Analysis of consumer response to fuel price fluctuations applying sample selection model to GPS panel data: Dynamics in individuals’ car use

The effects of fuel price increases on people’s car use have been widely discussed during the last few decades in travel behavior research. It is well recognized that fuel price has significant effects on driving distance and driving efficiency. However, most of this research assumed that these effects are invariant across individuals and weather conditions. Moreover, intrinsic variability in people’s preferences has not been given much attention due to the difficulty of collecting the necessary data. In this paper, we collected detailed travel behavior data of 276 respondents in the Netherlands, spanning a time period between one week and three months using GPS logs. These GPS data were fused with weather data, allowing us to estimate both exogenous (such as weather and fuel price) and endogenous effects (inertia and activity plans) on individual’s car use behavior. To further understand the effects of fuel price on the environment, we estimated the effects of fuel price fluctuation on CO₂ emissions by car. The results show a significant degree of inertia in car use behavior in response to increased fuel prices. Weather and fuel price showed significant effects on individual’s car using behavior. Moreover, fuel price shows two-week lagged effects on individual’s travel duration by car.     

Cluster-based correlation of severe driving events with time and location

In this article, a systematic strategy is proposed to identify severe driving events occurrence correlation with time and location. The proposed approach, which is constructed based on batch clustering and real-time clustering techniques, incorporates historical and real-time data to predict the time and location of severe driving events. Batch clustering is implemented with the combination of subtractive clustering and fuzzy c-means clustering to generate clusters representing the initial correlation patterns. Real-time clustering is then developed to create and update real-time correlation patterns on the foundation of the batch clustering using the evolving Gustafson–Kessel like (eGKL) algorithm. In both clustering processes, the correlation of the events within time domain is identified first, and then two different levels of accurate correlations are conducted for the location domain. Real-time data of operating vehicles each equipped with a data acquisition and wireless communication platform are used to validate the proposed strategy. Batch clustering results reveal the severe braking events distribution and concentration at daytime and nighttime. Real-time clustering provides and updates the variation of the correlations/intercorrelation of different regions. Drivers can be notified of the potential severe driving locations through maps showing the driving routes. Through the variation of the correlations, drivers can recognize the events occurrence at different times and locations. The generated time series can be potentially used to develop spatial-time models for regions to model and forecast the events occurrence.     

The socio-economics of travel behavior and environmental burdens: A Detroit,Michigan regional context

The transportation industry—particularly light-duty vehicles—is a significant contributor of greenhouse gasses, accounting for about one-third of overall emissions in the U.S. Research to date has studied various factors that impact travel behavior of residents with varying socio-economic characteristics. However, research on the socio-economic characteristics of residents and their impact on environmental burdens within a single urban region, as measured by fuel consumption and vehicular emissions, is recognized as under-represented in the U.S. planning and transportation literature. This study focuses on the Detroit region, Michigan, a unique case study due to the scale of suburbanization and urban decline, yet representative of many mid-western cities. The article explores how socio-economic characteristics impact travel patterns and environmental burdens within six Detroit region neighborhoods. Data on individual travel behavior and personal vehicle characteristics gathered from a mail survey enabled an analysis into how associated environmental burdens varied with socio-economic composition. The analysis explores contributions to environmental burdens between poorer urban and wealthier suburban populations.     

Personal travel management: the adoption and consideration of travel‐related strategies

Traveler behavior plays a role in the effectiveness of travel demand management (TDM) policies. Personal travel management is explored in this paper by analyzing individuals' adoption and consideration of 17 travel‐related alternatives in relation to socio‐demographic, mobility, travel‐related attitude, personality and lifestyle preference variables. The sample comprises 1282 commuters living in urban and suburban neighborhoods of the San Francisco Bay Area. Among the findings: females were more likely to have adopted/considered the more ‘costly’ strategies; those with higher mobility were more likely to have adopted/considered travel‐maintaining as well as travel‐reducing strategies; and those who like travel and want to do more are less likely to consider travel‐reducing strategies. These findings, when combined with those of earlier work on this subject, present a compelling argument for the need to further understand traveler behavior – particularly in response to congestion and TDM policies.     

Modeling household energy consumption behavior: A comparative analysis

Household decisions on the energy consumption behavior are with regard to the situations that multiple end-uses (e.g., domestic appliances and vehicles) are simultaneously hold and consumed. To deal with this issue, the multiple discrete–continuous models are the best choices from the behavioral perspective. This study compared two types of utility theory-based multiple discrete–continuous models, which are widely applied in the literature: multiple discrete–continuous extreme value (MDCEV) model and the improved resource allocation model based on the multi-linear function (RAM-MLF). A household energy consumption survey was carried out in Beijing in 2010, and the comparative analysis on the performance of these two models is carried out based on the survey data. Results show that the overall performance of RAM-MLF is slightly superior to the MDCEV model due to the incorporation of the inter-end-use interaction and the relative importance of end uses. Moreover, the utility structure by using the satiation parameters to represent the diminishing marginal utility with the increasing consumption shows better fitness than the structure only using the logarithmic function. These findings can be contributed to understand the household energy consumption behavior, while suggest the potential improvement of the model structure, which is mainly focused on the utility form and the decision making mechanism.     

基于OD分布的运输通道识别方法

为了弥补运输通道分析方法的缺陷,本文结合运输通道＂带状分布＂、＂客货流密集＂等特征,提出了基于OD分布的运输通道定量识别方法。首先利用图论,在OD分布图上识别出所有抽象的带状路径,并结合OD矩阵筛选出其中OD量最大的一条或多条路径,然后通过区域产业轴线、城镇轴线以及交通轴线的定性验证,确定运输通道。最后,对驻马店市运输通道进行实例分析,结果表明其精确度较高,具有科学合理的实用性。     

有限元法在载荷识别中的应用

用材料力学方法和准静态法,对大秦线2万t重载列车用转K6转向架交叉杆所受的动态载荷进行了识别,并分别编制了轴力谱、横向力谱和垂向力谱,进行了两种方法的载荷极值比较和误差分析。     

Factors Influencing Environmentally Responsible Behavior among Coastal Recreationists

Abstract

An increasing threat to the marine environment is the presence of debris in the ocean, which is predominantly a result of land-based sources and increasing use of single-use packaging items. To begin reducing the amount of debris entering the ocean, human behavior must be addressed. The purpose of this study is to evaluate how coastal recreationists behave towards the environment and whether their participation in recreation correlates to their reported behavior specific to marine debris control. Path analysis was used to determine how one’s attitudes, knowledge, recreational activity, and background characteristics influence behavior. Results showed that type of recreational activity had very little impact on behavior and other predictors. From the model, marine environmental concern, connection to the marine environment, and gender most directly influenced reported behavior. Therefore, to encourage change in recreationists’ relevant behavior, the results indicate social groups where behavior change efforts could initially be made. Additionally, the marine environmental concerns of different demographic groups should be considered and used in developing programs directed at increasing environmentally responsible behavior with emphasis on marine debris.     

Motivating small-displacement car purchasing in China

Due to the rapid development of the automobile industry and the ever-increasing quantities of produced and sold automobiles in China, many problems such as fuel scarcity and air pollution have emerged. To alleviate such problems, one solution is to promote households to purchase small-displacement (engine) cars (SDCs). Based on the theory of planned behavior, we develop a theoretical framework to examine how influencing factors such as environmental attitude, subjective norm, self-image and environmental knowledge motivate SDC purchasing behavior through SDC purchasing intention in China. We further extend the research framework to examine whether the factor of economic incentives moderates the relationships between SDC purchasing intention and behaviors. Using an empirical study with 232 usable questionnaire responses, we observe that SDC purchasing intention partly mediates the relationship between three of four influencing factors (environmental attitude, self-image, environmental knowledge) and SDC purchasing behavior. Statistical results also show that the factor of economic incentives moderates the relationship between SDC purchasing intention and behavior. Our results indicate that three of four influencing factors can yield SDC purchasing behavior, especially when SDC purchasing intention exists. Economic incentives such as financial support from the government could promote SDC purchasing intention to transform into purchasing behavior.