Evaluating crisis perturbations on urban mobility using adaptive reinforcement learning

Liu

Int J of Intelligent Sys

et al. 2021

Given the distribution difference of the vehicle flow in different urban areas, coordinating the optimization priorities of crossroads in the dynamic control of traffic lights is vital. To reduce traffic congestion at crossroads, in this study, an intelligent diverse optimization priority method (IDOPM) was developed for dynamic traffic light control at crossroads, where diverse optimization priorities can be flexibly and efficiently assigned to different crossroads. The IDOPM mainly consists of a dynamic state constructor and an optimization priority assigner. The dynamic state constructor controls the transformation of the signal combinations of traffic lights. The signal combinations of traffic lights at crossroads are abstracted as cells to be controlled by formulated rules. By designing duration particles and enhancing particles, the optimization priority assigner reconstructs the quantum particle swarm algorithm to assign crossroads with different optimization priorities. The results obtained by comparison with state‐of‐the‐art methods via extensive experiments confirmed the outstanding optimization performance of the proposed IDOPM in dynamic traffic light control.

Section: Intelligent Diverse Optimization Priority Methodsmentioning

confidence: 99%

Intelligent optimization of dynamic traffic light control via diverse optimization priorities

Liu

Int J of Intelligent Sys

et al. 2021

“…With location-based data, several studies have examined population mobility during disasters (Coleman, Gao, DeLeon, & Mostafavi, 2021;Gray & Mueller, 2012;Hsu, Fan, & Mostafavi, 2021;Lu, Bengtsson, & Holme, 2012;Pastor-Escuredo et al, 2014;Yabe, Ukkusuri, & C. Rao, 2019) and assessed disaster impacts (Bonaccorsi et al, 2020;Esmalian, Yuan, Xiao, & Mostafavi, 2022;Fan, Jiang, & Mostafavi, 2020;Lee, Maron, & Mostafavi, 2021;Q. Wang & Taylor, 2014;Yuan, Yang, Li, & Mostafavi, 2022); however, the majority of these studies focus on evacuation patterns (Deng et al, 2021;Song et al, 2016), disruption in mobility (Arrighi, Pregnolato, Dawson, & Castelli, 2019;Esmalian et al, 2021;Galeazzi et al, 2021), and mobility resilience (Fan, Jiang, & Mostafavi, 2021;Fan, Yang, & Mostafavi, 2021;Roy, Cebrian, & Hasan, 2019;Y. Wang, Wang, & Taylor, 2017).…”

Section: Introductionmentioning

confidence: 99%

Specifying Evacuation Return and Home-switch Stability During Short-term Disaster Recovery Using Location-based Data

Lee,

Chou,

Mostafavi

2022

Preprint

Self Cite

The objectives of this study are: (1) to specify evacuation return and home-switch stability as two critical milestones of short-term recovery during and in the aftermath of disasters; and (2) to understand the disparities among subpopulations in the duration of these critical recovery milestones. Using privacy-preserving fine-resolution location-based data, we examine evacuation return and home move-out rates in Harris County, Texas in the context of the 2017 Hurricane Harvey. For each of the two critical recovery milestones, the results reveal the areas with short-and long-return durations and enable evaluating disparities in evacuation return and home-switch stability patterns. In fact, a shorter duration of critical recovery milestone indicators in flooded areas is not necessarily a positive indication. Shorter evacuation return could be due to barriers to evacuation and shorter home move-out rate return for lower-income residents is associated with living in rental homes. In addition, skewed and non-uniform recovery patterns for both the evacuation return and home-switch stability were observed in all subpopulation groups. All return patterns show a two-phase return progress pattern. The findings could inform disaster managers and public officials to perform recovery monitoring and resource allocation in a more proactive, data-driven, and equitable manner.

“…The limitations of surveys could be overcome with the increasing availability and popularity of location-based big data. Location intelligence data, also known as location-based service data, could provide important insights regarding human activities and mobility both during normal times, as well as in times of crises ( (Fan, Jiang, & Mostafavi, 2021;Gao et al, 2020;Lee, Chou, & Mostafavi, 2022;Lee, Maron, & Mostafavi, 2021). Location intelligence data is usually collected passively from smartphone devices through GPS, Wi-Fi, and Bluetooth (Darzi, Frias-Martinez, Ghader, Younes, & Zhang, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Location intelligence data has shown promise in disaster research. Researchers have used largescale geo-based data to analyze population displacement pattern after the 2010 Haiti earthquake (Lu, Bengtsson, & Holme, 2012); developed methods to characterize flood impacts on a population (Pastor-Escuredo et al, 2014); simulated and predicted human movement pattern after disaster (Song et al, 2016); detected impacts of extreme events on human movement (Roy, Cebrian, & Hasan, 2019); assessed flood inundation status (farahmand, Wang, Mostafavi, & Maron, 2021); quantified resilience based on the magnitude of impacts and time-to-recovery (Hong, Bonczak, Gupta, & Kontokosta, 2021); evaluated hurricane perturbation on urban mobility (Fan et al, 2021); assessed short-term disaster recovery by evacuation return and home switch (Lee et al, 2022). Nevertheless, the majority of studies have harnessed locationbased data for examining human mobility and evacuation in post-disaster contexts, and the potential of these data for examining pre-disaster human preparedness activities has not been realized.…”

Section: Introductionmentioning

confidence: 99%

Location Intelligence Reveals the Extent, Timing, and Spatial Variation of Hurricane Preparedness

Li¹,

Mostafavi²

2022

Preprint

Self Cite

Hurricanes are one of the most catastrophic natural hazards faced by residents of the United States. Improving the public's hurricane preparedness is essential to reduce the impact and disruption of hurricanes on households. Inherent in traditional methods for quantifying and monitoring hurricane preparedness are significant lags, which hinder effective monitoring of residents' preparedness in advance of an impending hurricane. This study establishes a methodological framework to quantify the extent, timing, and spatial variation of hurricane preparedness at the census block group level using high-resolution location intelligence data. Anonymized cell phone data on visits to points-of-interest for each census block group in Harris County before 2017 Hurricane Harvey were used to examine residents' hurricane preparedness. Four categories of points-of-interest, grocery stores, gas stations, pharmacies and home improvement stores, were identified as they have close relationship with hurricane preparedness, and the daily number of visits from each CBG to these four categories of POIs were calculated during preparation period. Two metrics, extent of preparedness and proactivity, were calculated based on the daily visit percentage change compared to the baseline period.The results show that peak visits to pharmacies often occurred in the early stage of preparation, whereas the peak of visits to gas stations happened closer to hurricane landfall. The spatial and temporal patterns of visits to grocery stores and home improvement stores were quite similar. However, correlation analysis demonstrates that extent of preparedness and proactivity are independent of each other. Combined with synchronous evacuation data, CBGs in Harris County were divided into four clusters in terms of extent of preparedness and evacuation rate.The clusters with low preparedness and low evacuation rate were identified as hotspots of vulnerability for shelter-in-place households that would need urgent attention during response.Hence, the research findings provide a new data-driven approach to quantify and monitor the extent, timing, and spatial variations of hurricane preparedness. Accordingly, the study advances data-driven understanding of human protective actions during disasters. The study