scholarly journals Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data

2018 ◽  
Vol 108 ◽  
pp. 64-67 ◽  
Author(s):  
Susan Athey ◽  
David Blei ◽  
Robert Donnelly ◽  
Francisco Ruiz ◽  
Tobias Schmidt
Keyword(s):  
Mobile Phone ◽  
Travel Time ◽  
Consumer Preferences ◽  
Mobile Location ◽  
Location Data ◽  
Consumer Choices ◽  
Using Data

We estimate a model of consumer choices over restaurants using data from several thousand anonymous mobile phone users. Restaurants have latent characteristics (whose distribution may depend on restaurant observables) that affect consumers' mean utility as well as willingness to travel to the restaurant, while each user has distinct preferences for these latent characteristics. We analyze how consumers reallocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants, comparing our predictions to actual outcomes. We also address counterfactual questions such as what type of restaurant would attract the most consumers in a given location.

scholarly journals An Application of K-Means Clustering on Tourist Activity in Maui Using Mobile Location Data

2021 ◽  
Author(s):  
Anna Meg Sheilds Brooker
Keyword(s):  
Big Data ◽  
Mobile Phone ◽  
Average Distance ◽  
Activity Patterns ◽  
Human Behaviour ◽  
Mobile Data ◽  
Mobile Location ◽  
Location Data ◽  
Tourism Management ◽  
Insight Into

Mobile location data are a major form of Big Data that hold many possibilities for study and insight into human behaviour. This research used mobile location data to investigate the differences in the activity patterns of tourists in Maui, Hawai’i. Mobile data used in this study were app-based location data collected as a stream of mobile phone locations with a timestamp. Tourists were clustered using K-Means based on time spent at attraction types. Different travel experiences were analyzed based on traveler’s accommodation choices, the average distance travelled from accommodation to attraction, and vacation length, which all varied significantly between the tourist clusters. This work provided a new use for K-means clustering with mobile location data to provide insightful information to marketing professionals and tourism management bodies.

scholarly journals An Application of K-Means Clustering on Tourist Activity in Maui Using Mobile Location Data

2021 ◽  
Author(s):  
Anna Meg Sheilds Brooker
Keyword(s):  
Big Data ◽  
Mobile Phone ◽  
Average Distance ◽  
Activity Patterns ◽  
Human Behaviour ◽  
Mobile Data ◽  
Mobile Location ◽  
Location Data ◽  
Tourism Management ◽  
Insight Into

Mobile location data are a major form of Big Data that hold many possibilities for study and insight into human behaviour. This research used mobile location data to investigate the differences in the activity patterns of tourists in Maui, Hawai’i. Mobile data used in this study were app-based location data collected as a stream of mobile phone locations with a timestamp. Tourists were clustered using K-Means based on time spent at attraction types. Different travel experiences were analyzed based on traveler’s accommodation choices, the average distance travelled from accommodation to attraction, and vacation length, which all varied significantly between the tourist clusters. This work provided a new use for K-means clustering with mobile location data to provide insightful information to marketing professionals and tourism management bodies.

scholarly journals Japan’s voluntary lockdown: further evidence based on age-specific mobile location data

2021 ◽  
Author(s):  
Tsutomu Watanabe ◽  
Tomoyoshi Yabu
Keyword(s):  
Age Groups ◽  
Evidence Based ◽  
Intervention Effect ◽  
Information Effect ◽  
Mobile Location ◽  
Serious Illness ◽  
Location Data ◽  
Voluntary Response ◽  
Younger Age ◽  
Age Profile

AbstractChanges in people’s behavior during the COVID-19 pandemic can be regarded as the result of two types of effects: the “intervention effect” (changes resulting from government orders for people to change their behavior) and the “information effect” (voluntary changes in people’s behavior based on information about the pandemic). Using age-specific mobile location data, we examine how the intervention and information effects differ across age groups. Our main findings are as follows. First, the age profile of the intervention effect shows that the degree to which people refrained from going out was smaller for older age groups, who are at a higher risk of serious illness and death, than for younger age groups. Second, the age profile of the information effect shows that the degree to which people stayed at home tended to increase with age for weekends and holidays. Thus, while Acemoglu et al. (2020) proposed targeted lockdowns requiring stricter lockdown policies for the oldest group in order to protect those at a high risk of serious illness and death, our findings suggest that Japan’s government intervention had a very different effect in that it primarily reduced outings by the young, and what led to the quarantining of older groups at higher risk instead was people’s voluntary response to information about the pandemic. Third, the information effect has been on a downward trend since the summer of 2020. It is relatively more pronounced among the young, so that the age profile of the information effect remains upward sloping.

scholarly journals 12-Year Trends in Active School Transport across Four European Countries—Findings from the Health Behaviour in School-Aged Children (HBSC) Study

2021 ◽  
Vol 18 (4) ◽  
pp. 2118
Author(s):  
Ellen Haug ◽  
Otto Robert Frans Smith ◽  
Jens Bucksch ◽  
Catherina Brindley ◽  
Jan Pavelka ◽  
...  
Keyword(s):  
Czech Republic ◽  
Travel Time ◽  
Health Behaviour ◽  
Sociodemographic Factors ◽  
The Czech Republic ◽  
Family Affluence ◽  
School Aged Children ◽  
Recent Trends ◽  
Hbsc Study ◽  
Using Data

Active school transport (AST) is a source of daily physical activity uptake. However, AST seems to have decreased worldwide over recent decades. We aimed to examine recent trends in AST and associations with gender, age, family affluence, and time to school, using data from the Health Behaviour in School-Aged Children (HBSC) study collected in 2006, 2010, 2014, and 2018 in the Czech Republic, Norway, Scotland, and Wales. Data from 88,212 students (11, 13 and 15 years old) revealed stable patterns of AST from 2006 to 2018, apart from a decrease in the Czech Republic between 2006 and 2010. For survey waves combined, walking to and from school was most common in the Czech Republic (55%) and least common in Wales (30%). Cycling was only common in Norway (22%). AST differed by gender (Scotland and Wales), by age (Norway), and by family affluence (everywhere but Norway). In the Czech Republic, family affluence was associated with change over time in AST, and the effect of travel time on AST was stronger. The findings indicate that the decrease in AST could be levelling off in the countries considered here. Differential associations with sociodemographic factors and travel time should be considered in the development of strategies for AST.

scholarly journals Using Data Mining to Analyze Dwell Time and Nonstop Running Time in Road-Based Mass Transit Systems

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1217
Author(s):  
Teresa Cristóbal ◽  
Gabino Padrón ◽  
Alexis Quesada ◽  
Francisco Alayón ◽  
Gabriel de Blasio ◽  
...  
Keyword(s):  
Data Mining ◽  
Travel Time ◽  
Dwell Time ◽  
Mass Transit ◽  
Transit Systems ◽  
Running Time ◽  
Bus Stop ◽  
Transport Line ◽  
Using Data

Travel Time plays a key role in the quality of service in road-based mass transit systems. In this type of mass transit systems, travel time of a public transport line is the sum of the dwell time at each bus stop and the nonstop running time between pair of consecutives bus stops of the line. The aim of the methodology presented in this paper is to obtain the behavior patterns of these times. Knowing these patterns, it would be possible to reduce travel time or its variability to make more reliable travel time predictions. To achieve this goal, the methodology uses data related to check-in and check-out movements of the passengers and vehicles GPS positions, processing this data by Data Mining techniques. To illustrate the validity of the proposal, the results obtained in a case of use in presented.

Commuter Bicyclist Route Choice: Analysis Using a Stated Preference Survey

2003 ◽  
Vol 1828 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Monique A. Stinson ◽  
Chandra R. Bhat
Keyword(s):  
Travel Time ◽  
Stated Preference ◽  
Empirical Models ◽  
Policy Implications ◽  
Facility Planning ◽  
Quality Factors ◽  
Stated Preference Survey ◽  
Factors Affecting ◽  
Choice Analysis ◽  
Using Data

The importance of factors affecting commuter bicyclists’ route choices was evaluated. Both route-level (e.g., travel time) and link-level (e.g., pavement quality) factors are examined. Empirical models are estimated using data from a stated preference survey conducted via the Internet. The models indicate that, for commuter bicyclists, travel time is the most important factor in choosing a route. Presence of a bicycle facility (especially a bike lane or separate path), the level of automobile traffic, pavement or riding surface quality, and presence of a bicycle facility on a bridge are also very important determinants. Furthermore, there are policy implications of these results for bicycle facility planning.

scholarly journals Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Adrian M. Tompkins ◽  
Nicky McCreesh
Keyword(s):  
Mobile Phone ◽  
Malaria Transmission ◽  
Gravity Model ◽  
Mobile Phone Data ◽  
Agent Based ◽  
Migration Model ◽  
Location Data ◽  
Home Location ◽  
One Year ◽  
Overnight Stays

One year of mobile phone location data from Senegal is analysed to determine the characteristics of journeys that result in an overnight stay, and are thus relevant for malaria transmission. Defining the home location of each person as the place of most frequent calls, it is found that approximately 60% of people who spend nights away from home have regular destinations that are repeatedly visited, although only 10% have 3 or more regular destinations. The number of journeys involving overnight stays peaks at a distance of 50 km, although roughly half of such journeys exceed 100 km. Most visits only involve a stay of one or two nights away from home, with just 4% exceeding one week. A new agent-based migration model is introduced, based on a gravity model adapted to represent overnight journeys. Each agent makes journeys involving overnight stays to either regular or random locations, with journey and destination probabilities taken from the mobile phone dataset. Preliminary simulations show that the agentbased model can approximately reproduce the patterns of migration involving overnight stays.

scholarly journals Challenges in Contact Tracing by Mining Mobile Phone Location Data for COVID-19: Implications for Public Governance in South Africa

10.28945/4736 ◽  
2021 ◽  
Vol 16 ◽  
pp. 101-124
Author(s):  
Paul Kariuki ◽  
Lizzy O Ofusori ◽  
Prabhakar Rontala Subramanniam ◽  
Moses Okpeku ◽  
Maria L Goyayi
Keyword(s):  
Public Health ◽  
South Africa ◽  
Mobile Phone ◽  
Contact Tracing ◽  
Future Research ◽  
Public Governance ◽  
The Public ◽  
Location Data ◽  
Pertinent Data ◽  
Privacy And Confidentiality

Aim/Purpose: The paper’s objective is to examine the challenges of using the mobile phone to mine location data for effective contact tracing of symptomatic, pre-symptomatic, and asymptomatic individuals and the implications of this technology for public health governance. Background: The COVID-19 crisis has created an unprecedented need for contact tracing across South Africa, requiring thousands of people to be traced and their details captured in government health databases as part of public health efforts aimed at breaking the chains of transmission. Contact tracing for COVID-19 requires the identification of persons who may have been exposed to the virus and following them up daily for 14 days from the last point of exposure. Mining mobile phone location data can play a critical role in locating people from the time they were identified as contacts to the time they access medical assistance. In this case, it aids data flow to various databases designated for COVID-19 work. Methodology: The researchers conducted a review of the available literature on this subject drawing from academic articles published in peer-reviewed journals, research reports, and other relevant national and international government documents reporting on public health and COVID-19. Document analysis was used as the primary research method, drawing on the case studies. Contribution: Contact tracing remains a critical strategy in curbing the deadly COVID-19 pandemic in South Africa and elsewhere in the world. However, given increasing concern regarding its invasive nature and possible infringement of individual liberties, it is imperative to interrogate the challenges related to its implementation to ensure a balance with public governance. The research findings can thus be used to inform policies and practices associated with contact tracing in South Africa. Findings: The study found that contact tracing using mobile phone location data mining can be used to enforce quarantine measures such as lockdowns aimed at mitigating a public health emergency such as COVID-19. However, the use of technology can expose the public to criminal activities by exposing their locations. From a public governance point of view, any exposure of the public to social ills is highly undesirable. Recommendations for Practitioners: In using contact tracing apps to provide pertinent data location caution needs to be exercised to ensure that sensitive private information is not made public to the extent that it compromises citizens’ safety and security. The study recommends the development and implementation of data use protocols to support the use of this technology, in order to mitigate against infringement of individual privacy and other civil liberties. Recommendation for Researchers: Researchers should explore ways of improving digital applications in order to improve the acceptability of the use of contact tracing technology to manage pandemics such as COVID-19, paying attention to ethical considerations. Impact on Society: Since contact tracing has implications for privacy and confidentiality it must be conducted with caution. This research highlights the challenges that the authorities must address to ensure that the right to privacy and confidentiality is upheld. Future Research: Future research could focus on collecting primary data to provide insight on contact tracing through mining mobile phone location data. Research could also be conducted on how app-based technology can enhance the effectiveness of contact tracing in order to optimize testing and tracing coverage. This has the potential to minimize transmission whilst also minimizing tracing delays. Moreover, it is important to develop contact tracing apps that are universally inter-operable and privacy-preserving.

scholarly journals Measuring when Uber behaves as a substitute or complement to transit: An examination of travel-time differences in Toronto

2019 ◽  
Author(s):  
Mischa Young ◽  
Jeff Allen ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Ordinary Least Squares ◽  
Gap Filling ◽  
Service Levels ◽  
Transit Service ◽  
Logistic Regressions ◽  
Transit Agencies ◽  
Using Data ◽  
The City

Policymakers in cities worldwide are trying to determine how ride-hailing services affect the ridership of traditional forms of public transportation. The level of convenience and comfort that these services provide is bound to take riders away from transit, but by operating in areas, or at times, when transit is less frequent, they may also be filling a gap left vacant by transit operations. These contradictory effects reveal why we should not merely categorize all ride-hailing services as a substitute or supplement to transit, and demonstrate the need to examine ride-hailing trips individually. Using data from the 2016 Transportation Tomorrow Survey in Toronto, we investigate the differences in travel-times between observed ride-hailing trips and their fastest transit alternatives. Ordinary least squares and ordered logistic regressions are used to uncover the characteristics that influence travel-time differences. We find that ride-hailing trips contained within the City of Toronto, pursued during peak hours, or for shopping purposes, are more likely to have transit alternatives of similar duration. Also, we find differences in travel-time often to be caused by transfers and lengthy walk- and wait-times for transit. Our results further indicate that 31% of ride-hailing trips in our sample have transit alternatives of similar duration (≤ 15 minute difference). These are particularly damaging for transit agencies as they compete directly with services that fall within reasonable expectations of transit service levels. We also find that 27% of ride-hailing trips would take at least 30 minutes longer by transit, evidence for significant gap-filling opportunity of ride-hailing services. In light of these findings, we discuss recommendations for ride-hailing taxation structures.

Sign in / Sign up

Export Citation Format

Share Document