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Browsing by Author "Elouni, Maha"

Now showing 1 - 3 of 3
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    Adaptive Traffic Signal Control: Game-Theoretic Decentralized vs. Centralized Perimeter Control
    Elouni, Maha; Abdelghaffar, Hossam M.; Rakha, Hesham A. (MDPI, 2021-01-03)
    This paper compares the operation of a decentralized Nash bargaining traffic signal controller (DNB) to the operation of state-of-the-art adaptive and gating traffic signal control. Perimeter control (gating), based on the network fundamental diagram (NFD), was applied on the borders of a protected urban network (PN) to prevent and/or disperse traffic congestion. The operation of gating control and local adaptive controllers was compared to the operation of the developed DNB traffic signal controller. The controllers were implemented and their performance assessed on a grid network in the INTEGRATION microscopic simulation software. The results show that the DNB controller, although not designed to solve perimeter control problems, successfully prevents congestion from building inside the PN and improves the performance of the entire network. Specifically, the DNB controller outperforms both gating and non-gating controllers, with reductions in the average travel time ranging between 21% and 41%, total delay ranging between 40% and 55%, and emission levels/fuel consumption ranging between 12% and 20%. The results demonstrate statistically significant benefits of using the developed DNB controller over other state-of-the-art centralized and decentralized gating/adaptive traffic signal controllers.
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    Geographical Self Organizing Map Clustering in Large-Scale Urban Networks for Perimeter Control
    Elouni, Maha; Rakha, Hesham A.; Menendez, Monica; Abdelghaffar, Hossam M. (2024-05-02)
    Traffic congestion in urban areas presents a major challenge to efficient transportation systems. Recent advancements in traffic management provide promising solutions, with perimeter control emerging as a technique to tackle network-wide congestion. However, it is crucial to identify geographically connected homogeneously congested areas for effective implementation. This research explores the application of clustering techniques, particularly geographical self-organizing maps (GeoSOM), to identify spatially connected and homogeneously congested areas within transportation networks. While GeoSOM has found applications across various domains, its adaptation to transportation networks for congestion clustering is novel. This study introduces and implements an adaptation of the GeoSOM algorithm tailored for the large-scale urban environment of downtown Los Angeles. Its performance is assessed through a comparative evaluation with two other clustering algorithms, namely DBSCAN and K-means. The results demonstrate that GeoSOM surpasses other clustering algorithms, exhibiting improvements of up to 43% in traffic density variance, up to 61% in the spatial quantization error, and 15% in the quantization error. This finding demonstrates that the proposed clustering algorithm is effective in identifying a spatially homogeneous congested area within a large-scale transportation network.
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    Regulating Traffic Flow and Speed on Large Networks: Control and Geographical Self Organizing Map (Geo-SOM) Clustering
    Elouni, Maha (Virginia Tech, 2021-06-09)
    Traffic growth and limited roadway capacity decrease traveler mobility and increase traffic congestion and fuel consumption. Traffic managers employ various control techniques to mitigate the aforementioned problems. One well-known network-wide control strategy is perimeter control (or gating). Perimeter control is based on the Network Fundamental Diagram (NFD). NFD-based perimeter control techniques are used to solve congestion problems in transportation networks. One well-known method used in the literature is Proportional Integral Control (PIC). PIC solves the congestion problem, but suffers from sensitivity to parameter tuning and the need for model linearization. A weather-tuned perimeter control (WTPC) and a jam density-tuned perimeter controller (JTPC) were developed to cope with parameter sensitivity for different weather conditions and jam densities, respectively. In an attempt to overcome PIC problems, a sliding mode controller (SMC) was developed. SMC does not require model linearization and parameter tuning. It is also robust to varying demand patterns. SMC computes the flow that needs to enter a protected network and converts it to corresponding traffic signal timings to achieve the desired control strategies. Another approach to implementing the sliding mode controller is to control vehicle speeds on the links entering the protected network. Coupling speed harmonization (SH) with sliding mode control (SMC), an SMC-SH was developed and implemented in the INTEGRATION microscopic traffic simulator. The mentioned controllers are all tested on a mid-size grid network replicating downtown Washington DC. SMC-SH improved different performance metrics on the whole grid network compared to the no control case. Specifically, it improved average travel time, total delay, stopped delay, fuel consumption, CO2 emissions by 17.27%, 18.18%, 12.76%, 5.91%, and 7.04%, respectively. In order to test the SMC-SH on a real large-scale network, the downtown Los Angeles (LA) network is used. The LA network is known for its congested freeways, so a development of a Freeway-SMC-SH controller is performed and tested. It shows good results in improving the performance not only of freeways, but also the overall LA network performance. Particularly, the network-wide average travel time, total delay, stopped delay, fuel consumption and CO2 emissions improved with respect to the no control case by 12.17%, 20.67%, 39.58%, 2.6%, and 3.3%, respectively. An identification of a homogeneously congested area is needed to apply SMC-SH on LA roads (not freeways). The geographical self organizing maps (GeoSOM) clustering algorithm is applied and tested on the LA network. The clustering goal is to identify a geographically connected region with small density variance. GeoSOM is able to achieve that objective with better performance than the state-of-the-art Kmeans and DBSCAN clustering algorithms. The enhancements reached up to 15.15% for quantization error, 61.05% for spacial quantization error, and 43.96% for variance. Finally, the SMC-SH is tested on the protected region of the LA network identified by the GeoSOM algorithm. SMC-SH succeeds in improving network-wide vehicle travel time, total delay, stopped delay, fuel consumption and CO2 emissions by 6.25%, 9.4%, 16.47%, 1.7%, and 2.19%, respectively.