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

A Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery

Electric vehicle routing problems can be particularly complex when recharging must be performed mid-route. In some applications such as the e-commerce parcel delivery truck routing, however, mid-route recharging may not be necessary because of constraints on vehicle capacities and maximum allowed time for delivery. In this study, we develop a mixed-integer optimization model that exactly solves such a time-constrained capacitated vehicle routing problem, especially of interest to e-commerce parcel delivery vehicles. We compare our solution method with an existing metaheuristic and carry out exhaustive case studies considering four U.S. cities -- Austin, TX; Bloomington, IL; Chicago, IL; and Detroit, MI -- and two vehicle types: conventional vehicles and battery electric vehicles (BEVs). In these studies we examine the impact of vehicle capacity, maximum allowed travel time, service time (dwelling time to physically deliver the parcel), and BEV range on system-level performance metrics including vehicle miles traveled (VMT). We find that the service time followed by the vehicle capacity plays a key role in the performance of our approach. We assume an 80-mile BEV range as a baseline without mid-route recharging. Our results show that BEV range has a minimal impact on performance metrics because the VMT per vehicle averages around 72 miles. In a case study for shared-economy parcel deliveries, we observe that VMT could be reduced by 38.8\% in Austin if service providers were to operate their distribution centers jointly

Exploration of Corridor-Based Tolling Strategies for Virginia’s Express Toll Lanes

Virginia has invested significant resources in the development of express toll lanes (ETLs), which adjust toll rates dynamically based on the level of toll lane usage. A tool is needed to investigate the potential impact of the I-66 Outside-the-Beltway (OTB) ETLs on regional traffic patterns. This study developed a microscopic traffic simulation model in TransModeler to evaluate a set of corridor-based tolling strategies for the I-66 ETLs in NOVA. This model also considered the changes in vehicle occupancy, mode split, and departure time among travelers because of tolls based on locally collected data. An interactive map-based analyzer based on the simulation results was created to support quick scenario analysis and decision-making. I-66 OTB ETLs are estimated to bring tangible travel time improvements to the entire corridor. The simulation model showed that, compared to traffic conditions before the opening of the I-66 OTB ETLs, eastbound travel time along the general purpose lanes improved during the morning peak period by as much as 36.1% for the segment between Gainesville and Rt. 28, and 13.2% for the segment between Rt. 28 and I-495, respectively. During the afternoon peak period, Westbound travel time improved by as much as 17% for the segment between I-495 and Rt. 28, and 7.4% between Rt. 28 and Gainesville, respectively. The simulation model showed that the I-66 OTB ETLs would serve about 6,645 and 8,774 vehicles at a point right before the interchange with I-495, during the morning peak and the afternoon peak periods, respectively. When combined with the traffic on the general purpose lanes, the total throughputs increased to 30,783 (+6.8%) and 35,914 (+5.1%) vehicles, compared to the current throughputs of about 28,813 and 34,160 vehicles respectively during each peak period. The simulation model also showed that US 29 and US 50 do not serve as good alternatives for trips along I-66 OTB. The introduction of the ETLs created less than a 5% impact on the overall traffic volumes along the arterial roads. The choice of a dynamic pricing algorithm affected the number of ETL users and played a critical role in maintaining sufficient levels of service for the ETLs. Other factors, such as the value of time distribution, the vehicle occupancy requirement for free access, and the overall travel demand also have a significant impact on ETL usage and corridor traffic patterns. Among all the single factor scenarios, the policy of tolling only single occupant vehicles (HOT2+) instead of vehicles with one or two occupants (HOT3+) has the most significant impact on the performance of the corridor. The models developed in this study also have some limitations, such as the limited quantity of data for model calibration, the small number of scenarios tested, and uncertainties that may not be fully considered at this point (e.g., COVID19). Users should use these results with an appropriate understanding of the caveats. With these constraints considered, this study does provide a proactive assessment of the potential impact of the I-66 OTB ETLs under different scenarios, which can provide information to VDOT stakeholders for future decisions. The value of time, the vehicle occupancy, and the mode switch models estimated in this study can be applied in other studies in the region when no better data sources are available

HOMOCYSTEINE, PYRIDOXINE, FOLATE AND VITAMIN B12 LEVELS IN CHILDREN WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER

Background: In our study, we aimed to evaluate the serum homocysteine levels, pyridoxine, folate and vitamin B12 levels in children with attention deficit hyperactivity disorders (ADHD). Subjects and methods: This study included 30 newly diagnosed drug-naive children with ADHD (23 males and 7 female, mean age 9.3±1.8 years) and 30 sex-and age matched healthy controls. The diagnosis of ADHD was made according to DSM-V criteria. Children and adolescents were administered the Schedule for Affective Disorders and Schizophrenia for School Aged Children, Present and Lifetime Version, the Conners\u27 Parent Rating Scale-Revised, Long Form, the Conners\u27 Teacher Rating Scale and the Wechsler Intelligence Scale for Children Revised (WISC-R) for all participants. Homocysteine, pyridoxine, folate and vitamin B12 levels were measured with enzyme-linked immunosorbent assay. Results: Homocysteine, pyridoxine, folate and vitamin B12 levels were significantly lower in children with ADHD compared with their controls (p<0.05). A positive significant correlation was observed between the all WISC-R scores and vitamin B12 level in patients (r=0.408, p=0.025). Conclusions: The results obtained in this study showed that reduced homocysteine, pyridoxine, folate and vitamin B12 levels could be a risk factor in the etiology of ADHD

Determinants of high sensitivity troponin T concentration in chronic stable patients with heart failure: Ischemic heart failure versus non-ischemic dilated cardiomyopathy

Background: Cardiac troponin T is a marker of myocardial injury, especially when measured by means of the high-sensitivity assay (hs-cTnT). The echocardiographic and clinical predictors of hs-cTnT may be different in ischemic heart failure (IHF) and non-ischemic dilated cardiomyopathy (DCM).Methods: Sixty consecutive patients (19 female, 41 male; mean age 56.3 ± 13.9 years) with stable congestive heart failure (33 patient with IHF and 27 patients with DCM), with New York Heart Association functional class I–II symptoms, and left ventricular ejection fraction &lt; 40% were included.Results: In patients with IHF peak early mitral inflow velocity (E), E/peak early diastolic mitral annular tissue Doppler velocity (Em) lateral, peak systolic mitral annular tissue Doppler velocity (Sm) lateral and logBNP were univariate predictors of hs-cTnT above median. But only E/Em lateral was an independent predictor of hs-cTnT above median (p = 0.04, HR: 1.2,CI: 1–1.4). In patients with DCM; left atrial volume index, male sex, Sm lateral and global longitudinal strain (LV-GLS) were included in multivariate model and LV-GLS was detected to be an independent predictor for hs-cTnT above median (p &lt; 0.05, HR: 0.7, CI: 0.4–1.0).Conclusions: While LV-GLS is an independent predictor of hs-cTnT concentrations in patients with DCM, E/Em lateral predicted hs-TnT concentrations in patients with IHF

Semi-Automatic Simulation Initialization by Mining Structured and Unstructured Data Formats from Local and Web Data Sources

Initialization is one of the most important processes for obtaining successful results from a simulation. However, initialization is a challenge when 1) a simulation requires hundreds or even thousands of input parameters or 2) re-initializing the simulation due to different initial conditions or runtime errors. These challenges lead to the modeler spending more time initializing a simulation and may lead to errors due to poor input data. This thesis proposes two semi-automatic simulation initialization approaches that provide initialization using data mining from structured and unstructured data formats from local and web data sources. First, the System Initialization with Retrieval (SIR) approach allows for mining structured data from local and web sources. Second, the System Initialization with Search (SIS) approach allows for mining unstructured data from local and web sources. The SIS in pm1icular has a high level of automation that facilitates mining data for initialization from large data sets as found in social media allowing the modeler to tap into highly dynamic and data rich sources. Both approaches have been successfully applied to initialize two simulations; one that seeks to establish the impact of obesity-related illnesses on the clinical carrying capacity of an area. The other approach seeks to provide decision makers of an area (zip code, city or state) a baseline for course action analysis when affected by flooding due to sea level rise

Investigating Relationship Between Driving Patterns and Traffic Safety Using Smartphones Based Mobile Sensor Data

In spite of various advancements in vehicle safety technologies and improved roadway design practices, roadway crashes remain a major challenge. While certain hotspots may be unsafe primarily due to the geometric features of these locations, in many cases the safety risk seems to be an outcome of the unsafe driving patterns along the roadway stretching downstream and/or upstream of the actual crash locations. Even though there is plenty of research on correlating safety measures to roadway characteristics and some elements of traffic flow (e.g., exposure, speed), there is no significant literature on analyzing the correlation between high-resolution speed and acceleration data and crash risks along highway segments. Collecting such high-resolution data is now feasible with the mobile consumer devices such as smartphones. Smartphones are now equipped with sensors capable of recording vehicle performance data at a very fine temporal resolution in a cost-effective way. The current project used this mobile sensor data to identify unsafe driving patterns and quantified the relationship between these driving patterns and traffic crash incidences. The models with microscopic traffic measures were shown to be statistically better than traditional models that only control for roadway geometry and traffic exposure variables. Also, from a methodological standpoint, generalized count models that provide more flexibility through spatial dependency, heterogeneous dispersion, and random parameter heterogeneity were found to perform better than standard Poisson and Negative Binomial models

Formal thought disorder in patients with first-episode schizophrenia: Results of a one-year follow-up study

Formal thought disorder (FTD) refers to abnormal speech patterns that can be characterized by deficiencies in thought organization and direction. The present study aimed to assess the factor structure of FTD and to examine its relationship with cognition and clinical features at first admission in patients with first-episode schizophrenia. We also examined the course of FTD during the twelve months after first admission. We assessed FTD using the alogia items of the Scale for the Assessment of Negative Symptoms and FTD items of the Scale for the Assessment of Positive Symptoms in 160 drug-naive patients. A three-factor structure as a disorganization factor, poverty factor, and verbosity factor were found in principal component analysis. The poverty factor was correlated negatively with executive functions, attention, and global cognition. The poverty factor was also correlated with global functioning. Admission FTD factor scores were not related to global functioning and work/study status at one year. The positive-FTD score decreased from admission to the third month, but no change occurred from the third to the twelfth month. The negative-FTD score did not differ throughout the follow-up. Our findings showed that FTD had three factors. Each factor had a different relationship with cognition and functioning

Predictors of long-acting injectable antipsychotic prescription at discharge in patients with schizophrenia and other psychotic disorders.

Long-acting injectable antipsychotics (LAIs) increase drug compliance and offer a reliable treatment option with stable pharmacokinetics. The aim of our study is to examine the rate and predictors of LAIs' prescription at discharge in inpatients with schizophrenia and other psychotic disorders. This retrospective study included 400 inpatients. Sociodemographic and clinical characteristics of the patients, the treatments applied in the past and prescribed at discharge were obtained from the hospitalization files. We compared these characteristics of those who were given LAI treatment at discharge to the patients who were given oral treatments. Thirty-nine percent of the patients were prescribed a LAI at discharge. Duration of illness was longer, and number of previous hospitalizations was higher in the LAI group. Nonadherence to the antipsychotics before the hospitalization, the previous history of LAI use, lack of insight at the admission and no previous antidepressant use were found as independent contributors to LAI prescription as the treatment of discharge in logistic regression analysis. Our study showed that LAIs are used at a high rate in our clinic; however, they are still initiated at a later stage, mostly in chronic patients with a lack of insight and compliance at admission