Rising metropolitan populations call for modern and efficient transportation solutions in cities. As pollution worsens and commuter congestion rises, city officials seek comprehensive plans to improve metropolitan transportation. ‘Big Data’ is essentially the process of compiling data from multiple technological sources in order to make informed business decisions and execute logical planning; it increasingly plays a role in nearly every industry, and transportation technology is no exception. As more cities adopt smart city technology, Big Data’s role will only grow, making it vital to understand the ways in which Big Data is currently used by city transportation officials and how it may be utilized in the future.
Generally, Big Data has the potential to help industries expedite operations and process information quickly, but while Big Data can assist companies in the private sphere to maximize profits, it can also assist with meeting needs in the public sphere. Development of methodical transportation networks in metropolitan centers is vital, as metropolitan populations are consistently growing and thus requiring comprehensive transportation reform. Specifically, Big Data can help cities to improve reliability, efficiency and speed of transportation networks such as public transit, traffic light controls and even parking analytics.
In the case of public transit, there are many variables contributing to its sometimes unpredictable and unreliable nature. For example, traffic accidents happen unexpectedly, sometimes causing a delay in a city’s bus route. Historically, the frequency and location of traffic accidents was literally unpredictable, but thanks to maturing technology and expanding databases, this is no longer the case. This is where Big Data comes in to save the day for commuters and city officials alike. By analyzing massive amounts of data compiled by municipal technology and even mobile applications, city planners can mitigate some of this unpredictability by observing traffic patterns and flaws of public transit and developing plans to remedy them.
IBM recently announced it will help the city of Dublin utilize Big Data to identify the main causes of traffic congestion within the Irish city’s public transit network. By studying the root causes of the congestion, traffic flow will be improved for commuters. In this case, city officials will be able to monitor traffic in real time by integrating data from a network of citywide censors that compile geospatial data. Dublin’s road and traffic department, in partnership with IBM, will collect and analyze Big Data from bus timetables, traffic detectors, television cameras and GPS updates from the city’s 1,000 in order to construct a digital map of the city overlaid with real-time positions of Dublin’s buses. As the average number of cars on the road increases globally, pollution worsens and commute times increase, Big Data progressively plays a large role in solving this transportation issue in major cities worldwide.
Traffic Light Controls
In terms of traffic light technology, cities are already analyzing traffic patterns to ensure implementation and adoption of more advanced traffic light controls. Traffic lights currently rely heavily on sensors and cameras to more efficiently determine when there is congestion in an intersection. This information helps engineers determine how long a traffic light should stay red, yellow or green. Busier intersections with traffic traveling at higher speeds, for example, typically are equipped with traffic lights that have algorithms adjusted to display longer yellow lights. This technology helps to ensure that drivers have sufficient time to safely pass through the intersection. While these advances in traffic light technology have made the traffic light system more efficient, there are many other variables that would also prove helpful in evaluating patterns at traffic lights and adjusting technological advances accordingly.
In coming years, it is likely that city officials will rely more on Big Data from a wider variety of sources. For example, mobile navigation and ride sharing applications such as Google Maps, Waze, Uber and Lyft are already collecting data about transportation routes users are taking. By compiling and analyzing data on these routes, city officials may be able to incorporate this information into improving the efficacy of traffic lights. If certain intersections tend to be busier during specific hours during the day, this knowledge can help traffic light engineers identify which types of sensors to incorporate into traffic lights. Using Big Data from mobile applications to adjust traffic lights to ever-changing traffic patterns in real time will undeniably improve traffic light technology, and thus Smart City transportation networks.
City officials may also improve parking programs by incorporating Big Data into their parking analytics strategies. Big Data can assist cities in managing parking capacity and more accurately predicting variation in parking resulting from a variety of factors, from traffic accidents to construction. By observing parking occupancy trends, city officials can better adjust enforcement staff numbers according to the predicted number of drivers parking at any given time.
Additionally, with mobile parking application usage on the rise, city officials are able to more effectively observe trends such as average duration of parking spot occupancy per vehicle. Studying parking trends equips city officials with the capability to more pragmatically plan for large city events such as concerts, street fairs, farmers’ markets or sporting events. The ability of city officials to accurately predict parking occupancy of any given event enables them to ensure there is a sufficient number of parking spaces and that they are charging a reasonable fee for parking at a given time. Additionally, Big Data on parking meter functionality allows city officials to ensure electronic parking devices operate efficiently
In summary, the ways in which adoption and analysis of Big Data can help city officials in developing Smart City technology are plentiful. Public transit, traffic light technology and parking analytics are simply a few examples of Smart City technology that may be improved with Big Data analysis. It is crucial to understand that Big Data’s role in improving transportation in Smart Cities is significantly increasing. With new mobile applications constantly being designed to streamline and automate various everyday responsibilities and routines, Big Data is also increasingly becoming available from a wider variety of sources. As more information becomes available, city planners should capitalize on this data to ensure enhanced metropolitan infrastructure and accommodation. Smart City transportation networks will benefit from the use of Big Data, resulting in more reliable, more efficient and safer infrastructure.
Gregory Miller is a writer with DO Supply (https://www.dosupply.com) who covers Robotics, Artificial Intelligence and Automation. When not writing, he enjoys hiking, rock climbing and opining about the virtues of coffee.