With the acceleration of urbanization and the promotion of the “dual carbon” goal, the road transport system is facing the triple challenges of efficiency bottlenecks, excessive carbon emissions, and data security risks. In view of the shortcomings of the existing research in dynamic response, multi-objective collaboration and privacy protection, this paper proposes a three-in-one intelligent management framework: (1) construct a real-time dynamic path optimization model based on Deep Reinforcement Learning (DRL), and realize the precise regulation of traffic flow through multi-source data fusion and adaptive reward mechanism; (2) Design a multi-objective optimization model integrating carbon trading mechanism to quantify the synergistic relationship between transportation efficiency, carbon emissions and economic costs; (3) Develop a distributed data management framework based on blockchain, and use zero-knowledge proof and smart contract technology to protect user privacy. The peak simulation experiment based on the fifth ring road section of Beijing shows that the proposed method reduces the average traffic time by 18.7%, the carbon emission by 23.5%, and the risk of data leakage by 76% compared with the traditional algorithm. This study provides theoretical and technical support for the construction of a safe, efficient and low-carbon intelligent transportation system.
D. Obasuyi, Rume Elizabeth Yoro, Margaret Dumebi Okpor, A.M Ifioki · 13 authors
As competitive market and globalization continue to ripple a range of issues across the asset chain (i.e. safety, quality, tracing, and overall management efficiency). Pandemics are bound to occur without warning and has revealed the unpreparedness of many nations. Thus, the Nigerian Government aiming to shore up revenue/monetization via customs exercise duties to augment the nosedive in revenue of the oil sector – must formulate policies and adapt technology to harness its inherent benefits therein. Study advances a sensor-based blockchain NiCuSBlockIoT, which will provision a decision-support scheme for cargo goods traceability and asset movement on a value-chain by first ensuring that accurate records of cargo goods are registered, tagged and reported using the sensor-based units. These are then broadcasted on to the NiCuSBlockIoT as record and/or blocks via a P2P chain on the network as a decentralized framework executed on a distributed hyper-ledger fabric via smart-contract transaction logic. Result show model eliminate fraud that often accompanies a centralized scheme via its sensor-layered model that reports all such errors as data on NiCuSBlockIoT supply value chain. Keywords: BlockChain, Food supply chain, Nigerian Customs Service, NISBlockIoT framework CISDI Journal Reference Format Obasuyi, D.A., Yoro, R.E., Okpor, M.D., Ifioki, A.., Brizimor, S.., Ojugo, A.A., Odiakaose, C.C., Emordi, F.U., Ako, R.E., Geteloma, V.C., Abere, R.A., Atuduhor, R.R. & Akiakeme, E. (2024): NiCuSBlockIoT: Sensor-based Cargo Assets Management and Traceability Blockchain Support for Nigerian Custom Services. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 2, Pp 45-64. dx.doi.org/10.22624/AIMS/CISDI/V15N2P4. Available online at www.isteams.net/cisdijournal
He Peng, Yao Sun, Jianli Hao, Chunjiang An · 5 authors
Ground transportation, which includes the road and rail sectors, is a major source of greenhouse gas (GHG) emissions. An emissions trading system (ETS) is one of the environmental policies for controlling carbon emissions from fossil fuel combustion during transport activities. To investigate the status of existing ETS interventions on ground transportation emissions, a comprehensive literature review is conducted, including policy evaluation and comparison, scheme optimization and design, and specific transport behavior interventions. The results show that existing upstream policies are insufficient in stringency and effectiveness, while policy implementation for downstream interventions remains limited. To address these shortcomings, this study proposes a downstream ground transportation emissions trading system (GTETS), incorporating the Internet of Things and blockchain technology. As well as road and rail transportation emissions, the system includes the emissions-related activities of individuals and transportation companies, and its Web3-based technologies provide effective and efficient monitoring, reporting, and verification.
The emergence of decentralized finance has transformed asset trading on the blockchain, making traditional financial instruments more accessible while also introducing a series of exploitative economic practices known as Maximal Extractable Value (MEV). Concurrently, decentralized finance has embraced rollup-based Layer-2 solutions to facilitate asset trading at reduced transaction costs compared to Layer-1 solutions such as Ethereum. However, rollups lack a public mempool like Ethereum, making the extraction of MEV more challenging. In this paper, we investigate the prevalence and impact of MEV on Ethereum and prominent rollups such as Arbitrum, Optimism, and zkSync over a nearly three-year period. Our analysis encompasses various metrics including volume, profits, costs, competition, and response time to MEV opportunities. We discover that MEV is widespread on rollups, with trading volume comparable to Ethereum. We also find that, although MEV costs are lower on rollups, profits are also significantly lower compared to Ethereum. Additionally, we examine the prevalence of sandwich attacks on rollups. While our findings did not detect any sandwiching activity on popular rollups, we did identify the potential for cross-layer sandwich attacks facilitated by transactions that are sent across rollups and Ethereum. Consequently, we propose and evaluate the feasibility of three novel attacks that exploit cross-layer transactions, revealing that attackers could have already earned approximately 2 million USD through cross-layer sandwich attacks.
The gradual transition from a traditional transportation system to an intelligent transportation system (ITS) has paved the way to preserve green environments in metro cities. Moreover, electric vehicles (EVs) seem to be beneficial choices for traveling purposes due to their low charging costs, low energy consumption, and reduced greenhouse gas emission. However, a single failure in an EV’s intrinsic components can worsen travel experiences due to poor charging infrastructure. As a result, we propose a deep learning and blockchain-based EV fault detection framework to identify various types of faults, such as air tire pressure, temperature, and battery faults in vehicles. Furthermore, we employed a 5G wireless network with an interplanetary file system (IPFS) protocol to execute the fault detection data transactions with high scalability and reliability for EVs. Initially, we utilized a convolutional neural network (CNN) and a long-short term memory (LSTM) model to deal with air tire pressure fault, anomaly detection for temperature fault, and battery fault detection for EVs to predict the presence of faulty data, which ensure safer journeys for users. Furthermore, the incorporated IPFS and blockchain network ensure highly secure, cost-efficient, and reliable EV fault detection. Finally, the performance evaluation for EV fault detection has been simulated, considering several performance metrics, such as accuracy, loss, and the state-of-health (SoH) prediction curve for various types of identified faults. The simulation results of EV fault detection have been estimated at an accuracy of 70% for air tire pressure fault, anomaly detection of the temperature fault, and battery fault detection, with R2 scores of 0.874 and 0.9375.
Panagiota Katsikouli, Pietro Ferraro, Hugo Richardson, Hanson Cheng · 9 authors
The link between transport related emissions and human health is a major issue for municipalities worldwide and one of the main challenges to address in the context of Smart Cities. Specifically, Particulate Matter (PM) emissions from exhaust and non-exhaust sources are one of the main worrying contributors to air-pollution. In this paper, we challenge the notion that a ban on internal combustion engine vehicles will result in clean and safe air in our cities, since emissions from tyres and other non-exhaust sources are expected to increase in the near future. We support this claim through simple calculations, based on publicly available data from the city of Dublin, and we present a high level solution to this problem, in the form of a control mechanism and ride-sharing scheme to limit the number of vehicles and therefore maintain the amount of transport-related PM to safe levels. Thanks to the use of Distributed Ledger Technology our proposal is entirely distributed, fair and privacy preserving, which makes it ideal for application in the Smart City domain.
The I-710 and CA-60 highways are key transportation corridors in the Southern California region that are heavily used on a daily basis by heavy duty drayage trucks that transport the cargo from the ports to the inland transportation terminals. These terminals, which include store/warehouses, inland-railways, are anywhere from 5 to 50 miles in distance from the ports. The concentrated operation of these drayage vehicles in these corridors has had and will continue to have a significant impact on the air quality in this region whereby significantly impacting the quality of life in the communities surrounding these corridors. To reduce these negative impacts it is critical that zero and near-zero emission technologies be developed and deployed in the region. A potential local market size of up to 46,000 trucks exists in the South Coast Air Basin, based on near- dock drayage trucks and trucks operating on the I-710 freeway. The South Coast Air Quality Management District (SCAQMD), California Air Resources Board (CARB) and Southern California Association of Governments (SCAG) — the agencies responsible for preparing the State Implementation Plan required under the federal Clean Air Act — have stated that to attain federal air quality standards the region will need to transition to broad use of zero and near zero emission energy sources in cars, trucks and other equipment (Southern California Association of Governments et al, 2011). SCAQMD partnered with Volvo Trucks to develop, build and demonstrate a prototype Class 8 heavy-duty plug-in hybrid drayage truck with significantly reduced emissions and fuel use. Volvo’s approach leveraged the group’s global knowledge and experience in designing and deploying electromobility products. The proprietary hybrid driveline selected for this proof of concept was integrated with multiple enhancements to the complete vehicle in order to maximize the emission and energy impact of electrification. A detailed review of all technologies included in the demonstrator is presented in this report. The project was completed in July 2015 with a final demonstration of the concept vehicle on a simulated drayage route around Volvo’s North American headquarters in Greensboro, NC. The route included all traffic conditions typical of drayage operation in Southern California as well as geofences defined to showcase the zero emission capabilities of the truck. The demonstrator successfully completed four consecutive trips with a gross combined vehicle weight of 44,000 lb., covering approximately 2 miles out of a total distance of 9 miles per trip in the Zero Emission (ZE) geofence. This vehicle is expected to use approximately 30% less fuel than a typical drayage truck in daily operation, and it is designed to allow full electric operation whenever operating in a marine terminal in the ports of Los Angeles / Long Beach. A paper study on the feasibility of expanding the capabilities of the plug-in hybrid concept developed as part of this project was also delivered as an addendum to the regular progress reports.
This paper shows that the inefficiency of fiscal decentralization in the presence of spillovers, a main tenet of the decentralization literature, is overturned in a particular transportation context. In a monocentric city where road (bridge) capacity is financed by budget-balancing user fees, decentralized capacity choices (made by individual zones within the city) generate the social optimum despite the presence of spillovers. This conclusion is closely tied to the famous self-financing theorem of transporation economics.