The impact of car specifications,prices and incentives for battery electric vehicles in Norway: Choices of heterogeneous consumers

Electric vehicles (EVs), specifically Battery EVs (BEVs), can offer significant energy and emission savings over internal combustion engine vehicles. Norway has a long history of research and government incentives for BEVs. The BEV market in Norway allows us to fully examine consumers’ BEV choices influenced by car specifications, prices and government incentives (public bus lanes access, toll waiver and charging stations). The Random-Coefficient Discrete Choice Model (referred to as the BLP model) is applied to understand the choices of heterogeneous personal consumers and business buyers. Our study is instantiated on the entire EV sales data in Norway from 2011 to 2013, as well as a set of demographics at the municipality level. The results suggest significant positive effects of BEV technology improvement, space, toll waiver and charging station density on EV demand for both personal consumers and business buyers. However, the effects on business buyers may be generally less pronounced than on personal consumers. Interestingly, bus lanes access demonstrates a negative impact for personal consumers, possibly due to consumers’ concern regarding bus lane congestion. In addition, preferences on the BEV price can vary statistically among consumers with different income levels. Compared to the BEV technology development, demographical features and municipal incentives may have generally less impacts on market shares within the BEV market.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

Model-based predictive control for bicycling in urban intersections

In this paper, a model predictive control approach for improving the efficiency of bicycling as part of intermodal transportation systems is proposed. Considering a dedicated bicycle lanes infrastructure, the focus in this paper is to optimize the dynamic interaction between bicycles and vehicles at the multimodal urban traffic intersections. In the proposed approach, a dynamic model for the flows, queues, and number of both vehicles and bicycles is explicitly incorporated in the controller. For obtaining a good trade-off between the total time spent by the cyclists and by the drivers, a Pareto analysis is proposed to adjust the objective function of the MPC controller. Simulation results for a two-intersections urban traffic network are presented and the controller is analyzed considering different methods of including in the MPC controller the inflow demands of both vehicles and bicycles.     

Commuters’ willingness-to-pay for improvement of transfer facilities in and around metro stations – A case study in Kolkata

Although several cities in India are developing the metro system, there are lacunas associated with transfer facilities in and around metro stations. The present work aims to investigate the perception of commuters of Kolkata city, India in terms of their willingness-to-pay (WTP) for improvement of transfer facilities. A stated preference survey instrument was designed to collect choice responses from metro commuters and the database was analysed by developing random parameter logit (RPL) models. The decomposition effects of various socioeconomic and trip characteristics on mean estimates were also investigated in random parameter logit models with heterogeneity. The work indicates significantly high WTP of metro commuters as compared to the average metro fare for improvement of various qualitative attributes of transfer facility such as ‘facility for level change’, ‘visual communication’, ‘pedestrian crossing’, and ‘pedestrian environment’. The WTP values are also found to vary across different groups of commuter formed on the basis of ‘trip purpose’, ‘monthly household income’, ‘station type’ and ‘metro fare’. ‘Work trip’ commuters are found to have higher WTP for improvement of access time, pedestrian environment and use of an escalator over the elevator. On the other hand, ‘high-income group’ commuters have shown higher WTP for improvement of access time, pedestrian crossing, and pedestrian environment. While ‘high fare group’ commuters have higher WTP for access time and pedestrian environment, heterogeneity is also observed in WTP for facility for level change, pedestrian crossing, and pedestrian environment across commuters using different ‘station type’ (underground, at-grade, and elevated). The findings from the study provide a basis for formulating policies for the improvement of transfer facilities in and around metro stations giving due attention to the preference of commuters having different socioeconomic and trip characteristics.     

连锁零售业库存／配送一体化决策优化模型与算法

针对在一个区域内拥有一个配送中心和n个在地理位置上分散的连锁分店的连锁零售业．建立VMI思想下适合于A类商品和一些重要的B类和C类商品的单一商品确定型需求的库存／配送一体化优化模型，并采用逐步迭代的方法对模型进行求解，可得出使系统在一定时期总成本最小的经济订购批量，最后用一个算例使连锁零售业库存，配送一体化决策在物流成本节约方面的优越性得以验证。     

基于加权Kendall相关系数和序贯代理模型的有限元模型修正

为了提高桥梁结构有限元模型修正的效率和效果，提出基于加权Kendall相关系数和序贯代理模型的有限元模型修正方法。首先，建立基于目标响应误差和待修正设计参数灵敏度的加权Kendall相关系数指标，并采用凝聚层次聚类算法，合理确定待修正设计参数的数量和位置，保证待修正设计参数对目标响应具有合适的解耦能力；其次，为解决传统一次性代理模型法在构造代理模型过程中产生的欠采样或者过采样的问题，采用基于FLOLA-Voronoi通用序贯设计策略的序贯代理模型法，提高构造代理模型过程中试验设计样本的利用率和模型修正效率；最后，根据一座斜拉桥的静动力荷载试验数据进行有限元模型修正。结果表明：基于FLOLA-Voronoi算法的序贯代理模型能够合理地确定构造代理模型所需的样本数量和位置，使得在相同样本数量的情况下，序贯代理模型比一次性代理模型具有稍好的精度指标；采用加权Kendall相关系数指标的聚类方法可以得到具有合适解耦能力的待修正设计参数，使得修正前大误差目标响应的误差显著下降，小误差目标响应的误差基本都处于合理范围内，有效地降低了目标响应的整体误差；同时，有限元理论静动力响应与实测静动力响应的平均误差分别由5.56%、8.43%降低至1.87%、3.41%，并且相较于常规方法，修正精度更好，所提方法可以用于桥梁结构有限元模型修正，可得到准确的桥梁结构数值模型。     

铁道车辆轴箱弹簧断裂故障对动力学性能的影响

车辆运行过程中,当轴箱弹簧突发断裂故障,会造成动力学状态和性能突变,一系悬挂刚度突然减小,暂时失去承载能力,会威胁车辆行车安全。结合整车的动力学仿真,建立了轴箱弹簧断裂过程的力学模型,对整个断裂过程进行仿真,模拟了轴箱弹簧突然断裂工况下车辆动力学性能变化,分析了轴箱弹簧断裂条件下车辆直线行车安全性以及曲线通过安全性。计算分析结果表明:轴箱弹簧突然断裂导致一系悬挂刚度剧变,引起轮轨垂向力先减小后增大,轮重减载率、脱轨系数等参数增大直至超限,但对轮轨横向力影响不大。     

基于深度学习的破碎带盾构施工沉降预测分析

为进一步提高复杂地层条件下盾构沉降预测的准确性，以广州地铁7号线1期工程谢村站-钟村站区间盾构工程为依托，针对破碎带盾构隧道沉降控制难题，提出基于深度学习的人工智能预测模型。通过分析开挖面破碎带分布规律，确定将破碎带面积比作为地层特性参数。采用相关系数矩阵分析不同施工参数与破碎带面积比的相关性，确定采用刀盘转矩代表破碎带面积比实时描述地层分布特性。以刀盘转矩、盾尾间隙与注浆量作为输入值，地面沉降作为输出值训练深度学习模型，并利用训练后的深度学习模型进行沉降预测分析。通过分析预测结果与沉降实测值的对比验证预测模型的有效性。     

对于现行公路隧道工程定额的思考和建议

针对在设计及后续服务过程中关于隧道造价方面发现的问题和争议,对2007版《公路工程预算定额》、《公路工程概算定额》提出了笔者的想法,对定额修编工作提出了一些建议。     

Prioritizing bicycle paths in Belo Horizonte City,Brazil: Analysis based on user preferences and willingness considering individual heterogeneity

Using bicycles as a commuting mode has proven to be beneficial to both urban traffic conditions and travelers’ health. In order to efficiently design facilities and policies that will stimulate bicycle use, it is necessary to first understand people’s attitudes towards bicycle use, and the factors that may influence their preferences. Such an understanding will enable reliable predictions of bicycle use willingness level, based on which cycling facility construction can be reasonably prioritized.As people often have different perceptions on exercising, green transportation, and traffic conditions, effects of potentially influencing factors on people’s willingness of using bicycles tend to be highly heterogeneous. This paper uses a random parameter ordered probit model to analyze how travelers’ willingness of using bicycles is influenced by various socio-economic factors in Belo Horizonte City, Brazil, with the consideration of individual heterogeneity. The data was collected through the 2010 bicycle use survey in Belo Horizonte City. Results show that, first, the willingness of using bicycle is favored by middle income class household, and negatively related with commuting time. Second, people who rent apartments tend to be more willing to use bicycles. Third, if a person is currently walking a long time to work, he/she would be most willing to commute with a bicycle in the future. Those currently commuting a relatively short distance by motorcycle and bus follow this group in terms of willingness to commute by bicycle in the future. Car users seem to be difficult to convert to bicycle users. Moreover, the estimation shows clear evidence that significant individual heterogeneity indeed exists, especially for education level, necessitating the consideration of such an effect. With the calibrated model, residents’ willingness of using bicycle commuting is then estimated for the entire Belo Horizonte City using the 2010 Census and the 2012 O/D survey data. The results are cross validated using the bicycle path preference information, also obtained from the 2010 bicycle use survey.