Schellhorn's Research Finds A Silver Lining in Freeway Traffic


Sometimes innovation is born within the laboratory. It sometimes dawns throughout a walk in the woods. For Professor Henry Schellhorn, it came gradually, in a vehicle, stuck somewhere between Santa Monica and Claremont.

\”When you're in traffic for 2 hours over the course of 15 years, you begin understanding it pretty much,\” said Schellhorn, a member of the Institute of Mathematical Sciences faculty and co-director of CGU's financial engineering program. \”You ask yourself, 'Are there better ways of doing this?'\”

Yes, you will find, and Schellhorn's concept is disarmingly simple: Many people around the freeway want to get for their destination faster and could be willing to purchase the opportunity. Others are less time-sensitive and could be pleased to assist allowing the hurried and also the harried decline in front-for a cost.

Implementing such a system involves orchestrating countless moving parts, but it begins with what is called Level 5 automation-fully autonomous vehicles that can operate under any condition. These vehicles, mass-produced perhaps a decade from now, could be programmed using the mathematical algorithm that Schellhorn has patented, allowing a seamless monetary transaction by which one vehicle yields to another.

\”Suppose you want to move from Lane A to Lane B. You have to pay the cost of B, minus the cost of a,\” Schellhorn said. \”The cost change depends on location and current traffic conditions. Additionally, it works if you want to proceed to a slower lane to exit.\”

But who determines the lane price? This is where financial engineering is available in.

\”Financial engineering won't let you know the price. It tells you the price where buyers and sellers should meet,\” Schellhorn said.


An App & An Algorithm

The long-term goal is the development of an application that will be uploaded into these autonomous vehicles, eliminating the need to operate the app manually and introducing the potential of human error. Meanwhile, though, Schellhorn and his student researchers are working on a version that safely allows driver-to-driver transactions.

The app has two primary parts. The technical features, the backbone of any app, happen to be developed, says Boshen Feng, students researcher dealing with Schellhorn this fall. Next comes the pricing algorithm, which will be implemented step by careful step.

Feng, who's a master's student in the university's Center for Computer & Technology, can also be likely to polish the consumer interface to make the app attractive and simple to make use of.

Based on calculations, the entire freeway drive time wouldn't change, but lane buyers would reach their destination faster, and also the number of impulsive lane changers may likely decline, thus making the road safer for everyone.

The next step, Schellhorn said, would be to secure research funding for agent-based modeling, which, simply put ,, means building out mathematical models and refining them in a controlled setting-a gaming of sorts. Google and Waze curently have skin the sport with digital traffic mapping, and automakers for example Tesla might make lane buying technology a typical feature in their 2030 models.

Schellhorn describes the work as far as \”a wise investment\” that could acquire some mileage (pun intended) with tech companies and auto manufacturers in the future.

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