Preparing for Tomorrow:
Heat + Transit
Urban planning challenges typically present themselves as a series of tangled issues that need unraveling.
The City of Phoenix is getting increasingly hot. Located in the heart of the Sonoran Desert, America’s 5th largest city has a growing dilemma that puts its residents at risk. Decades of paving asphalt roads and parking lots were beginning to concentrate heat and make summers unbearable to residents. While summer temperatures and heat-related illness have both been steadily on the rise, the Department of Public Transit had been experiencing a seasonal dip in bus ridership as well.
I first joined this project as part of a Field-Lab course taught by Linda Bilmes at the Harvard Kennedy School. Over the course of the semester, I helped develop ways of addressing heat’s impact on vital city services, such as transportation, as part of a diligent team consisting of: Andrew Alesbury, Miriam Keller, Jill Ni, and Adam Watkins. Together we helped the city better understand the drivers of transit demand (by refining an existing Transit Propensity Model) and associated costs/benefits of providing improved transit service to residents.
I carried the team’s work forward over the summer of 2018 as a Bloomberg-Harvard City Leadership Fellow, creating an analytic framework for locating and prioritizing sites that were recommended as high priority areas. My analysis utilizes a betweenness algorithm to estimate pedestrian counts over a network consisting of homes and businesses as origins, with bus stops as destinations.
The work the group did on the Transit Propensity Model has been incorporated into the official operating procedures of Valley Metro, while the City of Phoenix’s first “misted” bus stop has already been rolled out. My work on the betweenness analysis covered various site-specific locations where the city has prioritized investment in the public provision of shade. Work on using the betweenness analysis for prioritizing municipal investment will be continued by the city staff I trained to use the tool, as well as researchers at Arizona State University.
What is a heat island?
Heat islands are locations where impervious surfaces absorb and radiate heat over an extended period. They trap heat at the ground level and cause higher than normal temperatures – especially at night. They are typically found in urban or built up areas that contain high quantities of concrete or other surfaces that do not allow oxygen to flow-in to cool the surface, causing the heat to release into ambient air. Heat islands are further exacerbated by smog and pollution build-up that traps the sun’s radiation at the surface.
Context
The city had a strong feeling that heat was contributing to heat-related sickness and decreased transit ridership in summer months. As a group, we quantified the impact of heat on bus ridership and refined Transit Propensity tool used to plan transit lines. The diagrams below provide context on Phoenix and show the estimated impact of heat on bus ridership in Phoenix.
Cost Benefit Matrix
The second element of our work involved researching 19 different shade structures and incorporating them into a plug-and-play cost-benefit model that city officials could use during project planning. Costs incorporated each intervention’s carbon impact while benefits included decreased surface temperatures and other cooling effects. Our highest scoring option – the bus-stop water mister – also happened to be the most popular suggestion voiced in community meetings held throughout the city. Our results validated the desire of many communities and I was present for the groundbreaking of the first misted bus shelter in the summer of 2018.
Betweenness Model
Now that the city knew the impact of heat on bus patronage, I noticed a gap in implementing the strategy. I continued analysis over the summer aimed at using a set of computer models to simulate pedestrian counts to and from bus stops. I looked at four different neighborhoods, used spatial data to locate the highest potential demand for transit, and calibrated the model. The whole process is shown below.
What does a Betweenness Analysis consist of?
1. An origin weight. In this instance I distributed all residents and employees to each origin point given current land use patterns.
2. A gravity “beta” weight: a distance-decay curve that assigns a higher distribution for shorter commutes, while assigning a lower distribution to longer commutes. The farther away an origin is from a bus stop, the less likely the origin weight is to reach the destination. A typical gravity index of .002 is typically applied without sufficient data, however an index of .004 was shown to be closer to actual measurements in Phoenix – meaning it is less pedestrian friendly.
3. A detour ratio that tells the algorithm to final all paths that are up to X% longer than the shortest path. A value of 1.2 was used to overlays paths that are up to 20% longer than the shortest path alone. This is particularly useful since studies often show pedestrians prefer a slightly longer path if perceived to be safer or has more shade.
Locating a High-Need Site
Running the Model
Conclusion
This body of work shows that addressing one city challenge without thinking comprehensively about the secondary issues that might impact the core challenge poses a unique struggle for cities. In this instance, dealing with urban heat turned into a transit solution that aims to improve transit service as a means to combat rising temperatures. If Phoenix residents manage to shift more towards embracing public transit, the impacts of urban heat islands will be significantly less severe down the road.
