InterTwin: Deep Learning Approaches for Computing Measures of Effectiveness for Traffic Intersections

Microscopic simulation-based approaches are extensively used for determining good signal timing plans on traffic intersections. Measures of Effectiveness (MOEs) such as wait time, throughput, fuel consumption, emission, and delays can be derived for variable signal timing parameters, traffic flow patterns, etc. However, these techniques are computationally intensive, especially when the number of signal timing scenarios to be simulated are large. In this paper, we propose InterTwin, a Deep Neural Network architecture based on Spatial Graph Convolution and Encoder-Decoder Recurrent networks that can predict the MOEs efficiently and accurately for a wide variety of signal timing and traffic patterns. Our methods can generate probability distributions of MOEs and are not limited to mean and standard deviation. Additionally, GPU implementations using InterTwin can derive MOEs, at least four to five orders of magnitude faster than microscopic simulations on a conventional 32 core CPU machine.

Helmet Wearing Detection for Riders Safety

The main purpose of paper is to find riders who neglect road safety, which leads to accidents and death. Thus most of the countries mandate the use of the helmets for two-wheeler riders. In order to discourage this behavior police force has been made for traffic to issue violation ticket. This process will be done manual, time consuming and very tedious. Hence proposed system will detect riders who wear the helmet while riding the motor vehicle and helps in finding riders without helmet to get imposed with fine. The system implements machine learning and image processing techniques to detect riders, riding two-wheeler, who are wearing helmets. The system takes a video of real time as the input and detects moving objects in the scene. The SIFT and SURF algorithm is used for detecting the helmet in the real time video, surf is faster than the sift algorithm in the machine learning and it is more efficient to detect the helmet object. Further, practically can be implemented in traffic intersections to monitor the rider’s safety by detecting helmet.

Intelligent Traffic Control System with Priority to Emergency Vehicles

In static road dividers the number of lanes on either side of the road is fixed and cannot be extended. This can be a major problem during peak traffic hours. The situation is abysmal when emergency vehicles are required to wait for other vehicles to give way at traffic intersections. This causes large time delays and may affect the emergency case. These traffic issues faced by emergency vehicles and daily commuters can be avoided by using this proposed traffic control system based on image processing and IoT. As a result, this project successfully analyzes and implements an Intelligent traffic control system with priority given to emergency vehicles.

Reduction of Plastic Deformation in Heavy Traffic Intersections in Urban Areas

This study aims to maximize the effects of reducing plastic deformation in heavy traffic intersections in urban areas by improving the aggregates and binders of asphalt mixtures in order to verify the strength effect of SMA (Stone Mastic Asphalt) mixtures compared with that of fluid-resistant asphalt mixtures. The authors examine the pavement performance and conduct an economic analysis for sustainable urban infrastructure. Additionally, to reduce plastic deformation, the study analyzed an improvement plan through experimental research based on the existing literature. First, we determined the mixing design specifications of the general asphalt fluid-resistant mixture and SMA mixture, which is known to reduce plastic deformation. Next, we confirmed the appropriateness of the raw materials and mixing design results. Finally, a performance test was conducted on plastic deformation resistance. A wheel tracking test was also conducted as a performance experiment. The test body—with a fiber grid reinforcing material installed in the SMA mixture—showed high dynamic stability, which was the most effective for reducing plastic deformation.