Impacts of Urban Rail Transit on City Growth: Evidence from China

Tao, Zhibin; Feng, Xia‐Ting; Li, Kemeng; Shi, Ruolin

doi:10.1007/s40864-022-00169-8

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Although other agencies (including WMATA) operate fixed-route buses in Alexandria, DASH is the city's major bus transit provider (31). Figure 3 shows that StreetLight data correctly captured the bimodal distribution of DASH ridership and identified the 5-p.m. peak, with an hourly ridership ratio close to DASH's ground truth value.…”

Section: Streetlight Analysis Of Bus Activity Levelsmentioning

confidence: 95%

Examining Transit Activity Data from StreetLight Using Ridership Data from Virginia Transit Agencies

Raida,

Ohlms,

Chen

2023

Transportation Research Record: Journal of the Transportation R

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Researchers and planners require ridership data to study factors that influence people’s choice to use transit. However, the data can be challenging to obtain directly from transit agencies. Crowdsourced big data platforms such as StreetLight promise easily accessible ridership-related data in standard formats. It is important to assess the reliability of these data, particularly for transit agencies serving small- to medium-sized cities, which are less likely than agencies in large cities to have ridership data in standard formats. In this study, hourly ridership data from 2019 were collected from four bus transit agencies and one rail agency in Virginia and compared with StreetLight data. Comparisons for rail data were made on a station-to-station basis. Bus data comparisons were made at the city-limit level and at an aggregated-route level for each agency. In sum, StreetLight could not provide 2019 bus activity data for more than half of the localities in Virginia. Comparisons between transit agency and StreetLight data showed smaller root mean square errors when longer periods were analyzed (e.g., 4 versus 2 months). Although order of magnitude of ridership may indicate whether StreetLight can provide bus activity data, the former was not found to be correlated with the accuracy of the latter. Using data from StreetLight’s current algorithm might not be appropriate without verification against agency data, especially for agencies in small- to medium-sized cities.

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Section: Streetlight Analysis Of Bus Activity Levelsmentioning

confidence: 95%

Examining Transit Activity Data from StreetLight Using Ridership Data from Virginia Transit Agencies

Raida,

Ohlms,

Chen

2023

Transportation Research Record: Journal of the Transportation R

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“…Tese refnements contribute to a more robust analysis of the spillover efects of HSR on urban economies. However, due to the diferent stages of HSR development and variations in urban economic foundations, the impact of HSR on economy varies across cities [34]. Many studies indicate that the economic efects of transportation infrastructure are positive for highly developed cities, as the accessibility and connectivity of HSR can promote economic growth in these cities.…”

Section: Huang and Xumentioning

confidence: 99%

“…where GDP it denotes the economic development level of city i in year t. X it is a vector composed of control variables illustrated in Section 2. According to Tao et al [20], when determining the spillover efects of transportation modes in city level in China, μ i ought to be regarded as fxed efect instead of random efect. Terefore, μ i represents individual fxed efect that difers across cities, and λ t denotes time fxed efect which measures the change of years in this research.…”

Section: Modeling Studymentioning

confidence: 99%

“…Econometric model plays an irreplaceable role in such studies [11][12][13][14]. Currently, the primary models used for analyzing the spillover efects of rail transit are the panel data model [2,15], spatial econometric model [6,[16][17][18][19][20], and DID model [21][22][23][24]. Table 1 lists some of the research on economic externalities associated with HSR and urban rail conducted using these three models.…”

Section: Introductionmentioning

confidence: 99%

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The Impacts of High-Speed Railway on Urban GDP and Its Agglomeration: Evidence from China

Li,

Guo,

Geng

et al. 2023

Journal of Advanced Transportation

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This study develops difference-in-differences (DID) models to examine the direct and indirect effects of high-speed railway (HSR) operation on the urban gross domestic product (GDP) and its agglomeration. The period from 2010 to 2019 is selected as the study period, and 30 HSR-operating cities across the Chinese mainland are chosen as the study sites to be investigated. Individual fixed effects and time fixed effects are introduced to panel data models to account for the heterogeneities between cities and the endogeneities of explanatory variables. Estimation results suggest that the operation of HSR can improve the development of the urban GDP by accelerating the migration of population to HSR-operating cities, promoting the upgrading of the urban industrial structure, and improving the level of urban scientific research. Moreover, the level of urban economic agglomeration can also be improved as a result of HSR. However, the ability of HSR-promoting economic development is more significant in cities with more developed economies. Therefore, when formulating a sensible plan for the development of HSR, policymakers should prioritize the construction of HSR in more developed cities.

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“…The theories and methods of space syntax also have their limitations. At present, they cannot reveal the inner laws of the behavior and function of micro space, but can only be used to explain the mode selection of the existing space form(Yang Tao, 2022). The space syntax shows that the development of Chen Clan Ancestral Temple in Guangzhou should highlight the role of Juxian Hall and carry out related work around this area.…”

mentioning

confidence: 99%

Optimization Of Spatial Form Of Ancestral Temples In Guangfu

Kehua

2024

EATP

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Ancestral temples are important spaces for village development, which contains profound clan culture. In the process of urbanization, the traditional spatial form of ancestral temples has been unable to adapt to the development of the times, and the existing spatial form of ancestral temples is not the optimal form. This papers carries out quantitative analysis based on three quantitative criteria of integration degree, selection degree and total depth value of spatial analysis. Using the strategies of "topological model" and "convex space model" in space syntax, this papers makes a comparative study on the specific cases of Chen Clan Ancestral Temple in Guangzhou and Yu Zhongxiang Ancestral Temple in Kaiping. Through the research, it is found that there are similarities and differences between the Chinese-style ancestral temple and the ChineseWestern hybrid style combination ancestral temple. According to the calculation of Depthmap software, the scientific analysis data were obtained, and finally the optimization strategies of "propagation-type" and "inheritance-type" were proposed for the spatial forms of Chen Clan Ancestral Temple in Guangzhou and Yu Zhongxiang Ancestral Temple in Kaiping. Keywords：Spacesyntax; Ancestral temples; Spatial form; Optimization strategy Declarations of interest: There are no conflicts to declare. 1 Background and introduction of Chen Clan Ancestral Temple in Guangzhou and Yu Zhongxiang Ancestral Temple in Kaiping 1.1 Background Located at No. 34 Enlongli, Zhongshan Seventh Road, Liwan District, Guangzhou City, Guangdong Province, Chen Ancestral Temple was built and completed during the reign of Guangxu in the late Qing Dynasty. Chen Ancestral Temple is a joint venture of the Chen clan, and it is the largest existing ancestral temple building in Guangfu area. Chen Ancestral Temple is the Guang Dong folk arts museum. It belongs to the national key cultural relic protection unit and is rated as the national AAAA historical scenic spot. Yu Zhongxiang Ancestral Temple in Kaiping is located in Fengcai Middle School, Taiping Road, Kaiping City, Jiangmen City, Guangdong Province. Surrounded by water on three sides, the building has an open river, extending to the Tanjiang River like a dragon at sea. It was built in the late Guangxu years of the Qing Dynasty and completed in the three years of the Republic of China. The Yu Zhongxiang Temple in Kaiping was built by the Yu clan in Taishan and Kaiping of Guangdong Province to commemorate their ancestor Yujing. It is now a provincial protected scenic spot. Guangzhou is a world-famous port. People from Guangfu migrated overseas in the Tang Dynasty with the continuous promotion of trade along the "Maritime Silk Road" (Mo Yuling & Sun Enle, 2014). Guangzhou and Kaiping area belong to the Guangfu culture, and the Tang Dynasty people called this area Guangfu. In the Ming

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Impacts of Urban Rail Transit on City Growth: Evidence from China

Cited by 9 publications

References 33 publications

Examining Transit Activity Data from StreetLight Using Ridership Data from Virginia Transit Agencies

Examining Transit Activity Data from StreetLight Using Ridership Data from Virginia Transit Agencies

The Impacts of High-Speed Railway on Urban GDP and Its Agglomeration: Evidence from China

Optimization Of Spatial Form Of Ancestral Temples In Guangfu

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