13 December 2012

Travel Tracking

Seasonal travel is one of the busiest times of the year for transportation services. On other occasions it is rush hour management that can cause severe delays and disruptions to commuters. Tracking best routes and mapping restrictions and disruptions in real-time is a very plausible and useful level of analytics to have at hand for any commuter. This process will not only allow them precise information in real-time but could also allow them to fully comprehend their travel plans from source to destination in a very fast and conducive way. Most transportation networks can be mapped as graphs. Graphs are a generalization of representing the structure and relationships between data points or so called nodes. In a train network each station could be a node and each route to another node as a weighted link or more precisely an edge. Various algorithms can then be applied to work out specific natural language search requirements or more precisely the semantic context of search. This could be a sub-optimal or a greedy option. In a sub-optimal option aspects like global optimization ideas can be applied where a solution sample only needs to be good enough based on the defined function as the search space is so huge. Graph algorithms like dijkstra's shortest path,  uniform-cost search, minimum spanning trees, best-first search, breadth-first search, depth-first search, travelling salesman problem can be applied based on specific semantic requirements of travel. So, one could be looking for an option of finding the shortest and fastest possible route to get to destination before or at a certain time. Another semantic requirement might be to find the best route without connections, delays, or disruptions to travel.  Further, another option could be to find all possible routes that can be reached from source to destination with the cheapest possible price for travel. There is so much data about when one travels that finding and discovering the right information can be difficult. Data only becomes information based on the contextual usefulness. Various mashup applications can be applied that utilize web apis from public transport agencies utilizing their feeds for precise information sharing. Google transit has started their own standardization process towards travel data. However, in every aspect accessibility to right information, at right time, in the right possible way becomes crucial. This is where responsive user interfaces can become the stepping stone for a barrier free access to easy and quick information navigability. We live in an overload of data, almost everything can be translated to some form of information. Knowing how to obtain, deliver, and present such data so it can become useful information is paramount.