Proceedings, MSVSCC 2011

Proceedings of the 5th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2011 at VMASC in Suffolk, Virginia. 186 pp

Truck Trailer Classification Using Side-Fire Light Detection And Ranging (LiDAR) Data

Classification of vehicles into distinct groups is critical for many applications, including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The Federal Highway Administration (FHWA) developed a standardized 13-category vehicle classification ruleset, which meets the needs of many traffic data user applications. However, some applications need high-resolution data for modeling and analysis. For example, the type of commodity being carried must be known in the freight modeling framework. Unfortunately, this information is not available at the state or metropolitan level, or it is expensive to obtain from current resources. Nevertheless, using current emerging technologies such as Light Detection and Ranging (LiDAR) data, it may be possible to predict commodity type from truck body types or trailers. For example, refrigerated trailers are commonly used to transport perishable produce and meat products, tank trailers are for fuel and other liquid products, and specialized trailers carry livestock. The main goal of this research is to develop methods using side-fired LiDAR data to distinguish between specific types of truck trailers beyond what is generally possible with traditional vehicle classification sensors (e.g., piezoelectric sensors and inductive loop detectors). A multi-array LiDAR sensor enables the construction of 3D-profiles of vehicles since it measures the distance to the object reflecting its emitted light. In this research 16-beam LiDAR sensor data are processed to estimate vehicle speed and extract useful information and features to classify semi-trailer trucks hauling ten different types of trailers: a reefer and non-reefer dry van, 20 ft and 40 ft intermodal containers, a 40 ft reefer intermodal container, platforms, tanks, car transporters, open-top van/dump and aggregated other types (i.e., livestock, logging, etc.). In addition to truck-trailer classification, methods are developed to detect empty and loaded platform semi-trailers. K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost), and Support Vector Machines (SVM) supervised machine learning algorithms are implemented on the field data collected on a freeway segment that includes over seven-thousand trucks. The results show that different trailer body types and empty and loaded platform semi-trailers can be classified with a very high level of accuracy ranging from 85% to 98% and 99%, respectively. To enhance the accuracy by which multiple LiDAR frames belonging to the same truck are merged, a new algorithm is developed to estimate the speed while the truck is within the field of view of the sensor. This algorithm is based on tracking tires and utilizes line detection concepts from image processing. The proposed algorithm improves the results and allows creating more accurate 2D and 3D truck profiles as documented in this thesis

IOT SYSTEMS FOR TRAVEL TIME ESTIMATION

This research introduces new approach for vehicle re-identification by computing Relative Entropy and Pearson correlation between ILD signatures, and then estimating TT based on the highest correlated signatures. To clear measure noise, TT for vehicles is assumed to follow the same pattern within a certain time frame. Thus, TT values are arranged in time series groups before applying a spike detection algorithm to determine the TT range with the highest number of vehicles. A data spike is considered for estimating TT. Given that the number of vehicles within the spike is greater than number of vehicles in all other data groups, TT will be the mean value of TT within the spike

Developing Algorithms to Detect Incidents on Freeways From Loop Detector and Vehicle Re-Identification Data

A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident scenarios and generating the input data. Two incident scenarios are generated by closing either one lane or two lanes of a four-lane highway. The third scenario involves bottleneck blocking two lanes of the freeway with an incident occurring in the upstream of the bottleneck. The highway network is five miles long and simulated in VISSIM. Traffic parameters like occupancy, speed, flow and number of vehicles passing through the loop detector are collected to assess the traffic condition between the sensors or detectors. The proposed performance test inspects whether the algorithms thus tested were able to detect any occurrences and incidences within the first minutes in different scenarios and compares their respective detection to identify the best performing algorithm in all the contingencies. The results indicate that the implementation of this new approach not only reduces the dilemma of selecting thresholds but also checks algorithm performance in different incident scenarios so that the response time for clearing such incidences is as short as possible. Likewise, making use of Re identification data and travel time makes the incident detection more trivial and self-evident and thus outperformed the algorithms using traditional data like occupancy speed and volume in uncontested traffic conditions. Further different SVM models were trained and tested inspecting the effects of change in location of incident concerning detectors. However, using data from loop detector performed well when the incident happened at the upstream detector while using that from re-identification encountered delays in overall detection time for the same

Bayesian Models for Reidentification of Trucks Over Long Distances on the Basis of Axle Measurement Data

Vehicle reidentification methods can be used to anonymously match vehicles crossing 2 different locations on the basis of vehicle attribute data. In this article, reidentification methods are developed to match commercial vehicles that cross 2 weigh-in-motion sites in Oregon that are separated by 145 miles. Using vehicle length and axle data as attributes to characterize vehicles, a Bayesian model is developed that uses probability density functions obtained by fitting Gaussian mixture models to a sample data set of matched vehicles. The reidentification model when applied to a test data set (where each downstream vehicle also crosses the upstream site) matches vehicles with an accuracy of 91% when both axle weight and axle spacings data are used. To account for the fact that not all vehicles in the downstream also cross the upstream site, an additional new step is developed to screen mismatched vehicles produced by the algorithm. For this step, several screening methods are developed that allow the user to trade off the total number of matched vehicles and error rate. For evaluating the effectiveness of the screening methods, 2 scenarios are considered. In the first scenario, only common vehicles that cross both the upstream and downstream sites are considered, whereas in the second scenario all downstream vehicles are considered. It is shown that the mismatch error can be reduced to as low as 1% and 5% at the expense of not matching about 25% of the common vehicles (crossing both sites) for the first and second scenarios, respectively

Federal Register

Daily publication of the U.S. Office of the Federal Register contains rules and regulations, proposed legislation and rule changes, and other notices, including "Presidential proclamations and Executive Orders, Federal agency documents having general applicability and legal effect, documents required to be published by act of Congress, and other Federal agency documents of public interest" (p. ii). Table of Contents starts on page iii