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For most ultralong bus lines, low punctuality rates and vehicle bunching are two main challenges. One potential strategy to solve these two problems is to split an ultralong line into two or three shorter lines according to historical passenger flow patterns as well as other presetting objectives and constraints. Thus, in this paper, a multiobjective optimization model is first established in consideration of both passenger flow distribution and coline metro networks. Then, the optimal truncated stop is determined by estimating the model results generated by enumerating each potential bus stop derived from related metro stops. The nondominated sorting genetic algorithm II (NSGA-II) is applied to solve the model efficiently. A case study conducted on a real bus line associated with metro networks in Beijing, China, demonstrates the efficiency of the optimization strategy adoption in terms of operating cost and passenger travel time, even with a slight increase in passenger cost. Besides, the multivehicle type is also referred in the experiments to verify economic efficiency to reduce operational costs. [ABSTRACT FROM AUTHOR]

The optimization of the e-commerce logistics distribution path has always been an important research object in intelligent control. Based on the geographic information system (GIS) platform, this paper proposes an improved ant colony algorithm for the logistics distribution path optimization model based on the GIS platform. Firstly, ArcGIS software is used to solve the problem of complex networks and realize the selection and output of optional routes. Secondly, the basic ant colony algorithm is improved, and a dynamic path planning method combining global planning information and local planning is proposed. It mainly includes the improvement of the heuristic function and the improvement of the update method of pheromone. Finally, through simulation experiments and case validation experiments, it is concluded that the improved algorithm outperforms the traditional ant colony algorithm. In the case validation part, a local area of Beijing is selected to simulate a natural distribution environment, and the path optimization experiments are validated by building an actual road network model. The results show that the model can plan the optimal path for logistics distribution according to the road congestion. The data analysis shows that the route optimization model proposed in this paper can effectively reduce the distribution cost of enterprises, increase vehicle loading, and increase the profit and industry competitiveness of logistics enterprises. [ABSTRACT FROM AUTHOR]

This paper proposed an effective method, the overflow control strategy, for identifying and controlling the frequent overflow of signalized intersections based on the unmanned aerial vehicle (UAV) remote sensing and vehicle trajectory data that provide multi-dimensional vehicle dynamic data. This strategy can effectively control the overflow intersection and restore normal operation as soon as possible. The strategy was implemented as follows. First, UAV remote sensing was used to quickly find the overflow precursor at the intersection. Second, by using moving trajectory data, the second-flow model of the road section was constructed, and overflow identification indexes were established. Third, considering the traffic demand of controlled intersection and non-overflow phase from the global perspective, the queue length model of the non-overflow phase based on moving trajectory data was established and overflow identification indexes were taken as the objective function to design overflow control strategy set. Finally, according to the sampling rate requirement of moving trajectory data and the field traffic survey, the Changping District in Beijing, which meets the requirements, is selected as the case study for simulation and verification. Usually, when overflow occurs, total delay time and the average number of stop cycles at intersections are relatively high. However, by taking the proposed method, overflow at intersections was identified quickly, and a control strategy was implemented immediately, which alleviated the overflow and eventually eliminated it. The results show that the method proposed in this paper can accurately detect the occurrence of overflow, restrain and eliminate the overflow eventually. [ABSTRACT FROM AUTHOR]

Online shopping has led to the rapid development of e-commerce, and at the same time, the pressure of offline distribution has increased abruptly. Therefore, a current development trend is to share end-to-end distribution against the background of the Internet. The main research content of this paper is the benefit distribution mechanism of shared end distribution. Based on an analysis of the current situation of interest distribution, this paper proposes factors that affect interest distribution from the perspectives of individuals and groups. The suitable income distribution mode of enterprise alliances is chosen from two dimensions—cooperation mode and coordination mechanism. Based on extant theory, this paper proposes a benefit distribution scheme-selection mechanism based on the modified Shapley value method and takes the terminal distribution in the Haidian District of Beijing as an example. The revised income distribution results better reflect the income-generating abilities of different enterprises within a cooperative organization and assign different benefit proportions to this cooperative organization based on different income-generating capacities to provide development incentives and, at the same time, better achieve income distribution. [ABSTRACT FROM AUTHOR]

Carsharing is regarded as a new mode of transportation that can meet the diversity of travel demands. Carsharing systems have different operating modes, and one-way systems are more widely used since cars can be dropped off at any station. However, their planning involves a series of joint decisions regarding the number, size, and location of stations, as well as the fleet size. This paper develops a data-driven mixed-integer linear programming (MILP) model for planning one-way carsharing systems that consider the spatial distribution of demand and the interacting decisions between stations. The characteristics of existing stations and their spatiotemporal correlations are an important part of the model. To solve the MILP model, the extension of the Benders decomposition algorithm is adopted. The practicality of the proposed approach is demonstrated in a case study in Beijing, China. The results show that the existing planning of carsharing could result in a serious waste of resources. In contrast, the proposed method can obtain effective results in a reasonable time. The location results corresponding to a different rate of satisfied demand show that increasing the parking spots to improve the interaction between stations can effectively reduce the cost of operations. It should be noted that this paper only considers the benefit of operators. Future works will be carried out to optimize the one-way carsharing system by considering the benefits of operators, as well as the benefits of users and society. In addition, the impact of COVID-19 will be taken into account in future modeling and case studies. [ABSTRACT FROM AUTHOR]

Accurate monitoring of air quality can no longer meet people's needs. People hope to predict air quality in advance and make timely warnings and defenses to minimize the threat to life. This paper proposed a new air quality spatiotemporal prediction model to predict future air quality and is based on a large number of environmental data and a long short-term memory (LSTM) neural network. In order to capture the spatial and temporal characteristics of the pollutant concentration data, the data of the five sites with the highest correlation of time-series concentration of PM2.5 (particles with aerodynamic diameter ≤2.5 mm) at the experimental site were first extracted, and the weather data and other pollutant data at the same time were merged in the next step, extracting advanced spatiotemporal features through long- and short-term memory neural networks. The model presented in this paper was compared with other baseline models on the hourly PM2.5 concentration data set collected at 35 air quality monitoring sites in Beijing from January 1, 2016, to December 31, 2017. The experimental results show that the performance of the proposed model is better than other baseline models. [ABSTRACT FROM AUTHOR]

The development of connected and autonomous vehicle (CAV) technology has received increasing attention in recent years. Although car-following behavior in mixed traffic with CAVs and human-driven vehicles (HDVs) is a core component of microscopic traffic simulation, intelligent traffic systems, etc., the current study of car-following behavior in mixed traffic has some limitations. Furthermore, actual data do not support its applicability to the Chinese traffic environment. To address this gap, this paper designs and organizes a car-following experiment in mixed traffic in Beijing, extracts the trajectory data of CAVs and HDVs based on video recognition, and reconstructs the extracted trajectory data using the Lagrangian theory and Kalman filter theory to ensure the accuracy of the data. Based on this data set, this paper develops an extended car-following model. The model considers the cooperation between drivers by reformulating the prospect theory (PT). The root mean square percentage error (RMSPE) is selected to calibrate and validate the parameters of the proposed model, and the results show that there is significant heterogeneity between CAVs and HDVs in mixed traffic, and the proposed model captures this heterogeneity well. The model presented in this paper provides theoretical support for microscopic traffic simulation in mixed traffic. [ABSTRACT FROM AUTHOR]

Road rescue can provide rescue services for faulty vehicles, such as fuel delivery, tire replacement, battery connection, on-site repair, clearing, and towing, which plays an important role in reducing casualties and property losses in traffic accidents. Based on the historical data of road rescue, this paper analyzes the influencing factors of the road rescue demand and establishes a prediction model of the road rescue demand without data grouping. In order to further improve the prediction accuracy, the data are divided into nine groups according to the importance of the influencing factors, and nine submodels are established for the nine groups of data. When the influencing factors are known, the submodel corresponding to the most important influencing factor is selected to predict the road rescue demand. A case study in Beijing is used to verify the effectiveness and superiority of the proposed models, which can effectively predict the road rescue demand under various conditions, including the normal condition, the Spring Festival, National Day, the three-day holiday (e.g., Qingming, May Day, the Dragon Boat Festival, the Mid-Autumn Festival, and New Year's Day,), and extreme weather (e.g., low temperature, high temperature, heavy snow, heavy rain, and rainstorm). The research findings can provide scientific basis for the rescue department to deploy rescue equipment and rescue personnel in advance, raise the efficiency and quality of rescue, and improve the resilience of the transportation system. [ABSTRACT FROM AUTHOR]

A detailed evaluation of the riding environment can help the government master the urban riding environment, identify problematic road sections, and improve riding quality. However, the current evaluation of riding environment is mainly subjective, lacking big data (e.g., shared bicycle trajectory data) as a data-driven objective evaluation system. The emergence of shared bicycle data has provided data support for data-driven riding environment evaluation, but there are few studies using shared bicycle data for riding evaluation at present. First, according to the characteristics of the data and the riding environment, a boxplot method and Bayesian probabilistic network model are used to exclude abnormal data and to match trajectories to road sections. Second, this paper proposes a data-driven evaluation framework based on riding influencing factors. An evaluation framework, which is composed of node-, link-, and block-level evaluation indicators, was constructed, using an evaluation model combining TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and KNN (K-Nearest Neighbors). The evaluation results can identify parking issues, intersection difficulties, and lane occupancy issues on bicycle sections, and visually reflect the riding environment. The significance of this article is to create an objective evaluation system based on a data-driven technology to accurately identify sections and causes of riding quality problems. The research results can be applied in the future to evaluate the cycling environment around the railway stations for bicycle parking planning and determine the foothold for traffic management. [ABSTRACT FROM AUTHOR]

With increasing energy consumption in urban rail transit systems, researchers have paid significant attention to energy-saving train control. In this paper, we propose an effective train trajectory optimization method to reduce the energy consumption based on coasting control, in which coasting control regimes are added to balance running time and energy consumption. For better determining the starting points of coasting control regimes, the whole train running process is divided into several subintervals. Then, aiming to achieve energy efficiency, coasting regimes are added to the subintervals with high energy-saving effects, in which more energy consumption can be reduced with the same running time addition. Based on this, a coasting control method is proposed to generate energy-saving trajectories considering train dynamics, safety, and punctuality. In addition, the proposed method can solve the multisection energy-saving train trajectory optimization problem to obtain optimal running time schemes and related trajectories. Finally, numerical examples based on one of the Beijing metro lines are implemented to verify the effectiveness of the proposed method. The results show that, for the single-section train control problem, the proposed coasting control algorithm can achieve significant energy-saving effects compared to the practical trajectory and calculate energy-saving trajectory in shorter computation times compared to the dynamic programming method. Meanwhile, for the multisection train control problem, energy consumption can be further reduced by optimizing trajectories and running times integratedly. [ABSTRACT FROM AUTHOR]

Urban public transit has been rapidly developed in recent years. However, given increases in travel volume, the level of service still needs to be improved to meet the satisfaction of passengers. Transit service providers and researchers have focused on improving transit devices, but the service level of public transit has not yet been effectively improved, so more and more research is interested in analyzing the travel patterns of passengers. Compared with traditional survey methods, smart card collection systems—which can collect spatial-temporal information about passengers' trips—are convenient for the study of bus and subway passengers' travel patterns. However, the data provided by smart cards have not yet been fully explored. Therefore, this paper proposed a multistep methodology to gather information on the travel patterns of bus and subway passengers in Beijing, China. We conducted statistical analyses and used an unsupervised clustering method to study and classify passengers based on travel patterns. Four groups have been identified: standard commuters, flexible commuters, and two types of low-frequency passengers. Then, a comprehensive analysis was conducted. We also discussed the changes of passengers' travel time consumption before and after the implementation of customized bus for high-frequency passengers. The analyses indicated that passengers' travel patterns can provide useful information for transit service providers and can help improve the level of service of urban public transit by promoting the promulgation of local public transport policies and the implementation of customized services. [ABSTRACT FROM AUTHOR]

Steel pipe columns are an important component in subway station construction and with stand stress state and internal force during the construction process. Based on the engineering background of Beijing subway, this paper introduces the geological conditions, station structure types, and internal force monitoring of steel pipe columns of 10 subway stations constructed by the PBA method. The field monitoring indicates that the greatest change in the internal force of the steel pipe column occurs during stage 1 (beam, primary support, and secondary lining buckle arch construction stage), no matter pile foundation station or strip foundation station, which accounts for about 60% to 65% of the total axial force. When the left and right spans are not synchronously arched or excavated, the steel pipe column is subjected to uneven forces and bears a large bending moment is produced. When the depth of overburden is between 7 m and 15 m, the internal force of the steel pipe changes positively correlated with the overburden depth. During the construction process, the internal force of the steel pipe varies widely in the silty sand stratum. Also, the steel pipe columns in stations affected by groundwater bears about twice of the axial force compared with other stations are not affected by groundwater. In addition, the construction period is also a factor that affects changes in internal forces. This research is conducive to update the database of the world subway projects and can serve as a practical reference for similar geological condition. [ABSTRACT FROM AUTHOR]

With the rapid development of urban rail transit in various parts of China, the supply and demand contradiction between the commercial development level of rail transit integrated transfer hubs and passenger demand has become increasingly obvious, and it is of great significance to clarify the consumption characteristics and influencing factors of passengers from the rail transit integrated transfer hubs to solve the contradiction between supply and demand. In this paper, passengers of major rail transit integrated transfer hubs in Beijing and Tianjin are selected as samples, and combined with questionnaire survey and data statistics, the consumption characteristics of passengers at hub stations are quantitatively analyzed, and the influence mechanism of passengers' basic attributes and travel behavior on their consumption characteristics is studied. Through data processing, correlation analysis, regression analysis, and other analysis methods, five key factors affecting passenger consumption and the corresponding influence mechanism are obtained, respectively, and the regression model of five consumption characteristics is obtained, which lays a foundation for the optimization and improvement of the commercial layout of the integrated transfer hub of rail transit in the future. [ABSTRACT FROM AUTHOR]

In the rapidly changing multimodal transportation market, not only the obligation in providing train services but also the responsibility in helping other transportation modes to meet emergencies are instrumental for maximizing the generalized benefit of railway companies, leading to an increased utilization. This paper proposes an optimization framework, which includes multiple dispatching measures of arranging residual seats, modifying stopping plans, and inserting additional trains, to design a rescheduling operation plan on a high-speed rail corridor to meet the passenger demand of unexpected large passenger flow (ULPF), which is generated from the disruption of other transportation modes. Considering the revenues of shifting passengers and the costs of the three dispatching measures as two objectives, we formulate a linear integer programming (LIP) model by employing a time-space network to obtain an optimization rescheduling operation plan. Several experiments based on the Beijing-Shanghai high-speed railway corridor are conducted to evaluate the effectiveness and efficiency of the proposed model. The experimental results demonstrate that the proposed model can be used to obtain a reasonable rescheduling operation plan for serving the passengers from ULPF within an acceptable calculation timeframe. [ABSTRACT FROM AUTHOR]

Summary. With the support of national policies and the holding of the 2022 Beijing Winter Olympic Games, China's sports market presents an unprecedented prosperity, but it also faces many challenges. The market environment in which the sports industry is located is full of uncertainty and complexity. Creative products, competitive sports enterprises, and clear business models are urgently needed. Research purpose. To explore the future development mode and the prospect of China's sports undertakings and enterprises through the analysis and research on the competitive advantage of China's sports industry. In this context, since 2016, this study has started with the micromarket players. Research methods. Using the case study method highly praised by the international economic management academic circles, combined with in-depth learning, this study has conducted a systematic and comprehensive study on a total of 50 Chinese sports enterprises by mining and analyzing the unstructured text, video, and other data obtained from public materials and interviews. This paper deeply analyzes the competitive strategy of China's sports enterprises, creates and maintains competitive advantages in the sports industry, and studies the model construction of case studies and analysis. Conclusion. The study found that the product and business model, the leadership of managers, the innovation ability of teams, and the external market environment are the core elements for sports enterprises to formulate competitive strategies and create and maintain competitive advantages. The organic combination of the four eventually forms a competitive advantage that is difficult to replicate and finally forms a value chain through the implementation of competitive strategies so as to realize the value promotion and sustainable development of enterprises. [ABSTRACT FROM AUTHOR]

Based on the PSR conceptual evaluation model of urban green and low-carbon development competitiveness, this paper evaluates the green and low-carbon development competitiveness of 13 cities in Beijing-Tianjin-Hebei from 2010 to 2019 and reveals their spatial effects and obstacle factors combined with the spatial autocorrelation and obstacle model. The results show the following: (1) In terms of the overall situation, the evaluation index of urban green and low-carbon development competitiveness in the Beijing-Tianjin-Hebei region shows a fluctuating upward trend as a whole from 2010 to 2019. Among them, the comprehensive evaluation indexes of Beijing, Zhangjiakou, and Tianjin are very competitive, while Hengshui, Handan, and Xingtai are relatively weak. (2) In terms of spatial data analysis, the global and local Moran's I of Beijing-Tianjin-Hebei cities from 2010 to 2019 were positive and passed the 1% significant level test. This shows that the comprehensive evaluation index of urban green low-carbon development competitiveness of Beijing-Tianjin-Hebei cities has a positive spatial correlation, and the spatial agglomeration effect is significant. On the whole, it presents a spatial agglomeration pattern of "high in the north, partial jump, and low in the south". (3) In the aspect of obstacle factor analysis, the proportion of the tertiary industry output value in GDP, energy consumption of ten thousand yuan of GDP, the urban green technology innovation level, and industrial soot emission of ten thousand yuan of GDP are the main obstacle factors restricting the improvement of urban green low-carbon development competitiveness in Beijing-Tianjin-Hebei cities. [ABSTRACT FROM AUTHOR]

Under the background of the promotion of the Olympic effect of the 2022 Beijing Winter Olympics and the in-depth implementation of the national ice and snow industry's strategy of "expanding from the south to the west and expanding from the east," China's ice and snow industry has ushered in a new period of development. By applying literature and logical analysis, etc., with in-depth analysis of various subjective and objective supplies for the integration of China's ice-snow industry and ecocultural tourism in Hunan Province, it is considered to be driven by the bidding and preparation effect of Beijing Winter Olympic Games in 2022. The supply capacity of ice-snow industry venues, professional facilities, and public services are gradually strengthened, and the soft power of construction of the ice-snow industry needs to be coordinated and improved. The problem of safety is prominent and it is urgent to build a safety prevention and emergency management of ice-snow tourism.The integration of ice-snow sports and ecotourism industry has gradually became a prototype, but the integration point, line, and surface still need to be further strengthened and expanded. Accordingly, this paper puts forward a method to realize the transformation and upgrading of the commercial operation mode of the integration of ice-snow industry and ecological culture tourism, comprehensively to improve the ecological innovation ability of ice-snow tourism public service, to create multi-industry ice-snow industry cluster area, and to construct the basic framework system of risk prevention and control and management with multisubject participation and other relevant strategies. [ABSTRACT FROM AUTHOR]

Since the limitation of carbon emissions, China's photovoltaic (PV) industry has developed vigorously, while some traditional heavy industries have been violently hit. Therefore, the industrial production data exhibits significant nonlinear and complexity characteristics, which may affect prediction accuracy, thus hindering the corresponding department's decision-making. Consequently, a novel structure-adaptive fractional Bernoulli grey model is presented in this paper to surmount this toughie, and the core innovations can be summarized as follows. Initially, a novel time function term is utilized to depict the accumulative time effect, which can smoothly represent the dynamic variations and significantly strengthen the robustness of the new model. Besides, the fractional-order accumulation technique, which could effectively improve the predicting accuracy, is employed in the proposed model. Furthermore, the adaptability and generalizability of the proposed model can be enhanced by the self-adaptive parameters optimized by the Particle Swarm Optimization. For illustration and verification purposes, experiments on forecasting the annual output of Photovoltaic modules in China and the annual output of steel in Beijing are compared with a range of benchmarks, including the classic GM (1, 1), conventional econometric technology, and machine learning methods. And the results confirmed that the proposed model is superior to all benchmark models, which indicates that the novel model is indeed suitable for industrial production forecasting. [ABSTRACT FROM AUTHOR]

Traffic prediction is the cornerstone of intelligent transportation system. In recent years, graph neural network has become the mainstream traffic prediction method due to its excellent processing ability of unstructured data. However, the network relationship in the real world is more complex. Multiple nodes and various associations such as different types of stations and lines in rail transit always exist at the same time. In an end-to-end model, the training accuracy will suffer if the same weights are assigned to multiple views. Thus, this paper proposes a framework with multi-view and multi-layer attention, which aims to solve the problem of node prediction involving multiple relationships. Specifically, the proposed model maps multiple relationships into multiple views. A graph convolutional neural network of multiple views with multi-layer attention learns the optimal regression of nodes. Furthermore, the model uses an autoencoder module to alleviate the over-smoothing problem during the training phase. With the historical dataset of Beijing rail transit, the experiment proves that the prediction accuracy of the model is generally better than the baseline traffic prediction algorithms. [ABSTRACT FROM AUTHOR]

With the advent of the big data era, the use of computers has spread to all walks of life, and the finance and taxation industry is also in the middle of it. The current taxation system is huge and complex, and different tax types are inevitably linked to different economic indicators at a deep level, so tax forecasting requires personalised forecasting analysis for different tax types. This paper selects several tax types that account for a large proportion of tax revenue for prediction analysis, respectively, and conducts fusion research on multi-source big data, including business tax, corporate income tax, and personal income tax. Based on the multi-source big data fusion method, the prediction research on fiscal taxation tax types is conducted, and experiments are conducted with the taxation data of Beijing from 1995 to 2020 to predict the three tax types from 2017 to 2020. The results show that the deviation of the forecast data from the real tax data is small, controlling the forecast deviation to within 14%, indicating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the feasibility and applicability of the test and evaluation methods for the temperature and energy consumption coefficient in the cold storage system are tested and verified which is specified in the Beijing local standard "Energy Conservation Monitoring of Refrigeration Storage." It also summarizes and analyses the test verification results of the evaluation index of the location of measuring points of temperature in refrigeration storage, data-collecting time interval of temperature in refrigeration storage, energy efficiency coefficient of the refrigeration unit, and energy consumption coefficient of the refrigeration. The study work provides technical support for making the monitor and evaluation methods which applies to the standard. [ABSTRACT FROM AUTHOR]

In order to tackle the congestion and environmental issues, customized bus services are proposed and deployed in metropolitan areas. As emerging public transportation services, customized bus services bring passengers more convenience and accessibility. Besides, conventional customized bus services generally organize homogeneous fleet and single location selection to passengers. In this paper, to enhance the mobility and flexibility of customized buses and increase companies' profit, we propose a new form of customized bus service with heterogeneous fleets and multiple candidate locations. First, a mixed-integer programming model (MIP) is developed to describe the customized bus problem. Compared with the conventional model, the proposed MIP is involved in the case of one passenger with multiple candidate pickup or delivery locations and can be solved by GUROBI on small scale, quickly and efficiently. Second, an improved adaptive large neighborhood search algorithm ( ALNS i p ) is utilized to address the large-scale problem more efficiently. Time slack calculation method is then designed to optimize vehicle timetables, which provides stable and excellent performance for searching feasible solutions. In addition, we propose two inserting operators to deal with the problem with multiple candidate locations and analyse its influence on the results. Finally, we test the performance of the proposed model and algorithm on the numerical experiments. And they are verified the effectiveness and implication in a small-scale case on a simplified Sioux waterfall network and a large-scale problem in Beijing, China. The result shows that ALNS i p outperforms other algorithms in searching for more satisfying solutions with higher efficiency. However, the GUROBI solver can obtain the solution to small-scale problems within a shorter time than ALNS i p . Furthermore, it can be suggested that the heterogeneous fleets service with multiple candidate locations is helpful to facilitate collaboration among vehicles and optimize pickup and delivery routes in consequence. [ABSTRACT FROM AUTHOR]

Large-scale activities such as the Winter Olympics are usually held in areas with low temperature or other harsh environments, which greatly affects the spectating experience of pedestrians. In order to improve the travel efficiency and reduce the safety risk of pedestrians, an adaptive information-distribution strategy of VMS (variable message sign) for road networks is proposed to guide the pedestrians. In the proposed strategy, the dynamic feedback mechanism between the VMS information distribution and the state of crowded pedestrians is established, and the dynamic optimization model of the VMS information release layout is formulated. To evaluate the effectiveness of the strategy, a multiagent-based simulation method is proposed. Through numerical simulation, it is found that the guidance strategy can improve the movement efficiency by adjusting releasing duration of VMS information or improving the information obedience rate of pedestrians. In this paper, a large-scale competition area in the Xiaohaituo Mountain in Beijing was taken as an example to simulate the scenarios of ingress and egress with and without the strategy. The results show that the average walking time and the road congestion can be significantly reduced in the road network with the strategy, and the proportion of pedestrians with shorter travel time can be increased. Therefore, the research can provide theoretical foundation and data support for managers to guide passenger flows and improve the spectating experience. [ABSTRACT FROM AUTHOR]

Public management service is the key to urban intelligent construction. This paper proposes an analysis method and model based on Spark big data framework and takes resident income, happiness index, urban planning, and ecological environment as the indicators of Spark big data. From the high difficulty of Spark big data cluster analysis of urban public management, we build the index weight by the entropy weight method, optimize the similarity calculation, and achieve the rapid understanding of urban public management. Subsequently, the Spark big data public management platform is applied to the public management of Beijing. The results indicate that the public management platform based on Spark big data framework can improve the public management level of the city and help to build an intelligent city. [ABSTRACT FROM AUTHOR]

The Olympic Games is a comprehensive social and cultural activity with the most complicated system and the largest scale. With the development of the Olympic Games, the ecological problems brought by the Olympic Games have attracted increasing attention, and the sustainable development of the Olympic Games has been put on the agenda. Based on the comprehensive index of Ecological Carrying Capacity (ECC), Environmental Kuznets Curve, and carbon footprint analysis of the Olympic Games host city competition cycle, this study analyzes three modes of ECC of the host city: light urban ecological burden mode, heavy urban ecological burden mode, and overload urban ecological burden mode. Based on the temperature sensor and GPS positioning, the land surface temperature change map of Tokyo, Japan, from 1990 to 2015 is obtained, and the heat island effect of Tokyo is obtained. This paper analyzes the case of using sensors for intelligent event management such as venue detection in the sustainable development plan of the 2020 Tokyo Olympics; the idea and practice of thrifty hosting of 2022 Beijing Winter Olympics and 2022 Hangzhou Asian Games holds that in most cases, the ECC of the host city of the Olympic Games is under great pressure, so it is necessary to pay attention to the ECC of the host city. The sustainable development of the Olympic Games is an important issue in the development of the Olympics, but the sustainable development plan of the Olympics is still being explored. It is suggested that the sustainable development of the Olympic Games needs to evaluate the ECC of the host city in the whole cycle, establish the principle of ecological priority to avoid the overload mode of ecological burden, strive to achieve carbon neutrality in the competition, and practice the idea of scientific frugality in running the competition. [ABSTRACT FROM AUTHOR]

A data-driven intelligent analysis method is proposed in this paper to explore and identify the enterprise's technological innovation influencing factors. Questionnaire surveys or expert interviews are usually adopted by the traditional evaluation methods for indicators of technological innovation selection. However, it inevitably involves human factors and experts' subjective judgments, which may affect the result of enterprises evaluation. The research presents an improved text clustering method based on a semantic concept model to explore and analyze the key influencing factors of enterprise's technological innovation. The study collects textual data from 400 enterprises in Beijing and smart analyzes the critical influencing factors of enterprise's technological innovation by using the proposed method. The influencing factors can be divided into seven categories. In addition, compared with the traditional K-means clustering method, the proposed method has a good effect. We proposed a methodology to conduct an intelligent analysis for enterprise's technological innovation under the data-driven. It can provide more objective and auxiliary suggestions for the evaluation of the enterprise's technology innovation. [ABSTRACT FROM AUTHOR]

As a national economic, political, and cultural center that ranks first in terms of openness to the outside world, the signing of the RCEP agreement has undoubtedly brought a series of opportunities and challenges to Beijing. Based on the analysis of the two main features embodied in the RCEP agreement, this paper precisely analyzes the areas where the RCEP agreement has a key impact on Beijing's economy and trade and points out the opportunities and challenges brought by the RCEP agreement to Beijing's economic and trade development. [ABSTRACT FROM AUTHOR]

In view of the fact that the density-based clustering algorithm is sensitive to the input data, which results in the limitation of computing space and poor timeliness, a new method is proposed based on grid information entropy clustering algorithm for mining hotspots of taxi passengers. This paper selects representative geographical areas of Nanjing and Beijing as the research areas and uses information entropy and aggregation degree to analyze the distribution of passenger-carrying points. This algorithm uses a grid instead of original trajectory data to calculate and excavate taxi passenger hotspots. Through the comparison and analysis of the data of taxi loading points in Nanjing and Beijing, it is found that the experimental results are consistent with the actual urban passenger hotspots, which verifies the effectiveness of the algorithm. It overcomes the shortcomings of a density-based clustering algorithm that is limited by computing space and poor timeliness, reduces the size of data needed to be processed, and has greater flexibility to process and analyze massive data. The research results can provide an important scientific basis for urban traffic guidance and urban management. [ABSTRACT FROM AUTHOR]

Historic districts represent an important characteristic of Beijing and are also a crucial carrier of Chinese historic culture. However, they are significantly affected by the rapid urban constructions. Thus, it is of great significance to maintain and promote the public space in historic districts. This paper uses a multisource data superposition method to select the evaluation index of public space. The AHP was also used to complete the single-level and total-level ranking and calculation of evaluation indexes. Finally, based on the DEA model, a vitality evaluation model of Beijing historic district public spaces was developed and its validity was verified through a case study of the Wanping historic district. [ABSTRACT FROM AUTHOR]

The Beijing Winter Olympic Games is an extremely important event, and the supply of electricity is the basis for it. In order to prevent the sudden loss of power to the event, it is necessary to carry out power repair and restore normal power supply in the shortest possible time. Contemporary research is less focused on the emergency repair of power systems. This thesis studies the dispatching of power emergency materials in two stages and in the first stage, the minimum gap rate and the shortest time for material dispatching and transportation are mainly considered, and the required types of power emergency materials are dispatched from the storage near the Olympic venues to various power failure points. In the second stage, aiming at the shortage of power emergency materials at each fault point, the power emergency materials will be dispatched from the rear national power grid regional material reserve to the power emergency materials reserve near the Olympic venues and then dispatched to each power fault point through the power emergency materials reserve. Finally, this paper verifies the effectiveness of the combination of the primary and secondary dispatching models in an example, providing a reference for further improving the emergency response capability of the Olympic venues and improving the safety system of the power system. [ABSTRACT FROM AUTHOR]

The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detection, and there are few researches on the real-time temporal anomaly detection. In this paper, a grid-based algorithm, which combines the spatial and temporal anomaly detection, is proposed for tracing the risk of urban freight vehicles trajectory by considering local temporal window. The travel time probability distribution of vehicle historical trajectory is analyzed to meet the time complexity requirements of real-time anomaly calculation. The developed methodology is applied to a case study in Beijing to demonstrate its accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections. [ABSTRACT FROM AUTHOR]

The accurate and rapid prediction of ticket prices for a public-private partnership (PPP) subway system, which is an important research topic in the field of civil engineering management, is of critical importance to ensure its smooth operation. To effectively cope with the effects of multiple influencing factors and strong nonlinearity among them, the mean impact value (MIV) method and the back-propagation (BP) feed-forward neural network improved by the sparrow search algorithm (SSA) are used in this study to develop an intelligent prediction model. First, we considered the relationship of the supply and the subway system service, which is a typical quasi-public product, and analyzed the relevant factors affecting its price adjustment. Then, we developed an intelligent method for the prediction of ticket prices based on the SSA-BP. This model not only makes full use of the powerful nonlinear modeling ability of the BP algorithm, but also takes advantage of the strong optimization ability and fast convergence speed of the SSA. Finally, this study screened out the key input factors by adopting the MIV method to simplify the structure of the BP algorithm and achieve a high prediction accuracy. In this study, Beijing Subway Line 4, Wuhan Metro Line 2, and Chengdu Metro Line 1 were selected as case study sites. The results showed that the linear correlations between influencing factors and ticket price for the PPP subway system service were weak, which indicated the need for using nonlinear analysis methods such as the BP algorithm. Compared with other prediction methods (the price adjustment method based on PPP contract, the traditional BP algorithm, the BP neural network improved by the genetic algorithm, the BP algorithm improved by the particle swarm optimization, and the support vector machine), the model proposed in this paper showed better prediction accuracy and calculation stability. [ABSTRACT FROM AUTHOR]

Redevelopment of abandoned coal mine industrial sites (ACMISs) has been one of the critical research topics in recent years. The ecological system of ACMISs, which involves environmental, economic, and social characteristics, is an environment-economy-society (EES) composite system. Many factors with conflicting effects are involved in the sustainable development of this system. The matter-element analysis method presented in this paper can transform counteracting problems among influencing factors into compatible problems. This analytical approach leads to a reduction of the uncertainty of these influencing factors and provides a suitable research method to solve the complexity when selecting the most suitable reuse mode of the ACMIS. In this study, eight state-owned ACMISs in the suburbs of Beijing were investigated, and a suitability evaluation index system based on the EES conceptual framework was built. Targeting four reuse modes, including the residential area, the park, the scientific research and office campus, and the business district, we established a matter-element decision-making model to evaluate the priority of reuse mode selections scientifically. The results of our calculations show that the optimal reuse mode for the Qianjuntai, Muchengjian, and Huapogen ACMISs is the park; for the Da'anshan and Changgouyu ACMISs is the business district; for the Datai ACMIS is the scientific research and office campus; and for the Wangping ACMIS is the residential area. Since the suitability level of reusing the Anjiatan ACMIS in any of the four reuse modes is relatively low, it is recommended to reserve it for governmental strategically reserved open space. These real cases demonstrate that the research method is feasible and can be applied to reuse mode decisions for similar abandoned coal mines. [ABSTRACT FROM AUTHOR]