Data Integration to Create Large-Scale Spatially Detailed Synthetic Populations

Zhu, Yi; Ferreira, Joseph

doi:10.1007/978-3-319-18368-8_7

Cited by 19 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study used the floor-area ratio (FAR) to indicate land use densities. Our FAR values were derived from a building database synthesizing a variety of datasets using the methodology proposed by Zhu and Ferreira (2015). We measured the land use density in four categories: public residential (i.e., HDB, named after the Housing and Development Board), private residential, commercial, and industrial areas.…”

Section: The Impact Of the Built Environmentmentioning

confidence: 99%

Understanding the usage of dockless bike sharing in Singapore

Shen

Zhang

Zhao

2018

International Journal of Sustainable Transportation

346

192

View full text Add to dashboard Cite

A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.

show abstract

Section: The Impact Of the Built Environmentmentioning

confidence: 99%

Understanding the usage of dockless bike sharing in Singapore

Shen

Zhang

Zhao

2018

International Journal of Sustainable Transportation

346

192

View full text Add to dashboard Cite

show abstract

“…Besides trip data, SimMobility includes a database of buildings in Singapore 56,57 . We estimate current parking supply from this database and publicly available data from official sources: the minimum parking requirements published by the Land Transport Authority (LTA) 58 , the list of parking spaces managed by the Urban Redevelopment Authority (URA) 59 , and the aggregate number of parking spaces managed by the Housing Development Board (HDB) 60 .…”

Section: Methodsmentioning

confidence: 99%

Addressing the “minimum parking” problem for on-demand mobility

Kondor

Santi

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Parking infrastructure is pervasive and occupies large swaths of land in cities. However, on-demand (OD) mobility has started reducing parking needs in urban areas around the world. This trend is expected to grow significantly with the advent of autonomous driving, which might render on-demand mobility predominant. Recent studies have started looking at expected parking reductions with on-demand mobility, but a systematic framework is still lacking. In this paper, we apply a data-driven methodology based on shareability networks to address what we call the “minimum parking” problem: what is the minimum parking infrastructure needed in a city for given on-demand mobility needs? While solving the problem, we also identify a critical tradeoff between two public policy goals: less parking means increased vehicle travel from deadheading between trips. By applying our methodology to the city of Singapore we discover that parking infrastructure reduction of up to 86% is possible, but at the expense of a 24% increase in traffic measured as vehicle kilometers travelled (VKT). However, a more modest 57% reduction in parking is achievable with only a 1.3% increase in VKT. We find that the tradeoff between parking and traffic obeys an inverse exponential law which is invariant with the size of the vehicle fleet. Finally, we analyze parking requirements due to passenger pick-ups and show that increasing convenience produces a substantial increase in parking for passenger pickup/dropoff. The above findings can inform policy-makers, mobility operators, and society at large on the tradeoffs required in the transition towards pervasive on-demand mobility.

show abstract

“…Although a certain level of interpretability could be maintained, ontology based 107 approaches are constrained to specific systems like buildings (Balaji et al 2016) example, recent work has demonstrated a strong link between transportation demand and energy 111 consumption in buildings (Karan et al, 2015). Moreover, a limitation of ontology based data integration 112 methods is that they must be fully expressive when defining their metadata schema and thus cannot adapt 113 easily to new urban data streams and types (Sinnott et al 2012;Zhu and Ferreira Jr 2015). While domain 114 centric methods are effective for data integration within individual system domains, they are limited in their 115 ability to both account for interactions across systems (extensibility) and easily adapt to growing and quickly 116 changing urban data streams (scalability).…”

Section: Domain Centric Integration Methods 95mentioning

confidence: 99%

Urban Data Integration Using Proximity Relationship Learning for Design, Management, and Operations of Sustainable Urban Systems

Gupta

Yang

Jain

2019

J. Comput. Civ. Eng.

View full text Add to dashboard Cite

10The world is rapidly urbanizing with 66% of the world's population expected to reside in cities by 2050. 11 This massive influx of new urban citizens is putting enormous pressure on city systems and bringing forth 12 challenges at the intersection of urban infrastructure, governance and the environment. As a result, 13 researchers and practitioners have turned to new advanced sensing and data analytics developed under the 14 burgeoning "smart city" movement to improve the design, management and operations of urban systems. 15However, data emerging from urban systems has been challenging to integrate, organize and analyze due 16 to their natural spatial, temporal and typological heterogeneity. In this paper, an Urban Data Integration 17 (UDI) framework is introduced that is capable of integrating heterogeneous urban data. The proposed UDI 18 framework is extensible to multiple types of urban systems, scalable to the growing amount and quickly 19 changing urban data streams and interpretable enough to help inform municipal decision-making. The UDI 20 framework utilizes a series of novel proximity relationship learning algorithms to automatically reconstruct 21 urban data in a graph database. The merits, applicability and efficacy of the proposed framework is 22 demonstrated by validating and testing it on data from a mid-size city in the United States and by 23 benchmarking its interpretability and computational performance for a typical urban analytics scenario 24 against current practice (i.e., a relational database). Results indicate that the UDI framework provides easier 25 and more computationally efficient exploration and querying of urban data and in turn can enable new 26 computational approaches to urban system design, management and operations. 27Keyword: data integration, graph database, proximity learning, smart city, urban data 28The heterogeneity of urban data and the nascent field of urban analytics both point towards the need for 53 integrating urban data early and often. For example, if new data becomes available on the air quality along 54 a main traffic corridor, a municipal official could want to map this new information to existing data sources 55 on related systems (e.g., traffic, roads), re-run analytical queries to understand mutual influences and then 56 take appropriate actions. Maintaining a high-level of interpretability is vital during the integration process 57 as the goal is to support urban design and operational decisions by municipal officials, policy-makers and 58 engineers. As a result, a useful urban data integration framework must be extensible to multiple urban 59 systems (and not system specific), scalable to the growing amounts of quickly changing urban data streams 60 and interpretable such that it can inform decision-making. 61In this paper, an Urban Data Integration (UDI) framework is introduced that integrates heterogeneous urban 62 data while maintaining extensibility, scalability and interpretability. The proposed UDI framework utilizes 63 a series of novel proxi...

show abstract

Data Integration to Create Large-Scale Spatially Detailed Synthetic Populations

Cited by 19 publications

References 14 publications

Understanding the usage of dockless bike sharing in Singapore

Understanding the usage of dockless bike sharing in Singapore

Addressing the “minimum parking” problem for on-demand mobility

Urban Data Integration Using Proximity Relationship Learning for Design, Management, and Operations of Sustainable Urban Systems

Contact Info

Product

Resources

About