Transportation and community resilience

Pavement condition prediction for communities: A low-cost, ubiquitous, and network-wide approach

Tao Tao and Sean Qian

Effective prediction of pavement deterioration is critical to forecast infrastructure performance and make infrastructure investment decisions under escalating environmental and traffic change. However, most communities often struggle to undertake such predictive tasks due to limited sensing capacity and lack of granular data. In addition, most studies focus on predicting the absolute pavement conditions rather than monthly/yearly changes, potentially overestimating the effect of current pavement conditions and thus underestimating the perceived effect of underlying driving factors. With the pavement condition rating (PCR) data generated from AI-powered computer vision technologies and multiple openly available datasets, we propose a low-cost and ubiquitous approach to predict system-level pavement conditions, using nine communities across the US as an example. In addition to predicting absolute PCRs as was done in classical models, we develop another set of models to predict the change in PCRs over any time increment (i.e., time lapse between a PCR observation and retrofit decision point). The findings show that the proposed low-cost prediction approach yields results comparable to existing studies, demonstrating its promising application in supporting pavement management. Furthermore, PCR change model indicates that, besides current PCR, weather, road classification, socioeconomics, and built environment attributes are important to predicting PCR change. The interactive impacts also show salient interactive effects between variables and current PCR, offering suggestions on better allocating the limited resources in pavement maintenance projects. Finally, the proposed model could enhance climate resiliency and transportation equity during the pavement management process.


Exploring the interaction effect of poverty concentration and transit service on highway traffic during the COVID-19 lockdown

Tao Tao and Jason Cao, 2021. Journal of Transport and Land Use, 14(1), 1149–1164.

During the COVID-19 lockdown, transit agencies need to respond to the decline in travel but also maintain the essential mobility of transit-dependent people. However, there are few lessons that scholars and practitioners can learn from. Using highway traffic data in the Twin Cities, US, this study applies a generalized additive model to explore the relationships among the share of low-income population, transit service, and highway traffic during the week right after the stay-at-home order. Our results substantiated that transportation impacts are spread unevenly across different income groups and low-income people are less able to reduce travel, leading to equity concerns. Moreover, transit supply influences highway traffic differently in the areas with different shares of low-income people. Our study suggests that transportation agencies should provide more affordable travel options for the areas of concentrated poverty during the lockdown time. In addition, transit agencies should manage transit supply strategically depending on the share of low-income people to better meet people’s mobility needs.