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The rapid development of urbanization has increased traffic pressure and made the identification of urban functional regions a popular research topic. Some studies have used point of interest (POI) data and smart card data (SCD) to conduct subway station classifications; however, the unity of both the model and the dataset limits the prediction results. This paper not only uses SCD and POI data, but also adds Online to Offline (OTO) e-commerce platform data, an application that provides customers with information about different businesses, like the location, the score, the comments, and so on. In this paper, these data are combined to and used to analyze each subway station, considering the diversity of data, and obtain a passenger flow feature map of different stations, the number of different types of POIs within 800 m, and the situation of surrounding OTO stores. This paper proposes a two-stage framework, to identify the functional region of subway stations. In the passenger flow stage, the SCD feature is extracted and converted to a feature map, and a ResNet model is used to get the output of stage 1. In the built environment stage, the POI and OTO features are extracted, and a deep neural network with stacked autoencoders (SAE–DNN) model is used to get the output of stage 2. Finally, the outputs of the two stages are connected and a SoftMax function is used to make the final identification of functional region. We performed experimental testing, and our experimental results show that the framework exhibits good performance and has a certain reference value in the planning of subway stations and their surroundings, contributing to the construction of smart cities.

Digital forensics has a vital effect in several domains and mainly focuses on reactive measures, especially when facing digital incidents. Gender identification becomes the important problem in the realm of forensic techniques and handwriting recognition. In this paper, attention-based two-pathway Densely Connected Convolutional Networks (ATP-DenseNet) is proposed to identify the gender of handwriting. There are two pathways in ATP-DenseNet: Feature pyramid could extract hierarchical page feature, and attention-based DenseNet (A-DenseNet) could extract the word feature by fusing Convolutional Block Attention Module (CBAM) and dense connected block. Finally, ATP-DenseNet makes the final prediction combining the two pathways. Experimental results show the efficiency of ATP-DenseNet, and the proposed method performs better than other researches. And the visualization of the feature maps can help us to know which part of the image contributes most to the gender identity.

Online-to-offline (O2O) commerce, e.g., the internet celebrity economy, provides a seamless service experience between online commerce and offline bricks-and-mortar commerce. This type of commerce model is closely related to location-based social networks (LBSNs), which incorporate mobility patterns and human social ties. Personalized point-of-interest (POI) recommendations are crucial for O2O commerce in LBSNs; such recommendations not only help users explore new venues but also enable many location-based services, e.g., the targeting of mobile advertisements to users. However, producing personalized POI recommendations for O2O commerce is highly challenging, since LBSNs involve heterogeneous types of data and the user-POI matrix is very sparse. LBSNs have substantially altered how people interact by sharing a wide range of user information, such as the products and services that users use and the places and events that users visit. To address these challenges in O2O commerce LBSNs, we analyze users’ check-in behaviors in detail and introduce the concept of a heterogeneous information network (HIN). Then, we propose a HIN-based POI recommendation system, which consists of two components: an improved singular value decomposition (SVD++) and factorization machines (FMs). The results of experiments on two real-world O2O commerce websites, namely, Gowalla and Foursquare, demonstrate that our method is more accurate than baseline methods. Additionally, a case study of the bricks-and-mortar brand of internet celebrity indicates that our proposed POI recommendation system can be used to conduct online promotion and purchasing to drive offline marketing and consumption.

Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million passengers, ensuring the safety of underground rail transit is particularly important. Big data shows that more than 4000 passengers participate in Long-term Stay in the Subway every day. However, the behaviors of these passengers have not been characterized. This paper proposes a method for identifying the Long-term Staying in Subway System (LSSS) in the subway based on the shortest path and analyze its travel mode. In combination with the past research of scholars, we try to quantify the suspected behavior with a database of assumed suspected behavior records. Finally, we extract the spatial-temporal travel characteristics of passengers and we propose a SAE-DNN algorithm to identify suspected anomalies; the accuracy of the training set can reach 95.7%, and the accuracy of the test set can also reach 93.5%, which provides a reference for the subway operators and the public security system.

Purpose The paper aims to identify and analyze passengers’ riding paths for providing better operational support for digital transformation in megacity metro systems.Design/methodology/approach The authors develop a method to leverage certain passengers’ deterministic riding paths to corroborate other passengers’ uncertain paths. Using Automatic Fare Collection data and train schedules, a witness model is built to recover the actual riding paths for passengers whose paths are unknown otherwise. The identification and analysis of passenger riding paths between three different types of origin–destination) pairs reveal the complexity of passenger path choice.Findings The results show that passenger path choice modeling is usually characterized by complexity, experience and partial blindness. Some passengers choose paths that are not optimal due to their experience and limited access to overall metro system information. These passengers could be the subject of improved path guidance in light of riding efficiency improved through digital transformation.Originality/value This research contributes to the improvement of metro management and operations by leveraging ongoing digital transformation in megacity metro systems. Based on the riding paths and trip chains of a large number of individual passengers identified by the proposed method, metro operation management could prevent risks in areas with concentrated passenger flow in advance, optimally adjust train schedules on a daily basis and deliver real-time riding guidance station by station, which would greatly improve megacity metro systems’ service safety, quality and operational efficacy over time.

Subways are essential for urban commuting, particularly in large metropolitan areas. However, as cities expand, subways face increasing challenges in balancing passenger demand with available service, leading to passengers being stranded at stations. A key issue is that such stranding can propagate across multiple stations, forming clusters that significantly impact global service efficiency. This phenomenon, termed “propagation of passenger stranding (PPS)”, is studied based on a data-driven model. A transition point, determined by the shifting balance between supply and demand, dictates whether PPS will grow or diminish, serving as a threshold for subway service resilience. A nonlocal correlation pattern, key to PPS formation, is revealed using the eigen microstate method, alongside the local balance of demand and service and network topology. The findings reveal the resilience mechanism of service systems in networks where trains serve as the medium and identify key factors for enhancing resilience.

Data-intensive computing has received substantial attention since the arrival of the big data era. Research on data mining in data-intensive computing environments is still in the initial stage. In this paper, a decision tree classification algorithm called MR-DIDC is proposed that is based on the programming framework of MapReduce and the SPRINT algorithm. MR-DIDC inherits the advantages of MapReduce, which make the algorithm more suitable for data-intensive computing applications. The performance of the algorithm is evaluated based on an example. The results of experiments showed that MR-DIDC can shorten the operation time and improve the accuracy in a big data environment.

Under the background of China’s continuous promotion of urbanization, urban underground integrated pipeline corridor has become an inevitable trend of future urban integrated management. After the completion of the pipeline corridor, how to effectively manage its risks in operation and maintenance management has become a topic at this stage. In this paper, through the combination of the classical AHP method and DSM method, based on a large number of literature studies, the risk relationship system of the integrated pipe corridor is constructed. AnyLogic software is applied to simulate the system dynamics, analyze the impact of dynamic changes of each risk factor on the risk accident of the integrated pipe corridor, carry out uncertainty reasoning from multiple perspectives, and realize the evaluation and analysis of the accident risk of the integrated pipe corridor. The results of the study could provide targeted support tools for integrated pipeline corridor risk operation and maintenance management.

In this paper, we adopt an ensemble machine learning framework—a Light Gradient Boosting Machine (LightGBM) and develop an algorithmic credit rating prediction model by innovatively incorporating firms’ extra supply chain information both from suppliers and customers. By utilizing data from listed firms in North America from 2006 to 2020, our results find that the accuracy of the prediction improves by incorporating supply chain information in the previous year, compared to the inclusion of supply chain information in the current year. Besides, we identify the most important factors the stakeholders should pay attention to. Interestingly, we show that the models utilizing the current year’s information perform better after the strike of the COVID-19, indicating that the epidemics may have accelerated the spread of credit risk along the supply chain. Furthermore, supplier information is found to be more valuable than customer information in predicting the focal firm’s credit rating. A comparison of our framework with the existing methods vindicates the robustness of our main results.

The highly dynamic nature of the real-world environment poses significant challenges for electric bus system operations (EBSOs), which are prone to serious accidents due to their complexity and a wide variety of risk factors. The accidents are often the result of ignoring the most serious risk sources because of a lack of comprehensive risk assessments. Therefore, this paper proposes an improved failure mode and effects analysis (FMEA) multicriteria group decision-making model to ensure the reliability and safety of EBSOs. First, an expert group is invited to evaluate the risk failure modes (FMs) of the EBSOs and transform them into a normal wiggly hesitant fuzzy set (NWHFS) form. Because the risk assessment process involves a large number of team members with different backgrounds, the experts are grouped based on scoring function values using the K-medoids clustering technique. Then, the evaluation values of the expert group are integrated using the normal hesitant fuzzy weighted geometric (NWHFWG) aggregation operator to obtain the final aggregation matrix, and the weights of the three criteria of occurrence (O), severity (S) and detection (D) are determined for each FM via the CCSD method. Finally, considering the cross-correlation between factors within the system, the relationships between FMs are analyzed, and their impact and importance are quantified using the gray correlation-based DEMATEL method, followed by the final ranking of the FMs using regret theory and the PROMETHEE II methodology to achieve a rational allocation of resources. The results are analyzed with sensitivity and comparative analyses to illustrate the superiority of the model.