Social media and urban mobility: Using twitter to calculate home-work travel matrices

Arjona, Joaquín Osorio; Palomares, Juan Carlos García

doi:10.1016/j.cities.2019.03.006

Cited by 56 publications

(34 citation statements)

References 35 publications

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“…Firstly, Twitter provides useful information about the whole activity-travel patterns of individuals throughout the city(Osorio-Arjona and García-Palomares 2019 ), which is a more accurate evaluation of individuals’ exposure to congestion than considering only commuting or labour trips (Kim and Kwan 2019 ). The work of Osorio-Arjona and García Palomares ( 2019 ) demonstrated that the level of precision offered by Twitter was adequate and efficient, permitting the analysis of flows between different zones of a specific city or study area. Second, Twitter data serve as a proxy of activity and provide sufficient disaggregation for application to different geographical areas, in our case, to Transport Zones.…”

Section: Research Design: Travel Times Data and Socio-economic Variabmentioning

confidence: 99%

Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods

et al. 2021

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This paper aims to evaluate the impacts of the economic context on traffic congestion and its consequential effects on private vehicle accessibility. We conduct a long-term analysis of spatiotemporal traffic congestion patterns in Madrid (Spain), comparing two urban realms: the 2008 economic crisis and the following post-crisis situation. We apply TomTom Speed Profiles data to assess daily variations in traffic congestion and their changes between both periods, and Twitter data to capture spatial patterns of the daily pulse of the city. Increased traffic, a by-product of economic recovery, resulted in higher congestion, particularly during peak hours. Nevertheless, these changes are spatially uneven. In the city core, an increase in congestion is relatively temporally homogeneous, while in the peripheral suburban zones, there has been only a marginal increase in travel times. On the other hand, in the urban outskirts, increased traffic congestion is more severe but visibly different between north and south. These differences have strong social connotations: over 40% of the population experienced a dramatic increase in travel times (more than 25%) during peak hours. Moreover, low-income groups are more likely to live in the more affected southern districts, suffering most the negative consequences of increased congestion.

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Section: Research Design: Travel Times Data and Socio-economic Variabmentioning

confidence: 99%

Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods

et al. 2021

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“…Since analyzing crowd mobility from social media is entirely dependent on data, the analysis and patterns cannot be used for other urban areas, which means that the specific patterns of a city are not suitable to fit into another city in order to extract features unless there will be similar patterns in the source and target data of the two different cities. Osorio et al [58] claimed that Twitter data provide useful insights, because it is easy to use online applications. Origin-destination (OD) analysis is an efficient tool for obtaining useful information from social media.…”

Section: Crowd Social Media Analysismentioning

confidence: 99%

“…Since traditional ways of obtaining data from surveys are static, time-consuming, and expensive, a new generation of applications using data from online sources was introduced to access larger sources of data free of charge and analyze the spatio-temporal aspects of those data [59]. Compared to previous works [60,61], Osorio et al [58] employed extra sources of data to both the origin and destination of the travel metrics in order to obtain better precision. Land Registry (cadaster) maps and population residential data from official sources (i.e., a census) from the city of Madrid were used to evaluate the origin travel metrics.…”

Section: Crowd Social Media Analysismentioning

confidence: 99%

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

Ebrahimpour

Wan

Cervantes

et al. 2019

IJGI

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Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed.

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“…The key required data are the regional travel demand based on the spatial element. One way is to combine landuse and population with new technology like mobile phone location signal data to estimate the demand [16]. However, as essentially a probability model, it cannot accurately confirm the quantitative relationship between the actual travel demand and land-use type.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Bus Accessibility Based on Hotspot Detection and Matter-Element Analysis

Chao

Quan

2020

IEEE Access

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The paper aims to assess bus accessibility considering the matching between supply and demand for effectively optimizing the level and fairness of urban public transport service, which realizes the quantification of regional balance and accurate positioning the area with the worst balance. We firstly employ hotspot detection procedure based on taxi trajectory data and kernel density analysis to identify the travel sensitive areas, the heat values of which are deployed to represent travel demand spatial-temporally and evaluate weight factors for bus accessibility modeling. Matter-element theory is selected to establish multi-parameter evaluation model of bus accessibility, which has potential for solving incompatibility problems by systematically considering all factors. The correlations between accessibility indexes and heat value are deployed to evaluate weight factors rather than analytic hierarchy process or expert assessment method to improve subjectivity and dynamic updating. An index called the Level Ratio of Accessibility to Demand (LRAD) is addressed finally to quantify the balance between accessibility supply and travel demand of travel sensitive areas, which identifies the regional imbalance to assist the public transport system assignment. Xi'an, a large city, is selected as a case study for methodology verification. Bus accessibility degree of the whole city as well as its travel sensitive areas is evaluated by the matter-element model. It is found the bus transport accessibility of Xi'an is moderate level(M 3). The LRAD results identify the priority-processing area with the poor balance between accessibility supply and travel demand, which is cross referenced with local urban plans for verification.

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Social media and urban mobility: Using twitter to calculate home-work travel matrices

Cited by 56 publications

References 35 publications

Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods

Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

Evaluation of Bus Accessibility Based on Hotspot Detection and Matter-Element Analysis

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