Browsing by Subject "Traffic Flow"

Now showing 1 - 4 of 4
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    Boosting Max-Pressure Signal Control Into Practical Implementation: Methodologies And Simulation Studies In City Networks
    (2023-08) Xu, Te
    This dissertation presents innovative modifications to the Max-Pressure (MP) control policy, an adaptive traffic signal control strategy tailored to various urban traffic conditions. The max-pressure control offers two pivotal advantages that underscore its significance for in-depth research and future implementation: Firstly, MP operates on a decentralized basis, enabling real-time solutions. Secondly, MP control guarantees maximum stability, implying it can accommodate as much given demand as any alternative signal timing strategy. Initially, the MP control policy was adapted to transit signal priority (MP-TSP). It delivered enhanced bus travel times, outperforming both fixed-time signal controls with TSP and other adaptive signal controls in efficiency. Subsequently, the pedestrian-friendly max-pressure signal controller (Ped-MP) was developed. This marked a pioneering effort in crafting an MP control to boost pedestrian access without compromising vehicle throughput. The Ped-MP, backed by analytical proof for maximum stability, illustrated an inverse relation between pedestrian delay and tolerance time during simulations on the Sioux Falls network. This suggests the potential for urban spaces that are more pedestrian-oriented, even in areas of elevated pedestrian traffic. The third innovation addressed the practical feasibility of the position-weighted back-pressure (PWBP) controller. Although the initial PWBP controller was effective in simulations, it was found to be impractical due to its need for density information from everywhere of the road link. This observation paved the way for the approximate position-weighted back-pressure (APWBP) control, which significantly reduces sensor requirements by utilizing only two loop detectors per link (one downstream and one upstream). A comparative analysis revealed that the APWBP's efficacy closely paralleled the original PWBP, validating its practicality. Finally, recognizing the MP controller’s deficit in coordinated phase selection, the Smoothing-MP approach was conceptualized. Incorporating signal coordination, this novel strategy not only maintained its maximum stability properties but also amplified traffic flow efficiency, as confirmed by mathematical proofs and numerical studies in both the Grid Network and the Downtown Austin Network.
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    Development and Application of the Network Weight Matrix to Predict Traffic Flow for Congested and Uncongested Conditions
    (2016-08-01) Ermagun, Alireza; Levinson, David M
    To capture a more realistic spatial dependence between traffic links, we introduce two distinct network weight matrices to replace spatial weight matrices used in traffic forecasting methods. The first stands on the notion of betweenness centrality and link vulnerability in traffic networks. To derive this matrix, we assume all traffic flow is assigned to the shortest path, and thereby we used Dijkstra's algorithm to find the shortest path. The other relies on flow rate change in traffic links. For forming this matrix, we employed user equilibrium assignment and the method of successive averages (MSA) algorithm to solve the network. The components of the network weight matrices are a function not simply of adjacency, but of network topology, network structure, and demand configuration. We tested and compared the network weight matrices in different traffic conditions using Nguyen-Dupuis network. The results led to a clear and unshakable conclusion that spatial weight matrices are unable to capture the realistic spatial dependence between traffic links in a network. Not only do they overlook the competitive nature of traffic links, but they also ignore the role of network topology and demand configuration. In contrast, the flow-weighted betweenness method significantly operates better than unweighted betweenness to measure realistic spatial dependence between traffic links, particularly in congested traffic conditions. The results disclosed that this superiority is more than 2 times in congested flow situations. However, forming this matrix requires considerable computational effort and information. If the network is uncongested the network weight matrix stemming from betweenness centrality is sufficient.
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    An Introduction to the Network Weight Matrix
    (2016-08-01) Ermagun, Alireza
    This study introduces the network weight matrix as a replacement for the spatial weight matrix to measure the spatial dependence between links of a network. This matrix stems from the concept of betweenness centrality and vulnerability in network science. The elements of the matrix are a function not simply of proximity, but of network topology, network structure, and demand configuration. The network weight matrix has distinctive characteristic, which are capable of reflecting spatial dependence between traffic links: (1) The elements are allowed to have negative and positive values, which capture competitive and complementary nature of links, (2) The diagonal elements are not fixed to zero, which takes the self-dependence of a link upon itself into consideration, and (3) The elements not only reflect the spatial dependence based on the network structure, but they acknowledge the demand configuration as well. We verified the network weight matrix by modeling traffic flows in a 3x3 grid test network with 9 nodes and 24 directed links connecting 72 origin-destination (OD) pairs. The results disclose models encompassing the network weight matrix outperform both models without spatial components and models with the spatial weight matrix. This leads inexorably to the conclusion that the network weight matrix represents a more accurate and defensible spatial dependency between traffic links, and thereby augments traffic flow prediction.
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    Using Temporal Detrending to Observe the Spatial Correlation of Traffic
    (2016-08-01) Ermagun, Alireza; Levinson, David M; Chatterjee, Snigdhansu
    This empirical study sheds light on the correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the correlation between 140 freeway traffic links in a sub-network of the Minneapolis - St. Paul highway system with a grid-like network topology. This topology enables us to juxtapose positive correlation with negative correlation, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective to the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted correlation structure can augment the accuracy of short-term traffic forecasting models.