Can Mobile Data Help Reduce Traffic Jams?

Can Mobile Data Help Reduce Traffic Jams?

Travel in the “everything is mobile” era is rapidly changing.

Mobile is rapidly becoming the primary access for people to seek information. The mobile application market is big and is still growing at a steady pace. According to a market research report, “World Mobile Applications Market (2010 – 2015)” released by MarketsandMarkets (M&M), total global mobile application revenues have increased around four times in just five years and are estimated to reach $25 billion U.S. dollars in 2015. It is widely believed that we are entering a new age of “everything is mobile”.

The use of mobile information is changing transportation whether you are ready for it or not. Transportation systems are complex with numerous unconnected infrastructures and users. System users all make their own decisions regarding travel and sometimes they are frustrated by slow-moving traffic and long transit wait time. In the “everything is mobile” era, these situations are totally changed.

The “Internet of cars” (or “Internet of travelers”) has been created to maximize the potential of physical transportation systems by connecting users and infrastructures to make them “smart”. This happens in large part because mobile applications are able to bring all the traffic information together as real time services and help users in the system to adjust routes and modes of transportation to avoid traffic jams.

How can mobile data help decrease traffic jams?

As the number of cars on highways and urban roadways keep growing, simply expanding the existing road spaces is no longer a viable option to solve urban gridlock problems. The strategy should be an emphasis on the so-called Intelligent Transportation Systems (ITS), which encompass a wide range of advanced applications to increase efficiency of existing infrastructure.

How well ITS performs relies heavily on the availability and quality of data. Mobile data provides new data sources to ITS in addition to the traditional data from road traffic sensors and traffic cameras.  This helps transportation authorities manage day-to-day traffic operations, respond to incidents and provide information to transportation system users which optimizes the total system performance and reduces traffic jams.

Academic researchers and government programs have made great progress using mobile data in developing innovative analytics and metrics to monitor traffic and measure system effectiveness. Some programs have taken this even further by developing new approaches to integrate mobile data into their transportation planning exercises.

Transportation planning is a process that develops information to help make decisions on future transportation systems. Typically it involves a forecast of travel patterns from 15 to 25 years into the future with the goal of developing a system that will work effectively in the forthcoming years.

Collecting data to understand how people move around is the very first step for forecasting exercises. Data collection was primarily the government’s job in the past, but with the rapid growth of the mobile data business that has changed.  More and more transportation authorities are working in partnership with private service providers to use their valuable data for variety of benefits, such as planning transportation infrastructure.

Mobile data is transforming the ways in which transportation data is collected, from infrastructure-based to probe-based. Mobile application users in the transportation system are not only the consumers of travel information, they are also the data collection sensors in the system.

As world consumers continue to embrace mobile devices with the Internet; an incredible amount of floating car data is being consumed, created and collected though mobile Internet applications such as travel planners, map navigation and social media. Today’s leading global traffic information service providers are able to collect data from over 100 million vehicles worldwide from commercial fleets, passenger cars and other application users. The snowballing mobile business model has provided new data sources for transportation.

The information on where (locations of origin and destination), when (departure and arrival times), how (modes of travel) and why (purpose of travel) people travel is critical for identifying travel patterns and trends.  In the recent decade, academic researchers have been trying to develop approaches to use the collected mobile data for transportation planning. Some of the researchers are very successful in converting large streams of mobile movement data including location, time of travel and speed of travel into travel information to answer the questions of where, when and how.

By combining the mobile data with other business location data and by using statistical and data fusion techniques, the question of why can also be answered with reasonable assumptions.  This information can be used by planners and engineers to identify the imbalance of supply and demand in available transportation infrastructures.

What‘s next?

Mobile data has tremendous potential for organisations and societies to manage traffic jams. Our transportation future will rely on the data collected through the increasing use of mobile Internet applications. Mobile data will continue to expand, improve in quality and become more dynamic through live feeds that are constantly updated. With these technological advancements, the main hurdles to the application of mobile data today, such as sample size and bias, can be eliminated or minimized in the future.

According to a study from the London-based Centre for Economics and Business Research, traffic jams cost the United States economy up to $124 billion dollars in 2013 and this cost is expected to increase 50% to $186 billion by 2030.

In the years to come, transportation authorities will continue to confront the challenges of the ever-growing complexity of traffic problems. The qualitative and quantitative mobile data can help authorities identify and address areas in transportation systems that cause stress. Both system users and authorities can benefit from the information generated by mobile data.

Article written by Jason Li
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