How public transit can benefit from data analytics

Needless to say, more and more residents in cities use public transportation to commute to work or school. How to plan routes and optimize service quality therefore becomes a top priorities for transit authorities, who can now rely on data analytics in this regard.
 
From a security point of view, analytics can help identify suspicious movements or behavior and thus help prevent crime or other malicious activities. Video showing an unaccounted-for bag in a train station or someone loitering around at the train platform can be evidence of an impending accident or mishap, and the situation can be dealt with immediately.
 
However analytics can do much more, for example helping retailers determine whether a customer is a man or woman or detecting a car stopped in the middle or traveling in the wrong lane of the road.
 
In public transit applications, analytics can be of big help for transit authorities, who face various challenges. Traffic congestions often cause bus delays. People no longer use a single mode of transportation – they may ride a shared bike to the bus stop or train station or park in a car garage near the station, before changing to the next mode of transportation. These present difficulties for transit officials and operators as they do related planning. Now, with analytics generated by various sensors, transit authorities can get a big helping hand in maximizing their service.
 
So how can analytics help? The benefits can be summarized as follows.
 

Get the complete picture

 
As mentioned, transit passengers today tend to use a multimodal approach. “Cities must design their infrastructure to accommodate a more multimodal system of transportation. By taking advantage of the combined data analytics from buses, trains, metros, transit-adjacent parking, tolling systems, and even bicycle traffic, a more accurate city-wide view of the transportation landscape begins to take form,” said a blogpost by Conduent. 
 

Predictive analysis

 
According to the post, decisive action requires the ability to predict successfully the possible positive and negative impacts that result from any proposed change in the transportation infrastructure. “Data analytics helps generate possible outcomes from what-if scenarios more accurately and nearly instantaneously,” the post said. “Public transport executives can now offer more trustworthy solutions by relying on predictive analysis strategies that use a combination of up-to-the-minute data and historical statistics.”
 

A unified platform

 
Data can be aggregated on and reported from a single platform for different agencies who can get a unified view. “Transit authorities save even more time because they no longer have to re-enter data into several different databases before printing out multiple spreadsheets at different times,” the post said.
 

Improving operational efficiency

 
Since data analytics tools calculate in real-time, identifying operational inefficiencies is more accurate and timely as a result, the post said. “Big data systems can instantly alert transit executives of decreasing ridership numbers on individual train lines or buses that spend too much time in a single location,” the post said. “Since guesswork is no longer a factor, corrections and modifications can occur more rapidly.”
 

Plan for the future

 
While in the past, planning for the future was an arduous process. But this is no longer the case with data science and mining. “Today, local transit authorities can combine the real-time information of data analytics with the historical statistics of years past to generate a more decisive and accurate plan for the future and in far less time,” the post said, adding ridership also be maximized. “The effective implementation of data analytics tools helps transit authorities to create new travel routes, or modify existing ones to maximize ridership numbers,” it said.