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.

