Urban commuting dynamics in response to public transit upgrades: A big data approach

Gao, Qi-Li; Li, Qingquan; Zhou, Yan; Yue, Yang; Liu, Zhenzhen; Li, Shuiquan; Sui, Daniel Z.

doi:10.1371/journal.pone.0223650

Cited by 15 publications

(7 citation statements)

References 35 publications

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“…Eight wind directions with 45 • between them (N, NE, E, SE, S, SW, W, and NW) are considered. Based on the observed wind speed at the top of the local boundary layer (∼ 500 m) (Chen et al, 2018;He et al, 2018), we choose 10, 6.5, and 3 m s −1 to represent high, median, and weak wind conditions, respectively. This results in a total of 48 scenarios, which is limited by our computational capacity.…”

Section: Palmmentioning

confidence: 99%

Large-eddy simulation of traffic-related air pollution at a very high resolution in a mega-city: evaluation against mobile sensors and insights for influencing factors

Chen

et al. 2021

Atmos. Chem. Phys.

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Abstract. Urban air pollution has tremendous spatial variability at scales ranging from kilometers to meters due to unevenly distributed emission sources, complex flow patterns, and photochemical reactions. However, high-resolution air quality information is not available through traditional approaches such as ground-based measurements and regional air quality models (with typical resolution > 1 km). Here we develop a 10 m resolution air quality model for traffic-related CO pollution based on the Parallelized Large-Eddy Simulation Model (PALM). The model performance is evaluated with measurements obtained from sensors deployed on a taxi platform, which collects data with a comparable spatial resolution to our model. The very high resolution of the model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network. The model results (0.92 ± 0.40 mg m−3) agree well with the measurements (0.90 ± 0.58 mg m−3, n=114 502). The model has similar spatial patterns to those of the measurements, and the r2 value of a linear regression between model and measurement data is 0.50 ± 0.07 during non-rush hours with middle and low wind speeds. A non-linear relationship is found between average modeled concentrations and wind speed with higher concentrations under calm wind speeds. The modeled concentrations are also 20 %–30 % higher in streets that align with the wind direction within ∼ 20∘. We find that streets with higher buildings downwind have lower modeled concentrations at the pedestrian level, and similar effects are found for the variability in building heights (including gaps between buildings). The modeled concentrations also decay fast in the first ∼ 50 m from the nearest highway and arterial road but change slower further away. This study demonstrates the potential of large-eddy simulation in urban air quality modeling, which is a vigorous part of the smart city system and could inform urban planning and air quality management.

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Section: Palmmentioning

confidence: 99%

Large-eddy simulation of traffic-related air pollution at a very high resolution in a mega-city: evaluation against mobile sensors and insights for influencing factors

Chen

et al. 2021

Atmos. Chem. Phys.

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“…The model results give hints for source contribution and hot-spot for urban air pollution, which could inform urban planning, air quality management, and risk mitigation. Combined with personal GPS data, the revealed very high-resolution of air quality map can inform epidemiological studies, health risk analysis, and alter personal behavior 5,43 .…”

Section: Discussionmentioning

confidence: 99%

“…Patterns of traffic-related air pollution in the urban environment has substantial temporal and spatial variability due to unevenly distributed emission sources, complex flow pattern, and physicochemical transformations 4 . Compounded with the complex and dynamic commuting behavior and crowd dynamics of urban residents, high-resolution air quality information is thus needed for smart-city designers and air pollution mitigation in a "big-data" era 5 . However, such information is generally not available as accurate ground-based monitoring of air quality at a high spatial resolution is too expensive due to the large number of required instruments even with relatively low-cost sensors 6 .…”

Section: Introductionmentioning

confidence: 99%

Large-eddy simulation of traffic-related air pollution at a very high-resolution in a mega-city: Evaluation against mobile sensors and insights for influencing factors

Ye¹,

Chen²,

He³

et al. 2020

Preprint

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Abstract. Urban air pollution has tremendous spatial variability at scales ranged from kilometer to meters due to unevenly distributed emission sources, complex flow patterns, and photochemical reactions. However, high-resolution air quality information is not available through traditional approaches such as ground-based measurements and regional air quality models (with typical resolution >1 km). Here we develop a ten-meter resolution air quality model for traffic-related CO pollution based on the parallelized large-eddy simulation model (PALM). The model performance is evaluated with measurements obtained from sensors deployed on a taxi platform, which collects data with a comparable spatial resolution to our model. The very high resolution of the model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network. The model results (0.92 ± 0.40 mg/m3) agree well with the measurements (0.90 ± 0.58 mg/m3, n = 114,502). The model has similar spatial patterns with that of the measurements, and the r2 value of a linear regression between model and measurement data is 0.50 ± 0.07 during non-rush hours with middle and low wind speeds. A non-linear relationship is found between average modeled concentrations and wind speed with higher concentrations under calm wind speeds. The modeled concentrations are also 20–30 % higher in streets that align with the wind direction within ~20°. We find that streets with higher buildings in the downwind have lower modeled concentrations at the pedestrian level, and similar effects are found for the variability in building heights (including gaps between buildings). The modeled concentrations also decay fast in the first ~50 m from the nearest highway and arterial road but change slower further away. This study demonstrates the potential of large eddy simulation in urban air quality modeling, which is a vigorous part of the smart city system and could inform urban planning and air quality management.

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“…The effects of COVID-19 and the effects of this metro network extension can, at least locally, not be clearly separated from each other. However, previous research has shown that the opening of new metro lines tends to increase metro ridership numbers (Fu and Gu 2018 ; Gao et al 2019 ), which means that it can be assumed that if the new Circular Line [Yellow Line] had not opened at the time, passenger volumes would have decreased even more strongly.…”

Section: Methodsmentioning

confidence: 99%

Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

Mützel

Scheiner

2021

Public Transp

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Modern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.

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Urban commuting dynamics in response to public transit upgrades: A big data approach

Cited by 15 publications

References 35 publications

Large-eddy simulation of traffic-related air pollution at a very high resolution in a mega-city: evaluation against mobile sensors and insights for influencing factors

Large-eddy simulation of traffic-related air pollution at a very high resolution in a mega-city: evaluation against mobile sensors and insights for influencing factors

Large-eddy simulation of traffic-related air pollution at a very high-resolution in a mega-city: Evaluation against mobile sensors and insights for influencing factors

Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

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