Don't cross that stop line: Characterizing Traffic Violations in Metropolitan Cities

TL;DR

This study analyzes over 3 million e-challans from Ahmedabad to understand user behavior, violation patterns, and spatial-temporal trends, proposing features for recidivism prediction with high accuracy.

Contribution

It introduces a comprehensive dataset and analysis of traffic violations, along with a novel feature set for recidivism prediction achieving 95% accuracy.

Findings

Frequent offenders tend to repeat violations and avoid paying higher fines.

Traffic violations spike during festival days and certain hotspots.

Proposed features effectively predict recidivism with high accuracy.

Abstract

In modern metropolitan cities, the task of ensuring safe roads is of paramount importance. Automated systems of e-challans (Electronic traffic-violation receipt) are now being deployed across cities to record traffic violations and to issue fines. In the present study, an automated e-challan system established in Ahmedabad (Gujarat, India) has been analyzed for characterizing user behaviour, violation types as well as finding spatial and temporal patterns in the data. We describe a method of collecting e-challan data from the e-challan portal of Ahmedabad traffic police and create a dataset of over 3 million e-challans. The dataset was first analyzed to characterize user behaviour with respect to repeat offenses and fine payment. We demonstrate that a lot of users repeat their offenses (traffic violation) frequently and are less likely to pay fines of higher value. Next, we analyze the data from a spatial and temporal perspective and identify certain spatio-temporal patterns present in our dataset. We find that there is a drastic increase/decrease in the number of e-challans issued during the festival days and identify a few hotspots in the city that have high intensity of traffic violations. In the end, we propose a set of 5 features to model recidivism in traffic violations and train multiple classifiers on our dataset to evaluate the effectiveness of our proposed features. The proposed approach achieves 95% accuracy on the dataset.