Civil engineering doctoral student claims spatial analysis, modeling award
Cyclists choose their routes from one location to another for a variety of reasons. Some want the quickest route, others the route with the least traffic, and still others the route with the newest pavement. And sometimes, they choose the route based on a combination of factors. Or, at least they would if that information was readily available.
A University of Missouri doctoral student in the Civil and Environmental Engineering Department just developed a multi-criterion model that can tell Columbia cyclists just that, and picked up a highly regarded award in the process.
Ashkan Gholamialam co-authored “Modeling bikeability of urban environments” with his adviser, Tim Matisziw, an associate professor with a joint appointment in the Civil and Environmental Engineering Department and Department of Geography. The paper, poster and accompanying presentation earned Gholamialam the John Odland Award from the Spatial Analysis and Modeling group of the Association of American Geographers.
“Winning the competition … I was happy they liked it, the poster and the presentation,” Gholamialam said.
“It was a year ago that we started this project with the Office of Sustainability (at MU). … The city was considering starting up a bike-share system, so we decided to look at the road network in terms of bikeability.”
Gholamialam used data provided by the City of Columbia. He developed an algorithm that allows a user to input their preferred set of criteria — including route length, number of intersections, traffic rate and more — and use the data provided to determine a set of optimal routes based on that criteria.
“One of the typical assumptions is that people will take the shortest path,” Matisziw said. “But given that a different individual conceptualizes what’s shortest in a different way — might be the length of travel time; safety issues, whether it’s crime rate or lighting or number of trucks or traffic volume — instead of just considering the shortest path, we should consider a suite of different shortest paths.”
Previous work had been done in regard to cyclists and one individual factor, but Gholamialam used a dynamic programming approach to create a solution that allowed for a variety of factors to be taken into account. What dynamic programming does is solve a more complicated problem by compiling solutions to individual facets of the problem, storing them, then drawing from them to solve the larger problem.
“Using that, we’re able to look at different portions of the city and see how routing opportunities change (based on the importance of different criteria),” Gholamialam said.
And, because of that, cyclists armed with this information can select the safest route possible for them.