Abstract-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.

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 sparsemeasurement traffic flow models with empirical inductive loopdetector 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.

The effectiveness of government financing is a challenge in various industries, including higher education universities. The funding source and the resources' size are the key determinants of quality education. The problems arise in multi-criteria decision-making, where many subjective opinions are needed from the experts. It is, therefore necessary to prioritize the limited budget available for important criteria. On the other hand, multi-criteria evaluation leads to technically rigorous and enlightened university budget decisions. This paper proposes the exploitation of the Analytic Hierarchy Process (AHP) in budget allocation at one of the public universities in Malaysia. This study’s participants were eight top management experts in managing expenditure at the faculty level. The findings showed that the most significant factors in deciding budget allocations are Teaching and Learning (0.30) and Maintenance (0.26). Furthermore, the most dominant sub-criteria were laboratory and equipment devices (S4) and training and conferences (S10), with a weighted mean of 0.682 and 0.664, respectively. The weights were aggregated by the geometric mean and median, as well as the simulated mean and median, which showed deviating results and rank reversals. The geometric mean weights differed significantly. In contrast, the aggregation using measures of the median was similar to the geometric median, with only a few rankings criteria differing. This highlights that the median score is significant in weight calculation.