Abstract

Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors. The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show that once the model is calibrated, the estimated travel times corresponds well with the ground truth travel times collected from Bluetooth sensors. In order to produce accurate short-term predictions for various networks and conditions it is vital to combine different methods. We have implemented and evaluated a hybrid prediction approach that assimilates parametric and non-parametric short-term traffic state prediction. To predict mainline sensor data we use a neural network, while the CTM-v is ran forward in time in order to predict future traffic states. The results show that both the hybrid approach and the CTM-v prediction without the additional predicted mainline sensor data is superior to a naïve prediction method for longer prediction horizons.

Abstract

Road transportation is an essential element of mobility in most countries and we can observe an increasing demand for both goods and passenger traffic. There are however important societal and economical problems related to road transportation in terms of congestions, traffic safety and environmental effects. During the last decades vehicles have increasingly been equipped with different types of Advanced Driver Assistance Systems (ADAS). These systems can to some extent compensate for human behaviour and errors that cause congestions, accidents and air pollution. Most studies conducted to evaluate ADAS have focused on ADAS impacts on the driver or on the vehicle. Since an ADAS might influence not only driving behaviour and vehicle dynamics, but also the interaction between equipped and non-equipped vehicles, it is also important to consider the resulting effect on the traffic system. A reliable and realistic evaluation approach needs to include estimations of drivers’ decisions in different traffic situations with respect to the ADAS functionality and how such decisions affect the traffic system as a whole. The overall aim of the thesis is to develop a simulation based evaluation framework for investigations of impacts of different types of cruise controllers on the traffic system. The objective is also to apply the framework to evaluate a fuel minimizing cruise controller for trucks, the Look Ahead Cruise Control (LACC). The framework developed consists of a combination of a microscopic traffic simulation model, and a vehicle and ADAS simulation model. When applied for a specific ADAS, as for example the LACC, the framework needs to be complemented with a driver model that captures the changes in driving behaviour due to the system of interest. In this thesis a driver model for LACC equipped trucks was developed based on results from a driving simulator experiment, a field operational test, and a focus group study. Simulation experiments were carried out to observe the LACC impacts on the traffic system with respect to penetration rate, traffic density, and variation in the desired speed. Environmental effects were estimated using emission calculations.

Abstract

Energy consumption of the Internet is already substantial and it is likely to increase as operators deploy faster equipment to handle popular bandwidth-intensive services, such as streaming and video-on-demand. Existing work on energy saving considers local adaptation relying primarily on hardware-based techniques, such as sleeping and rate adaptation. We argue that a complete solution requires a network-wide approach that works in conjunction with local measures. However, traditional traffic engineering objectives do not include energy. This paper presents Energy-Aware Traffic engineering (EATe), a technique that takes energy consumption into account while optimizing for low link utilization and high end-host sending rates. EATe uses a scalable, online technique to spread the load among multiple paths so as to increase energy savings. Our extensive ns-2 simulations over realistic topologies show that EATe succeeds in moving 21% of the links to the sleep state, while keeping the same sending rates and being close to the optimal energy-aware solution. Further, we demonstrate that EATe successfully handles changes in traffic load and quickly restores a low overall energy state. Alternatively, EATe can move links to lower energy levels, resulting in energy savings of 8%. Finally, EATe can succeed in making 16% of active routers sleep. "p"QC 20140704

Abstract

Intelligent transportation systems (ITS) are becoming more and more effective. Robust and accurate short-term traffic prediction plays a key role in modern ITS and demands continuous improvement. Benefiting from better data collection and storage strategies, a huge amount of traffic data is archived which can be used for this purpose especially by using machine learning. For the data preprocessing stage, despite the amount of data available, missing data records and their messy labels are two problems that prevent many prediction algorithms in ITS from working effectively and smoothly. For the prediction stage, though there are many prediction algorithms, higher accuracy and more automated procedures are needed. Considering both preprocessing and prediction studies, one widely used algorithm is k-nearest neighbours (kNN) which has shown high accuracy and efficiency. However, the general kNN is designed for matrix instead of time series which lacks the use of time series characteristics. Choosing the right parameter values for kNN is problematic due to dynamic traffic characteristics. This thesis analyses kNN based algorithms and improves the prediction accuracy with better parameter handling using time series characteristics. Specifically, for the data preprocessing stage, this work introduces gap-sensitive windowed kNN (GSW-kNN) imputation. Besides, a Mahalanobis distance-based algorithm is improved to support correcting and complementing label information. Later, several automated and dynamic procedures are proposed and different strategies for making use of data and parameters are also compared. Two real-world datasets are used to conduct experiments in different papers. The results show that GSW-kNN imputation is 34% on average more accurate than benchmarking methods, and it is still robust even if the missing ratio increases to 90%. The Mahalanobis distance-based models efficiently correct and complement label information which is then used to fairly compare performance of algorithms. The proposed dynamic procedure (DP) performs better than manually adjusted kNN and other benchmarking methods in terms of accuracy on average. What is better, weighted parameter tuples (WPT) gives more accurate results than any human tuned parameters which cannot be achieved manually in practice. The experiments indicate that the relations among parameters are compound and the flow-aware strategy performs better than the time-aware one. Thus, it is suggested to consider all parameter strategies simultaneously as ensemble strategies especially by including window in flow-aware strategies. In summary, this thesis improves the accuracy and automation level of short-term traffic prediction with proposed high-speed algorithms.

Abstract

This thesis is carried out in the context of Advanced Driver Assistance Systems, and especially autonomous vehicles. Its aim is to propose a method to enhance localization of vehicles on roads. I suggests using a camera to detect lane markings, and to match these to a map to extract the corrected position of the vehicle.The thesis is divided in three parts dealing with: the map, the line detector and the evaluation. The map is based on the OpenStreetMap data. The line detector is a based on ridge detection. The results are compared with an Iterative Closest Point algorithm. It also focuses on implementing the components under a real-time constraint. Technologies such as ROS, for synchronization of the data, and CUDA, for parallelization, are used.

Abstract

The main purpose of this thesis is to investigate potential synergies between an electronic waybill (e-Waybill) and other Intelligent Transport System (ITS) services. An e-Waybill service, as presented in this thesis, should be able to perform the functions of a paper waybill (which is an important transport document and contains essential information about a consignment) and it should contain at least the information specified in a paper waybill. To investigate synergies between the e-Waybill solutions and ITS services, we present 5 conceptual e-Waybill solutions that differ in where the e-Waybill information is stored, read, and written. These solutions are further investigated for functional and technical requirements (non-functional requirements), which can potentially impose constraints on a potential system that should implement the e-Waybill service. A set of 20 ITS services are considered for synergy analysis in this thesis. These services are mainly for road transport, however most of them are relevant to be considered for utilization in other modes of transport as well. For information synergy analysis, the e-Waybill solutions are assessed based on their synergies with ITS services. For different ITS services, the required input information entities are identified; and if at least one information entity can be provided by an e-Waybill at the right location we regard it to be a synergy. The result from our synergy analysis may support the choice of practical e-Waybill systems, which has the possibility to provide high synergy with ITS services. This may lead to a higher utilization of ITS services and more sustainable transport, e.g., in terms of reduced congestion and emissions. Additionally, a service design method has been proposed for supporting the process of designing new ITS services, which primarily utilizes on functional synergies with already existing ITS services. In order to illustrate the usage of the suggested method, we have applied it for designing a new ITS service, i.e., the Liability Intelligent Transport System (LITS) service. The purpose of the LITS service is to support the process of identifying when, where, and by whom a consignment has been damaged and who was responsible when the consignment was damaged.

Abstract

Traffic Engineering (TE) is the stage which deals with geometric design planning and traffic operation of networks, network devices and relationship of routers for the transportation of data. TE is that feature of network engineering which concentrate on problems of performance optimization of operational networks. It involves techniques and application of knowledge to gain performance objectives, which includes movement of data through network, reliability, planning of network capacity and efficient use of network resources. This thesis addresses the problems of traffic engineering and suggests a solution by using the concept of Multi-Protocol Label Switching (MPLS). We have done simulation in Matlab environment to compare the performance of MPLS against the IP network in a simulated environment. MPLS is a modern technique for forwarding network data. It broadens routing according to path controlling and packet forwarding. In this thesis MPLS is computed on the basis of its performance, efficiency for sending data from source to destination. A MATLAB based simulation tool is developed to compare MPLS with IP network in a simulated environment. The results show the performance of MPLS network in comparison of IP network. YASSAR AZIZ, +46 73 9135296 MUHAMMAD NAEEM ASLAM +46 70 4918985

Abstract

The Bluetooth technology is still in an early stage of development. Much more research can and will be done before the performance of Bluetooth reaches its peak. During the recent years, ideas to integrate Bluetooth units in larger networks have arose, with the Bluetooth unit in the role as access point to the network. This behavior opens up for new possibilities but also increases the requirements on performance. In this thesis the main topic is improvement of piconet performance. The piconet, with the Master unit as access point, is studied from a teletraffic engineering point of view. Different performance attributes and behaviors have been found and investigated. With the outcome of these investigations in mind, new and more efficient policies and algorithms are proposed for both data and voice. A policy increasing the utilization of available bandwidth in a piconet is presented. Furthermore, a proposal is presented where multiple Bluetooth units are used in an efficient manner to support voice calls. The proposed solution does also enable creation of simple teletraffic models to be used for dimensioning. Phone: +46709138850

Abstract

The automotive industry is undergoing a revolution where the more traditional mechanical values are replaced by an ever increasing number of Advanced Driver Assistance Systems (ADAS) where advanced algorithms and software development are taking a bigger role. Increased safety, reduced emissions and the possibility of completely new business models are driving the development and most automotive companies have started projects that aim towards fully autonomous vehicles. For industrial applications that provide a closed environment, such as mining facilities, harbors, agriculture and airports, full implementation of the technology is already available with increased productivity, reliability and reduced wear on equipment as a result. However, it also gives the opportunity to create a safer working environment when human drivers can be removed from dangerous working conditions. Regardless of the application an important part of any mobile autonomous system is the motion planning layer. In this thesis sampling-based motion planning algorithms are used to solve several non-holonomic and kinodynamic planning problems for car-like robotic vehicles in different application areas that all present different challenges. First we present an extension to the probabilistic sampling-based Closed-Loop Rapidly exploring Random Tree (CL-RRT) framework that significantly increases the probability of drawing a valid sample for platforms with second order differential constraints. When a tree extension is found infeasible a new acceleration profile that tries to brings the vehicle to a full stop before the collision occurs is calculated. A resimulation of the tree extension with the new acceleration profile is then performed. The framework is tested on a heavy-duty Scania G480 mining truck in a simple constructed scenario. Furthermore, we present two different driver assistance systems for the complicated task of reversing with a truck with a dolly-steered trailer. The first is a manual system where the user can easily construct a kinematically feasible path through a graphical user interface. The second is a fully automatic planner, based on the CL-RRT algorithm where only a start and goal position need to be provided. For both approaches, the internal angles of the trailer configuration are stabilized using a Linear Quadratic (LQ) controller and path following is achieved through a pure-pursuit control law. The systems are demonstrated on a small-scale test vehicle with good results. Finally, we look at the planning problem for an autonomous vehicle in an urban setting with dense traffic for two different time-critical maneuvers, namely, intersection merging and highway merging. In these situations, a social interplay between drivers is often necessary in order to perform a safe merge. To model this interaction a prediction engine is developed and used to predict the future evolution of the complete traffic scene given our own intended trajectory. Real-time capabilities are demonstrated through a series of simulations with varying traffic densities. It is shown, in simulation, that the proposed method is capable of safe merging in much denser traffic compared to a base-line method where a constant velocity model is used for predictions.

Abstract

In freight transportation, a waybill is an important document that contains essential information about a consignment. The focus of this thesis is on a multi-purpose electronic waybill (e-Waybill) service, which can provide the functions of a paper waybill, and which is capable of storing, at least, the information present in a paper waybill. In addition, the service can be used to support other existing Intelligent Transportation System (ITS) services by utilizing on synergies with the existing services. Additionally, information entities from the e-Waybill service are investigated for the purpose of knowledge-building concerning freight flows. A systematic review on state-of-the-art of the e-Waybill service reveals several limitations, such as limited focus on supporting ITS services. Five different conceptual e-Waybill solutions (that can be seen as abstract system designs for implementing the e-Waybill service) are proposed. The solutions are investigated for functional and technical requirements (non-functional requirements), which can potentially impose constraints on a potential system for implementing the e-Waybill service. Further, the service is investigated for information and functional synergies with other ITS services. For information synergy analysis, the required input information entities for different ITS services are identified; and if at least one information entity can be provided by an e-Waybill at the right location we regard it to be a synergy. Additionally, a service design method has been proposed for supporting the process of designing new ITS services, which primarily utilizes on functional synergies between the e-Waybill and different existing ITS services. The suggested method is applied for designing a new ITS service, i.e., the Liability Intelligent Transport System (LITS) service. The purpose of the LITS service isto support the process of identifying when and where a consignment has been damaged and who was responsible when the damage occurred. Furthermore, information entities from e-Waybills are utilized for building improved knowledge concerning freight flows. A freight and route estimation method has been proposed for building improved knowledge, e.g., in national road administrations, on the movement of trucks and freight. The results from this thesis can be used to support the choice of practical e-Waybill service implementation, which has the possibility to provide high synergy with ITS services. This may lead to a higher utilization of ITS services and more sustainable transport, e.g., in terms of reduced congestion and emissions. Furthermore, the implemented e-Waybill service can be an enabler for collecting consignment and traffic data and converting the data into useful traffic information. In particular, the service can lead to increasing amounts of digitally stored data about consignments, which can lead to improved knowledge on the movement of freight and trucks. The knowledge may be helpful when making decisions concerning road taxes, fees, and infrastructure investments.