Understanding loading patterns of construction waste hauling trucks: triangulation between big quantitative and informative qualitative data

Lu, Weisheng; Li, Yuan; Lee, Wendy M. W.

doi:10.1007/s11356-022-19387-5

Cited by 7 publications

(2 citation statements)

References 48 publications

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“…Lu (2019) used big data analytics to investigate the possible determinants of the illegal dumping behavior of CWHTs in Hong Kong using different transportation behavior as indicators. Lu et al (2022) also explored the loading patterns of CWHTs in Hong Kong, as well as the factors influencing these patterns, using a triangulation method based on big data and driver interviews. Wei et al (2022) analyzed the relationship between CWHT transportation characteristics and carbon emissions based on construction waste transportation records in Hong Kong.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-time Prediction of Construction Waste Hauling Trucks’ Transportation Activities: An Input‒Output Hidden Markov Modelling Approach

Yang,

Lei,

Han

et al. 2023

Preprint

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Construction waste hauling trucks (CWHTs), as one of the most commonly seen heavy-duty vehicles in major cities around the globe, are usually subject to a series of regulations and access restrictions because they not only produce significant NOx and PM emissions but also causes on-road fugitive dust. To limit their environmental impact, specific spatial-temporal access restrictions are implemented, and timely and accurate detection of possible infringements has become a key challenge facing many municipal managers. To address this challenge, we propose a prediction method based on an interpretable activity-based model, input-output hidden Markov model (IOHMM), and apply it to trajectories of 300 CWHTs in Chengdu, China for evaluation. Contextual factors including weather conditions, recent transportation activities, and historical work statistics are considered in the model to improve its prediction power. Results show that the IOHMM has an average percentage of 64% for correctly predicting the destination and an average \({R}^{2}\) value of 69% for predicting the duration of the transportation activity. These values are higher than the results of the baseline models, including Markov chains, linear regression, and long short-term memory. To investigate the factors that influence the predictability of CWHTs’ transportation activities, linear regression models are constructed using the percentage of correct destination predictions and \({R}^{2}\) of the duration prediction model as the dependent variables, respectively. Our findings indicate that the number of active days and the proportion of days with nighttime activities are positively correlated with the prediction accuracy of both models. The average number of daily transportation activities and the number of different staying activities have positive and negative effects on the prediction accuracy of the destination model, respectively. In conclusion, the proposed model holds promise in assisting government agencies by predicting the next transportation activities of CWHTs and detecting infringements in a timely and effective manner.

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Section: Literature Reviewmentioning

confidence: 99%

Real-time Prediction of Construction Waste Hauling Trucks’ Transportation Activities: An Input‒Output Hidden Markov Modelling Approach

Yang,

Lei,

Han

et al. 2023

Preprint

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“…Waste management is a process that includes the collection, transportation, processing, recycling or disposal, and monitoring of waste materials (Lu et al, 2022). Since unsustainable waste management has the potential to cause various environmental problems (Hao et al, 2021), it is important to create an integrated and effective system for waste management (Abdul & Syafrudin, 2018;Ma et al, 2022b).…”

Section: Construction Muck Waste Managementmentioning

confidence: 99%

Integrated and effective platform governance mode for managing construction muck waste for a circular economy

Wu¹,

Yin²,

Hao

et al. 2022

Preprint

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Construction waste is a type of solid waste that is inevitably produced in the process of urban development and has a dreadful environmental impact due to inadequate management of it by the public sector. The purpose of this paper is therefore to investigate platform governance as an alternative strategy for muck waste management that will contribute to a circular economy. A comparison between the traditional governance process and platform governance of muck truck management is made by using Petri-net. Nanjing’s muck smart supervision platform is used as a case study to assess effectiveness of the platform governance mode. Results from Petri-net simulation modeling software reveal that the platform governance mode is more effective than the traditional mode. Based on the case study of Nanjing’s muck smart supervision platform, it is found that the success of Nanjing's muck waste management can be attributed to the platform governance mode. This means that the platform management approach can contribute to the sustainability of muck waste governance. It is feasible to utilize the platform governance mode for muck waste as an integrated and effective management mode for current practices of muck waste management and resource recovery in China. The platform governance mode can significantly improve the efficiency of muck waste management and provide economic and environmental benefits as part of a circular economy.

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Integrated and effective management of muck waste under the platform governance mode for a circular economy

Yin

Hao

et al. 2023

Environ Sci Pollut Res

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Understanding loading patterns of construction waste hauling trucks: triangulation between big quantitative and informative qualitative data

Cited by 7 publications

References 48 publications

Real-time Prediction of Construction Waste Hauling Trucks’ Transportation Activities: An Input‒Output Hidden Markov Modelling Approach

Real-time Prediction of Construction Waste Hauling Trucks’ Transportation Activities: An Input‒Output Hidden Markov Modelling Approach

Integrated and effective platform governance mode for managing construction muck waste for a circular economy

Integrated and effective management of muck waste under the platform governance mode for a circular economy

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