•Raw water pipelines can remove a certain extent of long-chain PFASs.•Long-distance, open-channel water diversion can increase PFAS contamination risk.•O3-BAC and UV treatment processes can provide a ...certain removal effect on PFASs.•The PFAS distribution is affected by the loose deposit in branched DWDSs.•The PFAS concentration forms a relatively uniform distribution in the looped DWDSs.
Perfluoroalkyl substances (PFASs) can occur in water sources, pass through drinking water treatment plants (DWTPs), drinking water distribution systems (DWDSs), to the consumer taps. This investigation was carried out to present the transportation behaviors of 17 PFASs, involving seven DWTPs with different water sources, raw water transportation modes, treatment processes, and DWDS structures in eastern and northern China. The results showed that the long-distance raw water transportation pipelines removed a certain extent of PFASs from raw water, probably due to the accumulation of loose deposits. The long-distance, open-channel South-to-North water diversion increased PFAS contamination risk. In the DWTPs, granular activated carbon (GAC) adsorption and ultraviolet radiation removed less than 25% of PFASs, but ozonation-biological activated carbon (O3-BAC) was superior to GAC alone in removing PFASs. Loose deposits couldsignificantly influence PFAS accumulation and release within branch-structured DWDSs. In loop-structured DWDSs, finished water with different PFAS characteristics could mix along the pipeline, with the corresponding DWTP as the center, ultimately forming a relatively uniform distribution in the entire DWDS.
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•A novel one-stage transportation mode identification framework is proposed.•The PointNet architecture from point cloud processing is modified.•A post-processing algorithm is proposed for reducing ...prediction discontinuity.•The model can facilitate real-time applications.
Transportation mode identification is fundamental for transportation planning and management. With the popularization of ubiquitous GPS-enabled devices, leveraging travelers’ GPS trajectories to infer transportation modes becomes a cost-effective and appealing approach. The prevailing two-stage framework of transportation mode identification usually suffer from the inevitable segmentation errors in the first stage, and can hardly achieve real-time inference. The existing one-stage framework models either require multi-source data as input or solely enable fixed-size features, which may need to be further improved. In concern of the similar data structure and semantic segmentation task for point clouds and GPS trajectory points, this study proposes a novel one-stage method to directly predict pointwise transportation modes by introducing and improving PointNet, which is a widely used deep learning network in point cloud processing. Specifically, 1D convolution and pointwise pyramid pooling structure are embedded into the original PointNet to capture local features in various granularities for better distinguishing similar transportation modes. Moreover, a post-processing algorithm is further proposed to refine the pointwise classification by taking the nearby consistency into account. Experiments on the GeoLife dataset show that the proposed method achieves an accuracy of 0.849 in identifying five transportation modes, including walk, bike, bus, car, and train. Comparisons reveal that the proposed method significantly outperforms other state-of-the-art methods in terms of local context extraction capability, computational efficiency, and prediction accuracy, making the proposed approach more efficient and effective in practice.
Hydrogen energy has the advantages of being clean and carbon-free, convenient storage, and easy conversion to electrical energy. The use of renewable energy electrolysis for hydrogen production is an ...important solution to promote clean energy utilization and decarburization in the power and transportation industries. However, hydrogen has a low hydrogen storage density and is prone to explosion. The high cost and risk of hydrogen transportation pose challenges to the promotion and utilization of hydrogen. To address these difficulties, this paper proposes a day-ahead dispatch of electricity-hydrogen systems (EHS) under the solid-state transportation mode of hydrogen energy. Firstly, based on the Van't Hoff equation, the relationship between gas pressure and the reaction temperature is established during the gas-solid conversion process, and a hydrogen energy solid-state transport model based on a magnesium-based hydrogen transport vehicle (MHTV) is proposed. Secondly, a renewable energy uncertainty set is established based on the information-gap decision theory (IGDT) envelope constraint for addressing the uncertainty of renewable energy and a day-ahead bi-level dispatch model of the EHS is constructed based on the solid-state transportation mode of hydrogen energy. Finally, the IGDT approach considering fuzzy variables (FV-IGDT) is presented by using a fuzzy variable membership function, and the proposed bi-level model is transformed into a single-level model for a solution based on this approach. The effectiveness of the proposed model and solution method is verified through simulation on an EHS consisting of a modified IEEE-118 power system, 2 hydrogen production stations (HPS), and 10 hydrogen refueling stations (HRS).
•Hydrogen pressure & temperature during gas-solid conversion established by Van't Hoff equation.•Hydrogen energy solid-state transport model based MHTV is proposed using magnesium.•MHTV hydrogen transportation time, energy balance, time-coupling constraints are considered.•FV-IGDT model is proposed to address issue of traditional IGDT models unable to solve optimal value.•Optimize fluctuation range of uncertain variables in RSOS and RARS models and optimal values.
Predicting transportation mode choice is a critical component of forecasting travel demand. Recently, machine learning methods have become increasingly more popular in predicting transportation mode ...choice. Class association rules (CARs) have been applied to transportation mode choice, but the application of the imputed rules for prediction remains a long-standing challenge. Based on CARs, this paper proposes a new rule merging approach, called CARM, to improve predictive accuracy. In the suggested approach, first, CARs are imputed from the frequent pattern tree (
FP-tree
) based on the frequent pattern growth (FP-growth) algorithm. Next, the rules are pruned based on the concept of pessimistic error rate. Finally, the rules are merged to form new rules without increasing predictive error. Using the 2015 Dutch National Travel Survey, the performance of suggested model is compared with the performance of CARIG that uses the information gain statistic to generate new rules, class-based association rules (CBA), decision trees (DT) and the multinomial logit (MNL) model. In addition, the proposed model is assessed using a ten-fold cross validation test. The results show that the accuracy of the proposed model is 91.1%, which outperforms CARIG, CBA, DT and the MNL model.
With the advancement of location acquisition technologies, a large amount of raw global positioning system (GPS) trajectory data is produced by many moving devices. Learning transportation modes from ...the GPS trajectory data is an important problem in the domain of trajectory data mining. Traditional supervised learning‐based approaches rely heavily on data preprocessing and feature engineering, which require domain expertise and are time consuming. The authors propose a deep learning‐based convolutional long short term memory (LSTM) model for transportation mode learning, in which the convolution neural network is first used to extract deep high‐level features and then LSTM is used to learn the sequential patterns in the data that uses both GPS and weather features, thus making the full use of spatiotemporal operations. The authors have also analysed the impact of the geospatial region on human mobility. Experiments conducted on the Microsoft Geolife data set fused with the weather data set show that their model achieves the state‐of‐the‐art results. The authors compare the performance of their model with the benchmark models, which shows the superiority of their model having 3% improvement in accuracy using only GPS features, and the accuracy is further improved by 4 and 7% on including the impact of geospatial region and weather attributes, respectively.
•We analyze tourist behavior in relation to movement patterns and transport mode.•Implications are derived from bivariate probit model estimates and marginal effects.•The main driver in visiting more ...than one region is cultural novelty seeking.•Demographics and destination familiarity are highly affecting transport mode choice.
This paper proposes that movement patterns and transportation mode choices are linked, and then identifies the estimation of a bivariate probit model as an appropriate technique to investigate the two correlated choices. The two variables are described by a mixed combination of independent variables, wherein the transport mode choice can be explained by demographics, whereas movement patterns are influenced by trip characteristics. Moreover, the introduction of activity participation and motivation in the model allows for a better understanding of tourist behavior in relation to the two investigated variables. Finally, marginal effects are derived to quantify the impacts and draw policy implications in destination management and transport planning.
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•Proposed a process handling scheme that connects two terminals.•Studied the different transportation modes in the underground logistics channel.•The scheduling problem concerns the ...connection of three equipment.•Proposed an optimization model to reduce the waiting time among the three types of equipment.
With an increase in the container volume, the problems of container terminal congestion and resource shortage may become increasingly critical. To enhance the service of a container terminal, this article investigates a novel loading and unloading scheme that optimizes the underground container logistic system and configuration and scheduling of underground logistics vehicles, gantry cranes at the handling shaft, and automatically guided vehicles on the ground. First, a mathematical model of the problem is established with the objective function of minimizing the maximum completion time. In addition, another objective function of minimizing the total waiting time of the underground logistics vehicles at the handling shafts is considered to form a multiobjective cooperative optimization model. Second, the genetic algorithm is adopted to solve the single model, and the nondominated sorting genetic algorithm-III derived from the genetic algorithm is applied to efficiently solve the multiobjective problem, and the corresponding performance is experimentally verified. To verify the correctness of the models and effectiveness of the algorithms, numerical experiments of different scales with the solutions of two transportation modes are illustrated. The sensitivity analysis proves that the underground logistics vehicle grouping transportation mode can significantly increase the operation efficiency of the underground container logistic system and container terminal. The “superiority” performance at different scales indicates that instance No. 6, which includes 96 containers, 3 gantry cranes and 8 automatic guided vehicles, has the maximum effect and highest performance in the underground logistics vehicle grouping mode. The experimental results and analysis demonstrate that the proposed method, which can select the configuration and scheduling scheme for an underground container logistic system, is rational and valid.
Understanding people's transportation modes is beneficial for empowering many intelligent transportation systems, such as supporting urban transportation planning. Yet, current methodologies in ...collecting travelers' transportation modes are costly and inaccurate. Fortunately, the increasing sensing and computing capabilities of smartphones and their high penetration rate offer a promising approach to automatic transportation mode detection via mobile computation. This paper introduces a light-weighted and energy-efficient transportation mode detection system using only accelerometer sensors in smartphones. The system collects accelerometer data in an efficient way and leverages a deep learning model to determine transportation modes. Different architectures and classification methods are tested with the proposed deep learning model to optimize the system design. Performance evaluation shows that the proposed new approach achieves a better accuracy than existing work in detecting people's transportation modes.
The challenge of selecting the best transportation mode is one of the most significant issues that organizations deal with during the product delivery phase. Many criteria that influence the ...selection of an appropriate mode of transportation are indicative of how challenging this problem is. Therefore, in this study, a hybrid decision making methodology based on interval-valued intuitionistic fuzzy sets (IVIFS) is proposed to obtain the most suitable transportation mode by considering the determined criteria. The criteria are decided according to both experts’ opinions and literature review results. Afterwards, experts’ opinions in the company are consulted for the evaluation of the determined transportation mode alternatives. Then, relying on IVIF-CRITIC technique, criteria weights are determined. Subsequently, IVIF-TOPSIS method is applied to rank the alternatives. Because the combination of these methodologies has never been applied to solve the problem at hand, the developed methodology and the application area represent a novelty. According to the results of our study, the railway has been determined as the most proper transportation mode for the glass manufacturing company. To show the robustness of the proposed methodology, we conduct a sensitivity analysis by both changing the criteria weights and applied methodology. The results show that in all scenarios the ranking of alternatives is similar.
To reach the goal of becoming carbon neutral, there is a necessity to optimize the cold logistics due to its huge energy consumption and waste. Here, the life cycle assessment method was used to ...calculate the carbon emissions at each step of the fruit and vegetable cold chain. Based on the energy balance equation, the refrigerated transportation methods and carbon emissions under different transportation times was studied. The results show that the carbon emission of 1 kg of fruits and vegetables in the cold logistics is 0.098 kg: In this carbon emission, the transportation step accounts for 82%, while the emission of the pre-cooling step, storage (sales) step and consumption (abandonment) step account for 7%, 6% and 5%, respectively. If the transportation time is within 5 h, the pre-cooled fruits and vegetables are best to be transported by insulated transportation. If the transportation time is between 5 and 60 h, the preferable transportation method is the cold storage transportation. Moreover, 70 kg of cold storage agent is needed per cubic meter when the latent heat of the cold storage agent is 270 kJ/kg. Comparing the cold storage transportation with the mechanical refrigerated transportation, the transportation time is less than 87 h (1.5tons) and 98 h (10tons), that is the cold storage transportation has lower carbon emission. We also found that the driving carbon emission of a 1.5tons small refrigerated truck is three times that of a 10tons heavy refrigerated truck. In order to reduce the carbon emission in the transportation step, a smaller shape factor H, a cold storage agent with a large phase change latent heat and a thicker insulation layer have been suggested.
●The CO2 emission of three different transportation types was calculated during the cold chain.●The time used to decide on the transportation type was provided during the cold chain based on the CO2 emission●The method to reduce the CO2 emission of the transportation step has been suggested.
