Large Mobility Dataset Research Articles

Impact of apparent temperatures on park visitation behavior: A comprehensive analysis using large-scale mobility data

As many regions continue to show record temperatures and high frequencies of extreme climate events, climate change is disrupting human health and prosperity. This study focuses on how temperature changes and extreme heat as key climate components can impact park visitations (key physical activities indicator). Using the large mobility dataset SafeGraph, this study introduced a causal machine learning approach to estimate marginal heterogenous treatment effects (HTE) of daily apparent temperatures for different park properties and activity types. Our results revealed the declining effects of park visitations as apparent temperature increases and the significant disturbances under extreme heat. Different temperature thresholds that may harm park visitations were identified. Nearby green and public spaces (e.g. neighborhood parks) are the most susceptible category to summer heat. Dog parks, Playgrounds and Ball Field/Sports were well used during the heat. Pool/Splash pad and Community center have shown capabilities of mitigating heat stress. Effectiveness of other park facilities, including Restroom/Drinking Fountain, Water Body, Shade Area Facility were also presented. This study estimates climate impact on park behaviors for multiple usage and environmental situations. Our findings may help future planners, policymakers, landscape architects and park managers better understand climate-related risks when making investment and development decisions.

Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra.

Compartmental models are often used to understand and predict the progression of an infectious disease such as COVID-19. The most basic of these models consider the total population of a region to be closed. Many incorporate human mobility into their transmission dynamics, usually based on static and aggregated data. However, mobility can change dramatically during a global pandemic as seen with COVID-19, making static data unsuitable. Recently, large mobility datasets derived from mobile devices have been used, along with COVID-19 infections data, to better understand the relationship between mobility and COVID-19. However, studies to date have relied on data that represent only a fraction of their target populations, and the data from mobile devices have been used for measuring mobility within the study region, without considering changes to the population as people enter and leave the region. This work presents a unique case study in Andorra, with comprehensive datasets that include telecoms data covering 100% of mobile subscribers in the country, and results from a serology testing program that more than 90% of the population voluntarily participated in. We use the telecoms data to both measure mobility within the country and to provide a real-time census of people entering, leaving and remaining in the country. We develop multiple SEIR (compartmental) models parameterized on these metrics and show how dynamic population metrics can improve the models. We find that total daily trips did not have predictive value in the SEIR models while country entrances did. As a secondary contribution of this work, we show how Andorra's serology testing program was likely impacted by people leaving the country. Overall, this case study suggests how using mobile phone data to measure dynamic population changes could improve studies that rely on more commonly used mobility metrics and the overall understanding of a pandemic.

Does the COVID-19 Pandemic Affect People’s Social and Economic Preferences? Evidence from China

This study investigates the effect of the COVID-19 pandemic on people’s preferences including social preference in terms of trust and trustworthiness, risk attitude, and time preference. We compare the preferences of subjects in two sets of online experiments with samples drawn from provinces across mainland China before and after the COVID-19 pandemic. We test two competing hypotheses regarding social preference. First, we examine whether the outbreak as a collective threat induces more pro-social behavior by enhancing ingroup cohesion and cooperation. Second, we test whether the pandemic leads to a reduction in pro-social behavior due to the expectation that the related economic downturn will have a negative impact on income. We find that after the outbreak, the subjects overall are less trusting (8% lower) and more trustworthy (6% higher). We also find that the subjects are more risk averse and less patient after the outbreak, which is consistent with the idea that the fear generated by the pandemic induces risk aversion and impatience. Finally, using two large mobility datasets derived from anonymized mobile phone records, we show that people’s preferences are not affected by social distancing.

Data offloading capacity in a megalopolis using taxis and buses as data carriers

All means will be welcome to help data flow across smart cities. As a matter of fact, smart cities widely rely in sensing the environment, an action that is prone to generating huge amounts of data. A major challenge is how to collect such data in an efficient way without the need to deploy, whenever necessary, extra (costly) cellular infrastructure. In this paper, we examine the possibility of creating a delay-tolerant vehicular network in the city of Rio de Janeiro, Brazil, using the public transportation system as a data carrier. We evaluate the capacity of such a network by analyzing a large mobility dataset reporting GPS positions of 12,456 buses and 5,833 taxis during a 24-hour period. Our results confirm the viability of the approach and reveal that hundreds of Terabytes can circulate across the city on a daily basis while achieving significant city coverage.

A Semantic Model for Human Mobility in an Urban Region

The continuous development and complexity of many modern cities offer many research challenges for urban scientists searching for a better understanding of mobility patterns that happen in space and time. Very large trajectory datasets are often publicly generated thanks to the availability of multiple positioning sensors and location-based services. However, a successful integration of mobility data still requires the development of conceptual and database frameworks that will support appropriate data representation and manipulation capabilities. The research presented in this paper introduces a conceptual modeling and database management approach for representing and analyzing human trajectories in urban spaces. The model considers the spatial, temporal and semantic dimensions in order to take into account the full range of properties that emerge from mobility patterns. Several object data types and data manipulation constructs are developed and experimented on top of an urban dataset testbed currently available in the city of Beijing. The interest of the approach is twofold: first, it clearly appears that very large mobility datasets can be integrated in current extensible GIS; secondly, significant patterns can be derived at the database manipulation level using specifically developed query functions.

Boosting Ride Sharing With Alternative Destinations

People living in highly populated cities increasingly experience decreased quality of life due to pollution and traffic congestion. With the objective of reducing the number of circulating vehicles, we investigate a novel approach to boost ride-sharing opportunities based on the knowledge of the human activities behind individual mobility demands. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed in many different places. Therefore, when there is the possibility of sharing a ride, people having a pro-environment behavior or interested in saving money can accept to fulfill their needs at an alternative destination. We thus propose activity-based ride matching (ABRM), an algorithm aimed at matching ride requests with ride offers, possibly reaching alternative destinations where the intended activity can be performed. By analyzing two large mobility datasets extracted from a popular social network, we show that our approach could largely impact urban mobility by resulting in an increase up to 54.69% of ride-sharing opportunities with respect to a traditional destination-oriented approach. Due to the high number of ride possibilities found by ABRM, we introduce and assess a subsequent ranking step to provide the user with the top-k most relevant rides only. We discuss how ABRM parameters affect the fraction of car rides that can be saved and how the ranking function can be tuned to enforce pro-environment behaviors.

Never drive alone: Boosting carpooling with network analysis

Carpooling, i.e., the act where two or more travelers share the same car for a common trip, is one of the possibilities brought forward to reduce traffic and its externalities, but experience shows that it is difficult to boost the adoption of carpooling to significant levels. In our study, we analyze the potential impact of carpooling as a collective phenomenon emerging from people׳s mobility, by network analytics. Based on big mobility data from travelers in a given territory, we construct the network of potential carpooling, where nodes correspond to the users and links to possible shared trips, and analyze the structural and topological properties of this network, such as network communities and node ranking, to the purpose of highlighting the subpopulations with higher chances to create a carpooling community, and the propensity of users to be either drivers or passengers in a shared car. Our study is anchored to reality thanks to a large mobility dataset, consisting of the complete one-month-long GPS trajectories of approx. 10% circulating cars in Tuscany. We also analyze the aggregated outcome of carpooling by means of empirical simulations, showing how an assignment policy exploiting the network analytic concepts of communities and node rankings minimizes the number of single occupancy vehicles observed after carpooling.