Specification of regret-based models of choice behaviour: formal analyses and experimental design based evidence

Travel behaviour analysis has recently witnessed a rapidly growing interest in regret-based models of choice behaviour. Two different model specifications have been introduced in the transportation literature. Chorus et al. (Transportation Research B 42: 1–18, 2008a; in: Proceedings 87th Annual Meeting of the Transportation Research Board, Washington DC, 2008b) specified regret as a (non) linear function of the difference between the best-foregone choice alternative and the chosen alternative. Later, as an approximation to the original specification, Chorus (2010) suggested a logarithm function of utility differences between all choice alternatives, mainly for ease of estimation. This paper makes two contributions to this literature. First, formal analyses are conducted to identify the parameter space where the logarithmic specification becomes theoretically inferior to the original specification. Second, an empirical stated choice study on the choice of shopping centre is conducted to empirically test which specification best describes stated choices. Results suggest that for the collected data the original specification outperforms the new specification. Implications of this finding for the application of regret-based choice models in travel behaviour analysis are discussed.     

Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria,Australia

Autonomous vehicles (AVs) potentially increase vehicle travel by reducing travel and parking costs and by providing improved mobility to those who are too young to drive or older people. The increase in vehicle travel could be generated by both trip diversion from other modes and entirely new trips. Existing studies however tend to overlook AVs’ impacts on entirely new trips. There is a need to develop a methodology for estimating possible impacts of AVs on entirely new trips across all age groups. This paper explores the impacts of AVs on car trips using a case study of Victoria, Australia. A new methodology for estimating entirely new trips associated with AVs is proposed by measuring gaps in travel need at different life stages. Results show that AVs would increase daily trips by 4.14% on average. The 76+ age group would have the largest increase of 18.5%, followed by the 18–24 age group and the 12–17 age group with 14.6 and 11.1% respectively. If car occupancy remains constant in AV scenarios, entirely new trips and trip diversions from public transport and active modes would lead to a 7.31% increase in car trips. However increases in car travel are substantially magnified by reduced car occupancy rates, a trend evidenced throughout the world. Car occupancy would need to increase by at least 5.3–7.3% to keep car trips unchanged in AV scenarios.     

Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches

Integrated choice and latent variable (ICLV) model incorporates latent factors into standard discrete choice model with aim to provide greater explanatory power. Using simulated datasets, this study makes a comparison among three estimation approaches corresponding to the sequential approach and two simultaneous approaches including the maximum simulated likelihood with GHK estimator and maximum approximate composite marginal likelihood (MACML) approach, to evaluate their abilities to recover the underlying parameters of multinomial probit-kernel ICLV model. The results show that both simultaneous approaches outperform the sequential approach in terms of estimates accuracy and efficiency irrespective of the sample sizes, and the MACML approach is the most preferable due to its best performance on recovering true values of parameters with relatively small standard errors, especially when the sample size is large enough.     

Household structure and gender differences in travel time: spouse/partner presence,parenthood, and breadwinner status

Despite having more similar roles at work and home than ever before, US men and women continue to exhibit different travel behavior. An open question is whether the remaining gender differences in travel differ by traditional and emergent aspects of household structure such as spouse/partner presence, parenthood, and breadwinner status. Using data from the 2003–2010 American Time Use Survey, this study offers a unique, empirical travel time analysis of metropolitan workers stratified by household structure. Results show that gender differences in travel time respond to multiple aspects of household structure in complex and interactive ways. Gender difference in work travel time is only observable when spouse/partner presence and parenthood interact, i.e., in couple households with children. Gender difference in household support travel reacts to parenthood but not spouse/partner presence. Gender difference in travel time between employed females and employed males in single-breadwinner couples is no different from gender difference in double-breadwinner couples. The results call for policy initiatives and research inquiries that pay greater attention to the large gender disparities in work travel in couple households with children and the large gender disparities in household support travel in all households with children including single-parent households. Although incapable of ruling out the influences of internalized gender differences (e.g., preference theory) and gendered structural contexts (e.g., labor market segmentation), the findings provide clear evidence that traditional gender roles and relations remain operative in contemporary households in the US.     

The interactions between online shopping and personal activity travel behavior: an analysis with a GPS-based activity travel diary

Accompanying the widespread use of the Internet, the popularity of e-commerce is growing in developing countries such as China. Online shopping has significant effects on in-store shopping and on other personal activity travel behavior such as leisure activities and trip chaining behavior. Using data collected from a GPS-based activity travel diary in the Shangdi area of Beijing, this paper investigates the relationships between online shopping, in-store shopping and other dimensions of activity travel behavior using a structural equation modelling framework. Our results show that online buying frequency has positive effects on the frequencies of both in-store shopping and online searching, and in-store shopping frequency positively affects the frequency of online searching. Frequent online purchasers tend to shop in stores on weekends rather than weekdays. We also found a negative effect of online buying on the frequency of leisure activities, indicating that online shopping may reduce out-of-home leisure trips.     

About attitudes and perceptions: finding the proper way to consider latent variables in discrete choice models

We provide an in-depth theoretical discussion about the differences between individual-specific latent constructs (representing attitudes, for example, but also other characteristics such as values or personality traits) and alternative-specific latent constructs (that may represent perceptions) affecting the choice-making process of individuals; we also carry out an empirical exercise to analyze their effects. This discussion is of importance, as the majority of papers considering attitudinal latent variables just take these as attributes affecting directly the utility of a certain alternative, while systematic taste variations are rarely considered and perceptions are mostly ignored. The results of our case study show that perceptions may indeed affect the decision making process and that they are able to capture a significant part of the variability that is normally explained by alternative specific constants. Furthermore, our results indicate that attitudes may be a reason for systematic taste variations, and that a proper categorization of latent variables, in accordance with underlying theory, may outperform the customary assumption of linearity.     

Car today,gone tomorrow: The ephemeral car in low-income,immigrant and minority families

Most transportation research in the United States uses cross-sectional, “snapshot” data to understand levels of car access. Might this cross-sectional approach mask considerable variation over time and within households? We use a panel dataset, the Panel Study of Income Dynamics (PSID), for the years 1999–2011 to test this question. We find that for most families, being “carless” is a temporary condition. While 13 % of families in the US are carless in any given year, only 5 % of families are carless for all seven waves of data we examine in the PSID. We also find that poor families, immigrants, and people of color (particularly, blacks) are considerably more likely to transition into and out car ownership frequently and are less likely to have a car in any survey year than are non-poor families, the US-born, and whites.     

Predictors of car use habit strength in an urban Norwegian public

Car use in urban areas carries well known risks for negative effects on urban quality of life, the environment and land use. Previous psychological work has mainly studied the habit of using a car as a predictor variable, whereas only a few studies have focused on psychological factors that may underlie and shape the habit. The aim of the present study is to examine demographic characteristics, spatial variables and social–psychological factors associated with car habit use strength. The results are based on a survey conducted in a random urban population-based sample with car access recruited from the Norwegian population registry (n = 878). Car use habit strength was stronger among male than female respondents and tended to be weak with a lower income. Multivariate logistic regression showed that reporting pro-environmental attitudes and personal norms predicted a weak car use habit strength, while reporting strong priority of flexibility and a long distance from home to work predicted a strong car use habit strength. Car use habit is influenced by multiple demographic, spatial and social–psychological factors, which should be carefully addressed in interventions aimed at reducing the likelihood of developing a strong car habit strength.     

固体氧化物燃料电池船舶动力系统的动态仿真(英文)

Solid oxide fuel cell (SOFC) has been identified as an effective and clean alternative choice for marine power system. This paper emphasizes on the dynamic modeling of SOFC power system and its performance based upon marine operating circumstance. A SOFC power system model has been provided considering thermodynamic and electrochemical reaction mechanism. Subcomponents of lithium ion battery, power conditioning unit, stack structure and controller are integrated in the model. The dynamic response of the system is identified according to the inertia of its subcomponent and controller. Validation of the whole system simulation at steady state and transit period are presented, concerning the effects of thermo inertia, control strategy and seagoing environment. The simulation results show reasonable accuracy compare with lab test. The models can be used to predict performance of a SOFC power system and identify the system response when part of the component parameter is adjusted.     

一种基于模型的水声弱信号检测方法(英文)

Detection of weak underwater signals is an area of general interest in marine engineering. A weak signal detection scheme was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function (RBF) neural networks and an extended Kalman filter (EKF). In this method chaos theory was used to model background noise. Noise was predicted by phase space reconstruction techniques and RBF neural networks in a synergistic manner. In the absence of a signal, prediction error stayed low and became relatively large when the input contained a signal. EKF was used to improve the convergence rate of the RBF neural network. Application of the scheme to different experimental data sets showed that the algorithm can detect signals hidden in strong noise even when the signal-to-noise ratio (SNR) is less than −40d B.