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.

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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.

The early observation and elimination of non-recurring incidents is a crucial task in trafficmanagement. The performance of the conventional incident detection methods (trafficcameras and other sensory technologies) is limited and there are still challenges inobtaining an accurate picture of the traffic conditions in real time. During the last decade,the technical development of mobile platforms and the growing online connectivity made itpossible to obtain traffic information from social media and applications based on spatialcrowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receiveinformation about a more comprehensive number of incidents and can monitor areaswhich are not covered by the conventional incident detection systems. The crowdsourcedtraffic data can provide supplementary information for incidents already reported throughother sources and it can contribute to earlier detection of incidents, which can lead tofaster response and clearance time. Furthermore, spatial crowdsourcing can help to detectincident types, which are not collected systematically yet (e.g. potholes, traffic light faults,missing road signs). However, before exploiting crowdsourced traffic data in trafficmanagement, numerous challenges need to be resolved, such as verification of the incidentreports, predicting the severity of the crowdsourced incidents and integration with trafficdata obtained from other sources.During this thesis, the possibilities and challenges of utilizing spatial crowdsourcingtechnologies to detect non-recurring incidents were examined in form of a case study.Traffic incident alerts obtained from Waze, a navigation application using the concept ofcrowdsourcing, were analyzed and compared with officially verified incident reports inStockholm. The thesis provides insight into the spatial and temporal characteristics of theWaze data. Moreover, a method to identify related Waze alerts and to determine matchingincident reports from different sources is presented. The results showed that the number ofreported incidents in Waze is 4,5 times higher than the number of registered incidents bythe Swedish authorities. Furthermore, 27,5 % of the incidents could have been detectedfaster by using the traffic alerts from Waze. In addition, the severity of Waze alerts isexamined depending on the attributes of the alerts.

The early observation and elimination of non-recurring incidents is a crucial task in traffic management. The performance of the conventional incident detection methods (traffic cameras and other sensory technologies) is limited and there are still challenges in obtaining an accurate picture of the traffic conditions in real time. During the last decade, the technical development of mobile platforms and the growing online connectivity made it possible to obtain traffic information from social media and applications based on spatial crowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receive information about a more comprehensive number of incidents and can monitor areas which are not covered by the conventional incident detection systems. The crowdsourced traffic data can provide supplementary information for incidents already reported through other sources and it can contribute to earlier detection of incidents, which can lead to faster response and clearance time. Furthermore, spatial crowdsourcing can help to detect incident types, which are not collected systematically yet (e.g. potholes, traffic light faults, missing road signs). However, before exploiting crowdsourced traffic data in traffic management, numerous challenges need to be resolved, such as verification of the incident reports, predicting the severity of the crowdsourced incidents and integration with traffic data obtained from other sources. During this thesis, the possibilities and challenges of utilizing spatial crowdsourcing technologies to detect non-recurring incidents were examined in form of a case study. Traffic incident alerts obtained from Waze, a navigation application using the concept of crowdsourcing, were analyzed and compared with officially verified incident reports in Stockholm. The thesis provides insight into the spatial and temporal characteristics of the Waze data. Moreover, a method to identify related Waze alerts and to determine matching incident reports from different sources is presented. The results showed that the number of reported incidents in Waze is 4,5 times higher than the number of registered incidents by the Swedish authorities. Furthermore, 27,5 % of the incidents could have been detected faster by using the traffic alerts from Waze. In addition, the severity of Waze alerts is examined depending on the attributes of the alerts.