Effect of vehicle type on highway traffic flow: Effects of vehicle type on speed, delay and capacity characteristics of highway traffic flow in the United Kingdom and Saudi Arabia determined by an examination of traffic data.
Highway traffic flow
Traffic signal approaches
The University of Bradford theses are licenced under a Creative Commons Licence.
InstitutionUniversity of Bradford
DepartmentPostgraduate School of Studies in Civil and Structural Engineering
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AbstractThe t h e s i s c o n s i d e r s t h e e f f e c t s of v e h i c l e type on highway t r a f f i c flow. The e f f e c t s of v e h i c l e type on t h e c a p a c i t y of t r a f f i c s i g n a l approaches are examined by t h e experimental d e t e r m i n a t i o n of passenger c a r u n i t s a t i n t e r s e c t i o n s i n London and West Yorkshire and i n a d d i t i o n s a t u r a t i o n flows and lost t i m e s a r e examined. . Vehicle type e f f e c t s a t roundabout e n t r i e s a r e i n v e s t i g a t e d and t h e r e s u l t s of f i e l d o b s e r v a t i o n s r e p o r t e d . Details a r e given of t h e gap acceptance of varying v e h i c l e t y p e s , t h e e f f e c t of v e h i c l e type on delay and comparisons a r e made with e x i s t i n g recommendat i o n s f o r t h e c a p a c i t y design of roundabout e n t r i e s . Observations of t r a f f i c flow on a r u r a l motorway a r e used to demonstrate t h e e f f e c t of v e h i c l e type on speed and observed v a l u e s a r e f i t t e d t o a normal d i s t r i b u t i o n . Overtaking behaviour is a l s o examined and conclusions drawn of t h e r e l a t i v e e f f e c t on c a p a c i t y of v e h i c l e t y p e . A review is given of t h e e f f e c t s of v e h i c l e type on t h e design and o p e r a t i o n of t h e highway system in Saudi Arabia.
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Intelligent Real-Time Decision Support Systems for Road Traffic Management. Multi-agent based Fuzzy Neural Networks with a GA learning approach in managing control actions of road traffic centres.Dahal, Keshav P.; Hossain, M. Alamgir; Almejalli, Khaled A. (University of BradfordDepartment of Computing, 2010-03-16)The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.
Performance modeling of congestion control and resource allocation under heterogeneous network traffic. Modeling and analysis of active queue management mechanism in the presence of poisson and bursty traffic arrival processes.Awan, Irfan U.; Min, Geyong; Wang, Lan (University of BradfordDepartment of Computing, 2010-10-27)Along with playing an ever-increasing role in the integration of other communication networks and expanding in application diversities, the current Internet suffers from serious overuse and congestion bottlenecks. Efficient congestion control is fundamental to ensure the Internet reliability, satisfy the specified Quality-of-Service (QoS) constraints and achieve desirable performance in response to varying application scenarios. Active Queue Management (AQM) is a promising scheme to support end-to-end Transmission Control Protocol (TCP) congestion control because it enables the sender to react appropriately to the real network situation. Analytical performance models are powerful tools which can be adopted to investigate optimal setting of AQM parameters. Among the existing research efforts in this field, however, there is a current lack of analytical models that can be viewed as a cost-effective performance evaluation tool for AQM in the presence of heterogeneous traffic, generated by various network applications. This thesis aims to provide a generic and extensible analytical framework for analyzing AQM congestion control for various traffic types, such as non-bursty Poisson and bursty Markov-Modulated Poisson Process (MMPP) traffic. Specifically, the Markov analytical models are developed for AQM congestion control scheme coupled with queue thresholds and then are adopted to derive expressions for important QoS metrics. The main contributions of this thesis are listed as follows: iii ¿ Study the queueing systems for modeling AQM scheme subject to single-class and multiple-classes Poisson traffic, respectively. Analyze the effects of the varying threshold, mean traffic arrival rate, service rate and buffer capacity on the key performance metrics. ¿ Propose an analytical model for AQM scheme with single class bursty traffic and investigate how burstiness and correlations affect the performance metrics. The analytical results reveal that high burstiness and correlation can result in significant degradation of AQM performance, such as increased queueing delay and packet loss probability, and reduced throughput and utlization. ¿ Develop an analytical model for a single server queueing system with AQM in the presence of heterogeneous traffic and evaluate the aggregate and marginal performance subject to different threshold values, burstiness degree and correlation. ¿ Conduct stochastic analysis of a single-server system with single-queue and multiple-queues, respectively, for AQM scheme in the presence of multiple priority traffic classes scheduled by the Priority Resume (PR) policy. ¿ Carry out the performance comparison of AQM with PR and First-In First-Out (FIFO) scheme and compare the performance of AQM with single PR priority queue and multiple priority queues, respectively.
Performance modelling and analysis of congestion control mechanisms for communication networks with quality of service constraints. An investigation into new methods of controlling congestion and mean delay in communication networks with both short range dependent and long range dependent traffic.Woodward, Mike E.; Fares, Rasha H.A. (University of BradfordDepartment of Computing, School of Computing, Informatics and Media, 2012-05-24)Active Queue Management (AQM) schemes are used for ensuring the Quality of Service (QoS) in telecommunication networks. However, they are sensitive to parameter settings and have weaknesses in detecting and controlling congestion under dynamically changing network situations. Another drawback for the AQM algorithms is that they have been applied only on the Markovian models which are considered as Short Range Dependent (SRD) traffic models. However, traffic measurements from communication networks have shown that network traffic can exhibit self-similar as well as Long Range Dependent (LRD) properties. Therefore, it is important to design new algorithms not only to control congestion but also to have the ability to predict the onset of congestion within a network. An aim of this research is to devise some new congestion control methods for communication networks that make use of various traffic characteristics, such as LRD, which has not previously been employed in congestion control methods currently used in the Internet. A queueing model with a number of ON/OFF sources has been used and this incorporates a novel congestion prediction algorithm for AQM. The simulation results have shown that applying the algorithm can provide better performance than an equivalent system without the prediction. Modifying the algorithm by the inclusion of a sliding window mechanism has been shown to further improve the performance in terms of controlling the total number of packets within the system and improving the throughput. Also considered is the important problem of maintaining QoS constraints, such as mean delay, which is crucially important in providing satisfactory transmission of real-time services over multi-service networks like the Internet and which were not originally designed for this purpose. An algorithm has been developed to provide a control strategy that operates on a buffer which incorporates a moveable threshold. The algorithm has been developed to control the mean delay by dynamically adjusting the threshold, which, in turn, controls the effective arrival rate by randomly dropping packets. This work has been carried out using a mixture of computer simulation and analytical modelling. The performance of the new methods that have