Evolution of a Synthetic Population and Its Daily Mobility Patterns Under Spatial Strategies for Urban Growth

- The article focuses on the relationship between tramway networks and urban structure in Spain during the period 1900-1936. It states that this relationship should be studied after considering the use of transport and the mobility patterns of different classes, specially the working class. Once these factors have been studied it is possible to assert the impact of the tramway netmark on urban growth. The impact of the tramways on major Spanish cities did not take the form of a transport revolution that would radically changed the urban pattern. Tramways did not direct urban growth until use of tramway lines by the working class became general. This did not happen until World War I. Since then, skilled and some unskilled workers did change their mobility patterns and tramway use experienced a cycle of growth that continued until the late 1950s.

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Trip chain complexity: a comparison among latent classes of daily mobility patterns

Transportation ◽

10.1007/s11116-020-10084-1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Florian Schneider ◽

Danique Ton ◽

Lara-Britt Zomer ◽

Winnie Daamen ◽

Dorine Duives ◽

...

Keyword(s):

Latent Classes ◽

Mobility Patterns ◽

Trip Chain ◽

Daily Mobility

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Using mobile phone data to explore spatial-temporal evolution of home-based daily mobility patterns in Shanghai

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) ◽

10.1109/besc.2016.7804481 ◽

2016 ◽

Cited By ~ 2

Author(s):

Zhicheng Liu ◽

Jinbin Yu ◽

Weiting Xiong ◽

Jian Lu ◽

Junyan Yang ◽

...

Keyword(s):

Mobile Phone ◽

Temporal Evolution ◽

Mobile Phone Data ◽

Mobility Patterns ◽

Daily Mobility ◽

Home Based

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Animal daily mobility patterns analysis using resting event networks

Applied Network Science ◽

10.1007/s41109-021-00353-y ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Maxime Lenormand ◽

Hervé Pella ◽

Hervé Capra

Keyword(s):

Temporal Resolution ◽

Fish Species ◽

General Problem ◽

Animal Movement ◽

Movement Patterns ◽

Conservation Strategies ◽

Mobility Patterns ◽

Daily Mobility ◽

Recent Advances ◽

Spatio Temporal

AbstractCharacterizing the movement patterns of animals is crucial to improve our understanding of their behavior and thus develop adequate conservation strategies. Such investigations, which could not have been implemented in practice only a few years ago, have been facilitated through the recent advances in tracking methods that enable researchers to study animal movement at an unprecedented spatio-temporal resolution. However, the identification and extraction of patterns from spatio-temporal trajectories is still a general problem that has relevance for many applications. Here, we rely on the concept of resting event networks to identify the presence of daily mobility patterns in animal spatio-temporal trajectories. We illustrate our approach by analyzing spatio-temporal trajectories of several fish species in a large hydropeaking river.

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Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211031234 ◽

2021 ◽

pp. 036119812110312

Author(s):

Biao Yin ◽

Fabien Leurent

Keyword(s):

Mobile Phone ◽

Large Scale ◽

Travel Patterns ◽

Mobility Patterns ◽

Individual Activity ◽

Data Set ◽

Individual Mobility ◽

Daily Mobility ◽

Home Based ◽

The Individual

Data mining techniques can extract useful activity and travel information from large-scale data sources such as mobile phone geolocation data. This paper aims to explore individual activity-travel patterns from samples of mobile phone users using a two-week geolocation data set from the Paris region in France. After filtering the data set, we propose techniques to identify individual stays and activity places. Typical activity places such as the primary anchor place and the secondary place are detected. The daily timeline (i.e., activity-travel program) is reconstructed with the detected activity places and the trips in-between. Based on user-day timelines, a three-stage clustering method is proposed for mobility pattern analysis. In the method framework, activity types are first identified by clustering analysis. In the second stage, daily mobility patterns are obtained after clustering the daily mobility features. Activity-travel topologies are statistically investigated to support the interpretation of daily mobility patterns. In the last stage, we analyze statistically the individual mobility patterns for all samples over 14 days, measured by the number of days for all kinds of daily mobility patterns. All individual samples are divided into several groups where people have similar travel behaviors. A kmeans++ algorithm is applied to obtain the appropriate number of patterns in each stage. Finally, we interpret the individual mobility patterns with statistical descriptions and reveal home-based differences in spatial distribution for the grouped individuals.

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Assessing the Role of Daily Activities and Mobility in the Spread of COVID-19 in Montreal With an Agent-Based Approach

Frontiers in Built Environment ◽

10.3389/fbuil.2021.654279 ◽

2021 ◽

Vol 7 ◽

Author(s):

Ouassim Manout ◽

Francesco Ciari

Keyword(s):

Public Transportation ◽

Research Question ◽

Daily Activities ◽

Risk Of Infection ◽

Mobility Patterns ◽

Agent Based ◽

Synthetic Population ◽

Community Transmission ◽

Business As Usual

Daily activities and mobility dynamics play a central role in the spread of COVID-19. Close physical interactions involved by certain daily activities help transmit the virus. Travel required by the spatial distribution of activities contributes to the propagation of the virus. In order to control and limit this propagation, it is critical to understand the contribution of daily activities to the dynamics of COVID-19. This paper investigates the connection between daily activities, their distribution in space and time, the characteristics of the individuals performing them, and the transmission of the virus. A business-as-usual agent-based simulation scenario of Montreal, Canada is used. To address this research question, we use two agent-based models: MATSIM and EPISIM. MATSIM simulates daily activities and mobility dynamics of the population. EPISIM simulates the spread of the virus in the population using contact networks computed by MATSIM. A synthetic population of Montreal is defined to replicate the main observed sociodemographic characteristics of Montrealers as well as their activity and mobility patterns. The definition of the synthetic population relies on various data sources: household travel survey, census, real estate, car ownership, and housing data. In the business-as-usual scenario, findings underline the significant role of home, work, and school activities in community transmission of COVID-19. Secondary activities, including leisure and shopping, also help spread the virus, but to a lesser degree in comparison with primary activities. The risk of infection in the workplace depends on the economic sector. Healthcare workers are, by far, the most exposed workers to the virus. Workplace infections mirror the gender-biased job market of Montreal. Most infections in the healthcare and educational services are among women. Most infections in the manufacturing, construction, transportation, and warehousing industries are among men. In the business-as-usual scenario where community transmission is high, primary and secondary school-aged children are found to be a major transmission vector of the virus. Finally, simulation results suggest that the risk of infection in the public transportation system is low.

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Daily Mobility Patterns: Reducing or Reproducing Inequalities and Segregation?

Social Inclusion ◽

10.17645/si.v9i2.3850 ◽

2021 ◽

Vol 9 (2) ◽

pp. 208-221

Author(s):

Lina Hedman ◽

Kati Kadarik ◽

Roger Andersson ◽

John Östh

Keyword(s):

Residential Segregation ◽

Mobile Phone Data ◽

Strong Impact ◽

Good Representation ◽

Mobility Patterns ◽

Negative Effects ◽

Daily Mobility ◽

Home Area ◽

Low Levels

Theory states that residential segregation may have a strong impact on people’s life opportunities. It is unclear, however, to what extent the residential environment is a good representation of overall exposure to different people and environments. Daily mobility could reduce the negative effects of segregation if people change environments and/or become more mixed. They could also enhance existing segregation patterns if daily mobility produces more segregated environments. This article uses mobile phone data to track daily mobility patterns with regard to residential segregation. We test the extent to which patterns differ between residents in immigrant-dense areas and those from areas with a greater proportion of natives. Results suggest, in line with previous research, that daily mobility patterns are strongly segregated. Phones originating from more immigrant-dense areas are more likely to (1) remain in the home area and (2) move towards other immigrant-dense areas. Hence, although mobility does mitigate segregation to some extent, most people are mainly exposed to people and neighbourhoods who live in similar segregated environments. These findings are especially interesting given the case study areas: two medium-sized Swedish regions with relatively low levels of segregation and inequality and short journey distances.

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Unravelling daily human mobility motifs

Journal of The Royal Society Interface ◽

10.1098/rsif.2013.0246 ◽

2013 ◽

Vol 10 (84) ◽

pp. 20130246 ◽

Cited By ~ 217

Author(s):

Christian M. Schneider ◽

Vitaly Belik ◽

Thomas Couronné ◽

Zbigniew Smoreda ◽

Marta C. González

Keyword(s):

Time Scales ◽

Human Mobility ◽

Underlying Mechanism ◽

Mobility Patterns ◽

Daily Mobility ◽

Simple Rules ◽

Long Time ◽

Daily Scale ◽

Temporal And Spatial ◽

Spatial Trajectories

Human mobility is differentiated by time scales. While the mechanism for long time scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analysing the temporal and spatial trajectories of thousands of persons as individual networks. Using the concept of motifs from network theory, we find only 17 unique networks are present in daily mobility and they follow simple rules. These networks, called here motifs, are sufficient to capture up to 90 per cent of the population in surveys and mobile phone datasets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, daily human mobility can be reproduced by an analytically tractable framework for Markov chains by modelling periods of high-frequency trips followed by periods of lower activity as the key ingredient.

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