Interpretable Traffic Event Analysis with Bayesian Networks

Although existing machine learning-based methods for traffic accident analysis can provide good quality results to downstream tasks, they lack interpretability which is crucial for this critical problem. This paper proposes an interpretable framework based on Bayesian Networks for traffic accident prediction. To enable the ease of interpretability, we design a dataset construction pipeline to feed the traffic data into the framework while retaining the essential traffic data information. With a concrete case study, our framework can derive a Bayesian Network from a dataset based on the causal relationships between weather and traffic events across the United States. Consequently, our framework enables the prediction of traffic accidents with competitive accuracy while examining how the probability of these events changes under different conditions, thus illustrating transparent relationships between traffic and weather events. Additionally, the visualization of the network simplifies the analysis of relationships between different variables, revealing the primary causes of traffic accidents and ultimately providing a valuable reference for reducing traffic accidents.

Design Of Two Stage CMOS Operational Amplifier in 180nm Technology

In this paper a CMOS two stage operational amplifier has been presented which operates at 1.8 V power supply at 0.18 micron (i.e., 180 nm) technology and whose input is depended on Bias Current. The op-amp provides a gain of 63dB and a bandwidth of 140 kHz for a load of 1 pF. This op-amp has a Common Mode gain of -25 dB, an output slew rate of 32 $V / \mu s$, and a output voltage swing. The power consumption for the op-amp is $300\mu W$.