Combining taxi and social media data to explore urban mobility issues

Rodrigues, Diego O.; Boukerche, Azzedine; Silva, Thiago H.; Loureiro, Antônio A. F.; Villas, Leandro A.

doi:10.1016/j.comcom.2018.10.004

Cited by 19 publications

(12 citation statements)

References 31 publications

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“…However, data collection in the used case is specific compared to essential mobility elements. [Rodrigues et al 2018a] presented a framework called SMAFramework, to integrate heterogeneous urban data sources and find the correlation between them. The work aims to help city planners with urban mobility data and data-driven solutions to facilitate citizen trip experiences around the city.…”

Section: Related Workmentioning

confidence: 99%

Air Pollution Calculation for Location Based Social Networks Multimodal Routing Service

Brito¹,

Santos²,

Oliveira³

et al. 2022

Anais Do VI Workshop De Computação Urbana (CoUrb 2022)

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With the growth of the urban population, the urban mobility infrastructure suffers several types of problems, such as the more significant occurrence of traffic jams, which directly affects the quality of life of the population and the inhabitants who need to use different types of transport, also generating a more extraordinary occurrence of air pollution emitted by vehicles. This work addresses the need to integrate the generation of hybrid multimodal routes through the analysis of geographic data collected from location-based social networks, adding the calculation of greenhouse gas emissions by used vehicles. Further, performs a user experience analysis for the main identified flows of the analyzed urban environment, for users and urban planners analysis. The proposed algorithm proves its efficiency by offering less expensive, healthier trips for the population.

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Section: Related Workmentioning

confidence: 99%

Air Pollution Calculation for Location Based Social Networks Multimodal Routing Service

Brito¹,

Santos²,

Oliveira³

et al. 2022

Anais Do VI Workshop De Computação Urbana (CoUrb 2022)

View full text Add to dashboard Cite

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“…They also further revealed the usage and trajectories of road and pedestrian sidewalks at street level. Apart from these uniform data sources, scholars have also pointed out the necessity for mining mobility patterns from multi-source data, resulting in an increasing trend of using multi-source data with data fusion techniques [16]. For instance, Yang et al [17] described the integration and analysis of smart card data and points of interest from social media to understand urban mobility in Shenzhen, China.…”

Section: Data-driven Analysis On Vehicle Mobility Patternsmentioning

confidence: 99%

Exploring Resilient Observability in Traffic-Monitoring Sensor Networks: A Study of Spatial–Temporal Vehicle Patterns

Tang

Wan

Nochta

et al. 2020

IJGI

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Vehicle mobility generates dynamic and complex patterns that are associated with our day-to-day activities in cities. To reveal the spatial–temporal complexity of such patterns, digital techniques, such as traffic-monitoring sensors, provide promising data-driven tools for city managers and urban planners. Although a large number of studies have been dedicated to investigating the sensing power of the traffic-monitoring sensors, there is still a lack of exploration of the resilient performance of sensor networks when multiple sensor failures occur. In this paper, we reveal the dynamic patterns of vehicle mobility in Cambridge, UK, and subsequently, explore the resilience of the sensor networks. The observability is adopted as the overall performance indicator to depict the maximum number of vehicles captured by the deployed sensors in the study area. By aggregating the sensor networks according to weekday and weekend and simulating random sensor failures with different recovery strategies, we found that (1) the day-to-day vehicle mobility pattern in this case study is highly dynamic and decomposed journey durations follow a power-law distribution on the tail section; (2) such temporal variation significantly affects the observability of the sensor network, causing its overall resilience to vary with different recovery strategies. The simulation results further suggest that a corresponding prioritization for recovering the sensors from massive failures is required, rather than a static sequence determined by the first-fail–first-repair principle. For stakeholders and decision-makers, this study provides insightful implications for understanding city-scale vehicle mobility and the resilience of traffic-monitoring sensor networks.

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“…O trabalho de Rodrigues et al [Rodrigues et al 2018a] propõe uma metodologia para coletar e integrar dados de mobilidade urbana de fontes heterogêneas com o intuito de entender melhor a dinâmica das cidades. Os autores agregaram diferentes dados coletados das mídias sociais e do sistema de táxi da cidade de Nova York numa abordagem que utiliza a estrutura de um MAG para construir e avaliar uma ferramenta que permite entender a correlação espaço-temporal de dados urbanos de diferentes fontes.…”

Section: Trabalhos Relacionadosunclassified

Análise Espaço-Temporal da Mobilidade Urbana de Diferentes Grupos Socioeconômicos: Uma Abordagem Multicamadas

Gubert¹,

Santin²,

Munaretto³

et al. 2020

Anais Do Workshop De Computação Urbana (CoUrb 2020)

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Este trabalho analisa a mobilidade urbana de Curitiba por meio do uso do transporte público e com a população dividida em estratos socioeconômicos. Para estudar o grafo de mobilidade estabelecido e seus padrões espaço-temporais foi aplicada uma abordagem multicamadas denominada Multi-Aspect Graph (MAG) com quatro aspectos. Os resultados detalham padrões que não podem ser observados em um grafo tradicional, tais como os deslocamentos mais frequentes bem como os pontos mais centrais da cidade levando em consideração todos os aspectos modelados. A modelagem utilizada possibilita um entendimento estratégico do uso do sistema público de transporte benéfico para diferentes aplicações, tais como atuações em situações de emergência, como surtos de doenças de rápido contágio.

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Combining taxi and social media data to explore urban mobility issues

Cited by 19 publications

References 31 publications

Air Pollution Calculation for Location Based Social Networks Multimodal Routing Service

Air Pollution Calculation for Location Based Social Networks Multimodal Routing Service

Exploring Resilient Observability in Traffic-Monitoring Sensor Networks: A Study of Spatial–Temporal Vehicle Patterns

Análise Espaço-Temporal da Mobilidade Urbana de Diferentes Grupos Socioeconômicos: Uma Abordagem Multicamadas

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