An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: the case of Iran

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car ownership function and its determinants, ANN and FLR (including eight well-known FLR) approaches are applied to the collected data. Next, the preferred ANN is selected based on sensitivity analysis results for the test data while the preferred FLR is identified with regard to ANOVA and MAPE results. The results obtained from the performance comparison demonstrate the considerable superiority of the preferred ANN over the preferred FLR regarding the nonlinear and complex nature of the car ownership function in Iran. This is the first study that presents an ANN-FLR approach for car ownership forecasting capable of handling complexity and non-linearity, uncertainty, pre-processing, and post-processing.     

Shipment forecasting for supply chain collaborative transportation management using grey models with grey numbers

The sharing of forecasts is vital to supply chain collaborative transportation management (CTM). Shipment forecasting is fundamental to CTM, and is essential to carrier tactical and operational planning processes such as network planning, routing, scheduling, and fleet planning and assignment. By applying and extending grey forecasting theory, this paper develops a series of shipment forecasting models for supply chain CTM. Grey time-series forecasting and grey systematic forecasting models are developed for shipment forecasting under different collaborative frameworks. This paper also integrates grey numbers with grey models for analyzing shipment forecasting under partial information sharing in CTM frameworks. An example of an integrated circuit (IC) supply chain and relevant data are provided. The proposed models yield more accurate prediction results than regression, autoregressive integrated moving average (ARIMA), and neural network models. Finally, numerical results indicate that as the degree of information sharing increases under CTM, carrier prediction accuracy increases. This paper demonstrates how the proposed forecasting models can be applied to the CTM system and provides the theoretical basis for the forecasting module developed for supply chain CTM.     

站城融合模式下既有铁路车站城市功能开发体量预测研究

针对站城融合模式,提出对既有铁路车站及其影响区域的城市功能开发体量预测方法.考虑车站周边道路交通剩余能力和城市规划中对土地容积率的要求,协调枢纽城市功能和交通功能体量的比例关系,建立铁路既有车站城市功能的体量预测模型,并利用商业求解器对模型求解,得到各业态开发总体量的范围.以重庆沙坪坝综合交通枢纽城市功能开发为例,检验求解结果与实际开发体量的契合度及有效性.分析枢纽交通功能占比和容积率的敏感性,验证模型的适用性.计算结果表明,本文方法能有效利用道路承载能力,在适应城市整体发展要求的情况下,实现枢纽和土地的一体化发展,为投资者加强土地集约开发提供新思路.     

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对区域物流量进行研究与预测有助于把握区域物流的需求,实现区域物流供需相对平衡,提高区域物流规划质量和运行效率具有重要的理论和实际意义.本文将模糊粗糙集理论引入区域物流量的预测中,建立基于模糊粗糙集与支持向量机的区域物流量预测模型,用模糊粗糙集作为前端预处理器对数据进行约简,剔除冗余信息,以实现两种算法的优势互补.针对支持向量机在处理数据时无法将数据简化的问题,提出了基于模糊粗糙集与支持向量机的区域物流量预测方法,在支持向量机对样本数据进行处理之前,利用模糊粗糙集数据挖掘的能力对原始数据样本集进行预处理.结果表明,这种预测方法具有很好的精确性和有效性.     

内贸海轮进出长江驾引力量需求预测研究

针对目前长江内贸船舶压港、滞港现象,运用灰色系统预测GM(1,1)模型对长江内贸船舶进出港艘次进行预测,进而求得未来几年内贸驾引力量需求数量。结果表明,未来几年长江内贸驾引力量需求数量巨大,驾引力量短缺严重。     

基于预测有效度的组合模型在交通量预测中的应用

在已有交通量预测模型的基础上,建立了基于预测有效度的组合预测模型,应用规划方法求解出各单一预测模型的最优权重系数。通过实测数据的验证表明,该组合预测方法具有比回归分析和神经网络预测模型更高的精度,组合方法为交通量的预测提供了一种可靠而有效的新途径。     

组合预测模型在交通量预测中的应用

对当前组合预测模型进行分析,在已有交通量预测模型的基础上,建立基于预测有效度的组合预测模型,应用规划方法求解各单一预测模型的最优权重系数。实测数据验证表明,该组合预测方法具有比回归分析和神经网络预测模型具有更高精度,组合方法为交通量预测提供一种可靠而有效的新途径。     

基于模型组合的铁路集装箱运量预测

根据铁路集装箱运量预测受到多因素影响以及非线性的特点,本文采用灰色关联分析法选取了影响集装箱运量的主要因素,提出了一种基于非线性灰色模型和神经网络模型组合的铁路集装箱运量预测方法. 该方法将非线性灰色预测模型的预测值作为输入,相应的实际集装箱货运量作为输出,建立了神经网络模型结构,并提出了相应的算法. 最后以实例分析了该模型的可行性和科学性. 实例分析表明：非线性灰色模型预测的最大误差为10.52％,而组合模型的预测误差最大为8.72％,说明文中提出的组合预测模型充分考虑了多指标的共同作用,灰色预测模型提供了较完善的输入数据,神经网络模型考虑了各主要指标的关联关系.     

公众参与的交通需求管理

为了加强交通需求管理（TDM）整个过程中公众参与的程度,保障TDM的可实施性和有效性,提出了公众参与的交通需求管理概念。从公众的视角,分析了TDM与公众参与的关系和公众参与TDM的意义,总结提炼了公众参与TDM的原则、方法和策略,并初步探讨了公众参与交通需求管理的主要流程和实施框架,为合理制定交通需求管理政策提供了一种参考思路与方法。     

通过规划审核和综合决策完善轨道交通客流预测

客流预测结果是轨道交通发展建设的重要依据。首先分析了深圳市近期轨道交通发展的特点,并总结其对轨道交通客流成长和预测方面的启示。针对轨道交通客流预测普遍存在的技术问题,提出利用“客流预测规划审核”和“预测结果综合决策”的办法改进客流预测。前者探讨了重要基础数据的核实、轨道交通出行特征分析、敏感性分析等内容。后者包括确定系统规模的综合决策以及确定车站设计规模和运营组织方案的综合决策。最后提出客流预测发展机制的一些建议。