scholarly journals Analyzing walking route choice through built environments using random forests and discrete choice techniques

2016 ◽  
Vol 44 (6) ◽  
pp. 1145-1167 ◽  
Author(s):  
Calvin P Tribby ◽  
Harvey J Miller ◽  
Barbara B Brown ◽  
Carol M Werner ◽  
Ken R Smith
Keyword(s):  
Random Forests ◽  
Goodness Of Fit ◽  
Salt Lake ◽  
Human Mobility ◽  
Route Choice ◽  
Salt Lake City ◽  
Choice Models ◽  
Data Driven ◽  
Walking Behavior ◽  
Mobility Data

Walking is a form of active transportation with numerous benefits, including better health outcomes, lower environmental impacts and stronger communities. Understanding built environmental associations with walking behavior is a key step towards identifying design features that support walking. Human mobility data available through GPS receivers and cell phones, combined with high resolution walkability data, provide a rich source of georeferenced data for analyzing environmental associations with walking behavior. However, traditional techniques such as route choice models have difficulty with highly dimensioned data. This paper develops a novel combination of a data-driven technique with route choice modeling for leveraging walkability audits. Using data from a study in Salt Lake City, UT, USA, we apply the data-driven technique of random forests to select variables for use in walking route choice models. We estimate data-driven route choice models and theory-driven models based on predefined walkability dimensions. Results indicate that the random forest technique selects variables that dramatically improve goodness of fit of walking route choice models relative to models based on predefined walkability dimensions. We compare the theory-driven and data-driven walking route choice models based on interpretability and policy relevance.

A Human Mobility Data Driven Hybrid GNN+RNN Based Model For Epidemic Prediction

2021 ◽  
Author(s):  
Shohaib Mahmud ◽  
Haiying Shen ◽  
Ying Natasha Zhang Foutz ◽  
Joshua Anton
Keyword(s):  
Human Mobility ◽  
Data Driven ◽  
Mobility Data ◽  
Epidemic Prediction

scholarly journals Early Detection of COVID-19 Outbreaks Using Human Mobility Data

2021 ◽  
Author(s):  
Grace Guan ◽  
Yotam Dery ◽  
Matan Yechezkel ◽  
Irad Ben-Gal ◽  
Dan Yamin ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Human Mobility ◽  
Health Data ◽  
Decision Makers ◽  
Mobility Data ◽  
Country Level ◽  
Level Data ◽  
Categorization Scheme ◽  
Accuracy Of Prediction

Background Contact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be. Methods We analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted. Results Models using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998. Conclusions Using anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage of more global restrictions.

scholarly journals Early detection of COVID-19 outbreaks using human mobility data

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253865
Author(s):  
Grace Guan ◽  
Yotam Dery ◽  
Matan Yechezkel ◽  
Irad Ben-Gal ◽  
Dan Yamin ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Human Mobility ◽  
Health Data ◽  
Decision Makers ◽  
Mobility Data ◽  
Country Level ◽  
Level Data ◽  
Categorization Scheme ◽  
Accuracy Of Prediction

Background Contact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be. Methods We analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted. Results Models using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998. Conclusions Using anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage from more global restrictions.

scholarly journals Examining the Relationship between Urban Design Qualities and Walking Behavior: Empirical Evidence from Dallas, TX

2019 ◽  
Vol 11 (10) ◽  
pp. 2720
Author(s):  
Shima Hamidi ◽  
Somayeh Moazzeni
Keyword(s):  
New York ◽  
Urban Design ◽  
Salt Lake ◽  
Salt Lake City ◽  
Walking Behavior ◽  
Human Scale ◽  
Walking Activity ◽  
The Impact ◽  
The Relationship ◽  
The City

This study examines the relationship between street-level urban design perceptual qualities and walking behavior in the City of Dallas. While the city has the potential to experience growth in pedestrian activities, it exhibits a very low level of walking activity, placing it as one of the least walkable cities in the nation. To assess the impact of urban design qualities on walkability, we collected data on 23 features related to urban design, 11 built environment variables characterized as D variables comprising diversity, density, design, distance to transit, and destination accessibility. The sample included 402 street block faces in Dallas Downtown Improvement District. Accounting for spatial autocorrelation, we found that two urban design qualities, among five, including image-ability—such as a memorable quality of a place, and transparency—as to what degree people can see beyond the street’s edge—significantly influence pedestrian volume in downtown streets. These findings are in agreement with the two previous studies that used the same methodology in different cities (New York City, NYC and Salt Lake City, UT). According to the findings of these three studies, the other urban design qualities including human scale, complexity, as well as enclosure, are not playing a significant role in walkability, despite the theoretical justification and the extensive operationalization efforts. The findings of this study draw policy makers’ attention to creating more appealing and walkable places through the implementation of these urban design qualities.

scholarly journals Understanding individual routing behaviour

2016 ◽  
Vol 13 (116) ◽  
pp. 20160021 ◽  
Author(s):  
Antonio Lima ◽  
Rade Stanojevic ◽  
Dina Papagiannaki ◽  
Pablo Rodriguez ◽  
Marta C. González
Keyword(s):  
Choice Model ◽  
Cost Minimization ◽  
Human Mobility ◽  
Route Choice ◽  
Empirical Support ◽  
Opportunity To Learn ◽  
Choice Models ◽  
Transportation Systems ◽  
Smart Devices ◽  
Link Type

Knowing how individuals move between places is fundamental to advance our understanding of human mobility (González et al . 2008 Nature 453, 779–782. ( doi:10.1038/nature06958 )), improve our urban infrastructure (Prato 2009 J. Choice Model. 2, 65–100. ( doi:10.1016/S1755-5345(13)70005-8 )) and drive the development of transportation systems. Current route-choice models that are used in transportation planning are based on the widely accepted assumption that people follow the minimum cost path (Wardrop 1952 Proc. Inst. Civ. Eng. 1, 325–362. ( doi:10.1680/ipeds.1952.11362 )), despite little empirical support. Fine-grained location traces collected by smart devices give us today an unprecedented opportunity to learn how citizens organize their travel plans into a set of routes, and how similar behaviour patterns emerge among distinct individual choices. Here we study 92 419 anonymized GPS trajectories describing the movement of personal cars over an 18-month period. We group user trips by origin–destination and we find that most drivers use a small number of routes for their routine journeys, and tend to have a preferred route for frequent trips. In contrast to the cost minimization assumption, we also find that a significant fraction of drivers' routes are not optimal. We present a spatial probability distribution that bounds the route selection space within an ellipse, having the origin and the destination as focal points, characterized by high eccentricity independent of the scale. While individual routing choices are not captured by path optimization, their spatial bounds are similar, even for trips performed by distinct individuals and at various scales. These basic discoveries can inform realistic route-choice models that are not based on optimization, having an impact on several applications, such as infrastructure planning, routing recommendation systems and new mobility solutions.

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