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The key to generating tailored suggestions in the scenic location recommendation scene is how to model the diverse tourism environments in order to correctly acquire visitor preferences and scenic spot tourism features. However, most existing recommendation algorithms focus on the spatiotemporal background modeling of historical tourism trajectories. Only tourists' preferences are understood, and the rich heterogeneous tourism information such as tourists' personal tourism constraints and scenic spots' tourism attributes are ignored. In this paper, we proposed a multiple neural collaborative filtering attraction recommendation architecture (MNCF‐AR). To begin, we learn the tourism feature representation of tourists by modeling diverse tourism contexts, and then we create the tourism trajectory background of tourists using a large number of actual tourism logs to achieve the entire feature representation of tourists. Second, to learn the feature vector of scenic spots in the context, the tourism heterogeneous network map is used to build the scenic spot attribute background, the self‐attention network to learn the scenic spot sequence learning, and a neural network to project each scenic spot into a unified potential feature space. Finally, the multi‐neural collaborative filtering method is utilized to forecast the difference in scores between visitors and scenic sites so that tailored scenic spots may be recommended. Extensive experiments on mainstream datasets, such as MaFengwo, New York, Tokyo and Xi'an, show that the proposed method can effectively and accurately recommend attractions for users. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

• A reliable path finding model is proposed to minimize the fuel consumption budget. • Specified on-time arrival probability index is ensured by reliable path finding model. • A heuristic label setting algorithm is developed to exactly solve the formulated problem. • Real traffic data is applied for the applicability and efficiency verification. Transport activities are regarded as a major source of fuel consumption and CO 2 emission production. To reduce the negative impact of traffic-related CO 2 emission, this paper proposes a reliable path-finding algorithm for improving fuel efficiency in stochastic networks under the uncertainty of travel time and fuel consumption with the consideration of spatial correlation. A reliable constrained path-finding model is developed and formulated to minimize the fuel consumption budget while guaranteeing the specified on-time arrival probability. A heuristic label setting algorithm is developed to precisely solve the formulated problem. The proposed algorithm overcomes the time-consuming drawbacks of traditional path enumeration algorithms. The applicability and efficiency of the proposed algorithm are verified on real-world traffic data acquired from the Beijing and Xi'an networks in China. The experiments demonstrate that our proposed algorithm can significantly reduce fuel consumption compared to existing studies. The experiment in Beijing shows that using the proposed algorithm can reduce 0.9 kg of CO 2 emissions on average per trip compared to existing studies. [ABSTRACT FROM AUTHOR]

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM2.5 in buildings. This study used an algorithm to automatically estimate the long‐term time‐resolved indoor PM2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time‐resolved indoor/outdoor PM2.5 concentrations and occupants' window actions, which were easily obtained from the low‐cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time‐resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long‐term outdoor PM2.5 monitoring using low‐cost light‐scattering sensors. [ABSTRACT FROM AUTHOR]

High-energy consumption of a district heating system is mainly caused by low-efficiency operation. Real-time regulation and control of the system in the operation stage has great energy-savings potential, and accurate load prediction is the basis of system control and on-demand supply. The key for obtaining an accurate hourly heating load forecast value is to select reasonable input parameters. Therefore, this paper summarizes the methods of predicting heating loads and selecting input parameters, proposes the Eclat algorithm of association rules to obtain the best combination of input parameters, and introduces the Spearman parameter selection method for comparison; the Eclat-Support Vector Regression (E-SVR) and Spearman-Support Vector Regression (S-SVR) prediction models are established. The heating load data of a district in Xi'an, China is taken as an example, and the results show that the water supply temperature of the historical primary network was the most significant factor that affects the district ultrashort-term heating load prediction. The E-SVR prediction model has better performance than S-SVR with an R2 value of 0.92, an accuracy improvement of 8.2%, and a root mean square error (RMSE) reduction of 28.1%. Single factor analysis of different prediction models showed that with an increase in the length of the prediction cycle, the influences of the historical water supply temperature and flow rate on the heating load decrease gradually, and the influence of outdoor temperature increases gradually. • The methods of predicting heating load and selecting input parameters were reviewed. • The Eclat algorithm to obtain the best combination of input parameters were proposed. • The models were trained and compared using data from China as a case study. • Eclat can get better combination of input parameters than Spearman algorithm. [ABSTRACT FROM AUTHOR]

Most early research on route choice behavior analysis relied on the data collected from the stated preference survey or through small-scale experiments. This manuscript focused on the understanding of commuters' route choice behavior based on the massive amount of trajectory data collected from occupied taxicabs. The underlying assumption was that travel behavior of occupied taxi drivers can be considered as no different than the well-experienced commuters. To this end, the DBSCAN algorithm and Akaike information criterion (AIC) were first used to classify trips into different categories based on the trip length. Next, a total of 9 explanatory variables were defined to describe the route choice behavior, and and the path size (PS) logit model was then built, which avoided the invalid assumption of independence of irrelevant alternatives (IIA) in the commonly seen multinomial logit (MNL) model. The taxi trajectory data from over 11,000 taxicabs in Xi'an, China, with 40 million trajectory records each day were used in the case study. The results confirmed that commuters' route choice behavior are heterogenous for trips with varying distances and that considering such heterogeneity in the modeling process would better explain commuters' route choice behaviors, when compared with the traditional MNL model. [ABSTRACT FROM AUTHOR]