Use of Harsh-Braking Data from Connected Vehicles as a Surrogate Safety Measure

Traffic safety may be analyzed with the use of surrogate safety measures, measures of safety that do not incorporate collision data but rather rely on the concept of traffic conflicts. Use of these measures provides several benefits over use of more traditional analysis methods with historical crash data. Surrogate measures eliminate the need to wait for crashes to occur to conduct a safety analysis. The amount of time required for enough crash data to accumulate can be significant, delaying safety analyses. Similarly, these measures allow for safety analysis to be conducted prior to crashes occurring, potentially calling attention to hazardous areas which may be altered to prevent crashes. In addition to these benefits, traffic conflicts occur much more frequently than collisions, generating many more data points which in turn make statistical methods of analysis more effective. Evaluating surrogate safety measures for a particular transportation network is most effectively done with the use of traffic microsimulation or with connected vehicle data. Traffic microsimulation (such as the use of PTV VISSIM) will generate kinematic data that may then be used for computation of surrogate safety measures. A significant amount of research has been done on this topic, resulting in the establishment of algorithms for calculation of several different surrogate measures and validation of these measures. Kinematic data from connected vehicles has also been used for the calculation of surrogate safety measures. One data point collected by connected vehicles is harsh braking events which could serve as a surrogate safety measure. Because drivers usually brake more gently if given the opportunity to do so, harsh braking events indicate that a traffic conflict has occurred or is about to occur. Such events take away the driver’s opportunity to brake gently. This research establishes statistical models which relate harsh braking events to crashes on intersections and segments in Salt Lake City, Utah. The findings indicate that harsh braking events have the effect of reducing expected crashes because they represent traffic conflicts which were remedied through the use of harsh braking as an evasive action. The presence of schools and the presence of left turn lanes were also found to be statistically significant crash predictors. In addition to this research work a paper outlining the existing state of safety analysis with surrogate safety measures and evaluating the usefulness and practicality of various existing measures is presented

Cluster observes formation of high-beta plasma blobs

Late in a sequence of four moderate substorms on 26 July 2001, Cluster observed periods of a few minutes durations of high-beta plasma events (<i>B</i><10nT, β=2-30), connected with dipolarizations of the magnetic field. Cluster was located near 02:45 MLT, at <i>R</i>=19<i>R<sub>E</sub></i> and at about 5°N GSM. These events began late in the recovery phase of the second and about 5min before onset of the third substorm and lasted for three hours, way beyond the recovery phase of the fourth substorm. The most remarkable observation is that the onset coincided with the arrival of energetic (<i>E</i>~7keV) O<sup>+</sup> ions and energetic electrons obviously from the ionosphere, which tended to dominate the plasma composition throughout the remaining time. The magnetic flux and plasma transport is continuously directed equatorward and earthward, with oscillatory east-west movements superposed. Periods of the order of 5-10min and strong correlations between the magnetic elevation angle and log β (correlation coefficient 0.78) are highly reminiscent of the high-beta plasma blobs discovered with Equator-S and Geotail between 9 and 11<i>R<sub>E</sub></i> in the late night/early morning sector (Haerendel et al., 1999). <P style="line-height: 20px;"> We conclude that Cluster observed the plasma blob formation in the tail plasma sheet, which seems to occur predominantly in the recovery and post-recovery phases of substorms. This is consistent with the finding of Equator-S and Geotail. The origin is a pulsed earthward plasma transport with velocity amplitudes of only several tens of km/s

Driver’s behavior classification in vehicular communication networks for commercial vehicles

Vehicles are becoming more intelligent and connected due to the demand for faster, efficient, and safer transportation. For this transformation, it was necessary to increase the amount of data transferred between electronic modules in the vehicular network since it is vital for an intelligent system’s decision-making process. Hundreds of messages travel all the time in a vehicle, creating opportunities for analysis and development of new functions to assist the driver’s decision. Given this scenario, the dissertation presents the results of research to characterize driving styles of drivers using available information in vehicular communication network. This master thesis focuses on the process of information extraction from a vehicular network, analysis of the extracted features, and driver classification based on the extracted data. The study aims to identify aggressive driving behavior using real-world data collected from five different trucks running for a period of three months. The driver scoring method used in this study dynamically identifies aggressive driving behavior during predefined time windows by calculating jerk derived from the acquired data. In addition, the K-Means clustering technique was explored to group different behaviors into data clusters. Chapter 2 provides a comprehensive overview of the theoretical framework necessary for the successful development of this thesis. Chapter 3 details the process of data extraction from real and uncontrolled environments, including the steps taken to extract and refine the data. Chapter 4 focuses on the study of features extracted from the preprocessed data, and Chapter 5 presents two methods for identifying or grouping the data into clusters. The results obtained from this study have advanced the state-of-the-art of driver behavior classification and have proven to be satisfactory. The thesis addresses the gap in the literature by using data from real and uncontrolled environments, which required preprocessing before analysis. Furthermore, the study represents one of the pioneering studies conducted on commercial vehicles in an uncontrolled environment. In conclusion, this thesis provides insights into the development of driver behavior classification models using real-world data. Future research can build upon the techniques presented in this study and further refine the classification models. The thesis also addresses the threats to validity that were mitigated and provides recommendations for future research

Improvement of Inertial Profiler Measurements of Urban and Low-Speed Roadways

Inertial profilers can measure the longitudinal road elevation profile and the International Roughness Index (IRI) accurately when they are operated under favorable conditions. However, their performance deteriorates when they experience disturbances such as lateral and longitudinal accelerations, and when the profiler host vehicle travels very slowly or comes to a stop. Preliminary analytical and experimental work confirmed two major sources of measurement errors. First, slowly varying bias in vertical acceleration measurements causes drift in the signals derived from them after they are integrated twice to form a component of the measured profile. When the profiler host vehicle decelerates to very low speed or comes to a stop, the drift appears directly in the profile and causes a large artificial change in elevation that is concentrated over a small travel distance. Second, the inertial sensors experience dynamic changes in pitch and roll orientation as the profiler host vehicle reacts to driver inputs, such as braking and steering. Tilt of the sensitive axis causes contamination of the accelerometer signal by inputs along other axes. After double integration, this appears as errors in vertical curvature in the profile over the region where the braking or steering occurred. The errors reduce the accuracy of longitudinal profiles on urban road networks and low-speed roadways, and render the measurements of IRI unusable at important locations, such as intersections. This research proposed two solutions for addressing these measurement errors. First, the research investigated data processing algorithms that reduce measurement errors without the need for adding sensors to the typical inertial profiler design. These solutions combine specialized processing algorithms with standard filtering techniques to mitigate artificial roughness caused by drift and misalignment. Since no additional hardware is required, these algorithms offer low-cost options for immediate implementation within the existing fleet. The algorithms do not offer a complete solution, because they mitigate large upward biases in roughness measured at stops at the cost of reducing the validity of measured profile at low speed. Second, the research proposed and tested the use of additional sensors to improve profile measurement at low speed, during braking, and at stops. The augmented system includes inertial and GPS measurement of profiler kinematics. A multi-rate extended Kalman filter combines the inertial sensors with the GPS outputs to reduce drift and errors associated with profiler host-vehicle tilt. Use of a Rauch-Tung-Striebel smoother improves the mitigation of drift. A custom measurement system was designed and built for this research to enable an experimental evaluation of the proposed solutions. The performance of the error suppression algorithms and sensor augmentation proposed in this research was evaluated using the results from several test runs collected under challenging conditions, including operation at low speed, braking, and operation through a stop. For all test runs, performance is quantified using standard measures for accuracy of longitudinal profile and IRI used by road agencies for pavement network quality assurance and pavement network management. Use of inertial measurement in three dimensions and GPS measurement of profiler height and orientation is shown to give the best performance. The recommended processing algorithm integrates an additional mode of operation into the Kalman filter that applies an alternative measurement model at stops. Nearly equivalent performance was observed when the GPS outputs were replaced by artificial signals. This version of the system offers an option for measurement in urban canyons.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167920/1/stevemk_1.pd