Over the decades, the transportation system has evolved drastically bringing advanced, and major changes to human movements patterns increasing demands in customer experience. For example, back in the day before the establishment of ride-sharing companies, like Uber, you would need to rush all the way to stop a taxi passing in the middle of the road or its track, and if you are running to the airport, hope for no or fewer traffic congestions along the way.
Today, any average traveler has access to stay connected, thanks to the pervasiveness of big data. With these advancements, you can now know precisely when to leave, your choice of transportation, and reroute plans if there are any congestions like strikes, blizzards, or other traffic disruptions.
However, the increasing urbanization has resulted in many private, public and shared vehicles being crowded, and managing the entire problem becomes very challenging.
Growing population challenges
As per the United Nations, there have been 37 megacities - dense metro regions with a population of more than ten million people. This figure is expected to grow to 47 by 2030. This change to a more densely populated urban environment brings with it plenty of other concerns, including resource scarcity, waste management, and growing inequality. However, transportation management is likely the most serious problem (and, conversely, a solution to other issues).
For instance, if we look into the most heavily populated cities in North America, New York is one of the most popular and densely populated cities in the U.S, with approximately 8.419 million residents (2019). But New York City also has one of the worst traffic congestions causing several delays, pollution, and significant disturbance to the city residents.
After the emergence of wireless networks, New York's transportation system shifted from the old traffic management network to a better, faster, and more efficient system that is the Intelligent Transportation System (ITS). In 2020, the New York City Department of Transportation (NYC DOT) completed a large-scale intelligent transportation system deployment at the cost of more than $100 million. By collaborating with leading companies in the ITS industry, NYC DOT connected 14,000 signalized intersections along most of its 6300 miles of streets and highways.
ITS system runs on mobile networks like 4G/LTE and 5G cellular routers and a concurrent dual-carrier network for failover and high reliability at 99.99%. Its primary function is to collect extensive data and analyze it, which helps transportation planners to control, manage and plan their city transportation system.
Big Data Analytics for Transportation Demand Forecasting
Traffic Congestion Management
Many urban residents despise traffic congestion, which puts a lot of pressure on the municipal government.
There is no shortage of realistic and ambitious solutions for reducing traffic congestion, including road fees, public transportation improvements, tighter one-way lanes, tunnels, "bare" streets without traffic lights, and even flying automobiles and drone-based operations. Some work to a certain extent, while others ultimately fail. Nonetheless, no city is entirely free of traffic congestion. Weather conditions and natural disasters are the primary cause of traffic congestion in the United States, accounting for 15% of all cases.
While an extensive data-fueled intelligent transportation system or more targeted traffic optimization solutions can't eliminate the physical causes, they can improve the digital end of the challenge:
- Identify inefficient parking layouts that cause congestion.
- Create more efficient routes for commercial trucks and deliveries in metropolitan areas.
- Improve the timing of traffic light signals.
- Optimize micro-mobility and shared mobility solutions.
- Apply effective multi-modal transportation routes, persuade drivers to abandon their automobiles.
All of the above big data use cases in transportation and traffic engineering are technologically viable.
According to a report by Ben Bradford from marketplace.org, an average person is wired to tolerate one hour of commute per day to a maximum – so 30 mins one way. This concept is known as Marchetti's Constant, shaping city layouts and dwelling patterns for years.
Yet the rising population in urban areas and increased traffic congestion undermine the commute hours and our tolerance to wait long in these traffic jams. As a result, many travelers increased their use of public transportation. Some city authorities are also encouraging passengers to use public transportation rather than their own car by introducing tolls on public roads for all drivers and passengers with their cars, such as in New York City.
However, today's travel times should be improved. For ITS, a mix of big data and analytics can produce tremendous support. By operationalizing available sources, planners can:
- Develop more precise traveler systems and applications for trip planning.
- Instead of penalizing residents, create laws that encourage them to use public transportation.
- To accommodate known travel patterns, improve municipal planning, and develop road infrastructure.
- Expand commuting routes, including mobility-as-a-service and shared transportation projects, into the city's public transportation repertory.
Why is big data in transportation not mainstream yet?
As the examples above demonstrate, numerous technologically realistic methods exist to use big data in transit, transportation, and urban planning.
Today, however, parking a car in congested areas is still a problem. So what is the situation?
Big data is challenging to extract and process for transportation authorities. The majority of cities do not have enough road sensors to detect traffic patterns. A comprehensive, low-latency data governance and management framework is required for capturing and mining video data. Fusing data from multiple sources in various forms necessitates a level of technical infrastructure maturity that not all organizations have.
Then there's the big concern of data privacy which is considered very important. All big data should be cleaned, anonymized, and then safeguarded using available best-in-class security measures. After all, big data holding traces of the entire population's daily movements is a good tool for exploitation and hacker target.
However, the good news is that these difficulties are controllable and do not diminish the amount of beneficial improvements that greater use of big data in transportation might bring personally and socially.
Democratizing Location Data
Today, AirSage is one of the top mobile location data providers for location insights and human movement visualization. We have served the ITS industry for over 20 years continuously democratizing the location data. AirSage provides the newest and most innovative location data solution and advanced analytics in the market.
To improve transit and transportation systems, AirSage offers two advanced products:
1) Activity Density that helps understand population activities and density within a given location, and
2) Nationwide Trip Matrix, which provides high-resolution intelligence on population movement between areas and includes a wide array of attributes for person trips like origin, destination, and home zones down to a census block group.
AirSage offers self-service platforms to all their location data solutions, making it easy for transportation planners and engineers to access their data anytime from anywhere.
To democratize the data even more, the company launched the first free mobile app for location intelligence - ALADIN. By using ALADIN, industry professionals and academics can access location intelligence in minutes from their mobile phones and analyze location analytic studies based on high-quality population movement insights. With ALADIN, data has become accessible and free for everyone.
Are you interested in working together to make a better ride and a better city?
Contact AirSage to learn more about location data solutions in the transportation industry.