A credit charging scheme incorporating carpool and carbon emissions

Zong, Fang; Zeng, Mi; Lv, Jianyu; Wang, Chenyu

doi:10.1016/j.trd.2021.102711

Cited by 26 publications

(6 citation statements)

References 34 publications

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“…Numerous studies have also sought to assess the impact of different types of carpooling services (in terms of quality of service such as flexibility, price, etc.) and incentives introduced by governments, universities, employers or transit agencies: dedicated lanes on highways, reserved parking places or tradable credit schemes [48][49][50][51][52][53][54]. Research has also intensively looked at the numerous obstacles to carpooling, such as difficulties in finding a passenger or a driver and in matching schedules, but also fears or discomfort of sharing a trip with a stranger [18,55,56].…”

Section: Brief Literature Reviewmentioning

confidence: 99%

The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges

Aguiléra

Pigalle

2021

Sustainability

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This article proposes several new research directions regarding the future and sustainability of carpooling practices. The reflection is based on a systematic review of the literature (2010–2021) and a consideration of some of the main recent changes in carpooling, such as carpooling platforms and apps, and changes in lifestyles that may affect carpooling practices, such as telework. Carpooling is defined here as the sharing of short- or long-distance car rides between people who are not members of the same household, for a trip (or part of a trip) already scheduled by the driver, free of charge or expense sharing. After a description of the corpus selection method used, followed by a brief review of existing literature, we propose three new avenues for research. The first avenue is a call for greater consideration of forms of transport, other than carpooling, to work (or to the place of study), which remain poorly studied. The second avenue proposes the investigation of the role that the current digitalisation of carpooling, namely online carpooling platforms and their integration into mobility platforms, and also the use of social media by carpoolers, could play in the coming years. The third avenue argues that the consequences of the rise of collaborative consumption and the current pandemic, namely teleworking practices and the perception of shared mobility, could have an effect on carpooling, which merits specific investigation.

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Section: Brief Literature Reviewmentioning

confidence: 99%

The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges

Aguiléra

Pigalle

2021

Sustainability

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“…Second, since there is no crucial need to predict the exact value of the number of occurrences of MW in terms of early warning of actual unsafe driving behaviors, we have further processed the data. Here, trips with less than two single-trip MWs were defined as low-risk trips, and the remaining were defined as high-risk trips; then, the forecast of MW is transformed into a classification process [38,39]. ird, we compare four forecasting approaches based on machine-learning algorithms for the occurrence of MW with a real-life data set and provide the relative importance of the factors.…”

Section: Forecasting Approaches Based On Machine Learningmentioning

confidence: 99%

Factors Identification and Prediction for Mind Wandering Driving Using Machine Learning

Lin

Zhang

Gong

et al. 2021

Journal of Advanced Transportation

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Traffic safety is affected by many complex factors. Mind wandering (MW) is a fatal cause affecting driving safety and is hard to be detected and prevented due to its uncertain and complex occurrence mechanism. The aim of this study was to propose a framework for analyzing and predicting MW based on readily available driving status data. The data used in this study are the single-trip information collected by the questionnaire, which includes drivers’ personal characteristics, contextual information in which MW occurs, and in-vehicle environmental factors. After investigating the extent of factors that influence MW, these chosen factors are used to forecast MW. Based on these results, we select factors reliable to be obtained in real life to forecast MW. To verify that the new factors explored are useful in improving the forecast accuracy, the compared analysis is conducted with the results found by our approach and the existing approaches. We compare results obtained by four machine-learning-enabled forecasting approaches on a real-life data set. The result shows that the factors found in this paper can significantly improve forecast accuracy. The confusion matrix, ROC curves, and AUC are conducted, and the performance of the gradient boosting decision tree algorithm is better than other forecast approaches. The importance rankings of most factors obtained by the Gradient Boosting Decision Tree and questionnaire are the same.

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“…Previous studies on driver behaviour decision theory are generally based on two aspects: traffic environment and driver vision. The change of traffic environment is the main cause that forces drivers to change behaviours [14]. Rudin-Brown et al [15] divided the complexity of the traffic environment into three levels and studied the correlation between the levels and speed maintenance degree.…”

Section: Behaviour Decision Theorymentioning

confidence: 99%

“…The change of traffic environment is the main cause that forces drivers to change behaviours [14]. Rudin‐Brown et al.…”

Section: Introductionmentioning

confidence: 99%

A new theory of driver vision pressure energy field and its application in driver behaviour decision‐making model

Chen

et al. 2021

IET Intelligent Trans Sys

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The interpretation of driver behaviour decisions is an essential part of driver behaviour research. Unlike previous studies that use driver's vision indicators or behaviour indicators as the basis for behaviour decision-making models, this paper proposes a new concept of vision pressure energy field to describe driver's vision perception. Driver behaviours are regarded as the results of the energy fluctuation of the "Potential Energy-Field Energy-Kinetic Energy" cycle. The energy field model and corresponding classification method of driving risk level are presented. The micro-effect and macro-effect of driver behaviour decisions are considered in the decision effect evaluation process. These models are integrated into an RNN (Recurrent Neural Network) framework. After the field test data training, the model results show that the decision-making framework with a hidden layer can successfully describe the car-following and lane-changing behaviours. The phenomenon of continuous behaviour change can be explained by the prediction result of decision effect level. The vision pressure energy field theory integrates the driver behaviour into the physical energy field theory. It presents a new way to interpret driver's vision perception results. The driver behaviour changes can also be successfully predicted through this theory.

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A credit charging scheme incorporating carpool and carbon emissions

Cited by 26 publications

References 34 publications

The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges

The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges

Factors Identification and Prediction for Mind Wandering Driving Using Machine Learning

A new theory of driver vision pressure energy field and its application in driver behaviour decision‐making model

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