Building accurate travel demand models requires a detailed understanding of the ways behavior changes during specific conditions. Route choice, for example, can vary dramatically depending on time of day and other factors that influence drivers.
Let’s say that Sarah needs to drive from her home in Pittsburgh’s east end to the downtown business district. Most of the time, she finds Penn Avenue to be the fastest and most enjoyable route. But she also knows it would be foolish to take that route during rush hour. Or during a sports game, major concert or visit from the President.
Lately, I’ve been thinking a lot about the city of Atlanta, where a fire recently destroyed a portion of the I-85. It’s a major highway that hundreds of thousands of people use every day to access to their jobs, their schools, their groceries, and more. For me, the highway’s closure highlights how vital our transportation networks have become to quality of life in our communities. Even in a best case scenario, residents of the Atlanta region are likely to spend several months without this vital transportation connection – and the typical Atlanta resident already spends more than 70 hours in traffic each year. What can we, the transportation community, do to limit the negative consequences of unforeseen events like this? It's not a simple problem for anyone to solve, and we know that the folks in Atlanta are working day and night to solve it. In this blog post, I will describe a few data-driven tactics for reducing congestion misery on I-85 in Atlanta. We hope that this analysis will be useful for detour management in Atlanta in the coming months.
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Note: This is a guest blog post from Wendy Tao, the Head of Business Development and Strategy of the Intelligent Transportation Systems Group at Siemens Mobility. Wendy helps communities develop Smart Cities solutions related to advanced traffic management systems, adaptive signal control, connected vehicles and multi-modal applications.
From Intelligent Transportation Systems (ITS) to Massive Mobile Data, innovative technologies are tackling decades old challenges and creating new opportunities in the transportation industry. And it’s not just an idea. We’re seeing significant impacts derived from in-depth evaluations on project performance and cost-effectiveness. Siemens recently partnered with StreetLight Data to measure the impact of a Siemens’ SCOOT adaptive signal control implementation in Ann Arbor, MI. Our empirical before-and-after study showed that SCOOT can reduce travel times by 10 to 20 percent. The study used archival navigation-GPS data from connected cars.
Transportation planners today face a ton of challenges as they work to build efficient, safe, and sustainable urban transportation systems. From rising congestion to increased demand for public transit, the travel behavior and transportation preferences of modern city dwellers are changing fast. These challenges raise complicated questions for urban transportation planners; for example, “How do we handle the rise of ride hailing apps? If we add more public transit options, will people use them? How do we minimize the impact of construction if we do expand public transit? And how do we pay for all of this?”
When we founded StreetLight Data back in 2011, our sole focus was to help educate and plan for electric vehicles (EVs). But we quickly realized that our transportation analytics would have a more significant positive impact if we expanded our mission. However, EVs are still one of my deep interests: I drove a Chevy Volt for several years before going car-free just a few months ago, and I focused on EVs in my early career at the Rocky Mountain Institute and Federal Energy Regulatory Commission. Based on my personal experience, I know we can do a better job of planning and deploying EV charging infrastructure. If we want to see wide adoption of EVs, then we must make charging more convenient and affordable while minimizing its impact on our electrical grid. Given the wave of new charging station deployments in the US and abroad, now seems like the right time to explain how Big Data can help.
Autonomous vehicles (AVs) are beginning to dominate much of the public conversation about our transportation future. This was certainly the case at South by Southwest, where I participated in an excellent panel discussion at the C3 Smart Mobility Showcase: “Smart Cities and Data-Driven Deployment of Autonomous Vehicles.” Nearly every single panel at the showcase was related to AV technology. People in that tent were very excited about AVs. However, I found myself thinking back to the 12-lane urban highway that my taxi driver took from my hotel to the event. The local bus would have taken over three times as long, and the drive reminded me that AVs are not a panacea for all that ails our transportation system.
Don’t get me wrong: Talking about AVs at an event like SXSW makes sense, and I’m glad we’re having these conversations. But I think the broader discussion around AVs needs to be focused on accountability. The impact of AVs could be very positive or very negative, as many transportation experts have suggested. In this blog post, I’ll explore how a data-driven approach can help us strike the right balance with AVs, and hold ourselves accountable for achieving a positive outcome for all.