Impacts of the Extended-Weight Coal Haul Road System

The Extended-Weight Coal Haul Road System, created by the Kentucky Legislature in 1986, consists of all roads which carry over 50,000 tons of coal in a calendar year. Trucks hauling coal on this system are authorized to exceed normal weight limits through the payment of an annual decal fee. A research study was initiated in July of 1992 to analyze the impacts of the extended-weight system. Analyses in this report are based on the following: historical data on coal production and transportation: data from coal decal applications; interviews of legislators. transportation officials. coal company representatives. and coal trucking representatives: newspaper articles; vehicle classification data: analyses of pavement costs: pavement rideability data; and accident data. Primary conclusions include: I) The extended-weight 5)\u27Stem has apparently been somewhat successful in accomplishing the objective of enhancing the competitiveness and economic viability of the Kentucky coal industry; 2) Overall accident rates did not increase as a result of implementation of the extended-weight system. but the fatal accident injury rates were significantly higher on the extended-weight system and for trucks operating with the coal decal; 3) Advance-warning flashers have been evaluated and recommended as a means of reducing intersection accidents involving heavy/coal trucks; 4) The coal-decal fee structure results in a net annual loss in Road Fund revenue of approximately S2 million; 5) Forty percent of revenue from decal fees are allocated to counties even though county-maintained roads comprise only eight percent of the extended-weight system; 6) Heavier weights of coal-decal trucks add approximately $9 million annually to the pavement overlay costs; 7) Road users throughout the state are subsidizing the movement of Kentucky coal by participating in the cost of maintaining and improving tile highway system; and 8) Possibly reflecting the increased funding of extended-weight roads., the rideability index. has risen to a level above the statewide average. The primary recommendation was that the extended-weight system should evolve into a comprehensive trucking network. A Resource and Commodity Highway System was evaluated as a separate study and found to be a feasible and desirable means of providing a trucking highway network that is fully compatible with the dimensions and characteristics of large trucks

Multipath signal phase and timing broadcast project

The Multipath Signal Phase and Timing (SPAT) Broadcast Project demonstrates a Safe Green Passage traffic signal application that provides speed guidance to an approaching driver so that a vehicle may safely pass through the green phase of an upcoming traffic signal. This is accomplished by the signal system’s ability to send SPAT information to approaching vehicles even when they are several miles or multiple signals away. This project was developed in partnership with the Institute for Information Industry, a Taiwan ITS consortium, the Michigan Department of Transportation (MDOT), and the University of Michigan Transportation Research Institute (UMTRI). The Institute for Information Industry provided system components, software and a related Traffic Signal Violation Warning application. UMTRI provided a Paramics system simulation of the multipath SPAT application, functional requirements, and overall coordination of the project. The implementation at two signalized intersections was supported by the Road Commission of Oakland County. The system was developed and demonstrated on August 8, 2011. While the functional demonstration was successful, the traffic simulation of 25,000+ vehicles did not show the expected statistical increase in flow-through efficiency. Further, the Dedicated Short Range Communications (DSRC) technology requires line-of-sight placement, which significantly limits the ability to maximize the distance from the signal to the vehicle, and therefore limits the effectiveness of the application.Michigan Department of Transportation, ITS Program Officehttp://deepblue.lib.umich.edu/bitstream/2027.42/97024/1/102940.pd

Evaluation of Sustained Enforcement, Education, and Engineering Measures on Pedestrian Crossings

Pedestrian fatalities and injuries represent a growing percentage of all traffic fatalities and injuries. This project used a multifaceted approach to improving compliance to the Minnesota crosswalk law in Saint Paul, Minnesota, including: (1) education, (2) measurement, (3) enforcement efforts, (4) social norming, and (5) engineering treatment. The multifaceted activities were planned and implemented in Saint Paul with city traffic engineers and enforcement officers. The study initially observed 32% yielding and frequent multiple threat passing at 16 unsignalized, marked crosswalks throughout Saint Paul, measured through staged pedestrian crossings by the research team. A program was implemented that used a phased treatment approach of disseminating educational materials, conducting four waves of high visibility enforcement (HVE), displaying yielding averages on feedback signs across the city, and introducing low-cost engineering solutions through in-street signs. The results demonstrated a significant impact from education, HVE, and engineering to increase yielding to as high as 78% at enforcement sites and 61% at untreated sites. Multiple threat passing was also reduced. Overall, the study demonstrated that the HVE program and combined low-cost engineering were effective at improving compliance to the crosswalk law

Transportation System Performance Measures Using Internet of Things Data

The transportation system is undergoing a rapid change with innovative and promising technologies that provide real-time data for a variety of applications. As we transition into a technology-driven era and Internet of Things (IoT) applications, where everything is connected via a network of smart sensors and cloud computing, there will be an increasing amount of real-time data that will allow a better understanding of the transportation system. Devices emerging as a part of this connected environment can provide new and valuable data sources in a variety of transportation areas including safety, mobility, operations and intelligent transportation systems. Agencies and transportation professionals require effective performance measures and visualization tools to mine this big data to make design, operation, maintenance and investment decisions to improve the overall system performance. This dissertation discusses the development and demonstration of performance measures that leverage data from these emerging IoT devices to support analysis and guide investment decisions. Selected case studies are presented that demonstrate the impact of these new data sources on design, operation, and maintenance decisions. Performance measures such as vibration, noise levels and retroreflectivity were used to conduct a comprehensive assessment of different rumble strip configurations in the roadway and aviation environment. The results indicated that the 12 in sinusoidal wavelength satisfied the National Cooperative Highway Research Program (NCHRP) recommendations and reduced the noise exposure to adjacent homeowners. The application of low-cost rumble strips to mitigate runway incursions at general aviation airports was evaluated using the accelerations on the airframe. Although aircraft are designed for significant g-forces on landing, the results of analyzing accelerometers installed on airframes showed that long-term deployment of rumble strips is a concern for aircraft manufacturers as repeated traversal on the rumble strips may lead to excessive airframe fatigue. A suite of web dashboards and performance measures were developed to evaluate the impact of signal upgrades, signal retiming and maintenance activities on 138 arterials in the Commonwealth of Pennsylvania. For five corridors analyzed before and after an upgrade, the study found a reduction of 1.2 million veh-hours of delay, 10,000 tons of CO2 and an economic benefit of $32 million. Several billion dollars per year is expended upon security checkpoint screening at airports. Using wait time data from consumer electronic devices over a one-year period, performance dashboards identified periods of the day with high median wait times. The performance measures outlined in this study provided scalable techniques to analyze operating irregularities and identify opportunities for improving service. Reliability and median wait times were also used as performance measures to compare the standard and expedited security screening. The results found that the expedited screening was highly reliable than the standard screening and had a median wait time savings of 5.5 minutes. Bike sharing programs are an eco-friendly mode of transportation gaining immense popularity all over the world. Several performance measures are discussed which analyze the usage patterns, user behaviors and effect of weather on a bike sharing program initiated at Purdue University. Of the 1626 registered users, nearly 20% of them had at least one rental and around 6% had more than 100 rentals, with four of them being greater than 500 rentals. Bikes were rented at all hours of the day, but usage peaked between 11:00 and 19:00 on average. On a yearly basis, the rentals peaked in the fall semester, especially during September, but fell off in October and November with colder weather. Preliminary results from the study also identified some operating anomalies, which allowed the stakeholders to implement appropriate policy revisions. There are a number of outlier filtering algorithms proposed in the literature, however, their performance has never been evaluated. A curated travel time dataset was developed from real-world data, and consisted of 31,621 data points with 243 confirmed outliers. This dataset was used to evaluate the efficiency of three common outlier filtering algorithms, median absolute deviation, modified z-score and, box and whisker plots. The modified Z-score had the best performance with successful removal of 70% of the confirmed outliers and incorrect removal of only 5% of the true samples. The accuracy of vehicle to infrastructure (V2I) communication is an important metric for connected vehicle applications. Traffic signal state indication is an early development in the V2I communication that allows connected vehicles to display the current traffic signal status on the driver dashboard as the vehicle approaches an intersection. The study evaluated the accuracy of this prediction with on-field data and results showed a degraded performance during phase omits and force-offs. Performance measures such as, the probability of expected phase splits and the probability of expected green for a phase, are discussed to enhance the accuracy of the prediction algorithm. These measures account for the stochastic variations due to detectors actuations and will allow manufacturers and vendors to improve their algorithm. The application of these performance measures across three transportation modes and the transportation focus areas of safety, mobility and operations will provide a framework for agencies and transportation professionals to assess the performance of system components and support investment decisions

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Comprehensive Safety Analysis of Vulnerable Road User Involved Motor Vehicle Crashes

This dissertation explores, identifies, and evaluates a multitude of factors significantly affecting motor vehicle crashes involving pedestrians and bicyclists, commonly defined as vulnerable road users (VRUs). The methodologies are guided by the concept of safe behavior of different parties that are primary responsible for a crash, either a pedestrian, a bicyclist or a driver, pertaining to roadway design, traffic conditions, land use and built environment variables; and the findings are beneficial for recommending targeted and effective safety interventions. The topic is motivated by the fact that human factors contribute to over ninety percent of the crashes, especially the ones involving VRUs. Studying the effect of road users’ behavior, their responses to the dynamics of traveling environment, and compliance rate to traffic rules is instrumental to precisely measure and evaluate how each of the investigated variables changes the crash risk. To achieve this goal, an extensive database is established based on data collected from sources such as the linework from topologically integrated geographic encoding and referencing, Google maps, motor vehicle accident reports, Wisconsin Information System for Local Roads, and Smart Location Dataset from Environmental Protection Agency. The crosscutting datasets represent various aspects of motorist and non-motorists travel decisions and behaviors, as well as their safety status. With this comprehensive database, intrinsic relationships between pedestrian-vehicle crashes and a broad range of socioeconomic and demographic factors, land use and built environment, crime rate and traffic violations, road design, traffic control, and pedestrian-oriented design features are identified, analyzed, and evaluated. The comprehensive safety analysis begins with the structural equation model (SEM) that is employed to discover possible underlying factor structure connecting exogenous variables and crashes involving pedestrians. Informed by the SEM output, the analysis continues with the development of crash count models and responsible party choice models to respectively address factors relating to roles in a crash by pedestrians and drivers. As a result, factors contributing to crashes where a pedestrian is responsible, a driver is responsible, or both parties are responsible can be specified, categorized, and quantified. Moreover, targeted and appropriate safety countermeasures can be designed, recommended, and prioritized by engineers, planners, or enforcement agencies to jointly create a pedestrian-friendly environment. The second aspect of the analysis is to specify the crash party at-fault, which provides evidence about whether pedestrians, bicyclists or drivers are more likely to be involved in severe crashes and to identify the contributing factors that affect the fault of a specific road user group. An extensive investigation of the available information regarding the crash (i.e., issued citations, actions/circumstances that may have played a role in the crash occurrence, and crash scenario completed by the police officer) are considered. The goal is to recognize and measure the factors affecting a specific party at-fault. This provides information that is vital for proactive crisis management: to decrease and to prevent future crashes. As a part of the result, a guideline is proposed to assign the party at-fault through crash data fields and narratives. Statistical methods such as the extreme gradient boosting (XGboost) decision tree and the multinomial logit (MNL) model are used. Appealing conclusions have been found and suggestions are made for law enforcement, education, and roadway management to enhance the safety countermeasures. The third aspect is to evaluate the enhancements of crash report form for its effectiveness of reporting VRU involved motor vehicle crashes. One of the State of Wisconsin projects aiming to develop crash report forms was to redesign the old MV4000 crash report form into the new DT4000 crash report form. The modification was applied from January 1, 2017, statewide. The reason behind this switch is to resolve some matters with the old MV4000 crash report form, including insufficient reporting in roadway-related data fields, lack of data fields describing driver distraction, intersection type, no specification of the exact traffic barrier, insufficient information regarding safety equipment usage by motorists and non-motorists, unclear information about the crash location, and inadequate evidence concerning non-motorists actions, circumstances and condition prior to the crash. Hence, the new DT4000 crash form modified some existing data fields incorporated new crash elements and more detailed attributes. The modified and new data fields, their associated attribute values have been thoroughly studied and the effectiveness of improved data collection in terms of a better understanding of factors associated with and contributing to VRU crashes has been comprehensively evaluated. The evaluation has confirmed that the DT4000 crash form provided more specific, details, and useful about the crash circumstances

DATA-DRIVEN BAYESIAN METHOD-BASED TRAFFIC CRASH DRIVER INJURY SEVERITY FORMULATION, ANALYSIS, AND INFERENCE

Traffic crashes have resulted in significant cost to society in terms of life and economic losses, and comprehensive examination of crash injury outcome patterns is of practical importance. By inferring the parameters of interest from prior information and studied datasets, Bayesian models are efficient methods in data analysis with more accurate results, but their applications in traffic safety studies are still limited. By examining the driver injury severity patterns, this research is proposed to systematically examine the applicability of Bayesian methods in traffic crash driver injury severity prediction in traffic crashes. In this study, three types of Bayesian models are defined: hierarchical Bayesian regression model, Bayesian non-regression model and knowledge-based Bayesian non-parametric model, and a conceptual framework is developed for selecting the appropriate Bayesian model based on discrete research purposes. Five Bayesian models are applied accordingly to test their effectiveness in traffic crash driver injury severity prediction and variable impact estimation: hierarchical Bayesian binary logit model, hierarchical Bayesian ordered logit model, hierarchical Bayesian random intercept model with cross-level interactions, multinomial logit (MNL)-Bayesian Network (BN) model, and decision table/na\xefve Bayes (DTNB) model. A complete dataset containing all crashes occurring on New Mexico roadways in 2010 and 2011 is used for model analyses. The studied dataset is composed of three major sub-datasets: crash dataset, vehicle dataset and driver dataset, and all included variables are therefore divided into two hierarchical levels accordingly: crash-level variables and vehicle/driver variables. From all these five models, the model performance and analysis results have shown promising performance on injury severity prediction and variable influence analysis, and these results underscore the heterogeneous impacts of these significant variables on driver injury severity outcomes. The performances of these models are also compared among these methods or with traditional traffic safety models. With the analyzed results, tentative suggestions regarding countermeasures and further research efforts to reduce crash injury severity are proposed. The research results enhance the understandings of the applicability of Bayesian methods in traffic safety analysis and the mechanisms of crash injury severity outcomes, and provide beneficial inference to improve safety performance of the transportation system

Proceedings, MSVSCC 2012

Proceedings of the 6th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2012 at VMASC in Suffolk, Virginia