Utility of mixed-use development by reducing aggregated travel time for multiple non-work activities: A case of Seoul, Korea

Choi, Dae-Sik; Kang, Myounggu; Yoon, Jeong-Joong

doi:10.1016/j.cities.2020.103007

Cited by 5 publications

(6 citation statements)

References 49 publications

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“…Traditional neighborhoods are dense, mixed-use, and feature roadway networks organized in grids [20]. In addition, Choi et al [9] used multiple regression to analyze regional data to address this issue. Independent variables included density and MLU.…”

Section: Literature Reviewmentioning

confidence: 99%

“…DNN models are becoming more and more popular recently because of their better capacity to handle nonlinear data. However, recent studies indicate that ANN-based methods have a significant potential for modeling MLU changes [9]. The impact of transport supply on MLU changes was examined using the ANN methods.…”

Section: Multilayer Perceptron (Mlp) Modelmentioning

confidence: 99%

“…The impact of transport supply on MLU changes was examined using the ANN methods. In the corresponding layer, the unidirectional interconnections between neurons are represented by lines, while neurons are represented by circles [9]. Figure 12 depicts the MLP structure.…”

Section: Multilayer Perceptron (Mlp) Modelmentioning

confidence: 99%

“…A neural network (ANN) is a computational model that focuses on the structure and functions of ANN. DNN is one of several machine learning methods based on ANN [9]. The use of multiple layers in the network gives rise to the term 'deep' in DNN.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Impact of Transportation Infrastructure-Based Accessibility on the Development of Mixed Land Use Using Deep Neural Networks: Evidence from Jiang’an District, City of Wuhan, China

Almansoub,

Zhong,

Safdar

et al. 2023

Sustainability

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Mixed land use (MLU) plays a crucial role in fostering a sustainable urban development, vibrant communities, and efficient land utilization, providing a viable solution for smart growth, inclusive public transit, and urban sustainability. This study employs deep neural network (DNN) models: multilayer perceptron (MLP), and long short-term memory (LSTM), to analyze the effect of the transportation infrastructure-based accessibility on the prevalence of MLU patterns, based on the following data: infrastructure-based accessibility measures represented by the logsum (or transport supply), MLU patterns at the parcel level, and floor space prices by space type, for the years 2012 and 2015. Furthermore, the proposed methods are applied to the Jiang’an District of the city of Wuhan, China, at the parcel level as the case study. The study results reveal that MLU is predominantly accessible in areas close to the city center, characterized by a high density, and is relatively scarce on the city outskirts. Notably, parcels exhibiting mixed residential–commercial and residential land-use patterns underwent significant changes between 2012 and 2015, particularly in regions with robust accessibility via non-motorized modes and public transit, specifically in the central and southern parts of Jiang’an District. This transition is evident under scenario 3 (walk, bike, bus, subway) and scenario 6 (walk, bus, car) considered in this study. Furthermore, the study observed a substantial expansion in mixed commercial–residential and commercial districts, significantly near the high-transit accessibility area of subway line 1, as demonstrated in scenario 7 (bike, subway, taxi). The results from the MLP models show a mean relative error (MRE) of 4.7–14.08% for the MLU, and the LSTM models show an MRE of 3.74–10.38% for the MLU. More importantly, both the training and forecasting errors of the above models are lower, in most cases, than those reported in the literature. Moreover, these results indicate that the transportation supply or the infrastructure-based accessibility (represented by logsum) significantly influences MLU patterns.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Multilayer Perceptron (Mlp) Modelmentioning

confidence: 99%

Section: Multilayer Perceptron (Mlp) Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Impact of Transportation Infrastructure-Based Accessibility on the Development of Mixed Land Use Using Deep Neural Networks: Evidence from Jiang’an District, City of Wuhan, China

Almansoub,

Zhong,

Safdar

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…With the general improvement of social economy and urban expansion, the daily travel distance will increase, which leads to car travel. From the perspective of travel choice, high-density urban centers greatly improve people’s participation in non-work activities and reduce car travel ( 29 ). Van Dijk and Krygsman introduced point-of-interest (POI) data as an indicator to represent the availability of opportunities within an individual’s activity space, and their findings show a correlation between the availability of more opportunities within the activity space and the willingness to consider more sustainable transportation modes ( 30 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Deconstruction of Commuters’ Activity Space and Social Exclusion under Anchor Point Theory: A Case Study of Kunming, China

Zheng

Liu

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Activity space directly reflects residents’ utilization of urban space and their quality of life. However, the existing activity space measurement methods do not consider the differences in individual participation in different activities. To solve this problem, this study proposes a new method based on anchor theory to describe the activity space of urban commuters. First, the activity space is defined as the collection of space-time resources available in personal daily travel activities. According to the activity type, the activity space is divided into restricted space and free space. Second, using the survey data collected from Kunming city, China, three activity space patterns of commuters are identified, namely the restrict-oriented, free-oriented, and balance-oriented. Third, the influencing factors of different activity space patterns of commuters, including socio-demographics and built environment, are investigated using a multinomial logistic model. The results show that the method of deconstructing activity space can better describe the behavior patterns of urban residents. Besides, this study identifies the commuters who have small activity space but are not socially excluded and those who have large activity space but are at risk of social exclusion. This finding complements the previous identification of social exclusion. The research results can help policymakers develop suitable policies to avoid the mismatch between urban facilities and residents’ needs.

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Flow trace: A novel representation of intra-urban movement dynamics

Xing

Yuan²,

Huang³

et al. 2022

Computers, Environment and Urban Systems

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Utility of mixed-use development by reducing aggregated travel time for multiple non-work activities: A case of Seoul, Korea

Cited by 5 publications

References 49 publications

Modeling Impact of Transportation Infrastructure-Based Accessibility on the Development of Mixed Land Use Using Deep Neural Networks: Evidence from Jiang’an District, City of Wuhan, China

Modeling Impact of Transportation Infrastructure-Based Accessibility on the Development of Mixed Land Use Using Deep Neural Networks: Evidence from Jiang’an District, City of Wuhan, China

Deconstruction of Commuters’ Activity Space and Social Exclusion under Anchor Point Theory: A Case Study of Kunming, China

Flow trace: A novel representation of intra-urban movement dynamics

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