Can TODness improve (expected) performances of TODs? An exploration facilitated by non-traditional data

Zhou, Jiangping; Yang, Ye; Gu, Peiqin; Yin, Ling; Zhang, Fan; Zhang, Fang; Li, Dong

doi:10.1016/j.trd.2019.07.008

Cited by 41 publications

(31 citation statements)

References 25 publications

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“…Recent studies have extended "node-place" model to "node-tie-place" model (51) and "node-functionalityplace" model ( 52) by adding functionality indicators (51,52). Moreover, several previous studies have also emphasized pedestrian-oriented perspective as an important dimension in transit station areas (11,39,(53)(54)(55)(56)(57)(58). Nevertheless, pedestrianfriendly dimension quantitative studies are rarely conducted to comprehensively examine the TOD performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this context, Zhou analyzed a case of 167 metro stations in Shenzhen, combining traditional and non-traditional data (e.g., social media data, digital maps, PoIs, interviews, and surveys) to propose new indicators of TOD performance and its relationship to spatial and behavior characteristics ( 39 ). Nonetheless, this previous study mostly focused on transportation behavior on a citywide scale, paying less attention to pedestrian-oriented urban design as well as transit and land use integration.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Evaluation of TOD Performance Based on Multi-Source Data: A Case Study of Shanghai

Qiang

Zhang

Huang

2022

Front. Public Health

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Transit-oriented development (TOD) has been widely adopted as a primary urban planning strategy to better integrate transit and land use; further, the pedestrian-oriented perspective has been receiving increasing attention. However, most studies so far have only focused on few features and fail to capture comprehensive perceptions in the transportation (T), pedestrian-oriented accessibility (O), and urban development (D) dimensions. New emerging urban datasets provide a more refined and systematic approach to quantify the characteristics of metro station areas. This study offers a more efficient and convenient process and comprehensive approach to measure TOD performance. With a combination of traditional data collected by an official department, high-resolution open data, and innovative technology, large-scale analyses of 347 metro stations in Shanghai were conducted. Fifteen indicators for T, O, and D were chosen to categorize TOD performance into five clusters. Radar charts, boxplots, and colored maps were used to display numerous quantitative factors for each type. Combining the results with the Shanghai Comprehensive Plan (2017–2035) showed that the majority of Cluster 4 is located at the center of the Five New Towns. The correlation analysis between ridership and TOD performance showed that the transportation dimension indicator has a strong correlation with daily ridership, followed by the O and D indicators. Moreover, ridership per capita was found to be affected by resident density, employment density, O value, and D value, whereas no significant correlation was found between ridership per capita and T value. Population plays a pivotal role in metro passenger traffic, indicating ridership per capita had a high, strong correlation with resident density, with R = 0.658 for weekdays and R = 0.654 for weekends. This study reinterpreted the node-place method and 5Ds framework, resulting in a renewal method with new datasets and analysis tools. It contributes to providing pedestrian-oriented TOD planning methodology for urban planners and policymakers by combining T, O, and D dimensions and visualizing the results with current urban planning.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of TOD Performance Based on Multi-Source Data: A Case Study of Shanghai

Qiang

Zhang

Huang

2022

Front. Public Health

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“…Several studies have explored the effects of TOD on the travel behavior of populations in their study areas. For example, Zhou et al [13] found relationships between the volume of transit ridership and the TOD characteristics of density (population and employment), diversity (mix of land use type), design (degree of facility availability), and the number of the points of interest (POIs) in the study area. Kamruzzaman et al [14] differentiated the commuting behavior of people living in different types of neighborhoods based on their TOD characteristics, and found that TOD actively enhanced the use of public transport and reduced the number of private vehicles in urban environments.…”

Section: Transit-oriented Development (Tod) and Its Indicatorsmentioning

confidence: 99%

Evaluation of Relationships Between Ridership Demand and Transit-Oriented Development (TOD) Indicators Focused on Land Use Density, Diversity, and Accessibility: A Case Study of Existing Metro Stations in Bangkok

Nyunt

Wongchavalidkul

2020

Urban Rail Transit

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The purpose of this work is to support the transitoriented development (TOD) in Thailand. The main research objective is to explore the relationship between ridership demand and TOD indicators, which is expected to be the first question that a developer in Thailand must be able to answer in order to direct their development plan in the right direction. Using existing Bangkok metro stations as a case study, 22 variables categorized into three groups (density, diversity, and design) were collected for an 800-m buffer area around the stations. Results of the correlation analysis between transit ridership and other variables show a significant relation with the volume of transit ridership. Bus services have a stronger influence on transit ridership than railway stations and ferries (pier). Also, the interchange stations and park-and-ride buildings are found to be the main variables that correlate directly with the transit ridership numbers. Results from the principal component analysis are used to evaluate factors of TOD characteristics for the existing Bangkok metro stations, categorized into seven factor groups.

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“…ridership and pedestrian flow/density, can well reflect the performance. One of the most desirable outcomes of TOD is to encourage transit usage and walking and discourage car driving, particularly solo driving (Loo and Du Verle, 2017;Loo et al, 2010;Nasri and Zhang, 2014;Noland and DiPetrillo, 2015;Sung and Oh, 2011;Zhou et al, 2018Zhou et al, , 2019. These can potentially promote social interactions and economic activities.…”

Section: Msas' Vibrancy Proxiesmentioning

confidence: 99%

“…Out of various built-environment features of an MSA (e.g. density, diversity, and destination, namely 3Ds proposed by Cervero and Kockelman (1997)), which are costly to produce or cultivate, people often expect certain desirable outcomes/performances such as stable or even increased transit ridership and pedestrian activities (Loo and Du Verle, 2017;Loo et al, 2010;Nasri and Zhang, 2014;Noland and DiPetrillo, 2015;Sung and Oh, 2011;Zhou et al, 2018Zhou et al, , 2019, accessibility to jobs and inhabitants (Lyu et al, 2019;Papa and Bertolini, 2015), improved air quality (Gu et al, 2019), and property price premium (Kay et al, 2014;Xu et al, 2016;Yang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Predicting vibrancy of metro station areas considering spatial relationships through graph convolutional neural networks: The case of Shenzhen, China

Xiao¹,

Zhou

et al. 2020

Environment and Planning B: Urban Analytics and City Science

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Vibrancy is one of the most desirable outcomes of transit-oriented development (TOD). The vibrancy of a metro station area (MSA) depends partially on the MSA’s built-environment features. Predicting an MSA’s vibrancy with its built-environment features is of great interest to decision makers as these features are often modifiable by public interventions. However, little has been done on MSAs’ vibrancy in existing studies. On the one hand, seldom has the vibrancy of MSAs been explicitly explored, and measuring the vibrancy is essential. On the other hand, because MSAs are interconnected, one MSA’s vibrancy depends on the MSA’s features and those of relevant MSAs. Hence, selecting a suitable metric that quantifies spatial relationships between MSAs can better predict MSAs’ vibrancy. In this study, we identify four single-dimensional vibrancy proxies and fuse them into an integrated index. Moreover, we design a two-layer graph convolutional neural network model that accounts for both the built-environment features of MSAs and spatial relationships between MSAs. We employ the model in an empirical study in Shenzhen, China, and illustrate (1) how different metrics of spatial relationships influence the prediction of MSAs’ vibrancy; (2) how the predictability varies across single-dimensional and integrated proxies of MSAs’ vibrancy; and (3) how the findings of this study can be used to enlighten decision makers. This study enriches our understandings of spatial relationships between MSAs. Moreover, it can help decision makers with targeted policies for developing MSAs towards TOD.

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Can TODness improve (expected) performances of TODs? An exploration facilitated by non-traditional data

Cited by 41 publications

References 25 publications

Quantitative Evaluation of TOD Performance Based on Multi-Source Data: A Case Study of Shanghai

Quantitative Evaluation of TOD Performance Based on Multi-Source Data: A Case Study of Shanghai

Evaluation of Relationships Between Ridership Demand and Transit-Oriented Development (TOD) Indicators Focused on Land Use Density, Diversity, and Accessibility: A Case Study of Existing Metro Stations in Bangkok

Predicting vibrancy of metro station areas considering spatial relationships through graph convolutional neural networks: The case of Shenzhen, China

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