Exploring the Effects of Transportation Supply on Mixed Land-Use at the Parcel Level

Yunes Almansoub, Ming Zhong, Asif Raza, Muhammad Safdar, Abdelghani Dahou and Mohammed A. A. Al-qaness
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Yunes Almansoub: Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
Ming Zhong: Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
Asif Raza: Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
Muhammad Safdar: Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
Abdelghani Dahou: L.D.D.I. Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, Adrar 01000, Algeria
Mohammed A. A. Al-qaness: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Land, 2022, vol. 11, issue 6, 1-28

Abstract: The interactive relationship between transportation and land use has become more difficult to understand and predict, due to the economic boom and corresponding fast-paced proliferation of private transportation and land-development activities. A lack of coordination between transportation and land-use planning has created an imbalanced provision of transportation infrastructure and land-use patterns; this is indicated by places where a high-density land-development pattern is supported by a low-capacity transport system or vice versa. With this, literature suggests that Mixed Land-Use (MLU) developments have the potential to provide relevant solutions for urban sustainability, smart growth, inclusive public transit use, and efficient land-use. Therefore, this study employed deep neural network models—Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP)—for forecasting the effect of transportation supply on the MLU pattern at the parcel level in the Jiang’an District, Wuhan, China. The findings revealed a strong relationship between the supply of public transportation and MLU. Moreover, the study results indicated that MLU is widely available in areas with high accessibility, high density, and proximity to the city center. The forecasting results from the MLP and LSTM models showed an average error of 5.55–7.36% and 3.62–4.28% for mixed use, respectively, while most of their 90th percentile errors were less than 13.73% and 10.46% for mixed use, respectively. The proposed models and the findings from this study should be useful for stakeholders and policy makers for more precise forecasting of MLU at the urban level.

Keywords: mixed land-use; accessibility; transit-oriented development; machine learning; mixed land-use index; land parcel (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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