Signal Control Optimization – Engineering Assignment Help

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Annotated Bibliography for ‘Signal Control Optimization for  Automated Vehicles at Isolated Signalized Intersections’

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Li, Z., Elefteriadou, L. & Hanka, S., (2014), ‘Signal control optimization for automated vehicles at isolated signalized intersections’. Transportation Research Part C: Emerging Technologies. 49, pp.1-1S.

In this article. Li et al. developed a signal control algorithm that allows for vehicle paths and signal [;) control to be jointly optimized based OIL alkall•t’d cuuuuunicatiuu tliell11010gy between approawhin vehicles and signal controller by assuming that vehicle trajectories can he fully opti izae authors had assumed a simple intersection with two single-I through approaches ir ler o develop the optimization algorithm. A rolling horizon scheme    eveloped by authors o imple- ment the algorithm and to continually process newly arriving vt hicks. The algorithm was coded in MATLAB. The authors concluded that the proposed signal control optimization algorit hin could improve the intersection performance by reduce the average travel time delay by increase throughput by 2.7 20.2M depending on the demand scenario. The authors stated that t his algorithm works better for the more congested conditions.

Different Departing Scenarios Based on the Trajectory of the First Vehicle

Figure 1: Different Departing Scenarios Based on the Trajectory of the First Vehicle. Lost time of the first vehicle has significant impacts on efficiency.

In order to ensure the intersection capacity is fully utilized, the proposed algorithm minimised the lust time of the first vehicle and maximised the outflow during green light by maximising the mallow main] ioa I minimising t he headway (refer to Figure I ). In most of the papers, autonomous intersection management follows the first in first Mit rule, which means there is no actual phases like signal timings, as a result it is hard to accommodate pedestrians and cyclists in these cases (refer to Figure 2a), but in this study the authors had kept the phases, which made it easier to accommodate pedestrians and cyclists (refer to Figure 2b). In ease of system failure. the proposed management method by Li et al. will mostly results in rear-end collision, where as the commonly used management method will mostly results in T-bone collision which is significantly more dangerous that rear-end collision. It might also be more comfortable for human passengers not to observe other vehicles travelling in perpendicular directions in front of their vehicle.

I think this study is quite relevant to ms study. This study showed me how researchers use trajectories in their research work. I had always thought about putting loop detectors more upstream from the intersection and optimising the traffic light or green wave base on the real traffic flow. In this study, the author had assumed a communication range upstream from the intersect ion where the traffic controller communicates with the vehicles. I am writing my thesis on incorporating pedestrian metrics into intersection design, this study brings me the idea of setting

Comparison Between the Commonly Used Autonomous Intersection Management and the Proposed Autonomous Intersection Management

(a) Commonly Used Autonomous Intersection    (b) Proposed Autonomous Intersection Management
Management                                                                       by Li et al.

Figure 2: Comparison Between the Commonly Used Autonomous Intersection Management and the Proposed Autonomous Intersection Management by Li et al. There is no phases in the com¬monly used autonomous intersection management, meanwhile traffic timing phases still exist in the proposed autonomous intersection management. up communication range upstream from the intersection for pedestrians. where the pedestrians will be told by an app or even interactive ground to adjust their walking speed in order to minimise the delay and avoid queue spill-back at the intersection.

The atitlibia had assumed that there are only automated vehicles at the intersection and this automated vehicle environment enables bidirectional communication between vehicles and signal controller, which is not possible in the near future. but the proposed autonomous intersection man-agement will still improves the perfornnuice of intersections when not all vehicles are autonomous, whereas the commonly used autonomous intersection management has to require all of the vehicles to be autonomous. Overall the proposed autonomous intersection management is better in the near future. The authors stated that the proposed algorithm works best when the communication range is bigger than a certain value. If the communication range is smaller than that value, im¬provements brought by the proposed algorithm decrease as the communication range decreases. An intersection with only two phases with equally priorities were studied, intersection with more phases, different priorities in different directions and turning movements are recommended to be studied for future researches.

In considering with turning movement. the performance could be optimised by ordering traffic to have turning traffic separated from traffic going straight longitudinally so that the turning traffic does not slow other traffics down. In this study, the author had not optimised time performance by reducing the following spacing, which is achievable by letting the following autonomous vehicles to accelerate and decelerate at the same time. After minimising the following spacing, the proposed management method will be more efficient, whereas the commonly used autonomous intersection management method requires a significant amount of spacing in order to avoid collisions from ve-hicles travelling in different directions (refer to Figure 2a). This proposed autonomous intersection management method is now my favourite autonomous intersection management method, I will include this paper in my future research.

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