Multi-Gated Perimeter Flow Control of Transport Networks

This paper develops a control scheme for the multi-gated perimeter traffic flow control problem of urban road networks. The proposed scheme determines optimally distributed input flows (or feasible entrance link green times) for a number of gates located at the periphery of a protected network area. A macroscopic model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, we augment the basic state-space model with additional state variables to account for the queues at store-and-forward origin links at the periphery. We aim to equalise the relative queues at origin links and maintain the vehicle accumulation in the protected network around a desired point, while the system's throughput is maximised. The perimeter traffic flow control problem is formulated as a convex optimal control problem with constrained control and state variables. For real-time control, the optimal control problem is embedded in a rolling-horizon scheme using the current state of the whole system as the initial state as well as predicted demand flows at entrance links. A meticulous simulation study is carried out for a 2.5 square mile protected network area of San Francisco, CA, including fifteen gates of different geometric characteristics. Results demonstrate the efficiency and equity properties of the proposed approach to better manage excessive queues outside of the protected network area and optimally distribute the input flows

Monitoring and control of transport networks using parsimonious models

The growing number of vehicles on the roads coupled with inefficient road operations have generated traffic congestion. Consequently, traffic congestion increase trip time and indirectly contributes to poor quality of life and environmental pollution. Therefore, alleviating traffic congestion, especially in urban networks, is crucial and requires efficient traffic management and control. Recently, macroscopic operational scheme has become the preferred method for monitoring and mitigating traffic congestion due its simplicity in modeling complex large-scale cities and low computational effort. The schemes are based on parsimonious models known as Macroscopic or Network Fundamental Diagram (MFD or NFD) which provides an aggregated model of urban traffic dynamics, linking network circulating flow and average density. This thesis deals with an open problems associated with two main applications of NFD in transportation networks, namely: 1) Traffic monitoring and 2) Traffic flow control. Two parts of the thesis concentrates on each application separately. The implementation of NFD in perimeter control strategy requires an accurate estimation of NFD where its measurements are reflected from sensors located at appropriate locations in the network. First part of the thesis elaborates a new approach for studying sensor selection for the development of operational or sparse-measurement NFD, with less number of sensor and associated measurements. An information-theoretic based framework is proposed for the optimal sensor selection across a transport network to assist an efficient model selection and construction of sparse-measurement NFD. For the optimal sensor selection, a generalised set covering integer programming (GIP) is developed. Under this framework, several tools to assess GIP solutions are uitilised. First, a correlation between variables is introduced as a ''distance'' metric rather than spatial distance to provide sufficient coverage and information accuracy. Second, the optimal cost of GIP problem is used to determine minimum number of sensors. Third, the relative entropy or Kullback-Leibler divergence is used to measure the dissimilarity between probability mass functions corresponding to different solutions of the GIP program. The proposed framework is evaluated with experimental loop-detector data of one week from central business district with fifty-eight sensors. Results reveal that the obtained sparse-measurement diagrams from the selected models adequately preserve the shape and the main features similar to a full-measurement diagram. Specifically, the coverage level of 24% of the network demonstrated the effectiveness of GIP framework. Simulation results also disclose the Kullback-Liebler divergence as more generic and reliable metric of information loss. Such framework can be of great importance towards a cost-effective sensors installation and maintenance whilst improving the estimation of NFD for better monitoring and control strategy. Second part of the thesis discusses the traffic flow control problem involving single input flow distribution from perimeter control strategy towards number of gated links at the periphery of the network. It if often assumed that input flow ordered by perimeter control strategy should be equally distributed to a number of candidate junctions. There has not been considerable research into limited storage capacity/different geometric characteristics at gated links as well as equity properties for driver. A control scheme for the multi-gated perimeter flow control (MGC) problem is developed. The scheme determines optimally distributed input flows for a number of gates located at the periphery of a protected network area. A parsimonious model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, the basic state space model is augmented with additional state variables for the queues at store-and-forward origin links of the periphery. The perimeter traffic flow control problem is formulated as a convex optimal control problem with constrained control and state variables. For the application of the proposed scheme in real time, the optimal control problem may be embedded in a rolling-horizon scheme using the current state of the whole system as the initial state as well as predicted demand flows at origin/entrance links. This part also offers flow allocation policies for single-region network without considering entrance link dynamics namely capacity-based flow allocation policy and optimisation-based flow allocation policy. Simulation results are carried for a protected network area of downtown San Francisco with fifteen gates of different geometric characteristics. Results demonstrate the efficiency and equity properties of the MGC approach to better manage excessive queues outside of the protected network area and optimally distribute the input flows. The MGC outperforms the other approaches in terms of serving more trips in protected network as well as shorter queues at gated links. Such framework is particularly of interest to city managers because the optimal flow distribution may influence the network throughput hence serves maximum number of network users

Optimal Selection of Traffic Sensors: an Information-Theoretic Framework

This paper presents an information-theoretic framework for the optimal selection of sensors across a traffic network. For the selection of sensors a set covering integer programming (IP) problem is developed. A measure of correlation between random variables, reflecting a variable of interest, is introduced as a “distance” metric to provide sufficient coverage and information accuracy. The ultimate goal is to select sensors that are most informative about unsensed locations. The Kullback-Leibler divergence (relative entropy) is used to measure the dissimilarity between probability mass functions corresponding to different solutions of the IP program. Efficient model selection is a trade-off between the Kullback-Leibler divergence and the optimal cost of the IP program. The proposed framework is applied to the problem of developing sparse-measurement traffic flow models with empirical inductive loop-detector data of one week from a central business district with about sixty sensors. Results demonstrate that the obtained sparse-measurement rival models are able to preserve the shape and main features of the full-measurement traffic flow models

Distributed Perimeter Flow Control of Transport Networks

In this paper, we develop a distributed control scheme for the perimeter traffic flow control problem in urban road networks. The proposed scheme determines optimally distributed input flows for a number of gates located at the periphery of a protected network area. A parsimo- nious model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, we augment the basic state-space model with additional state variables for the queues at store-and-forward origin links at the periphery. We aim to equalise the relative queues at origin links and maintain the vehicle accumulation in the protected network around a desired point, while the system’s throughput is maximised. The perimeter traffic flow control problem is formulated as a convex optimal control problem with constrained control and state variables. Simulation results are carried for a protected area of downtown San Francisco with fifteen gates of different geometric characteristics. Results demonstrate the efficiency and equity properties of the proposed approach to better manage excessive queues outside of the protected area and optimally distribute the input flows

Path finding of indoor mobile robot using harmonic potentials via explicit decoupled group modified accelerated over relaxation method

The harmonic potential fields, a solution to the equation of Laplace are widely used in robot pathfinding as a suggestion for robot course-plotting in an identified environment. The computation of these harmonic functions often involves simulations on a high-performance computer. In the pursuit to solve the problem of robot navigation, this article suggests a technique called Half-Sweep Block Modified Accelerated Over-Relaxation or better known as Explicit Decoupled Group Modified Accelerated Over-Relaxation (EDGMAOR). To verify the effectiveness of EDGMAOR, simulations of robot navigation were applied in a static known enclosed environment. Experiments are provided to assess the performance of the suggested technique. In particular, different starting and goal positions are used to assess the paths generated from the simulations. The outcomes show the advantages of the proposed algorithm. In the end, the research indicates that the proposed method in computing harmonic functions is appealing and attainable for solving path planning problems

Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes

This study predicts student performance at Universiti Pertahanan Nasional Malaysia (UPNM) based on their socio-demographic profile; it also determines how a prediction algorithm can be used to classify the student data for the most significant demographic attributes. The analytical pattern in academic results per batch has been identified using demographic attributes and the student's grades to improve short-term and long-term learning and teaching plans. Understanding the likely outcome of the education process based on predictions can help UPNM lecturers enhance the achievements of the subsequent batch of students by modifying the factors contributing to the prior success. This study identifies and predicts student performance using data mining and classification techniques such as decision trees, neural networks, and k-nearest neighbors. This frequently adopted method comprises data selection and preparation, cleansing, incorporating previous knowledge datasets, and interpreting precise solutions. This study presents the simplified output from each data mining method to facilitate a better understanding of the result and determine the best data mining method. The results show that the critical attributes influencing student performance are gender, age, and student status. The Neural Networks method has the lowest Root of the Mean of the Square of Errors (RMSE) for accuracy measurement. In contrast, the decision tree method has the highest RMSE, which indicates that the decision tree method has a lower performance accuracy. Moreover, the correlation coefficient for the k-nearest neighbor has been recorded as less than one

Traffic Monitoring on Sparse-Measurement Network-Wide Fundamental Diagrams

This paper presents a rigorous information theoretic-based framework for the optimal place- ment of sensors across a transport network, the efficient model selection, and construction of sparse-measurement network-wide fundamental diagrams. For the optimal placement of sen- sors across the transport network a set cover integer programming (IP) problem is developed. A measure of correlation between random variables, reflecting occupancy observations, is in- troduced as a “distance” metric to provide sufficient coverage and information accuracy. The relative entropy or Kullback-Leibler divergence is used to measure the dissimilarity between probability mass functions corresponding to different solutions of the IP program. Efficient model selection is a trade-off between the Kullback-Leibler divergence and the optimal cost of the IP program. The proposed framework is evaluated with experimental loop-detector data of one week from a central business district with around sixty sensors. Results demonstrate that the obtained sparse-measurement rival diagrams are able to preserve the shape and main features of the operational full-measurement diagram. Therefore approximated fundamental diagrams, which are in principle less costly, can be used for the efficient monitoring and control of congested urban areas

Traffic Monitoring on Sparse-Measurement Network-Wide Fundamental Diagrams

This paper presents a rigorous information theoretic-based framework for the optimal place- ment of sensors across a transport network, the efficient model selection, and construction of sparse-measurement network-wide fundamental diagrams. For the optimal placement of sen- sors across the transport network a set cover integer programming (IP) problem is developed. A measure of correlation between random variables, reflecting occupancy observations, is in- troduced as a “distance” metric to provide sufficient coverage and information accuracy. The relative entropy or Kullback-Leibler divergence is used to measure the dissimilarity between probability mass functions corresponding to different solutions of the IP program. Efficient model selection is a trade-off between the Kullback-Leibler divergence and the optimal cost of the IP program. The proposed framework is evaluated with experimental loop-detector data of one week from a central business district with around sixty sensors. Results demonstrate that the obtained sparse-measurement rival diagrams are able to preserve the shape and main features of the operational full-measurement diagram. Therefore approximated fundamental diagrams, which are in principle less costly, can be used for the efficient monitoring and control of congested urban areas

Distributed Perimeter Flow Control of Transport Networks

In this paper, we develop a distributed control scheme for the perimeter traffic flow control problem in urban road networks. The proposed scheme determines optimally distributed input flows for a number of gates located at the periphery of a protected network area. A parsimo- nious model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, we augment the basic state-space model with additional state variables for the queues at store-and-forward origin links at the periphery. We aim to equalise the relative queues at origin links and maintain the vehicle accumulation in the protected network around a desired point, while the system’s throughput is maximised. The perimeter traffic flow control problem is formulated as a convex optimal control problem with constrained control and state variables. Simulation results are carried for a protected area of downtown San Francisco with fifteen gates of different geometric characteristics. Results demonstrate the efficiency and equity properties of the proposed approach to better manage excessive queues outside of the protected area and optimally distribute the input flows

Optimal Selection of Traffic Sensors: an Information-Theoretic Framework

This paper presents an information-theoretic framework for the optimal selection of sensors across a traffic network. For the selection of sensors a set covering integer programming (IP) problem is developed. A measure of correlation between random variables, reflecting a variable of interest, is introduced as a “distance” metric to provide sufficient coverage and information accuracy. The ultimate goal is to select sensors that are most informative about unsensed locations. The Kullback-Leibler divergence (relative entropy) is used to measure the dissimilarity between probability mass functions corresponding to different solutions of the IP program. Efficient model selection is a trade-off between the Kullback-Leibler divergence and the optimal cost of the IP program. The proposed framework is applied to the problem of developing sparse-measurement traffic flow models with empirical inductive loop-detector data of one week from a central business district with about sixty sensors. Results demonstrate that the obtained sparse-measurement rival models are able to preserve the shape and main features of the full-measurement traffic flow models