Alejandra Medina

Efficacy of roll stability control and lane departure warning systems using carrier-collected data.

INTRODUCTION Large truck crashes have significantly declined over the last 10 years, likely due, in part, to the increased use of onboard safety systems (OSS). Unfortunately, historically there is a paucity of data on the real-world efficacy of these devices in large trucks. The purpose of this study was to evaluate the two OSSs, lane departure warning (LDW) and roll stability control (RSC), using data collected from motor carriers. METHOD A retrospective cohort approach was used to assess the safety benefits of these OSSs installed on Class 7 and 8 trucks as they operated during normal revenue-producing deliveries. Data were collected from 14 carriers representing small, medium, and large carriers hauling a variety of commodities. The data consisted of a total of 88,112 crash records and 151,624 truck-years that traveled 13 billionmiles over the observation period. RESULTS The non-LDW cohort had an LDW-related crash rate that was 1.917 times higher than the LDW cohort (p=0.001), and the non-RSC cohort had an RSC-related crash rate that was 1.555 times higher than the RSC cohort (p<0.001). CONCLUSIONS The results across analyses indicated a strong, positive safety benefit for LDW and RSC under real-world conditions. PRACTICAL APPLICATIONS The results support the use of LDW and RSC in reducing the crash types associated with each OSS.

Simulation of Driver Behavior with Agent-Based Back-Propagation Neural Network

Two microscopic simulation methods are compared for driver behavior: the Gazis–Herman–Rothery (GHR) car-following model and a proposed agent-based neural network model. To analyze individual driver characteristics, a back-propagation neural network is trained with car-following episodes from the data of one driver in the naturalistic driving database to establish action rules for a neural agent driver to follow under perceived traffic conditions during car-following episodes. The GHR car-following model is calibrated with the same data set, using a genetic algorithm. The car-following episodes are carefully extracted and selected for model calibration and training as well as validation of the calibration rules. Performances of the two models are compared, with the results showing that at less than 10-Hz data resolution the neural agent approach outperforms the GHR model significantly and captures individual driver behavior with 95% accuracy in driving trajectory.

Linking roadway crashes and tire–pavement friction: a case study

Tire–pavement friction is a factor that can affect the rate of vehicle crashes. Several studies have suggested that reduced friction during wet weather conditions, due to water on the pavement surface reducing the contact area between the tire and the pavement, increases vehicle crashes. This study evaluates the effect of friction on both wet- and dry-condition crashes. The data for the study were provided by the New Jersey Department of Transportation. Regression analysis was performed to verify the effect of friction on the rate of wet- and dry-condition vehicle crashes for various types of urban roads. It was found that friction is not only associated with the rate of wet-condition vehicle crashes, but it also impacts the rate of dry-condition vehicle crashes. The analysis also suggested that the developed regression models could be used to define the friction demand for different road categories.

Geographic Information Systems-Based Pavement Management System: A Case Study

A case study in which the researchers developed a prototype low-volume roads pavement management system (PMS) using a geographic information system (GIS) platform for Fountain Hills, Arizona, is described. The approach used and the problems faced are discussed. The first stage of the study entailed the collection of all information available from the city. City engineers provided a database with inventory and condition data, and an AutoCAD map of the city streets. The research team then evaluated several software packages. They selected the Road Surface Management System (RSMS) package developed at Arizona State University for the PMS portion. This program was developed to help local Arizona agencies systematically manage low-volume road and street pavements. The researchers evaluated two GIS packages for the study, based on the software’s capabilities and the city’s needs. They selected Maplnfo because it was less expensive and easier to learn. A menu-driven MapInfo application that runs the RSMS software, imports the pavement maintenance and rehabilitation program, and interactively prepares and displays colored maps with the analysis results was finally prepared. The combination of RSMS and MapInfo significantly reduced the effort required to develop the prototype system. It allowed the development and implementation of a GIS-PMS for the city, based on the existing digital data. City engineers were very impressed with the prototype system’s capabilities.

Relationship between Infrastructure, Driver Error, and Critical Incidents

During the course of a Federal Highway Administration (FHWA)-sponsored research project ”Identification of Driver Errors,” ”driver errors” in crashes and near-crashes (i.e., critical incidents) were investigated and an analysis approach was developed to help identify infrastructure-related and non-infrastructure-related problems at intersections and other roadway sites. The research team carefully examined the relationship between the infrastructure, driver error, and critical incidents. This was accomplished by first determining which incidents appeared to have an infrastructure component (signing, signaling, delineation, alinement, geometry, etc.). These incidents were then reexamined to determine the precise nature of the infrastructure components. Once the problems had been identified, countermeasures were suggested. This work is a demonstration of the synergistic effects of combining human factors engineering techniques with traffic engineering. By broadening the earlier traffic-conflict technique to include greater consideration of driver behavior, with emphasis on generalized driver errors, a better understanding of critical incidents and their corresponding countermeasures has been obtained.

Statistical Analysis of Spatiotemporal Link and Path Flow Variability

In the absence of advanced traveler information systems, commuters tend to select their routes of travel, within a congested network, primarily based on historical average travel times. Typical traffic conditions can be sufficient if a specific day is similar to these average conditions. However, if traffic conditions vary considerably from the norm, historical information may not be sufficient for commuters to make optimum travel decisions. Under these conditions the provision of real-time traffic information could offer significant benefits. Consequently, the proposed research effort attempts to characterize typical variability in traffic conditions using traffic volume data obtained from 31 dual-loop detector stations along a section of 1-66 between Manassas and Vienna, VA during a 3-month period. The detectors logged time-mean speed, volume, and occupancy measurements for each station and lane combination. Using these data, the paper examines the spatiotemporal link and path flow variability on weekdays and weekends. The generation of path flows is made through the use of a synthetic maximum likelihood approach. Statistical analysis of variance (ANOVA) tests are performed on the data. The results demonstrate that in terms of link flows and total traffic demand Mondays and Fridays are similar to core weekdays (Tuesdays, Wednesdays, and Thursdays). In terms of path flows, Fridays appear to be different from core weekdays.

Identification of warning signs in truck driving behavior before safety-critical events

This research effort aims to shed light upon the behaviors that drivers show during safety-critical events. The 100-Car Naturalistic Driving Study conducted by the Virginia Tech Transportation Institute collected useful data about such behaviors. By instrumenting automobiles for the study and allowing them to be used during normal daily routines, the data collected included normal driving and safety-critical events. This allowed the two data sets to be compared in order to ascertain differences. A discriminant analysis was used for this task, which resulted in interesting results when analyzing the data immediately before safety-critical events for two drivers. The discriminant analysis resulted in a way to distinguish safety-critical event behavior as the discriminant scores of the data immediately before a safety-critical event show a deviation from normal car-following behavior.

Agent-based evaluation of driver heterogeneous behavior during safety-critical events

Heterogeneous driver behavior during safety-critical events is more complicated than normal driving situations and is difficult to capture by statistical models. This paper applies an agent-based reinforcement learning method to represent heterogeneous driving behavior for different drivers during safety-critical events. The naturalistic driving data of different drivers during safety-critical events are used in agent training. As an output of the Neuro-Fuzzy Actor Critic Reinforcement Learning (NFACRL) training technique, behavior rules are embedded in different agents to represent heterogeneous actions between drivers. The results show that the NFACRL is able to simulate naturalistic driver behavior and present heterogeneity.

Incident Clustering: Diagnostic Approach for Assessing Usability of Intersections and Other Road Sites

During the course of an FHWA-sponsored research project, driver errors in crashes and near-crashes (i.e., critical incidents) were investigated, and an analysis approach was developed with which to identify infrastructure-related and non-infrastructure-related problems at intersections and other roadway sites. Referred to as a specific site critical incident analysis, this analysis approach consisted of four general steps: (a) selection of a site, (b) careful review of the sites critical-incident data, (c) determination of the potential critical-incident contributing factors, and (d) identification of incident clusters. An incident cluster is a group of critical incidents with similar characteristics that occur at the same location. From these incident clusters, researchers gained insight into potential infrastructure-related and non-infrastructure-related causal factors associated with critical incidents and, subsequently, could redesign solutions. An overview is presented of the incident cluster approach, as is an example analysis conducted for the driver error project.

Development of a knowledge-based formula to prioritize pavement rehabilitation projects

The development of a formula to prioritize pavement rehabilitation projects based on experts’ opinion is presented. This formula is used for the preparation of the Arizona Department of Transportation (ADOT) 5 yr pavement preservation program. The knowledge to determine the prioritization formula and treatment assignment criteria was captured from a group of experts using a survey design based on the rational factorial methodology. The survey questionnaire provided a set of pavement sections with different characteristics and asked the experts to indicate which sections should receive a rehabilitation treatment, what type of treatment they would recommend for each section, and which priority should be assigned to the resulting preservation project. Eight pavement characteristics (functional classification, geographical region, structural number, traffic, pavement rideability, cracking, rutting, and average maintenance cost for the past 3 yr) were identified as influence variables. A fractional factorial experiment design with four blocks was selected for the study. A questionnaire with a different set of pavement sections was prepared for each experimental block. The questionnaires were sent to pavement experts from ADOT headquarters and maintenance districts. The responses were analyzed, using linear regression analysis, to select the best models for the priority and treatment selection equations. The study showed that the rational factorial methodology is a powerful tool for capturing and quantifying expert opinions. The average opinion of the experts surveyed indicated that rutting, functional classification, roughness, cracking, traffic, and maintenance cost significantly influence the priority assigned to a preservation project. The treatment assignment model was less reliable; a larger panel of experts would be required to obtain a reliable model.