Weather effects on human mobility: a study using multi-channel sequence analysis

Abstract

Widespread availability of geospatial data on movement and context presents opportunities for 10 applying new methods to investigate the interactions between humans and weather conditions. Understanding the influence of weather on human behaviour is of interest for diverse applications, such as urban planning and traffic engineering. The effect of weather on movement behaviour can be explored through Context-Aware Movement Analysis (CAMA), which integrates movement geometry with its context. More specifically, we use multi-channel sequence analysis (MCSA) to represent a 15 person's movement as a multi-dimensional sequence of states, describing either the type of movement or the state of the environment throughout time. Similar movement patterns can then be identified by comparing and aligning mobility sequences. In this paper we apply CAMA and MCSA to explore weather effects on human movement patterns. Data from a GPS tracking study in a Scottish town of Dunfermline are linked to weather data and converted into multi-channel sequences which are 20 clustered into groups of similar behaviours under specific weather typologies. Our findings show that the CAMA + MCSA method can successfully identify the response of commuters to variations in environmental conditions. We also discuss our findings on how travel modes and time spent at different places are affected by meteorological conditions, mainly wind, but also rainfall, daylight duration, temperature, comfort and relative humidity. 25

Convolutional LSTM based transportation mode learning from raw GPS trajectories

“…The GPS data is anonymously produced by many different devices such as vehicle tracking systems, mobile phones etc., therefore, using the GPS data for mobility inference is more practical and non‐trivial. Few studies like [19, 25, 37, 38] use only the GPS data for inference task, but at the same time only using the GPS data is not enough, because there are some hidden patterns that cannot be identified from GPS attributes as human mobility is highly correlated with the climate conditions [31, 39] and the weather data can be easily integrated with the GPS data. Therefore, we use weather attributes in addition to the GPS data, because using the weather data does not incur any cost and is easily available for all locations.…”

“…Therefore, we need additional weather features to uniquely identify the patterns among different transportation modes. Mobility patterns of the users are heavily dependent on weather conditions [31]. Based on this fact, we make use of the weather data which is easily available for all the locations and helps to overcome the limitations of GPS only attributes.…”

Towards an integrated science of movement: converging research on animal movement ecology and human mobility science

“…In human mobility studies, geographic context has traditionally received less attention than behavioral states or social, and/or demographic factors, although this is changing with newly available data (see, e.g. Horanont et al 2013, Siła-Nowicka et al 2016, Brum-Bastos et al 2018. Despite this increasing interest in understanding the geographic context of movement, there has been surprisingly little cross-over between animal movement ecology and human mobility science.…”

Quality of hybrid location data drawn from GPS‐enabled mobile phones: Does it matter?

“…They have been particularly useful in resource‐poor environments, although the vast number of mobile phone users provides an opportunity for tracking movement patterns of people throughout both the developed and developing world (Chen et al, ; Searle et al., ; Vazquez‐Prokopec et al., ; Wesolowski et al., ). Despite the popularity of mobile phone data in human mobility and transportation research (Alessandretti, Sapiezynski, Lehmann, & Baronchelli, ; Brum‐Bastos, Long, & Demšar, ; Chen et al., ; Feng & Timmermans, ; Gong, Chen, Bialostozky, & Lawson, ; Schneider, Belik, Couronné, Smoreda, & González, ; Sila‐Nowicka et al, ; Van Dijk, ; Williams, Thomas, Dunbar, Eagle, & Dobra, ), studies on the accuracy or the coverage of location data are relatively sparse. Here it is worth noting that the present paper focuses on “active mobile phone data” in which the location of the mobile phone is determined in response to queries specifically designed to collect location data at fixed time intervals or distance thresholds (Ahas, Aasa, Roose, Mark, & Silm, ; Sagl, Delmelle, & Delmelle, ).…”