Predictive–Tentative Transit Signal Priority with Self-Organizing Traffic Signal Control

Abstract

Reducing bus delay beyond what can be achieved with conventional transit signal priority requires making and responding to longer-range predictions of bus arrival time, which include dwell time at an upstream stop. At the same time, priority decisions based on such uncertain predictions should be reversible if the dwell time should be much longer than expected. Rules for applying these concepts are proposed for application in the framework of self-organizing traffic signal control developed by authors Cesme and Furth. Predicted arrival time is based on a calculation of expected remaining dwell time and is compared with the earliest time the bus phase can be expected to return to green. One possible decision is to expedite return to green so that secondary extensions (a feature of self-organizing control logic) are inhibited. The other is to hold the green; however, this decision can be reversed if updated predictions of expected remaining dwell time indicate that the bus will arrive after the maximum green extension has expired. Simulation tests on a corridor with nine signalized intersections showed a 75% reduction in bus delay, to only 5 s per intersection, with only a 3% increase in general traffic delay.

Short-Term Prediction of Signal Cycle on an Arterial With Actuated-Uncoordinated Control Using Sparse Time Series Models

“…Meanwhile, the Sparse VAR models perform better in predicting the next value of signal cycle among the five models, when the distance between signals is high (1000 meters) and the demand is high as well. Such mathematical findings can be interpreted in practical research that has been conducted by transportation scholars on self-organizing signal control (Moghimidarzi et al 2016, Furth and Cesme, 2013. It indicates that when signals are closely spaced, those signals should be synchronized together by having the same cycle length.…”

“…For instance, transit signal priority attempts to change/adjust signal timing in order to turn the signal green for transit vehicles, resulting in less delay to transit. It has been shown by many studies (Sun et al, 2007;Moghimidarzi et al, 2016;Wadjas and Furth, 2003;Li et al, 2012) that having longer prediction of transit arrival times can provide better conditions for transit to move faster. Thus, with the knowledge of a longer horizon of transit arrival times and more accurate signal cycle predictions, actuated control logics can gradually, rather than abruptly, change signal phases as the transit vehicle approaches the target intersection.…”

Transit Signal Priority in Smart Cities

“…There are other transit signal tactics that have been introduced, which in a sense is the combination of the above listed tactics; some of which include: transit phase truncation and queue dissipation [15], early red, flush-and-return [16], expedited return [17,18], etc.…”

“…Adaptive control with decentralized approach has also been developed, e.g. self-organizing system, that functioned well with signal priority [18].…”

Enhanced velocity based pore pressure prediction using lithofacies clustering: A case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

“…Through multiple iterations, the weights of the neurons converge as the neighborhood of the best matching unit (BMU) shrinks (Ciampi and Lechevallier, 2000). The robustness of SOM clustering method could be associated with its characterized non-linear projection from the higher dimensional space of inputs to a low dimensional grid, which facilitates the discovery of hidden patterns in the input data (Kohonen and Honkela, 2007;Moghimidarzi et al, 2016). The SOM proved to be able to handle large datasets with outliers effectively (Shahreza et al, 2011;Oyana et al, 2012), and it has been applied successfully in complex structures (Tasdemir and Merényi, 2009).…”