Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population

He, Brian Yueshuai; Zhou, Jiazhen; Ma, Ziyi; Chow, Joseph Y.J.; Özbay, Kaan

doi:10.1016/j.tra.2020.10.006

Cited by 18 publications

(28 citation statements)

References 34 publications

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“…The synthetic population approaches have shown to be effective in epidemic simulation applications and in deriving synthetic population from the country-wide census for privacy protection of the individuals (Wickramasinghe et al, 2020). This approach also has vast policy implications, because by using synthetic populations, policymakers can evaluate the city-scale built environment policies (He et al, 2020), analyze the risk for cardiovascular disease (Krauland et al, 2020), and evaluate electricity consumption in a neighborhood (van Dam et al, 2017).…”

Section: Johnsonmentioning

confidence: 99%

Exploring city digital twins as policy tools: A task-based approach to generating synthetic data on urban mobility

Papyshev

Yarime

2021

Data & Policy

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This article discusses the technology of city digital twins (CDTs) and its potential applications in the policymaking context. The article analyzes the history of the development of the concept of digital twins and how it is now being adopted on a city-scale. One of the most advanced projects in the field—Virtual Singapore—is discussed in detail to determine the scope of its potential domains of application and highlight challenges associated with it. Concerns related to data privacy, availability, and its applicability for predictive simulations are analyzed, and potential usage of synthetic data is proposed as a way to address these challenges. The authors argue that despite the abundance of urban data, the historical data are not always applicable for predictions about the events for which there does not exist any data, as well as discuss the potential privacy challenges of the usage of micro-level individual mobility data in CDTs. A task-based approach to urban mobility data generation is proposed in the last section of the article. This approach suggests that city authorities can establish services responsible for asking people to conduct certain activities in an urban environment in order to create data for possible policy interventions for which there does not exist useful historical data. This approach can help in addressing the challenges associated with the availability of data without raising privacy concerns, as the data generated through this approach will not represent any real individual in society.

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Section: Johnsonmentioning

confidence: 99%

Exploring city digital twins as policy tools: A task-based approach to generating synthetic data on urban mobility

Papyshev

Yarime

2021

Data & Policy

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“…Agent-based simulation models are effective in capturing the interactions between agents and the transportation system to output the equilibrated simulation results at the agent level. A synthetic population of NYC was created that incorporates the demographic information and travel patterns of 8.24 million people for the base year of 2016 ( He et al, 2020 ). The synthetic population includes personal attributes like age, gender, school enrollment status, work status, and work industry, and household attributes like income group, household size, and number of cars owned.…”

Section: Methodsmentioning

confidence: 99%

“…The built synthetic population is validated and fit well with the real data: an average 4% difference in the distribution of the employment industry proportions compared to 2016 Longitudinal Employer-Household Dynamics (LEHD), and 3% to 9% difference compared to different attributes in the NYMTC 2040 SED Forecasts. A summary is provided with details of the synthetic population available in He et al (2020) .…”

Section: Methodsmentioning

confidence: 99%

“…3 (a)) was developed for NYC at normal conditions (pre-COVID) based on the synthetic population along with transit schedules from General Transit Feed Specification (GTFS) data ( Chow et al, 2020 , He et al, 2021 ). The agent-based simulation tool is used for this study for four reasons: Work-from-home rates are modeled differently by industry of employment, which can be captured with a synthetic population ( He et al, 2020 ). MATSim-NYC, the underlying model, is the only citywide travel demand modeling tool that includes emergent mobility modes like Citi Bike and Uber/Lyft ( He et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…Agent-based simulation models are effective in capturing the interactions between agents and transportation systems to output the equilibrated simulation results at the agent level. A synthetic population was developed for the 8 M+ population in NYC ( Chow et al, 2020 , He et al, 2020 ) that includes employment in North American Industry Classification System (NAICS) industries. Based on the synthetic population along with calibrated transit schedules, a multi-agent simulation model, MATSim-NYC ( Chow et al, 2020 , He et al, 2021 ) was developed for NYC.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit

Wang

Tayarani

et al. 2021

Transportation Research Part A: Policy and Practice

Self Cite

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COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially.

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Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population

Cited by 18 publications

References 34 publications

Exploring city digital twins as policy tools: A task-based approach to generating synthetic data on urban mobility

Exploring city digital twins as policy tools: A task-based approach to generating synthetic data on urban mobility

Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit

A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City

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