A City Where Big Data, Analytics Drive Mobility Choices
Planning and transportation employees in San Jose, California use a cloud-based decision support system (DSS) to inform their plans as part of a mobility and access plan city scale.
On August 19, the city council voted to adopt Move San Jose and a Transit Priority Policy that would prioritize transit operations in planning and decision-making. An important part of these initiatives is DSS, a software tool that uses big data and analytics to help city governments determine the next best course of action.
“Moving to San Jose usually points us in the right direction. The decision support system actually takes a lot of information about big data sources and land use, current street layout, current transport availability, current bike paths and things like that , and said, "Hey, these are the places, and this is the city . department." transport plans, policies and sustainable development. Ramses Madu, Head of Department
Built by data analytics startup UrbanLogiq, the datasets that inform the DSS include internal street data, including the number of lanes in the city, their location, and the presence of sidewalks.
For example, UrbanLogiq geocoded 10 years of incident data and five years of traffic data into various file formats, primarily PDF and Microsoft Excel documents. After cleaning and validating the data, it was uploaded and merged into DSS. From here, users can query and view patterns and trends in the data, and download raw historical data offline anytime, anywhere.
Other data on electric scooters, land use, census blocks, and wealth data from the American Community Survey are also included in the DSS. Also urban public lighting data and traffic data from copies and open source data compliant with general traffic specifications provided by public transport operators. DSS centralizes everything on a single platform.
“It all comes down to key performance indicators,” Madow said. The city and Arup, a global sustainability consulting firm, have jointly developed 50 KPIs that support Move San Jose's nine goals for environmental impact, equity and safety. "We normalized all [data sets] so that KPIs are measured on a 10-point scale, where 10 is pass or better, and 1 is essentially fail."
In summary, here's how DSS works: when a road direction needs to be changed, users make changes to the system, click the Run button, and watch the KPIs change based on that adjustment.
“How a platform is configured affects many areas of user experience. A simple data visualizer,” said Jörg Thandorf, Associate Director of Arup. “You come here and you can view one set of data at a time. Getting there is very easy; it really can be done by anyone. This is a very simple geospatial mapping tool that can be found on the Internet.
Another possibility is post-project evaluation. “We decided to get into Project Y because it moved the needle from side to side. Has it done so two years later, and should we adjust our assumptions? Madhu said. “The real idea is…to be able to make informed decisions about the projects that will best help us achieve these goals and to be able to understand if the actions we have taken in the past are consistent with what we are saying. . What are we doing."
Updated data will be required for the assessment. “Where possible, we tie datasets to APIs to provide near-automated updates,” Tondorf said. "Obviously some of the other information provided by the vendor will need to be updated manually every few years, but the feature makes it as seamless as possible."
Also, as new datasets useful for DSS become available, Tondorff says, the system can scale to use them. “The platform definitely lets you scale and definitely lets you add more custom features,” he said.
He notes a growing interest from the public sector for such systems. “San Jose looks to the future knowing the world is changing faster than ever,” Thondorf said. “There are more uncertainties like the Covid and its impact or the current economic situation. Cities therefore need to have a tool that is fundamentally much more responsive and quick to do their planning work,” he said. "San Jose and other cities need to address immediate needs and have long-term planning options."
About three months ago, San Jose won a grant from the California Department of Transportation to fund the second phase of the program, which includes more data cleaning and automation, identifying more projects to run through DSS and a public interface. Madhu said. He anticipates that the work will begin next March and will last 9 to 12 months.
Historically, the city has run models for a day or two to determine the potential impact of transportation changes. But the models operate on mathematical representations of behavior and assumptions, Madou says. Because DSS uses observational data, it eliminates guesswork and reduces complexity. Also, DSS can work within an hour versus 24-48.
"Every time you say, 'Here's what we're going to do,' you've just done your research, and over time the assumptions start to dwindle," he said. “We gave ourselves the opportunity to make better decisions over time in changing circumstances. Here is the big difference. The system will be upgraded next year as all major road networks will be upgraded to the system and the new projects we are considering will be in the system. Of course, we assume that our goals are likely to be the same, but they can change and we can change the system to accommodate that.
Stephanie Kanowitz is a freelance writer based in Northern Virginia.
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