety-critical events (SCEs) and driving hours, work hours, and breaks was investigated. The data used in the analyses were collected in the Naturalistic Truck Driving Study and included 97 drivers and about 735,000 miles of continuous driving data. The assessment of the drivers' workday determined that, on average, drivers spent 66 percent of their shift driving, 23 percent in non-driving work, and 11 percent resting. Analyses on driving hours (i.e., driving only) and SCE risk found a time-on-task effect across hours. Analyses on work hours (i.e., driving in addition to non-driving work) found that risk of being involved in an SCE increased as work hours increased. This suggests that time-on-task effects may not be related to driving hours alone, but implies an interaction between driving hours and work hours: if a driver begins the day with several hours of non-driving work, followed by driving that goes deep into the 14- hour workday, SCE risk was found to increase. The finding from the workday characterization that drivers spent approximately 23 percent of their workday performing non-driving work provides a possible explanation for this time-on-task effect across work hours. Breaks from driving were found to be beneficial in reducing SCEs (during 1- hour window after a break) and were effective to counteract the negative effects of time-on-task.
  • Enhanced Night Visibility Series, Volume XIII Phase III - Study 1: Evaluation of Discomfort Glare During Nighttime Driving in Clear Weather
    McLaughlin, Shane B.; Hankey, Jonathan M.; Dingus, Thomas A. (United States. Federal Highway Administration, 2005)
    Phase III-- Study 1 was performed to further explore findings on far infrared (FIR) systems from Phase II, to investigate near infrared (NIR) and high intensity discharge (HID) technologies, and to investigate detection and recognition of retroreflective infrastructure components. The empirical testing for this study was performed at the Virginia Smart Road testing facility during clear weather conditions. A total of 18 participants were involved in the study. A 6 by 3 by 17 mixed-factorial design was used to investigate the effects of 6 different types of vision enhancement systems, 3 age groups, and 17 object presentations on detection and recognition distances; subjective evaluations were obtained for the different systems as well. The results of the empirical testing suggest that infrared (IR) systems, when designed correctly, can provide pedestrian detection benefit in clear weather, particularly for pedestrians in dark clothing and veiled in the glare of oncoming headlamps. A wider field of view display appears to facilitate detection in curves of 1,250-m radius. Retroreflective objects may be detected earlier in an NIR display, but require direct visual observation to recognize the object or read signage. HID systems did not provide detection benefit over the baseline halogen headlamps tested.
  • Empirical Studies on Traffic Flow in Inclement Weather
    Hranac, Robert; Sterzin, Emily; Krechmer, Daniel; Rakha, Hesham A.; Farzaneh, Mohamadreza (United States. Federal Highway Administration, 2006-10)
    Weather causes a variety of impacts on the transportation system. While severe winter storms, hurricanes, or flooding can result in major stoppages or evacuations of transportation systems and cost millions of dollars, day-to-day weather events such as rain, fog, snow, and freezing rain can have a serious impact on the mobility and safety of the transportation system users. These weather events can result in increased fuel consumption, delay, number of accidents, and significantly impact the performance of the transportation system. The overall goal of the research work undertaken in this study was to develop a better understanding of the impacts of weather on traffic flow. The research was intended to accomplish the following specific objectives: (1)Study the impact of precipitation on macroscopic traffic flow parameters over a full range of traffic states; 2) Study the impact of precipitation on macroscopic traffic flow parameters using consistent, continuous weather variables; 3) Study the impact of precipitation on macroscopic traffic flow parameters on a wide range of facilities; 4) Study regional differences in reaction to precipitation; and 5) Study macroscopic impacts of reduced visibility. The work documented in this report was conducted in two parts: 1) literature review and development of a data collection and analysis plan, and 2) analysis and interpretation of the results. The recommended plan combined the use of macroscopic traffic data archives with archived weather data in order to meet the research goals that include achieving better understanding of the impacts of weather on macroscopic traffic flow. The results of the research conducted for this study were helpful in identifying weather impacts of traffic flow in the three cities studied, Minneapolis-St. Paul, Baltimore and Seattle. No impacts were found on traffic stream jam density, but both rain and snow did impact traffic free-flow speed, speed-at-capacity and capacity and parameters varied with precipitation intensity. The results of these analyses are documented in the report. This report concludes with some recommendations of future research related to weather and traffic flow. Several ideas are presented including enhancing the macroscopic analysis used in this study. Additional work is proposed related to human factors and microscopic traffic modeling.
  • Methodology to Evaluate Teen Driver Training Programs : [brief]
    Trimble, Tammy E. (Wisconsin. Department of Transportation, 2014-03)
    In the United States, teenage drivers are more at risk of being involved in crashes than any other age group. Statistics reveal a clear need for improving teenagers' driving skills, judgment and behavior. Driver education programs are a crucial part of training drivers. These programs are managed on a state-by-state basis, and therefore significant variability can exist between states, and to some degree even within each state.
  • Agent-Based Game Theory Modeling for Driverless Vehicles at Intersections
    Rakha, Hesham A.; Zohdy, Ismail H.; Kamalanathsharma, Raj Kishore (United States. Department of Transportation, 2013-02-19)
    This report presents three research efforts that were published in various journals. The first research effort presents a reactive-driving agent based algorithm for modeling driver left turn gap acceptance behavior at signalized intersections. This model considers the interaction between driver characteristics and vehicle physical capabilities. The model explicitly captures the vehicle constraints on driving behavior using a vehicle dynamics model. In addition, the model uses the driver's input and the psychological deliberation in accepting/rejecting a gap. The model is developed using a total of 301 accepted gaps and subsequently validated using 2,429 rejected gaps at the same site and also validated using 1,485 gap decisions (323 accepted and 1,162 rejected) at another site. The proposed model is considered as a mix between traditional and reactive methods for decision making and consists of three main components: input, data processing and output. The input component uses sensing information, vehicle and driver characteristics to process the data and estimate the critical gap value. Thereafter, the agent decides to either accept or reject the offered gap by comparing to a driver-specific critical gap (the offered gap should be greater than the critical gap for it to be accepted). The results demonstrate that the agent-based model is superior to the standard logistic regression model because it produces consistent performance for accepted and rejected gaps (correct predictions in 90% of the observations) and the model is easily transferable to different sites. The proposed modeling framework can be generalized to capture different vehicle types, roadway configurations, traffic movements, intersection characteristics, and weather effects on driver gap acceptance behavior. The findings of this research effort is considered as an essential stage for modeling autonomous/driverless vehicles The second effort develops a heuristic optimization algorithm for automated vehicles (equipped with cooperative adaptive cruise control CACC systems) at uncontrolled intersections using a game theory framework. The proposed system models the automated vehicles as reactive agents interacting and collaborating with the intersection controller (manager agent) to minimize the total delay. The system is evaluated using a case study considering two different intersection control scenarios: a four-way stop control and the proposed intersection controller framework. In both scenarios, four automated vehicles (a single vehicle per approach) were simulated using a Monte Carlo simulation that was repeated 1000 times. The results show that the proposed system reduces the total delay relative to a traditional stop control by 35 seconds on average, which corresponds to an approximately 70 percent reduction in the total delay. The third effort presents a new tool for optimizing the movements of autonomous/driverless vehicles through intersections: iCACC. The main concept of the proposed tool is to control vehicle trajectories using Cooperative Adaptive Cruise Control (CACC) systems to avoid collisions and minimize intersection delay. Simulations were executed to compare conventional signal control with iCACC considering two measures of effectiveness - delay and fuel consumption. Savings in delay and fuel consumption in the range of 91 and 82 percent relative to conventional signal control were demonstrated, respectively. It is anticipated that the findings of this report may contribute in the future of advanced vehicles control and connected vehicles applications.
  • Enhanced Night Visibility Series, Volume XII: Overview of Phase II and Development of Phase III Experimental Plan
    Hankey, Jonathan M.; Blanco, Myra; Neurauter, Michael L.; Gibbons, Ronald B.; Porter, Richard J.; Dingus, Thomas A. (United States. Federal Highway Administration, 2005-12)
    This volume provides an overview of the six studies that compose Phase II of the Enhanced Night Visibility project and the experimental plan for its third and final portion, Phase III. The Phase II studies evaluated up to 12 vision enhancement systems in terms of drivers' ability to detect and recognize objects, visibility of pavement markings, and discomfort caused by glare from oncoming headlamps. Drivers' ability to detect and recognize objects was assessed in clear, rain, fog, and snow conditions. The results indicated that supplemental ultraviolet headlamps do not provide sufficient benefit to justify further testing. The performance of supplemental infrared (IR) vision enhancement systems, on the other hand, was robust enough to suggest further investigation. As a result, additional IR testing, disability glare testing, and off-axis object detection on the Virginia Smart Road were proposed as a replacement for public road Phase III testing with UV-A. The details of the experimental plan for each of these testing areas are provided in the Phase III portion of this report.
  • Field and Modeling Framework and Case Study of Truck Weigh Station Operations: Final report
    Katz, Bryan J.; Rakha, Hesham A. (Virginia Tech. Virginia Tech Transportation Institute, 2002-01)
    Weigh-in-Motion (WIM) systems improve the capacity of weigh station operations significantly by screening trucks while traveling at high speeds and only requiring trucks within a threshold of a maximum permissible gross of axle weight to be weighed on more accurate static scales. Consequently, the operation of a weigh station is highly dependent on the accuracy of the screening WIM system. This thesis develops a procedure for relating axle accuracy to gross vehicle accuracy and develops a field and modeling framework for evaluating weigh station operations. The WIM scale operation at the Stephens City weigh station in Virginia is examined to demonstrate how the field and modeling framework can be applied to evaluate the operation of a weigh station. Specifically, the field evaluation evaluated the accuracy of the WIM technology in addition to the operations of the weigh station in terms of service time, system time, and delay incurred at the static scales. During the field evaluation of the Stephens City WIM load cell system, the WIM technology was found to estimate truck weights to within 6 and 7 percent of the static weights 95 percent of the time. The modeling framework provides a methodology that can be used to determine the effects of the truck demand, the WIM accuracy, the system threshold, and the WIM calibration on system performance. The number of vehicles sent to the static scale and bypass lanes as well as the amount of delay experienced were analyzed for various system characteristics. The proposed framework can be utilized to estimate vehicle delay at a weigh station.
  • Naturalistic Driving Data for the Analysis of Car-following Models
    Rakha, Hesham A.; Sangster, John; Du, Jianhe (United States. Department of Transportation, 2013-02-21)
    This report presents two research efforts that have been published as conference papers through the Transportation Research Board Annual Meeting, the first of which is under review for journal publication. The first research effort investigates the general application of naturalistic driving data to the modeling of car following behavior. The driver-specific data available from naturalistic driving studies provides a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. The first research effort seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models. The second effort examines how insights gained from naturalistic data may serve to improve existing car following models. The research presented analyzes the simplified behavioral vehicle longitudinal motion model, currently implemented in the INTEGRATION software, known as the Rakha-Pasumarthy-Adjerid (RPA) model. This model utilizes a steady-state formulation along with two constraints, namely: acceleration and collision avoidance. An analysis of the model using the naturalistic driving data identified a deficiency in the model formulation, in that it predicts more conservative driving behavior compared to naturalistic driving. Much of the error in simulated car-following behavior occurs when a car-following event is initiated. As a vehicle enters the lane in front of a subject vehicle, the spacing between the two vehicles is often much shorter than is desired; the observed behavior is that, rather than the following vehicle decelerating aggressively, the following vehicle coasts until the desired headway/spacing is achieved. Consequently, the model is enhanced to reflect this empirically observed behavior. Finally, a quantitative and qualitative evaluation of the original and proposed model formulations demonstrates that the proposed modification significantly decreases the modeling error and produces car-following behavior that is consistent with empirically observed driver behavior.
  • Establishing a Methodology to Evaluate Teen Driver-training Programs
    Trimble, Tammy E.; Baker, Stephanie Ann; Schaudt, William A.; Schrader, Taryn (Wisconsin. Department of Transportation. Library and Research Unit, 2013-11)
    The goal of this research project was to develop a methodology to assist the Wisconsin Department of Transportation (WisDOT) in the evaluation of effectiveness of teen driver education programs over the short and long terms. The research effort was divided into two phases. Phase I focused on the development of an evaluative methodology that was based upon a review of the relevant literature and Wisconsin-specific policies and available data sources. This review culminated in a program assessment tool focused on four contributing areas of teen driver training and education: 1) Guardian Involvement, 2) Driver Education and Training Curricula Requirement, 3) GDL Coordination, and 4) Instructor Qualifications. The proposed methodology was presented to the Project Oversight Committee and was validated through two rounds of pilot testing using materials provided by programs and schools under the oversight of both WisDOT and the Wisconsin Department of Public Instruction. The resulting methodology informed the Phase II implementation plan recommendations. Work products included within this report are an annotated bibliography, a knowledge base documenting best practices and Wisconsin-specific data source, a methodology that may be used to analyze and evaluate the effectiveness of driver-training programs as they relate to the demonstrated safety and behavior of teen drivers in Wisconsin, and a three-phase implementation plan.
  • Calibration of Steady-state Car-following Models using Macroscopic Loop Detector Data
    Rakha, Hesham A.; Gao, Yu (Virginia Tech. Virginia Tech Transportation Institute, 2010-05)
    The paper develops procedures for calibrating the steady-state component of various car following models using macroscopic loop detector data. The calibration procedures are developed for a number of commercially available microscopic traffic simulation software, including: CORSIM, AIMSUN2, VISSIM, Paramics, and INTEGRATION. The procedures are then applied to a sample dataset for illustration purposes. The paper then compares the various steady-state car-following formulations and concludes that the Gipps and Van Aerde steady-state car following models provide the highest level of flexibility in capturing different driver and roadway characteristics. However, the Van Aerde model, unlike the Gipps model, is a single-regime model and thus is easier to calibrate given that it does not require the segmentation of data into two regimes. The paper finally proposes that the car-following parameters within traffic simulation software be link-specific as opposed to the current practice of coding network-wide parameters. The use of link-specific parameters will offer the opportunity to capture unique roadway characteristics and reflect roadway capacity differences across different roadways.
  • Evaluation and Development of a University Visitor Parking Management Framework
    Gurram, Sashikanth; Rakha, Hesham A. (Virginia Tech. Virginia Tech Transportation Institute, 2010-05)
    Visitors constitute an important component of a university business. Given that visitors are typically unfamiliar with university campus layouts, special assistance may be needed to assist them with their parking needs. For example, personal and follow-up interviews conducted with Virginia Tech visitors revealed that approximately 28% of the visitors did not know the location of the most convenient parking lot to access their destination and approximately 32% of the visitors had to visit at least two parking lots before finding a parking space. This paper attempts to develop a simple web-based interactive parking management framework that uses the PHP Hypertext Preprocessor (PHP) and My Structured Query Language (MySQL) platform. This framework identifies the best possible parking lot(s) for a selected destination considering various factors including "the distance of the parking lot to the visitors" destination and historical occupancy of the parking lot at specific times-of-the-day and days-of-the-week. The web-based interactive system is designed to provide the visitor with two or more choices for parking that meet the visitors search criteria.
  • Predictive Eco-Cruise Control (ECC) System: Model Development, Modeling and Potential Benefits
    Rakha, Hesham A.; Ahn, Kyoungho; Park, Sangjun (United States. Department of Transportation. Research and Innovative Technology Administration, 2013-02-19)
    The research develops a reference model of a predictive eco-cruise control (ECC) system that intelligently modulates vehicle speed within a pre-set speed range to minimize vehicle fuel consumption levels using roadway topographic information. The study includes five basic tasks: (a) develop a vehicle powertrain model that can be easily implemented within eco-driving tools, (b) develop a simple fuel consumption model that computes instantaneous vehicle fuel consumption levels based on power exerted, (c) evaluate manual driving and conventional cruise control (CC) driving using field-collected data, (d) develop a predictive ECC system that uses the developed vehicle powertrain and fuel consumption models, and (e) evaluate the potential benefits of the proposed predictive ECC system on a pre-trip and fleet-aggregate basis. This study develops a predictive ECC system that can save fuel and reduce CO2 emissions using road topography information. The performance of the system is tested by simulating a vehicle trip on a section of Interstate 81 in the state of Virginia. The results demonstrate fuel savings of up to 15 percent with execution times within real time. The study found that the implementation of the predictive ECC system could help achieving better fuel economy and air quality.
  • Linear Regression Crash Prediction Models : Issues and Proposed Solutions
    Rakha, Hesham A.; Arafeh, Mohamadreza; Abdel-Salam, Abdel-Salam Gomaa; Guo, Feng; Flintsch, Alejandra Medina (Virginia Tech. Virginia Tech Transportation Institute, 2010-05)
    The paper develops a linear regression model approach that can be applied to crash data to predict vehicle crashes. The proposed approach involves novice data aggregation to satisfy linear regression assumptions namely error structure normality and homoscedasticity. The proposed approach is tested and validated using data from 186 access road sections in the state of Virginia. The approach is demonstrated to produce crash predictions consistent with traditional negative binomial and zero inflated negative binomial general linear models. It should be noted however that further testing of the approach on other crash datasets is required to further validate the approach.