Publications
85 results found
Yang Z, Shang W-L, Miao L, et al., 2024, Pricing decisions of online and offline dual-channel supply chains considering data resource mining, Omega, Vol: 126, Pages: 103050-103050, ISSN: 0305-0483
Abdulkareem KH, Subhi MA, Mohammed MA, et al., 2024, A manifold intelligent decision system for fusion and benchmarking of deep waste-sorting models, Engineering Applications of Artificial Intelligence, Vol: 132, ISSN: 0952-1976
Increases in population and prosperity are linked to a worldwide rise in garbage. The “classification” and “recycling” of solid waste is a crucial tactic for dealing with the waste problem. This paper presents a new two-layer intelligent decision system for waste sorting based on fused features of Deep Learning (DL) models as well as a selection of an optimal deep Waste-Sorting Model (WSM) based on Multi-Criteria Decision Making (MCDM). A dataset comprising 1451 samples of images of waste, distributed across four classes – cardboard (403), glass (501), metal (410), and general trash (137), was used for sorting. This study proposes a Multi-Fused Decision Matrix (MFDM) based on identified fusion score level rules, evaluation criteria, and deep fused waste-sorting models. Five fusion rules used in the sorting process and the evaluation perspectives into the MFDM are sum, weighted sum, product, maximum, and minimum rules. Additionally, each of entropy and Visekriterijumska Optimizacija i Kompromisno Resenje in Serbian (VIKOR) methods was used for weighting selected criteria as well as ranking deep WSMs. The highest accuracy rate of 98% was scored by ResNet50-GoogleNet- Inception based on the minimum rule. However, under the same rule, an insufficient accuracy rate of sorting was presented by ResNet50-GoogleNet-Xception. Since Qi = 0 for Inception-Xception, the final output based on MCDM methods indicates that the fused Inception-Xception model outperforms the other fused deep WSMs, which achieved the lowest values of Qi. Thus, Inception-Xception was chosen as the best deep waste-sorting model based on images of waste, multiple evaluation criteria, and different fusion perspectives. The mean and standard deviation metrics were both used to validate the selection findings objectively. The suggested approach can aid urban decision-makers in prioritizing and choosing an Artificial Intelligence (AI)-optimized optimal sorting model.
Li X, Shang W-L, Liu Q, et al., 2024, Towards a sustainable city: Deciphering the determinants of restorative park and spatial patterns, Sustainable Cities and Society, Vol: 104, Pages: 105292-105292, ISSN: 2210-6707
Huo H, Wang C, Han C, et al., 2024, Risk disclosure and entrepreneurial resource acquisition in crowdfunding digital platforms: Evidence from digital technology ventures, Information Processing & Management, Vol: 61, Pages: 103655-103655, ISSN: 0306-4573
Chen X, Lv S, Shang W-L, et al., 2024, Ship energy consumption analysis and carbon emission exploitation via spatial-temporal maritime data, Applied Energy, Vol: 360, Pages: 122886-122886, ISSN: 0306-2619
Zaidan AA, Deveci M, Alsattar HA, et al., 2024, Neutrosophic bipolar fuzzy decision-based approach for developing sustainable circular business model innovation tools, Computers and Industrial Engineering, Vol: 189, ISSN: 0360-8352
The circular economy (CE) has been identified as a possible catalyst for sustainable development by business, academics, and policymakers. To aid company developers in creating and improving business models that incorporate circularity, a variety of tools for circular business model innovation (CBMI) have been proposed. Nevertheless, the existing tools failed to consider sustainability or CE in their advancements. Currently, there is no research that has presented a complete dataset including all potential tools that may be created based on the CE’ sustainability performance attributes. Moreover, there has been a dearth of research conducted to assess and model these tools in order to determine the most efficient ones, which has resulted in a research gap. This paper constructs a decision matrix of CBMI tools by intersecting 100 CBMI tools with 10 CE’ sustainability performance attributes. The modeling of CBMI tools falls under Multiple Attribute Decision Making (MADM) due to the presence of many attributes, varying importance levels of these attributes, and the and variation in data. Thus, the fuzzy weighted with zero inconsistency (FWZIC) method is reformulated under neutrosophic bipolar fuzzy sets (NBFS) to determine the weight of CE's sustainability performance attributes. The matrix that has been constructed and the resulting weight values are fed into the CODAS method in order to model CBMI tools and identify the most sustainable tool. The results indicate that the NBFS-FWZIC method gave a weight value of 0.1031 to A7, which is the greatest weight value. On the other hand, A3 had the lowest weight value of 0.0944. The CODAS method modeled the 100 CBMI tools, with Tool39 being identified as the most sustainable tool and Tool26 as the least sustainable tool. The robustness and durability of the proposed method are evaluated using a sensitivity analysis, Spearman's rank correlation test, and comparison analysis.
Ma Z, Yang X, Shang W, et al., 2024, Resilience analysis of an urban rail transit for the passenger travel service, Transportation Research Part D: Transport and Environment, Vol: 128, Pages: 104085-104085, ISSN: 1361-9209
Zhu L, Shang W-L, Wang J, et al., 2024, Diffusion of electric vehicles in Beijing considering indirect network effects, Transportation Research Part D: Transport and Environment, Vol: 127, Pages: 104069-104069, ISSN: 1361-9209
Shang W-L, Zhang J, Wang K, et al., 2024, Can financial subsidy increase electric vehicle (EV) penetration---evidence from a quasi-natural experiment, Renewable and Sustainable Energy Reviews, Vol: 190, Pages: 114021-114021, ISSN: 1364-0321
Liu X, Shang W-L, Correia GHDA, et al., 2024, A sustainable battery scheduling and echelon utilization framework for electric bus network with photovoltaic charging infrastructure, Sustainable Cities and Society, Vol: 101, Pages: 105108-105108, ISSN: 2210-6707
Shang WL, Song X, Chen Y, et al., 2024, Congestion and Pollutant Emission Analysis of Urban Road Networks Based on Floating Vehicle Data, Urban Climate, Vol: 53, ISSN: 2212-0955
Global warming caused by greenhouse gas (GHG) is receiving increasingly attention from all over the world, and urban transportation is a significant source of greenhouse gas and pollutant emission. However, the research on traffic state of urban road networks (URNs) based on sparse floating vehicle data (FVD) is insufficient. Therefore, we mainly utilize big data techniques to explore the congestion and pollutant emission of URN with FVD. Firstly, the location of vehicles is identified and matched with the URN. We then grid the FVD and city maps to more accurately identify areas of congestion and emission in later section. Following this, we use the congestion index and K-means clustering algorithm to evaluate the traffic state over time, pollutant emission is calculated based on emission calculation standards and carbon emission is estimated by using the fuel consumption-speed model. The results indicate that congestion and emission are very severe during peak hours (e.g., 8:00 a.m.), particularly in some transportation hub areas, such as high-speed rail stations. During off-peak hours (e.g., 11:00 p.m.), congestion and emission are relatively lower. The negative correlation between congestion index and emission is also revealed. This study provides some practical approaches to more accurately estimate the overall urban traffic state by using sparse traffic data, and may offer support to urban traffic managers in managing traffic congestion and pollutant emissions.
Shang W-L, Chen Y, Yu Q, et al., 2023, Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data, Applied Energy, Vol: 352, Pages: 121859-121859, ISSN: 0306-2619
Shang W-L, Zhang M, Wu G, et al., 2023, Estimation of traffic energy consumption based on macro-micro modelling with sparse data from Connected and Automated Vehicles, Applied Energy, Vol: 351, Pages: 121916-121916, ISSN: 0306-2619
Li Z, Liu A, Shang W-L, et al., 2023, Sustainability Assessment of Regional Transportation: An Innovative Fuzzy Group Decision-Making Model, IEEE Transactions on Intelligent Transportation Systems, Vol: 24, Pages: 15959-15973, ISSN: 1524-9050
Guo Z, Zhuang Z, Tan H, et al., 2023, Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets, Renewable Energy, Vol: 219, Pages: 119471-119471, ISSN: 0960-1481
Jeevaraj S, Gokasar I, Deveci M, et al., 2023, Adoption of energy consumption in urban mobility considering digital carbon footprint: A two-phase interval-valued Fermatean fuzzy dominance methodology, ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, Vol: 126, ISSN: 0952-1976
Zhang X, Fan H, Liu F, et al., 2023, Coupling coordination between the ecological environment and urbanization in the middle reaches of the Yangtze River urban agglomeration, Urban Climate, Vol: 52, Pages: 101698-101698, ISSN: 2212-0955
Tan Z, Shao S, Zhang X, et al., 2023, Sustainable urban mobility: Flexible bus service network design in the post-pandemic era, Sustainable Cities and Society, Vol: 97, Pages: 104702-104702, ISSN: 2210-6707
Zheng S, Jia R, Shang W-L, et al., 2023, Promote transport facility Resilience: Persuasion or Subsidy?, Transportation Research Part A: Policy and Practice, Vol: 176, Pages: 103822-103822, ISSN: 0965-8564
Shang W-L, Tao X, Bi H, et al., 2023, Audio Related Quality of Experience Evaluation in Urban Transportation Environments With Brain Inspired Graph Learning, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, ISSN: 1524-9050
Yuan M, Liu C, Wang B, et al., 2023, Accelerating the Net Zero Transition in Asia and the Pacific: Low-Carbon Hydrogen for Industrial Decarbonization, Philippines, Publisher: Asian Development Bank
Wang Y, Jin H, Zheng S, et al., 2023, Bike-sharing duopoly competition under government regulation, APPLIED ENERGY, Vol: 343, ISSN: 0306-2619
Shang W, Song X, Liao Q, et al., 2023, History, Status, and Future Challenges of Hydrogen Energy in the Transportation Sector, ADBI Working Paper Series
Yang L, Zhan J, Shang W-L, et al., 2023, Multi-Lane Coordinated Control Strategy of Connected and Automated Vehicles for On-Ramp Merging Area Based on Cooperative Game, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, ISSN: 1524-9050
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Shang W-L, Lv Z, 2023, Low carbon technology for carbon neutrality in sustainable cities: A survey, Sustainable Cities and Society, Vol: 92, Pages: 104489-104489, ISSN: 2210-6707
Chen X, Wang Z, Hua Q, et al., 2023, AI-Empowered Speed Extraction via Port-Like Videos for Vehicular Trajectory Analysis, IEEE Transactions on Intelligent Transportation Systems, Vol: 24, Pages: 4541-4552, ISSN: 1524-9050
Liu A, Li Z, Shang W-L, et al., 2023, Performance evaluation model of transportation infrastructure: Perspective of COVID-19., Transp Res Part A Policy Pract, Vol: 170, ISSN: 0965-8564
The transportation systems are facing major challenges due to changes social environment caused by the COVID-19 pandemic. How to construct a suitable evaluation criterion system and suitable assessment method to evaluate the status of the urban transportation resilience has become a predicament nowadays. Firstly, the criteria for evaluating the current state of transportation resilience involve many aspects. New features of transportation resilience under epidemic normalization are exposed, and previous summaries focusing on resilience characteristics under natural disasters can hardly reflect the current state of urban transportation resilience comprehensively. Based on this, this paper attempts to incorporate the new criteria (Dynamicity, Synergy, Policy) into the evaluation system. Secondly, the assessment of urban transportation resilience involves numerous indicators, which make it difficult to obtain quantitative figures for the criteria. With this background, a comprehensive multi-criteria assessment model based on q-rung orthopair 2-tuple linguistic sets is constructed to evaluate the status of transportation infrastructure from perspective on the COVID-19. Then, an example of urban transportation resilience is given to demonstrate the feasibility of the proposed approach. Subsequently, sensitivity analysis about parameters and global robust sensitivity analysis are conducted, and comparative analysis of existing method is given. The results reveal that the proposed method is sensitive to global criteria weights, so it is suggested that more attention should be paid to the rationality of the weight of criteria to avoid the influence on the results when solving MCDM problems. Finally, the policy implications regarding transport infrastructure resilience and appropriate model development are given.
Yang Z, Ahmad S, Bernardi A, et al., 2023, Evaluating alternative low carbon fuel technologies using a stakeholder participation-based q-rung orthopair linguistic multi-criteria framework, Applied Energy, Vol: 332, ISSN: 0306-2619
It is widely believed that alternative low carbon fuels (ALCF) can be instrumental in achieving the transportation sector's decarbonization goal. Unlike conventional fossil-based fuels, ALCF can be produced through a combination of different chemical processes and feedstocks. The inherent complexity of the problem justifies the multi-criteria decision-making (MCDM) approach to support decision-making in the presence of multiple criteria and data uncertainty. In this paper, we propose a novel stakeholder participation-based MCDM framework integrating experts' perspectives on ALCF production pathways using the analytics hierarchy process (AHP) and the q-rung orthopair linguistic partition Bonferroni mean (q-ROLPBM) operator. The key merit of our approach lies in treating criteria of different dimensions as heterogeneous indicators while considering the mutual influence between criteria within the same dimension. The proposed framework is applied to evaluate four ALCF production pathways against 13 criteria categorised under economic, environmental, technical, and social dimensions for the case of the United Kingdom (UK). Our analysis revealed the environmental and the economic dimensions to be the most important, followed by the social and technical evaluation dimensions. The e-fuel followed by the e-biofuel are found to be the two top-ranked production pathways that utilise the electrochemical reduction process and its combination with anaerobic digestion. These findings, along with our recommendations, provide decision-makers with guidelines on ALCF production pathway selection and formulate effective policies for investment.
Liu Z, Ma X, Liu X, et al., 2023, Optimizing Electric Taxi Battery Swapping Stations Featuring Modular Battery Swapping: A Data-Driven Approach, Applied Sciences, Vol: 13, Pages: 1984-1984
<jats:p>Optimizing battery swapping station (BSS) configuration is essential to enhance BSS’s energy savings and economic feasibility, thereby facilitating energy refueling efficiency of electric taxis (ETs). This study proposes a novel modular battery swapping mode (BSM) that allows ET drivers to choose the number of battery blocks to rent according to their driving range requirements and habits, improving BSS’s economic profitability and operational flexibility. We further develop a data-driven approach to optimizing the configuration of modular BSS considering the scheduling of battery charging at the operating stage under a scenario of time-of-use (ToU) price. We use the travel patterns of taxis extracted from the GPS trajectory data on 12,643 actual taxis in Beijing, China. Finally, we test the effectiveness and performance of our data-driven model and modular BSM in a numerical experiment with traditional BSM as the benchmark. Results show that the BSS with modular BSM can save 38% on the investment cost of purchasing ET battery blocks and is better able to respond to the ToU price than to the benchmark. The results of the sensitivity analysis suggest that when the peak electricity price is too high, additional battery blocks must be purchased to avoid charging during those peak periods.</jats:p>
Liu J, Li J, Chen Y, et al., 2023, Multi-scale urban passenger transportation CO<inf>2</inf> emission calculation platform for smart mobility management, Applied Energy, Vol: 331, ISSN: 0306-2619
Passenger transportation is one of the primary sources of urban carbon emissions. Travel data acquisition and appropriate emission inventory availability make estimating high-resolution urban passenger transportation carbon emissions challenging. This paper aims to establish a method to estimate and analyze urban passenger transportation carbon emissions based on sparse trip trajectory data. First, a trip chain identification and reconstruction method is proposed to extract travelers' trip information from sparse trip trajectory data. Meanwhile, a city-scale trip sampling expansion method based on population and checkpoint data is proposed to estimate population movements. Second, the identified trip information (e.g., trip origin and destination, and travel modes) is used to calculate multimodal passenger transportation CO2 emissions based on a bottom-up CO2 emissions calculation approach. Third, we develop a multi-scale high-resolution transportation carbon emission calculation and monitoring platform and take the city of Hangzhou, one of China's leading cities, as our case study, with around 10 million daily trips data and a quarter million road links. Five modes of passenger transportation are identified, i.e., walking, cycling, buses, metro, and cars. Hourly carbon emissions are calculated and attributed to corresponding road links, which build up passenger transportation carbon emissions from road links to region and city levels. Results show that a typical working day's total passenger transportation CO2 emission is about 36,435 tonnes, equivalent to CO2 emissions from 4 million gallons of gasoline consumed. According to our analysis of the carbon emissions produced by approximately 40,000 km of roadways, urban expressways have the most hourly carbon emissions at 194 kg/(h·km). Moreover, potential applications of the developed methods and platform linking to smart mobility management (e.g., Mobility as a Service, MaaS) and how to work in tandem to supp
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