城市土地利用中停车时长建模的机器学习方法
A Machine Learning Approach for Modelling Parking Duration in Urban Land-use
AI总结：
本研究提出一种基于人工神经网络并融合Garson算法与LIME解释的机器学习模型，分析印度两种土地利用下驾驶员社会经济及出行特征对停车时长的影响，以支持停车政策制定。
摘要：
停车是快速发展的发展中国家面临的一个不可避免的问题。越来越多的车辆需要分配越来越多的城市土地用于停车。然而，在印度等发展中国家，停车问题却很少受到关注。本研究提出了一个模型，用于分析汽车用户的社会经济特征和出行特征对停车时长的影响。具体而言，采用人工神经网络（ANNs）来捕捉驾驶员特征与停车时长之间的相互关系。人工神经网络在学习和识别参数之间的联系以最佳预测结果方面非常高效。然而，由于人工神经网络的“黑箱”性质严重限制了其实用性，本研究采用Garson算法和局部可解释模型无关解释（LIME）进行模型解释。LIME通过用开发的可解释模型局部逼近任何分类的预测来展示预测结果。本研究基于通过访谈调查现场收集的微观数据，考虑了两种土地利用类型：办公商务和市场/购物。结果显示，通过LIME的预测概率更高，因此该方法可以被广泛采用。此外，基于两种土地利用的结果讨论了政策影响。这项独特的研究可能有助于加强停车政策和管理，以实现可持续发展目标。
英文摘要：
Parking is an inevitable issue in the fast-growing developing countries. Increasing number of vehicles require more and more urban land to be allocated for parking. However, a little attention has been conferred to the parking issues in developing countries like India. This study proposes a model for analysing the influence of car users' socioeconomic and travel characteristics on parking duration. Specifically, artificial neural networks (ANNs) is deployed to capture the interrelationship between driver characteristics and parking duration. ANNs are highly efficient in learning and recognizing connections between parameters for best prediction of an outcome. Since, utility of ANNs has been critically limited due to its Black Box nature, the study involves the use of Garson algorithm and Local interpretable model-agnostic explanations (LIME) for model interpretations. LIME shows the prediction for any classification, by approximating it locally with the developed interpretable model. This study is based on microdata collected on-site through interview surveys considering two land-uses: office-business and market/shopping. Results revealed the higher probability of prediction through LIME and therefore, the methodology can be adopted ubiquitously. Further, the policy implications are discussed based on the results for both land-uses. This unique study could lead to enhanced parking policy and management to achieve the sustainability goals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
