Search

The Zero Litter Initiative (ZLI) of bringing self-generated litter down the mountain is a new attempt to reduce tourism litter generation on climbing trails in China. The extended theory of planned behavior (TPB) was used to analyze the influencing factors of tourists' environmentally responsible behavioral intention to participate in the ZLI, including three additional explaining variables of personal norms (moral obligation to do ZLI), past behavior, and incentive measures, as well as three control variables of gender, age, and educational level. A total of 372 valid questionnaires were collected from tourists in Huangshan National Park. The results of the hierarchical regression analysis showed that: (1) the extended TPB model had stronger predictive power than the original TPB model; (2) tourists' intention to participate in the ZLI was significantly and positively influenced by their attitude toward the behavior (positive or negative opinions), subjective norms (perceived social pressures from referents), perceived behavioral control (perceived ease or difficulty), past behavior, and the strongest predictor of personal norms; (3) incentive measures mainly referred to economic incentives (e.g., lower ticket prices) and non-economic incentives (e.g., a gift and honorary title), which had no significant influences on tourists' intention, and there may be a "crowding-out effect" on intrinsic motivations for collective action in public space; (4) gender had significant influences on tourists' intention, which showed females had stronger participation intention than males. Age had significant and positive influences on tourists' intention, which indicated older tourists were more willing to participate in the ZLI than younger tourists. However, the educational level had significant and negative influences on tourists' intention. Lastly, the study presented practical suggestions for enhancing tourism litter management and achieving sustainable development in mountainous tourism areas., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Previous research on the environmentally responsible behaviour (ERB) of tourists has primarily focused on the personal psychological factors of tourists. However, the studies overlook the effect of contextual factors of a tourist site. According to the broken window theory, a disorderly, unkempt environment might lead more people to be involved in environmental destruction, which would form a vicious cycle. Therefore, it can be deduced that the environmental background (EB) of a tourist site is an important contextual factor. In this paper, the effect of the EB of a tourist site on the ERB was examined by the theory of planned behaviour (TPB) adopting structural equation model (SEM) multi-group analysis (MGA). The results show the following: first, both the attitude toward environmental behaviour (ATT) of tourists and their subjective norm (SN) have a positive impact on the environmentally responsible behavioural intention (ERBI) of tourists. In addition, perceived behavioural control (PBC) exerts a remarkable influence on the ERB of tourists and their behavioural intention. Second, the relationship between the ERBI and ERB of tourists is positively regulated by the EB of a tourist site. Additionally, this paper presents policy proposals for the environmental management of tourist sites., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Economic incentives have played an essential role in stimulating residents to participate in online recycling, but the internal influence mechanism has not been effectively revealed. This study discusses the influencing mechanisms of non-monetary incentives and monetary incentives on residents' intention to participate in online recycling through an experimental study based on the S (stimulus) - O (organism) - R (response) model. The results show that both non-monetary incentives and monetary incentives have significant positive effects on residents' intention to participate in online recycling, and there is no significant difference between them. Perceived value plays a mediating role in the relationship between non-monetary incentives and participation intention and between monetary incentives and participation intention. Temporal distance plays a moderating role in the relationship between non-monetary incentives and perceived value and between monetary incentives and perceived value. Residents' perceived value of non-monetary incentives is higher in the near future than in the distant future while the perceived value of monetary incentives is higher in the distant future than in the near future. Our results are theoretically important and have implications for the practical development of online recycling platform enterprises. [Display omitted] [ABSTRACT FROM AUTHOR]

Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural crossing sequence data of pedestrians and vehicles are first collected by a laser scanner and HD camera, then 1980 effective crossing samples of pedestrians are selected. Influencing parameter sets of pedestrian crossing intention are then obtained through statistical analysis. Finally, long short-term memory network with attention mechanism (AT-LSTM) model is proposed. Compared with the support vector machine (SVM) model, results show that when the pedestrian crossing intention is recognized 0 s prior to crossing, the recognition accuracy of the AT-LSTM model for pedestrian crossing intention is 96.15%, which is 6.07% higher than that of SVM model; when the pedestrian crossing intention is recognized 0.6 s prior, the recognition accuracy of AT-LSTM model is 90.68%, which is 4.85% higher than that of the SVM model. The determination of pedestrian crossing intention parameter set and the more accurate recognition of pedestrian intention provided in this work provide a foundation for future fully automated driving vehicles. [ABSTRACT FROM AUTHOR]

The connected environment provides information on surrounding traffic and areas beyond the visual range to improve driving behavior and avoid dangerous incidents. However, due to the novelty of the connected environment and the scarcity of connected data, current research on driver lane change intention in this field has received little attention. In this work, we designed a typical lane change scenario in the connected environment based on a driving simulator and real-time collection of multi-modal data from eye trackers, driving simulators, and a connected platform. The driver's eye movement, head rotation, vehicle movement, and the driver's maneuver parameters were analyzed, revealing a significant difference between the lane change intention and lane keep stages in the connected environment. In addition, the length of the intention time window with connected information (6.5 s) was longer than that without connected information (4 s). The bi-directional long and short-term memory network based on the attention mechanism (AT-BiLSTM) was used to establish a lane change intention model. The accuracy of the lane change intention model based on the proposed AT-BiLSTM algorithm surpassed that of existing machine learning algorithms. The recognition accuracy of the lane change intention model was 93.33% at 3 s prior to the lane change. The conclusions of this study are of great significance for the development of a side warning assist system in future connected environments. • Compared with the traditional environment, the number of lane change intention feature parameters in the connected environment has increased. • The length of the intention time window in the connected environment is more than that in the traditional environment. • We established a driver's lane change intention model based on AT-BiLSTM, and the recognition accuracy of the model was 93.33% at 3s prior to lane change. [ABSTRACT FROM AUTHOR]