Use of machine learning in understanding transport dynamics of land use and public transportation in a developing city

Dorosan, Michael; Dailisan, Damian; Valenzuela, Jesus Felix; Monterola, Christopher

doi:10.1016/j.cities.2023.104587

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…The SHAP method has been widely applied across various domains for interpreting machine learning models, including finance [89], healthcare [90], and environmental sciences [91], to provide interpretable explanations of complex machine learning models. In our study, we leverage the predictive capabilities of an XGBoost ML model and enhance its interpretability using SHAP.…”

Section: Explainable Artificial Intelligencementioning

confidence: 99%

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

Trindade Neves,

Aparicio,

de Castro Neto

2024

Applied Sciences

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In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities.

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Section: Explainable Artificial Intelligencementioning

confidence: 99%

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

Trindade Neves,

Aparicio,

de Castro Neto

2024

Applied Sciences

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“…To address issues on mobility, including traffic congestion, several approaches have been taken, such as synergy of the transport system [19], traffic signal control [20], and predictive analytics [21], among many others. Additionally, extensive measures of a transport or mobility index have been proposed for different purposes under different perspectives, such as urban planning, environmental impact, traffic system performance monitoring, urban development, human mobility patterns in relation to work, and transport sustainability [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Urban Mobility Index from Clustering of Vehicle Speeds in a Tourist-Heavy City

Liponhay,

Valenzuela,

Dorosan

et al. 2023

Applied Sciences

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The rapid urbanization of cities often brings about complex mobility issues, such as traffic congestion that, when unplanned, results in decreased productivity and quality of life. While many cities have adopted smart city initiatives to capture and monitor mobility, applying these in a developing country context remains a challenge when infrastructure and high-resolution spatial and temporal data are lacking. In this work, we use GPS data obtained from probe vehicles (a mix of public and private transport vehicles) within the city of Baguio, The Philippines, to develop and propose the Zone-based Speed Index (ZSI), a mobility index based on the speed clusters observed in this city. The ZSI dynamically infers monthly speed thresholds to classify zones as fast or slow and successfully captures the decrease in vehicle mobility associated with the impact of typhoons and holidays. Thus, it can be used to characterize urban vehicle mobility with high (hourly) resolution. Insights from the use of our dynamic mobility index are useful in the development and optimization of transportation systems, in monitoring the ease of vehicle mobility, and in the performance assessment of smart city initiatives, which are much needed in tourism hotspots.

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Is greener always healthier? Examining the nonlinear relationships between urban green spaces and mental health in Wuhan, China

Wang,

Cheng,

et al. 2024

Urban Forestry & Urban Greening

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Use of machine learning in understanding transport dynamics of land use and public transportation in a developing city

Cited by 6 publications

References 15 publications

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

A Dynamic Urban Mobility Index from Clustering of Vehicle Speeds in a Tourist-Heavy City

Is greener always healthier? Examining the nonlinear relationships between urban green spaces and mental health in Wuhan, China

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