| 1. |
- Liu, Z., et al.
(författare)
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Targeted opportunities to address the climate-trade dilemma in China
- 2016
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Ingår i: Nature Climate Change. - : Springer Science and Business Media LLC. - 1758-678X .- 1758-6798. ; 6:2, s. 201-206
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Tidskriftsartikel (refereegranskat)abstract
- International trade has become the fastest growing driver of global carbon emissions, with large quantities of emissions embodied in exports from emerging economies. International trade with emerging economies poses a dilemma for climate and trade policy: to the extent emerging markets have comparative advantages in manufacturing, such trade is economically efficient and desirable. However, if carbon-intensive manufacturing in emerging countries such as China entails drastically more CO 2 emissions than making the same product elsewhere, then trade increases global CO 2 emissions. Here we show that the emissions embodied in Chinese exports, which are larger than the annual emissions of Japan or Germany, are primarily the result of China's coal-based energy mix and the very high emissions intensity (emission per unit of economic value) in a few provinces and industry sectors. Exports from these provinces and sectors therefore represent targeted opportunities to address the climate-trade dilemma by either improving production technologies and decarbonizing the underlying energy systems or else reducing trade volumes.
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| 2. |
- Luo, H., et al.
(författare)
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Real-time Characterization Model of Carbon Emissions Based on Land-use Status : A Case Study of Xi'an City, China
- 2024
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Ingår i: Journal of Cleaner Production. - : Elsevier Ltd. - 0959-6526 .- 1879-1786. ; 434
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Tidskriftsartikel (refereegranskat)abstract
- The traditional carbon accounting method, with a lag of over 2 years due to the release time of statistical yearbooks, impedes timely policy adjustments in urban planning and management. Hence, there is an urgent need to establish a real-time carbon emissions characterization model. Xi'an which has a complex land-use structure was chosen as the study site and its carbon emissions were calculated using the Emission Factor Method. The GIS-Kernel Density (KD) model was constructed, and land use was subdivided based on Point of Interest (POI) and road network data. Based on the results of carbon emissions accounting and land-use subdivision, a Multilayer perceptron (MLP) model was established. The remote sensing (RS) images of Xi'an underwent supervised classification, and the carbon emissions of Xi'an were characterized based on the subdivision results and MLP model. The results show that: (1) The accuracy of the characterization model is more than 90%, and with the improvement of RS technology, the accuracy will be further improved; (2) Compared with the existing model, this model can real time reflect the spatial distribution of carbon emissions; (3) Atmospheric emission of Xi'an will be 41.92 million tons at the end of 2022, a decrease of 2.80 million tons compared with that of 2020, but an increase of 0.33 million tons from 2021. The north of Xi'an and periphery of the central urban area are the main carbon sink loss areas, while the east of Xi'an and north foot of the Qinling Mountains are carbon sink growth areas.
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| 3. |
- Tang, R., et al.
(författare)
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A literature review of Artificial Intelligence applications in railway systems
- 2022
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Ingår i: Transportation Research Part C. - : Elsevier Ltd. - 0968-090X .- 1879-2359. ; 140
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Tidskriftsartikel (refereegranskat)abstract
- Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges.
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| 4. |
- Fatima, R., et al.
(författare)
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Sharing information online rationally : An observation of user privacy concerns and awareness using serious game
- 2019
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Ingår i: Journal of Information Security and Applications. - : Elsevier Ltd. - 2214-2134 .- 2214-2126. ; 48
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Tidskriftsartikel (refereegranskat)abstract
- Recent studies have shown that excessive online information disclosure is a major reason of privacy breach. It makes it easy for social engineers to gather information about their targets. The objective of this study is to gather user privacy concerns reported in the literature and categorize them into themes, then design a serious game covering the categorized privacy concerns and evaluate the educational effect of the game regarding dangers associated with excessive online information disclosure. We have conducted a literature review and extracted user privacy concerns reported in 109+ publications. Then we designed a serious game and empirically evaluated the game players awareness of dangers associated with excessive online information disclosure. We find that privacy awareness has a positive long-term impact on users online behavior in terms of controlled information sharing. However, social networking needs drive users to share information online, even knowing the potential risks. The proposed serious game shows positive effect in improving the privacy awareness of participants.
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| 5. |
- Liu, Q., et al.
(författare)
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Modeling interpretable social interactions for pedestrian trajectory
- 2024
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Ingår i: Transportation Research Part C. - : Elsevier Ltd. - 0968-090X .- 1879-2359. ; 162
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Tidskriftsartikel (refereegranskat)abstract
- The abilities to understand pedestrian social interaction behaviors and to predict their future trajectories are critical for road safety, traffic management and more broadly autonomous vehicles and robots. Social interactions are intuitively heterogeneous and dynamic over time and circumstances, making them hard to explain. In this paper, we creatively investigate modeling interpretable social interactions for pedestrian trajectory, which is not considered by the existing trajectory prediction research. Moreover, we propose a two-stage methodology for interaction modeling - “mode extraction” and “mode aggregation”, and develop a long short-term memory (LSTM)-based model for long-term trajectory prediction, which naturally takes into account multi-types of social interactions. Different from previous models that do not explain how pedestrians interact socially, we extract latent modes that represent social interaction types which scales to an arbitrary number of neighbors. Extensive experiments over two public datasets have been conducted. The quantitative and qualitative results demonstrate that our method is able to capture the multi-modality of human motion and achieve better performance under specific conditions. Its performance is also verified by the interpretation of predicted modes, of which the results are in accordance with common sense. Besides, we have performed sensitivity analysis on the crucial hyperparameters in our model. Code is available at: https://github.com/xiaoluban/Modeling-Interpretable-Social-Interactions-for-Pedestrian-Trajectory.
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| 6. |
- Yu, Q., et al.
(författare)
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GPS data in urban bicycle-sharing : Dynamic electric fence planning with assessment of resource-saving and potential energy consumption increasement
- 2022
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 322
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Tidskriftsartikel (refereegranskat)abstract
- As a newly-emerging option of shared transportation, Internet-enabled dockless bicycle sharing is well accepted by the public. The implementation of electric fences has great potential to tackle the problem of random parking in bicycle sharing services. However, the deployment of electric fences would have a negative impact on the convenience of bicycle sharing services, which might lead to an increase in energy consumption among customers who switch their methods of transportation. This paper proposes a dynamic electric fence planning method with an assessment of resource-saving and potential energy consumption increasement. An agent-based model is proposed to simulate the trips and evaluated the performance of static and dynamic electric fences. The results show that dynamic electric fences require significantly shorter walking distances than static electric fences. The implementation of electric fences in the city center can significantly avoid random parking and improve the parking tidiness of bicycles. The implementation of dynamic and static electric fences can averagely save 25.31% and 27.76% bicycle resources. By estimating travel mode shifting, dynamic electric fence can reduce energy consumption by 5.79% per day compared to the static electric fence situation.
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