Abstract
Fixed-time traffic signal control strategy in an isolated pedestrian crossing tends to reduce traffic capacity and expose vulnerable road users to more danger. To mitigate the negative impact of previous control strategy, this study proposed an optimal real-time signal timing strategy to protect pedestrian crossing and at the same time minimize the system-wide traffic delay. With the application of a wide-area radar data, the features of vehicles, pedestrians, and the passing time of non-motor vehicles and pedestrian were captured considering conflicts and traffic delay. The support vector machine for regression was utilized to hypothesize traffic delay by training. The discrete values of hypothetical passing time will be tested. The minimum value of delay can be recognized and the corresponding hypothetical passing time will be recommended as the green time for crossing. The performance of the proposed ORSTS outperformed the fixed-time traffic signal control strategy in reducing traffic delay by 22.3%.
Highlights
China’s urban traffic has been experiencing exponential growth in recent years
A considerable number of pedestrian crossings in China are actualized without signals
Support vector machine for regression (SVR) is a subfield of supervised learning utilized to give prediction
Summary
China’s urban traffic has been experiencing exponential growth in recent years. In busy commercial blocks or arterial roads with heavy traffic, vulnerable road users, pedestrians in particular, are more likely to face greater danger when crossing roads. Studies[3,4] have shown that non-signal control tends to obstruct traffic flows and deteriorate the safety of pedestrians. To better capture the features of road users, computer image processing has shown its advantages for real-time performance.[6,7] Machine learning techniques play important roles in optimizing the strategies for traffic control. An optimal real-time signal timing strategy (ORSTS) for an isolated pedestrian crossing was proposed. Support vector machine for regression (SVR) was utilized to solve the problem of insufficient amount of data in short-term forecasting, since it offers nonlinear function approximation and global optimal solutions in comparison to other algorithms.[16] The sum of vehicular delay and pedestrian delay is employed as the evaluation method for the proposed strategy.
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