This research contributes to improved risk analysis of performance measure forecasts in road safety engineering by designing and applying a method to characterize uncertainty associated with forecast input data in cases where input uncertainty is not known. The research applies this method to quantify uncertainty in three categories of inputs used in risk analysis of performance measure forecasts in road safety engineering: (1) estimates of pedestrian exposure to collision risk; (2) estimates of vehicular exposure to collision risk; and (3) estimates of engineering economics parameters that assign valuations to mortality risk reductions based on individual willingness to pay. The common methods used in each of these categories are repeated comparisons of input ground truth to input estimations, the use of simulation approaches (e.g. the simulation of short-term counts by sampling permanent count data), and the use of non-parametric techniques to characterize input uncertainty. Some highlights of quantified input uncertainty levels include: (1) when obtaining pedestrian risk exposure estimates at a site in Winnipeg, MB by expanding two-hour short-term counts using the National Bicycle and Pedestrian Documentation Project method, 90% of errors are between 62% and 170%; (2) when obtaining estimates of vehicle exposure to collision risk by expanding two 48-hour counts using the individual permanent counter method for Manitoba highways, 92 % of errors are between 9.5% and 10.8%; and (3) when applying an income-disaggregated transfer function to estimate value of a statistical life for road safety in developing countries, 90% of errors are between 53% and 54%. The results provide further detail on the structure of these input uncertainties. Analytic and computational capabilities in forecasting and risk analysis have advanced beyond our understanding of corresponding input uncertainty levels; this research closes some of this gap and enables better risk analysis of performance measure forecasts in road safety engineering.

This study examined the drivers' perception of the SAHER (means "watchful" in Arabic) system in Jeddah, Saudi Arabia. The purpose of this study was to analyze the perception of the SAHER system on impacting the overall traffic conditions in Jeddah, Saudi Arabia including its effectiveness and flaws. A survey was conducted and distributed to 70 drivers and residents of Jeddah. Drivers were divided into two groups based on their age. Five hypotheses were tested in this study. Hypotheses one through four were tested using the averages of related questions. Hypothesis five was tested statistically using a z-test for differences between the means. The overall conclusion of drivers' perception of SAHER on increasing road safety and reducing loss of life was generally positive. The conclusion for hypothesis 1, 2, and 3 was positive. The conclusion for hypothesis four was inconclusive. The conclusion for hypothesis five was retained to the null hypothesis with a 95% confidence level. A key recommendation from the study is that to measure the overall effectiveness of the system it will be prudent to observe how the system is perceived in other major cities of Saudi Arabia apart from Jeddah.

[pt] Sistemas computadorizados de vigilância de veículos têm despertado grande interesse devido à demanda para automatizar tarefas que atualmente são realizadas por operadores humanos. Porém, para realizar estas tarefas é preciso resolver alguns problemas clássicos de visão computacional como sombras, oclusões e variação de iluminação. Este trabalho propõe algoritmos em tempo real para máquinas de baixo custo com o objetivo de rastrear, classificar e determinar a velocidade de cada veículo de uma rodovia. / [en] Vehicle surveillance computerized systems have grown great interest due to the automatizing duties demand, which recently executed by computer vision like shadows, occlusion and light variation have to be solved. The present work proposes real time algorithms for low cost machines focused on tracking, classifying and determining each vehicle`s speed on a highway.

Recently, adding more lanes becomes less and less feasible, which is no longer an applicable solution for the traffic congestion problem due to the increment of vehicles. Using the existing infrastructure more efficiently with better traffic control and management is the realistic solution. An effective traffic management requires the use of monitoring technologies to extract traffic parameters that describe the characteristics of vehicles and their movement on the road. A three-dimension glass fiber-reinforced polymer packaged fiber Bragg grating sensor (3D GFRP-FBG) is introduced for the traffic monitoring system. The proposed sensor network was installed for validation at the Cold Weather Road Research Facility in Minnesota (MnROAD) facility of Minnesota Department of Transportation (MnDOT) in MN. A vehicle classification system based on the proposed sensor network has been validated. The vehicle classification system uses support vector machine (SVM), Neural Network (NN), and K-Nearest Neighbour (KNN) learning algorithms to classify vehicles into categories ranging from small vehicles to combination trucks. The field-testing results from real traffic show that the developed system can accurately estimate the vehicle classifications with 98.5 % of accuracy. Also, the proposed sensor network has been validated for low-speed and high-speed WIM measurements in flexible pavement. Field testing validated that the longitudinal component of the sensor has a measurement accuracy of 86.3% and 89.5% at 5 mph and 45 mph vehicle speed, respectively. A performed parametric study on the stability of the WIM system shows that the loading position is the most significant parameter affecting the WIM measurements accuracy compared to the vehicle speed and pavement temperature. Also the system shows the capability to estimate the location of the loading position to enhance the system accuracy.

The article introduces a general survey of concepts used in traffic monitoring applications. It describes different approaches for solving particular steps of vehicle detection process. Analysis of these methods was performed. Furthermore this work focuses on the design and realization of complex robust algorithm for real-time vehicle detection. It is based on analysis of video-sequence acquired from static camera situated on highway. Processing consists of many steps. It starts with background subtraction and ends with traffic monitoring results, i.e. average speed, number of cars, level of service etc.

The notion of the networked society is more than ever true nowadays. The Internet has a big impact on our daily lives. Network operators provide the underlying infrastructure and continuously deploy services in order to meet customer demands. The amount of data transported through operator networks is also increasing with the introduction of new high band width services and over the network content. That being said, operators, most often deploy or operate networks to meet these demands without any regard to energy-efficiency. As the price of electricity continues to grow,  tends to become a problem with serious implications. To solve this problem a trend towards more energy efficient networks has emerged. In this thesis, we investigate a way to facilitate the introduction of new energy efficiency paradigms for fixed networks. Towards this end, we investigate the energy efficiency schemes proposed up to now and select one that we believe is more realistic to deploy. Furthermore, we specify the inputs required for the selected “green” routing approach. Moreover, we study existing and new protocols that can provide basic network monitoring functionality that enables the acquirement of these inputs. In the end, a Software Defined Networking (SDN) approach is proposed to facilitate the development of energy-efficient aware networks. The details of a basic SDN monitoring application are presented from an abstract architectural point of view and three designs stemming from this basic architecture are discussed. The three designs are namely All_Flow, First_Switch and Port_FlowRemoved. The first two were implemented as steps towards understanding the full capabilities of performing monitoring in SDN enabled networks and provided useful input towards realizing the third one as a proof of concept. Their usage and faults are discussed as they can provide useful insight for possible future implementations. The Port_FlowRemoved is the design and implementation that is suggested as providing the most fitting results for the monitoring purpose at hand. This purpose is to retrieve the identified inputs for the selected “green” networking approach. The differentiation factor among the three designs is how they collect the required inputs from the network. A fast-prototype is created as a proof of concept in order to validate the proposed architecture and thus empower the validity of the idea.

In present road traffic system, drone-network based traffic monitoring using the Internet of Vehicles (IoVs) is a promising solution. However, camera-based traffic monitoring does not collect complete data, cover all areas, provide quick medical services, or take vehicle follow-ups in case of an incident. Drone-based system helps to derive important information (such as commuter's behavior, traffic patterns, vehicle follow-ups) and sends this information to centralized or distributed authorities for making traffic diversions or necessary decisions as per laws. The present approaches fail to meet the requirements such as (i) collision free, (ii) drone navigation, and (iii) less computational and communicational overheads. This work has considered the collision-free drone-based movement strategies for road traffic monitoring using Software Defined Networking (SDN). The SDN controllable drone network results in lesser overhead over drones and provide efficient drone-device management. In simulation, two case studies are simulated using JaamSim simulator. Results show that the zones-based strategy covers a large area in few hours and consume 5 kWs to 25 kWs energy for 150 drones (Case study 1). Zone-less based strategies (case study-2) show that the energy consumption lies between 5 kWs to 18 kWs for 150 drones. Further, the use of SDN-based drones controller reduces the overhead over drone-network and increases the area coverage with a minimum of 1.2% and maximum of 2.6%. Simulation (using AnyLogic simulator) shows the 3D view of successful implementation of collision free strategies.

In the passing decades, the monitoring of pavements and passing vehicles was developed vigorously with the growth of information and sensing technology. Pavement monitoring is an essential part of pavement research and plays an important role in transportation system. At the same time, the monitoring system about the traffic, such as Weigh-in-Motion (WIM) system and traffic classification system, also attracted lots of attention because of their importance in traffic statistics and management. The monitoring system in this dissertation combines the monitoring for pavements and traffic together with the same sensing network. For pavement health monitoring purpose, the modulus of the asphalt layer can be back-calculated based on the collected mechanical responses under corresponding environmental conditions. At the same time, the actually strain and stress in pavements induced by each passing vehicle are also used for pavement distress prediction. For traffic monitoring purpose, the horizontal strain traces are analyzed with a Gaussian model to estimate the speed, wandering position, weight and classification of each passing vehicle. The whole system, including the sensing network and corresponding analysis method, can monitor the pavement and the traffic simultaneously, and is called transportation monitoring system. This system has a high efficiency because of its low cost and easy installation; multi-functionality to provide many important information of transportation system. Many related studies were made to improve the prototyped transportation monitoring system. With the assistance of numerical simulation software ABAQUS and 3D-Move, the effect of many loading and environmental conditions, including temperature, vehicle speed, tire configuration and inflation pressure, are taken into consideration. A method was set up to integrate data points from many tests of similar environmental and loading conditions based on Gaussian model. Another method for consistent comparison of variable field sensor data was developed. It was demonstrated that variation in field measurement was due to uncontrollable environmental and loading factors, which may be accounted for by using laboratory test and numerical simulation based corrections. / Ph. D.

No description available.

No description available.