481 research outputs found
Dynamic Left-turn Phase Optimization Using Fuzzy Logic Control
The left-turn movement at an intersection has long been a concern of traffic engineers as it is a major capacity reduction factor. Different left-turn signal phasings have been shown to result in significant differences in delay, intersection capacity, and even safety level.
First, past studies about leading and lagging signal phases and signal control application are overviewed. Then this research gives a theoretical analysis of signal left-turn phase operations at both isolated and coordinated signalized intersections, compares the difference in delay based on leading and lagging left-turn signal phase designs, analyzes the influences of traffic control delay components for leading and lagging left-turn, identifies the main control factors, and gives a new model to guide the choosing between the leading and lagging left-turn phases.
In the third part of this research, some basic mathematical definitions and rules of fuzzy logic control are described. A four-level fuzzy logic control model is designed. To implement this control model, observed approaching traffic flows are used to estimate relative traffic intensities in the competing approaches. These traffic intensities are then used to determine whether a leading or lagging signal phase should be selected or terminated.
Finally, this research designs a dynamic traffic signal left-turn phase control system, and implements the four-level fuzzy logic control model to optimize signalized intersection operation. The performance of this dynamic traffic signal left-turn phase fuzzy logic control system compared favorably in all categories to fixed time control, actuated control, and traditional fuzzy control based on simulation using field data. The results suggest that the proposed dynamic traffic signal left-turn phase fuzzy logic control system is a superior and efficient tool for reducing intersection traffic delay. The study also demonstrated that the successful implementation of the proposed model does not rely on the installation of expensive or complicated equipment
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Intersection Signal Control and Design for Improved Person Mobility and Air Quality in Urban Multimodal Transportation Systems
Alternative geometric designs (e.g. roundabouts) and multi-objective signal control strategies are promising measures to improve sustainability of traffic networks. However, roundabouts are mostly used because of their safety and operational advantages. There has been less attention to the environmental performance of roundabouts. Also, the existing studies have been mostly used through field measurements and current simulation models, which need high calibration efforts and they are not inclusive in terms of considering all influencing factors on vehicular emissions at roundabouts. Furthermore, the existing real-time signal control strategies do not account for the emission rates of different vehicle types (e.g. cars and buses). In addition, the real-time multi-objective signal control systems does not consider environmental objectives. This dissertation develops a real-time bi-objective signal control system for isolated intersections, which operate at undersaturated traffic conditions that minimizes a weighted combination of vehicle delay (or person delay) and emissions of auto and transit vehicles. Pareto Frontiers of the optimal solutions are presented to help decision makers select the most appropriate combinations of objectives to achieve desirable levels of delay and emissions. Additionally, a simple simulation tool based on Cellular Automata (CA) model of traffic simulation is developed to estimate delay and reproduce vehicle trajectories for emission estimation. The models are used to compare the operational and environmental performance of roundabouts and signalized intersections and perform sensitivity analysis with respect to total traffic demand, left turn ratio, and pedestrian volume. Evaluation tests show that replacing a signalized intersection with a roundabout results in improved delay and emissions at undersaturated traffic conditions and any pedestrian volume. It also shows that roundabouts’ performance is less affected by high left turning demand compared to signalized intersections. On the contrary, roundabouts’ performance is sensitive to frequent pedestrian crossings while the performance of signalized intersections is not affected by pedestrian crossings
Neurofuzzy control to address stochastic variation in actuated-coordinated systems at closely-spaced intersections
This dissertation documents a method of addressing stochastic variation at closely-spaced signalized intersections using neurofuzzy control. Developed on the conventional actuated-coordinated control system, the neurofuzzy traffic signal control keeps the advantage of the conventional control system. Beyond this, the neurofuzzy signal control coordinates the coordinated phase with one of the non-coordinated phases with no reduction of the green band assigned to the coordination along the arterial, reduces variations of traffic signal times in the cycle caused by early return to green , hence, makes more sufficient utilization of green time at closely-spaced intersections. The neurofuzzy signal control system manages a non-coordinated movement in order to manage queue spillbacks and variations of signal timings.Specifically, the neurofuzzy controller establishes a secondary coordination between the upstream coordinated phase (through phase) and the downstream non-coordinated phase (left turn phase) based on real-time traffic demand. Under the fuzzy logic signal control, the traffic from the upstream intersection can arrive and join the queue at the downstream left turn lane and be served, and hence, less possibly be blocked on the downstream left turn lane. This secondary coordination favors left turn progression and, hence, reduces the queue spillbacks. The fuzzy logic method overcomes the natural disadvantage of currently widely used actuated-coordinated traffic signal control in that the fuzzy logic method could coordinate a coordinated movement with a non-coordinated movement. The experiment was conducted and evaluated using a simulation model created using the microscopic simulation program - VISSIM.The neurofuzzy control algorithm was coded with MATLAB which interacts with the traffic simulation model via VISSIM\u27s COM interface. The membership functions in the neurofuzzy signal control system were calibrated using reinforcement learning to further the performance. Comparisons were made between the trained neurofuzzy control, the untrained neurofuzzy control, and the conventional actuated-coordinated control under five different traffic volumes. The simulation results indicated that the trained neurofuzzy signal control outperformed the other two for each traffic case. Comparing to the conventional actuated-coordinated control, the trained neurofuzzy signal control reduced the average delay by 7% and the average number of stops by 6% under the original traffic volume; as traffic volume increasing to 120%, the reductions doubled
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