Traffic congestion prediction – Mind mapping 

 Challenge and Selection : 

 Mapping complex systems and generating network analysis require abstract preparations and must   be simplified at its initiation , before transferring the analysis into code and computation time . In   order to tackle problems consisting of multiple variables , data attributes and outcomes at a massive   scale , a simple mind map may speed up the process of clarifying such analysis , save computing time   and resources which is critical for the feasibility of any related project . Mobility analysis of cars within   a city , for example , require massive amounts of data crunching , computing power and algorithm   optimization . Before tackling such challenge , a simple mind map , consisting of three nodes should be   plotted . This is general abstraction before a detailed spider mind map topology . These nodes are the   input nodes ( let ’s call it A ) , the raw data attributes provided by data sources , the processing node ( B ) ,   to be explained later on and output nodes ( C ) , which are the outcomes waiting to be solved . The   process node should include a bulk option of multiple analytic models , be it basic econometric   regressions , machine learning algorithms , deep learning algorithms and so on . 

 Application : 

 The three nodes ( A , B , C ) , mentioned before in the mind map , will have connections to smaller nodes   ( a1 , a2 , a3 , etc . ) . The input nodes ( A ) containing data attributes include vehicle category , plate   number , vehicle make , vehicle model , vehicle size , mileage , mpg , range , car age , timestamp , day ,   date , time , latitude , longitude , average speed , etc . The processing node ( B ) will include possible   mobility analysis related algorithms , such as reinforcement learning ( b1 ) , supervised learning ( b2 ) ,   etc . , and the output node ( C ) will include outcomes needed to be measured , such as high traffic   congestion , medium traffic congestion and low traffic congestion . The A nodes will have connections   to sub - attributes such as in the case of vehicle category(a1 ) ; this would be passenger car(a11 ) , truck   ( a12 ) , lorry ( a13 ) etc . , which directly effect other attributes such as vehicle model and vehicle size .   This gives us an abstraction of a neural network . The output node , in which is the result of our   prediction will have three possible outcomes being high , medium and low traffic congestion ( c1 , c2   , c3 ) . 

 After extending the mind map into a spider diagram ( large nodes and small nodes ) , we start   cancelling out the listed algorithms in the processing node ( B ) . This processing node includes   algorithms such reinforcement learning(b1 ) , supervised learning ( b2 ) , etc . The sub attributes , or   smaller nodes connecting to , for example , the reinforcement learning ( b1 ) would be Q - table learning   ( b11 ) and so on . After brain storming and leaving the most suitable algorithm , let ’s say b11 , we can   start cancelling out the data attributes which may seem like noise , and use less attributes to   generate optimum results in a computationally cost - effective way . This method is not an exact   approach to tackle such complex systems analysis , but does speed up the process of getting the work   done in a feasible way . 

 Insight and Approach 

 The insights I have obtained through this mind mapping strategy has helped me filter out data   attributes that are noise at the A node level , I ’ve mentioned as the input node . Cancelling out noise   has sped up analysis generated while saving computational resources , meanwhile helping me select   the most optimum algorithm at the B node level , without decreasing the quality or precision and   accuracy of the predictions , which I ’ve mentioned as the C node . This forms the basis of a feed back   loop where more result - affecting input attributes , such as rainfall per meter squared can be added   and removed from the mind map and hence deliver enriched output results such as traffic   congestion scores . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Traffic congestion prediction – Mind mapping 

 Challenge and Selection : 

 Mapping complex systems and generating network analysis require abstract preparations and must   be simplified at its initiation , before transferring the analysis into code and computation time . In   order to tackle problems consisting of multiple variables , data attributes and outcomes at a massive   scale , a simple mind map may speed up the process of clarifying such analysis , save computing time   and resources which is critical for the feasibility of any related project . Mobility analysis of cars within   a city , for example , require massive amounts of data crunching , computing power and algorithm   optimization . Before tackling such challenge , a simple mind map , consisting of three nodes should be   plotted . This is general abstraction before a detailed spider mind map topology . These nodes are the   input nodes ( let ’s call it A ) , the raw data attributes provided by data sources , the processing node ( B ) ,   to be explained later on and output nodes ( C ) , which are the outcomes waiting to be solved . The   process node should include a bulk option of multiple analytic models , be it basic econometric   regressions , machine learning algorithms , deep learning algorithms and so on . 

 Application : 

 The three nodes ( A , B , C ) , mentioned before in the mind map , will have connections to smaller nodes   ( a1 , a2 , a3 , etc . ) . The input nodes ( A ) containing data attributes include vehicle category , plate   number , vehicle make , vehicle model , vehicle size , mileage , mpg , range , car age , timestamp , day ,   date , time , latitude , longitude , average speed , etc . The processing node ( B ) will include possible   mobility analysis related algorithms , such as reinforcement learning ( b1 ) , supervised learning ( b2 ) ,   etc . , and the output node ( C ) will include outcomes needed to be measured , such as high traffic   congestion , medium traffic congestion and low traffic congestion . The A nodes will have connections   to sub - attributes such as in the case of vehicle category(a1 ) ; this would be passenger car(a11 ) , truck   ( a12 ) , lorry ( a13 ) etc . , which directly effect other attributes such as vehicle model and vehicle size .   This gives us an abstraction of a neural network . The output node , in which is the result of our   prediction will have three possible outcomes being high , medium and low traffic congestion ( c1 , c2   , c3 ) . 

 After extending the mind map into a spider diagram ( large nodes and small nodes ) , we start   cancelling out the listed algorithms in the processing node ( B ) . This processing node includes   algorithms such reinforcement learning(b1 ) , supervised learning ( b2 ) , etc . The sub attributes , or   smaller nodes connecting to , for example , the reinforcement learning ( b1 ) would be Q - table learning   ( b11 ) and so on . After brain storming and leaving the most suitable algorithm , let ’s say b11 , we can   start cancelling out the data attributes which may seem like noise , and use less attributes to   generate optimum results in a computationally cost - effective way . This method is not an exact   approach to tackle such complex systems analysis , but does speed up the process of getting the work   done in a feasible way . 

 Insight and Approach 

 The insights I have obtained through this mind mapping strategy has helped me filter out data   attributes that are noise at the A node level , I ’ve mentioned as the input node . Cancelling out noise   has sped up analysis generated while saving computational resources , meanwhile helping me select   the most optimum algorithm at the B node level , without decreasing the quality or precision and   accuracy of the predictions , which I ’ve mentioned as the C node . This forms the basis of a feed back   loop where more result - affecting input attributes , such as rainfall per meter squared can be added   and removed from the mind map and hence deliver enriched output results such as traffic   congestion scores .
### Output: 