At this point, it seems clear that autonomous vehicles are on the verge of technical feasibility. Just last week, Waymo announced that it is testing self-driving minivans without a human back-up in the front seat. Its employees will be riding in the back with an emergency stop button – but no steering wheel. But do these technical advances mean that we’re ready for AVs? How should we manage the non-technical aspects of AV deployment to ensure they achieve promised improvements in safety and accessibility?
I decided to write this article to address these issues after participating in the Intelligent Transportation System World Congress earlier this month. There were tons of panels focused on autonomous vehicles, and I was lucky enough to be speak on one that dove into the critical questions for civic leaders and transportation professionals. We went beyond technical readiness to ask ourselves if should we deploy AVs, and, if so, how should we deploy them?
Getting the data you need to make savvy transportation planning decisions can be a lengthy, expensive process if you’re using surveys, license plate counts, or road sensors. These methods also make it difficult to evaluate the performance of a policy or project after implementation.
For example, when planners rely on old or modeled data to design their project, they don’t have a real-world baseline to evaluate current performance after a project is completed. Most road sensors are removed after a few days, so they must be re-installed to collect more data. Surveys can take months or even years to complete. If planners want to make performance-based improvements as they implement policy or infrastructure changes, they often have to start from scratch with data collection. This could create real flaws if your community has seasonal variation based on weather or school - or if your community has new transportation patterns derived from the rise of ride share or urban population growth, for example.
In today’s world, there are much more efficient, up-to-date, and accurate ways to obtain the information you need. Nearly 77% of the US population uses a smartphone, and the data they produce is creating new options for transportation planners. In this article, I’ll share four advantages that Big Data has over conventional data sources for transportation planning.
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Traffic congestion negatively effects the economy, roads and our quality of life. Some people tend to blame pass-through trips, commercial attractions or employers, but expectations don’t always line up with hard data about what really causes congestion.
In California’s Napa County, for example, it is common to blame traffic on wine-tasting tourists. However, when planners used Big Data to map out travel behavior in traffic congestion studies, they found out that commuters actually contributed as much to congestion as tourists. High housing costs in Napa are a major part of the problem.
It’s not easy to figure out why congestion happens, especially in downtown districts. Learn how to improve the accuracy of your traffic congestion studies by using analytics derived from Big Data.
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.
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?”
The smart city movement’s first wave brought tons of stationary sensors to our cities, especially in the context of transportation. These sensors are passively collecting valuable travel pattern information at traffic lights, parking lots, bus stops, sidewalks, and more. But if we want cities that are truly smart – if we want to solve the challenges exposed by our stationary sensors – we have to go beyond them. In this blog post, I will use New York City as a case study to explain why.