A bus that runs on time has enough room, and gets people where they need to go is an essential public utility. The less well-off city dwellers that cannot afford or do not want to bother with buying a car rely on public transportation almost daily. Then there are also the benefits of reduced congestion, noise pollution, and emissions into the atmosphere, which a healthy public transportation system can bring to a city. Ensuring that the city provides an excellent bus service is a crucial task for the municipal authorities. In the digital age, certain tools and applications might make this task more manageable, and the solutions more effective.
A comprehensive bus service overhaul would probably tie into the smart city initiative. As such, the first set of analytic tools to use would be big data analysis. The big data approach can be used to analyze massive blocks of text to extract the most common words or combinations of words. It could be useful to announce the initiative to the city’s population and let them write freeform complaints and comments on the bus service in either social media or a special form on a municipal website.
Combing through the dataset of these complaints can help set priorities by highlighting the most commonly-lamented problems with the public transportation system. It may not be necessary for a small city, where relatively few comments are likely to be posted, but it would be a must in a vast urban sprawl with millions of dwellers. Another application for big data analytics would be processing the data from around the city and determining what time of day is the busiest, which lines get an overflow or a lack of passengers, and which streets are the most likely to suffer from congestion.
For example, adjusting the number of buses during the rush hour could help decrease passenger load per bus. The dispatch software suite could feed information about traffic or accidents directly to the drivers so they could make better decisions. Some lines could benefit from allowing drivers to adjust the route according to real-time information, especially those where stops are few and far between.
The location of every bus and their route can also be transmitted to the users in real-time via an app so they know exactly how much they have to wait and which bus would be the best option. It can also be beneficial to equip bus stops to track how many people are waiting for the bus and which direction they usually arrive from or leave to. This measure might help determine if adjusting routes and creating new stops would help alleviate passenger load and allow travel to more places.
These solutions also utilize location analytics, as, logically, the city is a geographical location, and a bus is expected to move citizens from place to place. Predictive analytics may be useful as well, for example, in determining the public reaction to peripheral changes, such as ticket price increase, infrastructure changes, or even adopting a particular paint scheme. These predictive analytics tools can be integrated with the feedback collection mechanisms to measure and predict the public reaction. However, that is a secondary concern, as improving the actual operation of the bus service should probably take priority over more minute details.
Many city dwellers depend on public transportation every day for various reasons. Ensuring that the city buses run efficiently, accommodate passengers well, and can get people everywhere they need to be is the first priority. Location data and big data will be the primary focus for the implementation of the most important solutions regarding the flow of traffic and passenger load. The secondary concern would be measuring and predicting public reaction to more minute changes. Utilizing all available digital solutions will help create a well-rounded public utility for the people’s convenience.