Problem: Crime patterns are often reactive rather than proactive, leading to resource strain and inefficient crime prevention.  

Challenge: Utilize AI and advanced data analysis to predict high-risk areas for specific crime types, enabling targeted patrolling, resource allocation, and preventative measures.

Problem Statement:

Police generate and store a large volume of data relating to crime and criminals. However, the challenge does not end in storing and processing the data but in predicting crime hotspots, forecasting crime trends, and predicting offender characteristics. The problem includes identifying areas of specific crimes like murder, property offenses, and other bodily offenses and predicting future hotspots, using the data to predict when and where these crimes occur and also to link the pattern of crime with that of the offenders and predict the likelihood of future crimes based on demographic information and criminal history.

Solution Expected: 

1. Spatial analysis of the crime, distribution of crimes over a particular location, and crime hotspots.
2. Location-based analysis of the crimes, beat-wise distribution of crimes.
3. Trend of occurrence of crimes at a particular time or in a particular day/ month/season of the year.
4. Analysis of the accused age, occupation, socio-economic, status, location, etc, and prediction of criminal behavior.
5. Analysis of the victim, socio-economic background, gender, location, and prediction of vulnerable populations and areas.
6. Comparison of beat duties, patrolling areas with that of the crime occurrence, and analyzing the performance of the police
7. Training an AI model that not only predicts the occurrence of crimes but also suggests a deployment plan for the police.

Problem: Urban traffic congestion leads to economic losses, environmental impact, and decreased quality of life. Traditional traffic management methods are often limited in effectiveness.  

Problem Statement 1 - Evaluating the traffic congestion shown by map engine services against the calculated actual congestion using the Drone.

Expected Solution - Map engine services like Google, and Tom Tom have traffic congestion data which is based on the number of GPS devices used. Using a Drone for geo fenced area or a corridor to calculate the actual congestion. Evaluate the calculated drone-based congestion against the congestion shown by the map engine services and suggest the level of accuracy.

Problem statement 2 - Mechanism to measure the traffic flow optimization at each junction once signal time optimization is achieved.

Expected Solution - Based on Vehicle actuated signals / Queue length using camera or radars, the signal optimal time is calculated. The generated Green time (optimal signal time) from the junction handles how many vehicles during the cycle time? How many vehicles it could handle in an ideal situation when traffic discipline is followed in the same Green time? Comparison and analysis of both outcomes to provide actionable inputs on achieving the traffic flow optimization visa-vis signal optimal time.

Problem statement 3 - Detecting any bottlenecks like parking /encroachment of road & footpath/road condition (pothole, bad road, road marking) in the vicinity of a junction which result in congestion and reduce the traffic flow.

Expected Solution - Physical identification of all such bottlenecks surrounding 100 Mts of a junction and generating an alert for communicating to the control room, concerned PS, and nearest junction officials. Evaluating the corrective measure taken on each alert generated surrounding the junction on the predefined time intervals. The predefined interval varies from type of bottleneck.

Problem: Lack of comprehensive data-driven insights hampers effective police performance assessments and resource allocation optimization.  

Challenge: Create an analytical dashboard that tracks key performance indicators (response times, crime clearance rates) across different units and locations, providing actionable insights for strategic decision-making and resource optimization.

Introduction

The Police Station is the basic unit for the functioning of the Police Department. There are numerous functions performed at this unit level. In a police station, the Police Sub-Inspector (PSI) provides leadership at a cutting-edge level. A Police Inspector (PI) supervises the running of the Police Station and the work done by all staff and PSIs. Above PIs, a Deputy Superintendent of Police (DySP) is a senior police officer looking after the day-to-day execution of the responsibilities of police stations.

The gamut of activities, duties, and responsibilities entrusted to a police station is so vast that it becomes extremely difficult to monitor work at each of these levels. Similarly, the manpower available requires allocation to all such duties, the distribution of which is complicated. For this purpose, all the duties at a police station have been categorized into different themes – L&O, Crime, Station Management, Investigation, and Court Monitoring. Each of these themes has been further divided into sub-themes and work items have been listed for each such sub-theme. These work items may further be broken down into smaller action items as per requirement.

Problem Statement

How can superior officers supervise the execution of duties and responsibilities by a police station, by a PSI, by a PI and supervision activities by ACP/DySP?

Expected Solution: - 

Prepare a dashboard with a combination of tables, bars, and charts with the above-given themes (and sub-themes) at a police station level - There should be a grouping of police stations and the same data must be aggregated with multiple police stations to indicate the work output at the next supervisory level (i.e. PI) and a group of PIs must be aggregated for work done at DySP/ACP level. - The data should be updated automatically by fetching from different sources – PoliceIT, IRAD, etc. - The output should be capable of indicating progress updates for certain work items. - There should be automated alerts for non-completion of work items as per user-defined rules. - There should be a provision for entry of all staff and officers and for PSI/PI to map manpower to work items through a weekly duty roster/chart. 

Problem: Balancing data collection and utilization for effective law enforcement with public privacy concerns requires innovative solutions.

Challenge: Develop frameworks and technologies that anonymize sensitive data, ensure secure storage and access, and provide clear guidelines for data use in compliance with privacy regulations.

Problem Statement

While data privacy is important in police departments, analysis of the data using various third-party tools is also needed. For this anonymizing the identity details without comprising data quality is the mandate for this exercise.

Expected Solution -

In any dataset of crimes (FIR/Chargesheet/statements etc.), all the personal identifiers like names, places, addresses, times, and places should be identified in the dataset from various documents and formats and replaced with random placeholders in such a manner that, analysis of that data should give valid actionable results. - The same exercise should also contain a tool to anonymize any information that is fed by a web application. Key in both the exercises for this would be the logic and reliability of data should remain valid. - While OCR tools for Kannada or English language need not be developed, identifying the personal information out of the data set or file format has to be completed. - The option to pick and choose which parts of private data to be anonymized should be given clearly in a drop-down format at the interface of this tool.

Problem: Traditional methods for analyzing accident data lack insights into patterns and contributing factors, limiting the potential for effective prevention strategies.  

Challenge: Develop an advanced data analysis system that identifies accident patterns, high-risk locations, and contributing factors (weather, road conditions, driver behavior) to inform targeted safety interventions and infrastructure improvements.

Problem statement:

Police store and record a huge volume of data relating to accidents, this data is seldom used to analyze and predict the accident pattern. The problem is to analyze the accident data to determine the cause or causes of an accident to identify the contributing factors of the accident which may be related to the behavior of the person or may be related to the structural design or infrastructure of the road and to predict the future occurrences of the accidents It also includes a plan to prevent further accidents of a similar kind by influencing the people through awareness and enforcement and also through engineering solutions.

Expected Solution:

1. Analysis of Black spots of accidents as well as predicting future black spots (Grey Spots).
2. Analysis of the date and time of the accidents and predicting the probability of occurrence of such accidents.
3. Analysis of the roads on which the accidents are taking place like National highways, state highways, village roads, etc
4. Contributing factors for multiple injuries/fatalities and solutions.
5. Analysis of landmarks near the accidents like the presence of railway crossings, schools, religious places, pubs, bars, office areas, residential areas, etc.
6. Analysis of road signages, problems with the roads, and suggestions for improvements.
7. Analysis of pedestrian accidents and their behavior.
8. Analysis of the type of vehicles involved and predicting the accident zones.
9. Analysis of the accused based on gender, age, and behavior. Analysis of the victim based on gender, age, and behavior.
10. Superimposition of all the causative /contributing factors and building an analysis model.
11. Analysis of Traffic deployment, enforcement, and patrolling areas.
    Combining the Causative factors with that of the traffic deployment and building a model to analyze the correlation between the two.
    An AI model that learns and predicts accidents and also suggests traffic deployment plans.