Uncertainty in Cell-Phone Generated Bike and Pedestrian Volumes

Urban emissions hotspots: Quantifying vehicle congestion and air pollution using mobile phone GPS data

To support increasingly complex planning activities, many agencies are facing the challenges of obtaining highly nuanced travel behavior data while managing shrinking financial resources. Recent advancements in smartphones and GPS technologies present new opportunities to track travelers’ trips. Many studies have applied GPS-based data to planning and demand analysis, but cell phone (mobile phone) GPS data have not received much attention. Google location history (GLH) data provide an opportunity to explore the potential of cell phone GPS data. This paper presents the findings of a study that used GLH data, including the data-processing algorithm used to derive travel information, and their potential applications to understanding travel patterns. For the pilot study, GLH data were obtained from 25 participants over a 1-month period. The data showed that GLH provides a sufficient amount of high-resolution data that can be used to study people’s movement without a burden on the respondent. The algorithms developed in this study worked well with the pilot data. However, because of the limitations of the pilot data as a result of the sample size and sample representation, conclusions cannot be drawn from the results of the analysis conducted in this study. Nevertheless, this pilot study shows the potential of mobile phone GPS data as a supplement or complement to conventional data. Given the high rate of penetration of smartphones and the low respondent burden, these data could facilitate the investigation of various issues, such as the reason for less frequent long-distance travel, daily variations in travel behavior, and human mobility patterns on a large spatiotemporal scale.

To evaluate the association between social distancing quantified by mobile phone data and the current prevalence of COVID-19 infections in the U.S. per capita. Data were accessed on April 4, 2020, from Centers for Disease Control and Prevention, Google COVID-19 Community Mobility Report, and the United States Census Bureau to report prevalence of COVID-19 infections, mobility data, and population per state, respectively. Mobility data points were defined as daily length of visit or time spent in a single location based on mobile phone users shared locations from February 7 - March 29, 2020. Multivariable linear regression was used to evaluate relationships between normalized per capita infection prevalence and six parameters of social distancing. Mobility data indicated the following percent changes compared to median values of baseline activity: -50% in transit stations, -45% in retail/recreation, -36% in workplaces, -23% in grocery/pharmacy, -19% in parks, and +12% in residential living areas. Multivariable linear regression revealed significant correlation between prevalence of infection per capita and parameters of social distancing (R= 0.604, P= 0.002). Time at home was not an independent predictor for prevalence of infection per capita (beta= 0.016; 95% CI, -0.003 to 0.036; P= 0.09). Based on mobility reports from mobile phone GPS data and six characteristics of social distancing, significant associations were identified between geographic activity and prevalence of COVID-19 infections in the U.S. per capita. Mobile phone data utilizing 'location history' may be warranted to monitor the effectiveness of social distancing parameters on reducing prevalence of COVID-19 in the U.S.

Mobile phone GPS data in urban bicycle-sharing: Layout optimization and emissions reduction analysis

The study on individual mobility patterns supports our better understanding of spatiotemporal characteristics of people’s travel behavior and social activities. The mobile phone GPS data are advantageous due to the large size of their data coverage. This paper aims to identify individual activity anchor places and to analyze related patterns based on the GPS data collected from thousands of mobile phone users over four months in Greater Paris, France. We propose a method to refine the identification of home and secondary activities. Based on this, the mobility spatial characteristics are aggregated by applying a three-stage clustering method. As a consequence, the clusters of activity types, the daily mobility patterns (day types), and the user groups with similar daily mobility patterns are obtained stage by stage. This allows us to analyze the obtained clusters in a cascading maneuver by three different levels: activity level, day level, and individual level. Inversely, the mobility characteristics per user group are interpreted with respect to the interpretation of day types and then activity types. From the interpretable clusters, it is facilitated for us to find the daily mobility differences by user groups across weekdays and weekends, transport modes, as well as the mobility variability over the study period.

Predicting spatial behaviors of an individual (e.g., frequent visits to specific locations) is important to improve our understanding of the complexity of human mobility patterns, and to capture anomalous behaviors in an individual’s spatial movements, which can be particularly useful in situations such as those induced by the COVID-19 pandemic. We propose a system called Deep Spatio-Temporal Predictor (DST-Predict), that can predict the future visit frequency of an individual based on one’s past mobility behaviour patterns using GPS trace data collected from mobile phones. Predicting such spatial behavior is challenging, primarily because individuals’ patterns of location visits for each individual consists of both systematic and random components, which vary across the spatial and temporal scales of analysis. To address these issues, we propose a novel multi-view sequence-to-sequence model that uses Convolutional Long-short term memory (ConvLSTM) where the past history of frequent visit patterns features is used to predict individuals’ future visit patterns in a multi-step manner. Using the GPS survey data obtained from 1,464 participants in western New York, US, we demonstrated that the proposed system is capable of predicting individuals’ frequency of visits to common places in an urban setting, with high accuracy.

This chapter describes a framework for using a mobile phone GPS data to investigate the effects of weather on people’s daily activity routines. Temperature, rainfall, and wind speed are among the weather variables considered in our case study discussed in this chapter. We describe a method for inferring people’s daily activity patterns, including the places they visit and when they do so, as well as the duration of the visit, based on GPS position traces of their mobile phones and Yellow Pages information. An analysis of 31,855 mobile phone users reveals that people are more likely to stay at restaurants or food outlets for longer periods of time, and to a lesser extent at retail or shopping sites, when the weather is extremely cold or the ambiance is calm (non-windy). People’s activity habits are affected by certain weather conditions when compared to their usual patterns. People’s motions and activities are evident at different times of the day. The weather has a wide range of effects on different geographical areas of a large city. When urban infrastructure data is employed to characterize areas, significant connections between weather conditions and people’s accessibility to public rail network are observed. This chapter gives a new perspective of how mobile phone GPS data can be utilized in the context of weather’s influence on human behavior, specifically choices of daily activities, as well as the impact of environmental factors on urban life dynamics. The conceptual framework and analysis discussed in this chapter are based on the original research by Phithakkitnukoon et al. (PLoS One. 8:12, 2013; PLoS One. 7:10, 2012; Activity-aware map: Identifying human daily activity pattern using mobile phone data. LNCS, 2010).KeywordsGPS dataWeather effectAtmospheric conditionDaily activity patternsWeather variablesHuman behavior

Digital data help data scientists and epidemiologists track and predict outbreaks of disease. Mobile phone GPS data, social media data, or other forms of information updates such as the progress of epidemics are used by epidemiologists to recognize disease spread among specific groups of people. Targeting groups as potential carriers of a disease, rather than addressing individuals as patients, risks causing harm to groups. While there are rules and obligations at the level of the individual, we have to reach a stage in the development of data analytics where groups are protected as entities. This chapter offers a deeper examination of harms to the groups.

Abstract. Nowadays, for the estimation of traffic demand or people flow, modelling route choice activity in road networks is an important task and many algorithms have been developed to generate route choice sets. However, developing an algorithm based on a small amount of data that can be applied generally within a metropolitan area is difficult. This is because the characteristics of road networks vary widely. On the other hand, recently, the collection of people movement data has lately become much easier, especially through mobile phones. Lately, most mobile phones include GPS functionality. Given this background, we propose a data-oriented algorithm to generate route choice sets using mobile phone GPS data. GPS data contain a number of measurement errors; hence, they must be adjusted to account for these errors before use in advanced people movement analysis. However, this is time-consuming and expensive, because an enormous amount of daily data can be obtained. Hence, the objective of this study is to develop an algorithm that can easily manage GPS data. Specifically, at first movement data from all GPS data are selected by calculating the speed. Next, the nearest roads in the road network are selected from the GPS location and count such data for each road. Then An algorithm based on the GSP (Gateway Shortest Path) algorithm is proposed, which searches the shortest path through a given gateway. In the proposed algorithm, the road for which the utilization volume calculated by GPS data is large is selected as the gateway. Thus, route choice sets that are based on trends in real GPS data are generated. To evaluate the proposed method, GPS data from 0.7 million people a year in Japan and DRM (Digital Road Map) as the road network are used. DRM is one of the most detailed road networks in Japan. Route choice sets using the proposed algorithm are generated and the cover rate of the utilization volume of each road under evaluation is calculated. As a result, the proposed route generation algorithm and GPS data cleaning process work well and a huge variety of routes that have high potential to be used in the real world can be generated.

Predicting travel time reliability using mobile phone GPS data

Urban greenspace is a valuable component of the urban form that has the potential to improve the health and well-being of residents. Most quantitative studies of relationships between health and greenspace to date have investigated associations only with what greenspace exists in the local environment (i.e. provision of greenspace), rather than to what extent it is used. This is due to the difficulty of obtaining usage data in large amounts. In recent years, GPS functionality integrated into mobile phones has provided a potential solution to this problem by making it possible to track which parts of the environment people experience in their day-to-day lives. In this paper, we demonstrate a method to derive cleaned, trip-level information from raw GPS data collected by a mobile phone app, then use this data to investigate the characteristics of trips to urban greenspace by residents of the city of Sheffield, UK. We find that local users of the app spend an average of an hour per week visiting greenspaces, including around seven trips per week and covering a total distance of just over 2.5 km. This may be enough to provide health benefits, but is insufficient to provide maximal benefits. Trip characteristics vary with user demographics: ethnic minority users and users from more socioeconomically deprived areas tend to make shorter trips than White users and those from less deprived areas, while users aged 34 years and over make longer trips than younger users. Women, on average, make more frequent trips than men, as do those who spent more time outside as a child. Our results suggest that most day-to-day greenspace visits are incidental, i.e. travelling through rather than to greenspace, and highlight the importance of including social and cultural factors when investigating who uses and who benefits from urban greenspace.

Mobile phone GPS data in urban customized bus: Dynamic line design and emission reduction potentials analysis

In order to understand what is happening in the underlying economy it is useful to be able to gain a picture of what people are doing. For example, when people go out on the weekend, they may do so in order to engage in some kind of economic activity. In this research, we measured the level of economic activity by gaining a macro picture of people's movements. Specifically, we used the location data (GPS data) of mobile phones owned by the customers of major Japanese mobile carrier au to measure changes in the movements of people in key urban areas, and to show the relationship between these changes and macroeconomic variables. Our results found a notable correlation between the number of visitors to Shinjuku area in Tokyo on weekends/holidays and GDP consumer spending and spending-related government statistics such as the Indices of Tertiary Industry Activity and the Current Survey of Commerce. In addition, in Japan, the spread of COVID-19 has had a marked impact on people's economic activity in 2020. In order to confirm the details of this change, we analyzed the weekly change in the number of visitors to some commercial area in Tokyo. As a result, we confirm that there has been a dramatic change in people's behavior since the middle of February, when people became more aware of COVID-19 in Japan.

Using Specific Model Statements to Elicit Information and Cues to Deceit in Information-Gathering Interviews

Prelims