Abstract
The adaptability of traffic lights in the control of vehicle traffic heavily affects the trafficability of vehicles and the travel efficiency of traffic participants in busy urban areas. Existing studies mainly have focused on the presence of traffic lights, but rarely evaluate the impact of traffic lights by analyzing traffic data, thus there is no solution for practicably and precisely self-regulating traffic lights. To address these issues, we propose a low-cost and fast traffic signal detection and impact assessment framework, which detects traffic lights from GPS trajectories and intersection features in a supervised way, and analyzes the impact range and time of traffic lights from intersection track data segments. The experimental results show that our approach gains the best AUC value of 0.95 under the ROC standard classification and indicates that the impact pattern of traffic lights at intersections is high related to the travel rule of traffic participants.
Highlights
Traffic Lights via GPS VehicleTraffic signal information can achieve vehicle safety, global scheduling and increase traffic efficiency, and can improve the quality of application services, such as intelligent navigation and route planning, and effectively estimate the driving time of unmanned vehicles
According to the Chinese road traffic signal settings and installation specifications (GB14886-2016) [5], the intersection traffic lights are set based on three aspects: intersection type, intersection traffic flow and intersection traffic accident condition, which are not related to the rest of the intersection features
We explore the effect of using GPS trajectories and intersection features for detecting intersection traffic lights, and the influence of sampling trajectories in different intersection ranges on classification performance for setting traffic lights
Summary
Traffic signal information can achieve vehicle safety, global scheduling and increase traffic efficiency, and can improve the quality of application services, such as intelligent navigation and route planning, and effectively estimate the driving time of unmanned vehicles It has become an indispensable source of input data in various urban navigation applications. A detection framework based on deep learning algorithm is proposed, which focuses on three decisive metrics specified by the specifications for road traffic signal setup and installation. It differs from other frameworks in terms of physical feature vectors and statistical feature vectors extracted from trajectory data.
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