Congestion control is an important aspect of network technology, where traffic load is balanced to not cause an overflow in the system. Google has proposed its own protocol, QUIC, which is described as being set to supersede the TCP protocol. QUIC has several advantages, namely having high efficiency and low latency, but also a more flexible congestion control due to it being situated in the user space. To be used in tandem with QUIC, Google developed a new congestion control algorithm called BBR meant to fully exploit these advantages, by reducing latency and increasing throughput. However, as BBR is still a rather new algorithm, there are many different improvements possible to make it more efficient. In this paper, a modified BBR algorithm (mBBR) is proposed, which is comprised of three other algorithms meant to improve BBR by adjusting the otherwise static congestion window and pacing rate gain values based on the round-trip time flow, and is compared to the CUBIC, NewReno, and QUIC/TCP BBR algorithms. mBBR has a greatly lower RTT over CUBIC and NewReno, and reduces it by as much as 20% over the default QUIC BBR algorithm, while maintaining the same level of throughput. This improvement makes mBBR more suitable for usage in RAN-applications and other areas where a lower delay is crucial, without sacrificing network speeds.

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Motivated by the desire to explore future traffic flows that will consist of a mixture of classical vehicles and vehicles equipped with advanced driver assistance systems, new mathematical theories and models are developed. The basis for this theory was borrowed from the kinetic description of gas flows, where we replaced the behaviour of the molecules by typical human driving behaviour. From a methodological point of view, this 'human-kinetic' traffic flow theory provides two major improvements with respect to existing theory. Firstly, the model builds exclusively on a mathematical description of individual driver behaviour, whereas traditionally field measurements of traffic flow variables like flow rate and average speed of the flow are needed. This is of major importance for the exploration of future traffic flows with vehicles and equipment that are not yet on the market, and for which at best individual test results from driving simulator experiments or small scale field trials are available. Secondly, the model accounts for the more refined aspects of individual driver behaviour by considering the 'internal' state of the driver (active/passive, aware/unaware,...) and the variations of driving strategy that occur during driving. This is important when the ambition is to capture refined congestion patterns like the occurrence of stop-and-go waves, oscillating congestion and long jams, where the driving strategy may depend for instance on the motivation of the driver to follow closely. This new theory links together the worlds of traffic engineers and behavioural scientists. As such, it is a promising tool that increases the insight in the human behaviour as a basis of various dynamic congestion patterns, and it facilitates the design and evaluation of electronic systems in the vehicle that assist the driver to behave safer, more comfortable and more efficient in busy traffic flows. Herewith, the results of this research are relevant, both for the theoretical interest of the TRAIL research school, and for the more practically oriented work of TNO, who provided financing for this research in the joint T3 research program.

The main objective of this research is to develop a model to predict congestion. This model is developed using the techniques of simulation and as the model requires dynamic modeling, DYNAMO is used. This model incorporates the three-regime linear model for establishing a relationship between speed and density of the traffic stream. The input to this model is obtained from a presence type detector system. These measurements are then used to calculate various parameters and then the state of the traffic flow for the vehicular stream in the test zone is determined. This model also predicts the state of the traffic stream in any other section of the highway behind the test section. The model developed is flexible and easy to incorporate in any traffic control system. This research is also intended to simulate the various traffic stream models and evaluate their performance regarding their capability to represent highway traffic flow conditions. A thorough review of the fundamentals of traffic flow is required to achieve these objectives. The simulation models developed for these traffic stream formulae incorporate various measures of effectiveness to determine congestion. These measures of effectiveness are used to define congestion. The study of the various traffic stream models is necessary in order to develop a flexible and efficient model to predict congestion. The congestion prediction model developed incorporates all the parameters required to define congestion. / Master of Science

The fact that traffic congestion upstream of a bottleneck causes a reduction in the discharge flow rate through the bottleneck has been well documented in several empirical studies. However, what has been missing is an understanding of the causes of these empirically observed flow reductions. An identification of these causes is important in order to develop various mitigation schemes through the use of emerging technology. The concept of capacity drop can be introduced at time-independent bottlenecks (e.g. freeways) as well as time-dependent bottlenecks (e.g. signalized intersections). While to the author's knowledge no one has attempted to link these phenomena, the research presented in this thesis serves as a first step in doing so. The research uses the INTEGRATION simulation software, after demonstrating its validity against empirical data, to simulate time-independent and time-dependent bottlenecks in an attempt to characterize and understand the contributing factors to these flow reductions. Initially, the INTEGRATION simulation software is validated by comparing its results to empirically observed traffic stream behavior. This thesis demonstrates that the discharge flow rate is reduced at stationary bottlenecks at the onset of congestion. These reductions at stationary bottlenecks are not recovered as the traffic stream propagates downstream. Furthermore, these reductions are not impacted by the level of vehicle acceleration. Alternatively, the drop in the discharge flow rate caused by time-dependent bottleneck is recoverable and is dependent on the level of acceleration. The difference in behavior is attributed to the fact that in the case of a stationary bottleneck the delay in vehicle headways exceeds the losses caused by vehicle accelerations and thus is not recoverable. In the case of vehicles discharging from a backward recovery wave the dominant factor is the delay caused by vehicle acceleration and this can be recuperated as the traffic stream travels downstream. / Master of Science

Yes / This paper presents a new analytical framework for the congestion control of Internet traffic using a queue threshold scheme. This framework includes two discrete-time analytical models for the performance evaluation of a threshold based congestion control mechanism and compares performance measurements through typical numerical results. To satisfy the low delay along with high throughput, model-I incorporates one threshold to make the arrival process step reduce from arrival rate ¿1 directly to ¿2 once the number of packets in the system has reached the threshold value L1. The source operates normally, otherwise. Model-II incorporates two thresholds to make the arrival rate linearly reduce from ¿1 to ¿2 with system contents when the number of packets in the system is between two thresholds L1 and L2. The source operates normally with arrival rate ¿1 before threshold L1, and with arrival rate ¿2 after the threshold L2. In both performance models, the mean packet delay W, probability of packet loss PL and throughput S have been found as functions of the thresholds and maximum drop probability. The performance comparison results for the two models have also been made through typical numerical results. The results clearly demonstrate how different load settings can provide different tradeoffs between throughput, loss probability and delay to suit different service requirements.

No / Traffic congestion arising from the shared nature of uplink channels in wireless networks can cause serious problems for the provision of QoS to various services. One approach to overcome these problems is to implement some effective congestion control mechanisms at the downlink buffer at the mobile network link layer or at gateways on the behalf of wireless network access points. Active queue management (AQM) is an effective mechanism to support end-to-end traffic congestion control in modern high-speed networks. Initially developed for Internet routers, AQM is now being also considered as an effective congestion control mechanism to enhance TCP performance over 3G links. This paper proposes an analytical performance model for AQM using various dropping functions. The selection of different dropping functions and threshold values required for this scheme plays a critical role on its effectiveness. The model uses a well-known Markov-modulated Poisson process (MMPP) to capture traffic burstiness and correlations. The validity of the model has been demonstrated through simulation experiments. Extensive analytical results have indicated that exponential dropping function is a good choice for AQM to support efficient congestion control.