A study on possibility of commuting trip using private motorized modes in cities around the world: Application of multilevel model

The focus of the current research was to evaluate how the individual’s social characteristics and urban infrastructure impacts the usage of Private Motorized Modes (PMM). Based on individual and urban characteristics a multilevel analysis was conducted on the possibility of commuting trip by private motorized modes on the rush time of 78 cities around the world. Also the selected cities were classified through a principal component analysis, and based on the classification the impact of and urban variables on the possibility of commuting trips made by private motorized modes (PCTP) was verified. Results showed a diverse range of variables related to the usage of PMM, as well as the urban structure and railway lengths being an important variable in travel behavior.     

The impact of competition on container port (in)efficiency

There are many studies on container port efficiency and that seek to understand what factors, such as technical and scale efficiency, private versus public terminal management or macro-economic factors, play on the efficiency score of a given port. There are fewer studies that focus on the role played by the inter-port competitive environment. This role remains difficult to assess. In fact, on the one hand, a port subject to high inter-port competition may record higher efficiency scores due to the pressure from the competitive environment. On the other hand, a port subject to high competition may be forced to over-invest and could therefore records a lower efficiency score. This article investigates this issue and examines how the degree of competition measured at different levels (local, regional and global level) impacts the efficiency score of a given container port. To do so, we implement a truncated regression with a parametric bootstrapping model. The model applied to information gathered for 200 container ports in 2007 and 2010 leads to the following conclusions: port efficiency decreases with competition intensity when measured in a range of 400–800 km (regional level); and the effect from competition is not significant when competition is measured at a local (less than 300 km) or at a global (more than 800 km) level. Estimates also show a tendency for ports who invested from 2007 to 2010 to experience a general decrease in efficiency scores, an element which could be explained by the time lag between the investment and the subsequent potential increase in container throughput.     

Determinants of the property damage costs of tanker accidents

This study investigates determinants of the vessel, oil cargo spillage, and other-property damage costs of tanker accidents. Tobit estimation of a three-equation recursive model suggests that, among types of tanker accidents, fire/explosion accidents incur the largest vessel damage costs, but the smallest oil cargo spillage costs. Alternatively, grounding accidents incur the smallest vessel damage costs, but the largest oil cargo spillage costs, reflecting the difficulty of controlling oil cargo spillage subsequent to such accidents. Also, oil cargo spillage costs are lower for US flag tanker accidents. A dollar of vessel damage cost increases other-property damage cost by $0.06, whereas a dollar of oil cargo spillage increases this cost by $1.55.     

Exploration of correlation between environmental factors and mobility at signalized intersections

Characterizing the relationship between environmental factors and mobility is critical for developing a sustainable traffic signal control system. In this study, the authors investigate the correlation of the environmental impacts of transport and mobility measurements at signalized intersections. A metamodeling-based method involving experimental design, simulations, and regression analysis was developed. The simulations, involving microscopic traffic modeling and emission estimation with an emerging emission estimator, provide the flexibility of generating cases with various intersection types, vehicle types, and other parameters such as driver behavior, fuel types, and meteorological factors. A multivariate multiple linear regression (MMLR) analysis was applied to determine the relationship between environmental and mobility measurements. Given the limitations of using the built-in emissions modules within current traffic simulation and signal optimization tools, the metamodeling-based approach presented in this paper makes a methodological contribution. The findings of this study set up the base for extensive application of simulation optimization to sustainable traffic operations and management. Moreover, the comparison of outputs from an advanced estimator with those from the current tool recommend improving the emissions module for more accurate analysis (e.g., benefit-cost analysis) in practical signal retiming projects.     

Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China

In transportation studies, variables of interest are often influenced by similar factors and have correlated latent terms (errors). In such cases, a seemingly unrelated regression (SUR) model is normally used. However, most studies ignore the potential temporal and spatial autocorrelations across observations, which may lead to inaccurate conclusions. In contrast, the SUR model proposed in this study also considers these correlations, making the model more behaviorally convincing and applicable to circumstances where a three-dimensional correlation exists, across time, space, and equations. An example of crash rates in Chinese cities is used. The results show that incorporation of spatial and temporal effects significantly improves the model. Moreover, investment in transportation infrastructure is estimated to have statistically significant effects on reducing severe crash rates, but with an elasticity of only −0.078. It is also observed that, while vehicle ownership is associated with higher per capita crash rates, elasticities for severe and non-severe crashes are just 0.13 and 0.18, respectively; much lower than one. The techniques illustrated in this study should contribute to future studies requiring multiple equations in the presence of temporal and spatial effects.

利用层次分析建立的房地产价格回归法

根据房地产的不同性质和用处,找出影响其价格的各种因素,利用层次分析法将各因素条理化,层次化,求出三个主要因素的权重;再借助于一些销售良好的楼盘的相关数据供成多元线性回归方程组,利用最小二乘法解此方程组,从而给出房地产价格的定价模型.     

Modeling Car Ownership in Turkey Using Fuzzy Regression

Abstract

This article develops a model which can be used to determine car ownership in Turkey. Because of the lack of disaggregated data, the model is based on aggregated data. As owning a car is mainly affected by economic, social and demographic factors, the car ownership model has a multi-variable form. In order to explain the effects of these factors on car ownership in Turkey, a fuzzy multiple-regression model is used. The major reason for applying fuzzy regression is to overcome the intercorrelation problem associated with the independent variables. In this study, the urbanization rate, average family size, gross national product per capita, average car cost, gasoline price and total length of roads are selected as independent variables. The results show that, by applying a multi-variable approach to possibilistic regression, the model provides not only a crisp output but also an output range for car ownership in Turkey between 1970 and 2000.     

Reprint of: Modelling the impact of traffic incidents on travel time reliability

Traffic incidents are recognised as one of the key sources of non-recurrent congestion that often leads to reduction in travel time reliability (TTR), a key metric of roadway performance. A method is proposed here to quantify the impacts of traffic incidents on TTR on freeways. The method uses historical data to establish recurrent speed profiles and identifies non-recurrent congestion based on their negative impacts on speeds. The locations and times of incidents are used to identify incidents among non-recurrent congestion events. Buffer time is employed to measure TTR. Extra buffer time is defined as the extra delay caused by traffic incidents. This reliability measure indicates how much extra travel time is required by travellers to arrive at their destination on time with 95% certainty in the case of an incident, over and above the travel time that would have been required under recurrent conditions. An extra buffer time index (EBTI) is defined as the ratio of extra buffer time to recurrent travel time, with zero being the best case (no delay). A Tobit model is used to identify and quantify factors that affect EBTI using a selected freeway segment in the Southeast Queensland, Australia network. Both fixed and random parameter Tobit specifications are tested. The estimation results reveal that models with random parameters offer a superior statistical fit for all types of incidents, suggesting the presence of unobserved heterogeneity across segments. What factors influence EBTI depends on the type of incident. In addition, changes in TTR as a result of traffic incidents are related to the characteristics of the incidents (multiple vehicles involved, incident duration, major incidents, etc.) and traffic characteristics.     

时空因素影响下在线短时交通量预测

考虑交通流的时空因素进行短时交通流预测,能够提高预测的精度.为此,引入径向基核函数,将复杂的预测问题转化为高维空间的回归问题;然后,基于支持向量回归机并考虑时空因素影响作用建立在线的短时交通量预测模型,通过网格搜索的方法对模型参数进行优化;最后,构造时间-空间状态向量,通过不同的状态向量对时间和空间维度的影响进行了分析.利用高速公路检测器数据,对比不同模型的精度,对在线短时交通量预测模型的有效性和可行性进行了验证.结果表明：在线模型精度优于传统的支持向量回归模型,考虑时空因素影响后交通量预测模型具有更高的精度和稳定性.     

城市出租汽车保有量预测分析

分析影响出租车分担率的因素,选择出影响分担率的主要因子,把主要因子作为多元线性回归模型中的自变量,得出分担率预测模型,然后基于分担率预测模型得出基于供需平衡的出租车保有量预测模型。最后以重庆市为例,运用SPSS软件作线性回归分析,利用模型预测出2015年重庆主城区出租车的分担率,将分担率代入供需平衡模型,预测出2015年重庆主城区出租车的合理保有量。