Amplitudes distribution over two copies of small quantum subsystems

We propose a new formulation of quantum algorithm which allows to distribute amplitudes over two copies of small quantum subsystems. The new method gives a fixed number of copies and applied to the control of multi-qubit system. The analysis for the amount of error due to the distribution process has been presented for a system of 10 qubits with a small quantum subsystems to be copied. The present scheme provides a new way to distribute amplitudes over small quantum subsystems.     

Aggregate nonparametric safety analysis of traffic zones

Exploring the significant variables related to specific types of crashes is vitally important in the planning stage of a transportation network. This paper aims to identify and examine important variables associated with total crashes and severe crashes per traffic analysis zone (TAZ) in four counties of the state of Florida by applying nonparametric statistical techniques such as data mining and random forest. The intention of investigating these factors in such aggregate level analysis is to incorporate proactive safety measures in transportation planning. Total and severe crashes per TAZ were modeled to provide predictive decision trees. The variables which carried higher weight of importance for total crashes per TAZ were – total number of intersections per TAZ, airport trip productions, light truck productions, and total roadway segment length with 35 mph posted speed limit. The other significant variables identified for total crashes were total roadway length with 15 mph posted speed limit, total roadway length with 65 mph posted speed limit, and non-home based work productions. For severe crashes, total number of intersections per TAZ, light truck productions, total roadway length with 35 mph posted speed limit, and total roadway length with 65 mph posted speed limit were among the significant variables. These variables were further verified and supported by the random forest results.     

Analysis and prediction of traffic fatalities resulting from angle collisions including the effect of vehicles' configuration and compatibility

Although the rapid growth in light truck vehicle (LTV) sales, including minivans, sports utility vehicles (SUVs), and light-duty trucks, has not been associated with an overall increase in collisions or traffic deaths in the US, there is a need for a research program to determine whether particular types of collisions have become more frequent or injurious because of the increase in the percent of LTVs in traffic. This paper presents an analysis of the effect of the increasing number of LTV registrations on fatal angle collision trends in the US. The analysis investigates the number of annual fatalities that result from angle collisions as well as collision configuration (car-car, car-LTV, LTV-car, and LTV-LTV). The analysis uses the Fatality Analysis Reporting System (FARS) crash databases covering the period 1975-2000. Results showed that death rates differ based on the collision configuration. Time series modeling results showed that fatalities in angle collisions will increase in the next 10 years, and that they are affected by the expected increase in the percentage of LTVs in traffic. Forecast showed that the total number of annual deaths is expected to reach 6300 deaths by the year 2010 (an increase of 12% over 2000). Analysis into the configuration of the collision indicated the seriousness of angle collisions involving an LTV striking a common passenger car (LTV-car). A time series model illustrated the significance of time lag and percent of LTVs in traffic on the increase of this type of fatal collisions. Forecasts from the time series model indicated a 32% increase in deaths due to this type of collisions in the next 10 years.     

Investigation of road network features and safety performance

The analysis of road network designs can provide useful information to transportation planners as they seek to improve the safety of road networks. The objectives of this study were to compare and define the effective road network indices and to analyze the relationship between road network structure and traffic safety at the level of the Traffic Analysis Zone (TAZ). One problem in comparing different road networks is establishing criteria that can be used to scale networks in terms of their structures. Based on data from Orange and Hillsborough Counties in Florida, road network structural properties within TAZs were scaled using 3 indices: Closeness Centrality, Betweenness Centrality, and Meshedness Coefficient. The Meshedness Coefficient performed best in capturing the structural features of the road network. Bayesian Conditional Autoregressive (CAR) models were developed to assess the safety of various network configurations as measured by total crashes, crashes on state roads, and crashes on local roads. The models’ results showed that crash frequencies on local roads were closely related to factors within the TAZs (e.g., zonal network structure, TAZ population), while crash frequencies on state roads were closely related to the road and traffic features of state roads. For the safety effects of different networks, the Grid type was associated with the highest frequency of crashes, followed by the Mixed type, the Loops & Lollipops type, and the Sparse type. This study shows that it is possible to develop a quantitative scale for structural properties of a road network, and to use that scale to calculate the relationships between network structural properties and safety.     

Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway

While rural freeways generally have lower crash rates, interactions between driver behavior, traffic and geometric characteristics, and adverse weather conditions may increase the crash risk along some freeway sections. This paper examines the safety effects of roadway geometrics on crash occurrence along a freeway section that features mountainous terrain and adverse weather. Starting from preliminary exploration using Poisson models, Bayesian hierarchical models with spatial and random effects were developed to efficiently model the crash frequencies on road segments on the 20-mile freeway section of study. Crash data for 6 years (2000–2005), roadway geometry, traffic characteristics and weather information in addition to the effect of steep slopes and adverse weather of snow and dry seasons, were used in the investigation. Estimation of the model coefficients indicates that roadway geometry is significantly associated with crash risk; segments with steep downgrades were found to drastically increase the crash risk. Moreover, this crash risk could be significantly increased during snow season compared to dry season as a confounding effect between grades and pavement condition. Moreover, sites with higher degree of curvature, wider medians and an increase of the number of lanes appear to be associated with lower crash rate. Finally, a Bayesian ranking technique was implemented to rank the hazard levels of the roadway segments; the results confirmed that segments with steep downgrades are more crash prone along the study section.     

Motor vehicle-bicycle crashes in Beijing: irregular maneuvers, crash patterns, and injury severity

This research presents a comprehensive analysis of motor vehicle–bicycle crashes using 4 years of reported crash data (2004–2007) in Beijing. The interrelationship of irregular maneuvers, crash patterns and bicyclist injury severity are investigated by controlling for a variety of risk factors related to bicyclist demographics, roadway geometric design, road environment, etc.Results show that different irregular maneuvers are correlated with a number of risk factors at different roadway locations such as the bicyclist age and gender, weather and traffic condition. Furthermore, angle collisions are the leading pattern of motor vehicle–bicycle crashes, and different irregular maneuvers may lead to some specific crash patterns such as head-on or rear-end crashes. Orthokinetic scrape is more likely to result in running over bicyclists, which may lead to more severe injury. Moreover, bicyclist injury severity level could be elevated by specific crash patterns and risk factors including head-on and angle collisions, occurrence of running over bicyclists, night without streetlight, roads without median/division, higher speed limit, heavy vehicle involvement and older bicyclists.This study suggests installation of median, division between roadway and bikeway, and improvement of illumination on road segments. Reduced speed limit is also recommended at roadway locations with high bicycle traffic volume. Furthermore, it may be necessary to develop safety campaigns aimed at male, teenage and older bicyclists.     

Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data

Growing concern over traffic safety has led to research efforts directed towards predicting freeway crashes in Advanced Traffic Management and Information Systems (ATMIS) environment. This paper aims at developing a crash-likelihood prediction model using real-time traffic-flow variables (measured through series of underground sensors) and rain data (collected at weather stations) potentially associated with crash occurrence. Archived loop detector and rain data and historical crash data have been used to calibrate the model. This model can be implemented using an online loop and rain data to identify high crash potential in real-time. Principal component analysis (PCA) and logistic regression (LR) have been used to estimate a weather model that determines a rain index based on the rain readings at the weather station in the proximity of the freeway. A matched case-control logit model has also been used to model the crash potential based on traffic loop data and the rain index. The 5-min average occupancy and standard deviation of volume observed at the downstream station, and the 5-min coefficient of variation in speed at the station closest to the crash, all during 5-10 min prior to the crash occurrence along with the rain index have been found to affect the crash occurrence most significantly.     

Reflection and refraction of incident p-, T-, and SV-waves at interface between magnetized two solid-liquid media with heat sources and initial stress with and without thermal relaxation times

We investigate the problem of reflection and refraction of thermoelastic waves at a magnetized solid-liquid interface in presence of initial stress. In the context of Green-Lindsay and coupled thermoelastic theories of thermoelasticity, the problem has been solved and the effect of magnetic field, external heat sources, and initial stress on p-, T-, and SV-waves propagation have been discussed. The boundary conditions at the interface for displacement continuity, vanishing the tangential displacement, continuity of normal force, tangential force, and continuity of temperature are applied. The amplitudes ratios for the incident p-, T-, and SV-waves have been obtained. The effect of the initial stress, heat sources, and magnetic field on the reflection and transmitted coefficients for the incident waves have been discussed.     

Exploring the safety implications of young drivers’ behavior,attitudes and perceptions

The present study aims at identifying and quantifying significant factors (i.e., demographic, aberrant driving behavior) associated with young drivers’ involvement in at-fault crashes or traffic citations at the ages of 16–17 (while having the Operational License) and 18–24 years old (while having the Full License). A second objective was to investigate the main reason(s) for involvement in risky driving behavior by young drivers.     

Presence of passengers: does it increase or reduce driver's crash potential?

This study examines the impact of passengers on the driver's crash potential on freeways. To estimate the impact, a set of bivariate probit models were developed using the 5-year (1999-2003) crash records on a 36.3-mile stretch of Interstate-4 freeway (I-4) in Orlando, Florida. Bivariate probit models identify the correlation between potentially inter-related choices of three passenger characteristics and three crash characteristics. The analysis using bivariate probit models showed that there exist strong correlations between passenger and crash characteristics. It was found that drivers generally display safer driving behavior when they are accompanied by passengers, and more passengers reduce driver's crash potential. It was also found that younger driver's crash potential increases with the presence of a younger passenger only. In addition, the analysis of crash type using traffic flow parameters at the time of crashes showed that young drivers with only younger passengers are more likely to be involved in single-vehicle crashes in high-speed and low-volume conditions. The findings in this study provide insight into how the presence of passengers has an impact on driver behavior and traffic safety in various conditions.