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With the continuous development of public transportation, the impact of unexpected events on the operation of bus networks has become increasingly severe due to the growing demand for public transportation and passenger volume. To accurately assess the impact of unexpected events on the operation of bus networks and scientifically evaluate their resilience, this paper proposes a framework for analyzing the resilience of bus networks. With the aim of providing scientific evidence to enhance the reliability of public transportation networks, this framework can be used to determine the resilience of bus networks to unexpected events. The main contributions of this framework include three aspects: 1. Construction of the CRITIC–entropy weighting model for screening and calculating key indicators of the resilience of the bus network; 2. Use of resilience cycle theory to construct a model for analyzing the resilience of bus routes, and design a set of resilience quantification factors to calculate the resilience of bus routes; 3. Use of complex network theory to construct a model for analyzing the resilience of the bus network, by taking the bus route resilience obtained in the second step as the edge weight to calculate the resilience of the bus network. This paper takes the Beijing public transit system as an example and uses real data to verify the accuracy, scientificity, and feasibility of the proposed framework for analyzing the resilience of public transit networks to sudden events. The resilience analysis framework constructed in this paper has improved the existing research on transportation network resilience in theoretical aspects. Furthermore, the results outputted by this framework can provide a decision-making basis for network adjustment and disaster recovery for the management departments of public transportation networks in practical applications. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous improvement of urban public transportation capacity, citizens' travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression (MLR), K-Nearest Neighbor (KNN), eXtreme Gradient Boosting (XGBoost), and Gated Recurrent Unit (GRU) as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the "Huitian Area" of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation. [ABSTRACT FROM AUTHOR]

With the increasing number of car trips in cities, energy consumption and environmental pollution have become key issues in urban transportation. In particular, the increasing use of private cars not only leads to more energy consumption and produces more waste gas, but also makes the traffic structure unbalanced. Therefore, it is necessary to establish and optimize the reasonable traffic structure in order to promote the sustainable development of urban traffic. Based on the characteristics of different transportation modes, this paper proposes a multi-objective optimization model that maximizes transportation utility, minimizes ecological impact, and minimizes generalized cost. The ideal point method, linear weighting method, and hierarchical sequence method were used to solve and compare the model. It has been concluded that the ideal point method is more suitable for the research of this paper and can be applied to optimize the traffic structure of Beijing. Through example analysis, the optimized urban passenger traffic turnover and sharing rate are more scientific and reasonable, which verifies the feasibility of the model. This model not only guarantees the interests of passengers and reduces carbon emissions, it also maximizes the utility of urban traffic and the lowest generalized cost. It reflects the concept of sustainable development of urban transportation. Finally, we are able to make reasonable suggestions to relevant departments based on the optimization results. [ABSTRACT FROM AUTHOR]

Short-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, passenger flow prediction is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction model. The model is built using intelligent data provided by a large-scale urban traffic flow warning system, such as accurate passenger flow data, collected using the Internet of things and sensor networks. The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction. The obtained pre-diction fits well with the measured data. Therefore, the SARIMA–SVM model can fully charac-terize traffic variations and is suitable for passenger flow prediction. [ABSTRACT FROM AUTHOR]

The rapid growth of passenger flows has brought a series of challenges to the environment and safety management of tourist attractions. It is vital to establish an accurate passenger flow prediction model to reduce the risks associated with human flows. Owing to the limitation of a single data source, the existing research on the prediction of tourist flows in scenic spots ignores the impact of public transport passengers on the internal tourist flow in scenic areas. The prediction model lacks the learning process of data samples, and the ability of generalization and self-study is weak. In this paper, we propose a new method of predicting passenger flow in scenic areas based on a convolutional neural network and long short-term memory (CNN-LSTM) hybrid neural network (HNN) model, which considers the multisource traffic flow around a scenic area. It uses a series of HNNs to mine the temporal and spatial correlation between the passenger flows from multiple sources and solves the problem of data stability dependence. The time series of the passenger flow in the study area was designed and extracted on the basis of the spatial analysis of South Luogu Lane in Beijing, and the input structure was constructed by combining the multisource traffic passenger flow dimension. This model for predicting passenger flow in scenic areas based on CNN-LSTM provides a reference for the comprehensive application of multisource data in scenic areas and has high accuracy and robustness. [ABSTRACT FROM AUTHOR]

Using the calculation method of the United Nations World Tourism Organization (UNWTO), this paper measured the carbon emissions of tourism traffic and their evolution process in Beijing from 2005 to 2017. The Tapio model and Logarithm Mean Divisia Index (LMDI) approach were used to discuss the decoupling relationship between economic development and the change of tourism traffic carbon emissions and the influencing factors. There are six major indicators in our analysis, including (1) tourists scale, (2) per capita tourism consumption level, (3) contribution rate of tourism industry to Gross Domestic Product (GDP), (4) passenger traffic volume per unit GDP, (5) energy consumption per unit passenger traffic volume, and (6) energy structure. The results showed that: the main positive drivers are tourists scale, per capita tourism consumption level and energy consumption per unit of passenger traffic volume. In addition, per unit GDP passenger traffic volume is an effective factor to restrain the growth of carbon emissions. And the economic development of Beijing is developing in step with the change of carbon emissions in Beijing's tourism traffic. The research results have important theoretical and practical significance for Beijing to formulate emission reduction policies and develop low-carbon economy. [ABSTRACT FROM AUTHOR]

Predicting the passenger flow of public transport in a newly developed area of a city is very urgent for designing a precise and efficient public transport network. This paper proposes a new prediction model by exploring the relationship between the passenger flow of a station and its surrounding area's factors. First, in order to obtain more accurate factors affecting the passenger flow, the city is divided into multiple regions with similar internal traffic properties and moderate spatial size using the data of urban road network and buildings. Second, to effectively solve the problem of fuzziness of the station's attraction scope, the concept of the membership degree and fuzzy processing method is proposed. Finally, the station's passenger flow prediction model is launched based on Xgboost. The experimental results on three districts in Beijing show that our method outperforms all baselines significantly, which improves the accuracy by more than 20%. [ABSTRACT FROM AUTHOR]

Railway transport becomes a more popular transportation in many countries due to its large transport capacity, low energy consumption, and benign environment. The passenger train service planning is the key of the rail operations system to balance the transport service and the passenger demand. In this paper, we propose a mixed binary linear programming formulation for the passenger train service planning to optimize the train route, frequency, stop schedule, and passenger assignment simultaneously. In addition, we analyze the computational complexities of the model and develop a Benders decomposition algorithm with valid inequalities to solve this problem. Finally, our model and algorithm are tested on a real-world instance of the Beijing-Shanghai high-speed railway line. The computational results show that our approach can solve these problems within reasonable solution time and small optimality gaps (less than 2.5%). [ABSTRACT FROM AUTHOR]

The transport sector accounts for a large proportion of energy demand and environmental emissions and is growing rapidly. The city is the hub of modern human life and the urban transport sector is developing fast. Specifically, in the Internet era, the development of public transport has brought new challenges and opportunities to urban passenger transport. Thus, research on the energy demand and environmental emissions of the urban transport sector is significant and timely. This paper takes Beijing’s public transport as an example and applies the Long-range Energy Alternatives Planning (LEAP) model to analyze the energy demand and the main greenhouse gas (GHG) emissions under different scenarios during the period 2016–2030. The results show that: 1) if the government fails to guide the development of passenger transport appropriately, its energy demand and GHG emissions will rise rapidly and under the baseline scenario, the energy demand and emissions in 2030 are about 1.8 times those oft 2015; 2) the scenarios of developing public transport and transport sharing have a significant effect of energy saving and emission reduction and in 2030 the energy demand and GHG emissions falls by 25%–30% compared with that of the baseline scenario, so the combination of public transport and transport sharing is a wise choice for the low carbon development of the passenger transport sector in Beijing; 3) scenarios of easing road congestion and new-energy and clean-energy vehicle development have huge potential for energy saving and emission reduction, particularly the internal adjustment of the energy structure in the scenario of developing new-energy and clean-energy vehicles, which can greatly reduce the demand for traditional energy sources and reduce GHG emissions. This paper illustrates the development trends of energy demand and GHG emissions in Beijing’s passenger transport sector under the scenarios of transport sharing development and traditional development in the Internet era, providing a basis for decision making. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the railway passenger volume data are abnormal due to holidays and major events interfering with the prediction accuracy, the spline interpolation method is introduced to replace the abnormal passenger volume data. In addition, an improved particle swarm optimization (IPSO) is proposed to optimize the gradient difference acceleration law to combine and improve the predicted value and further improve the prediction accuracy of the railway passenger traffic. Finally, taking Beijing as the research object, the Holt exponential smoothing method and the BP neural network are selected to verify the effect of spline interpolation and IPSO-gradient difference acceleration law on prediction accuracy. The research results show that the spline interpolation method has a better prediction effect after processing abnormal passenger traffic data, and the improved particle swarm algorithm also shows better optimization ability and convergence speed when solving the double difference postulate. In comparison with the BP neural network, Holt exponential smoothing, simple averaging, and conventional redifference approaches, the IPSO-redifference acceleration method achieves a superior prediction performance, and the absolute values of the forecast error are reduced by 3.320%, 1.518%, 2.419%, and 0.602%. [ABSTRACT FROM AUTHOR]

To reveal the complexity of China's urban network, this paper proposes an empirical approach to examining the evolutionary mechanism of, rather than just mapping, the spatial pattern of the Chinese urban network from 1997 to 2015. The results show that the Chinese urban network is a complex structure that is embedded in China's unique context, where two interwoven processes, namely, globalization and decentralization, have shaped the network's spatial pattern. Moreover, the Chinese urban network is shifting from a single-core structure, with Beijing as its center, towards a diamond structure, anchored by the four 'national centers' of Beijing, Shanghai, Guangzhou, and Chengdu. Along with the transformation of their network structure, Chinese cities are interacting more closely and frequently. The results of the exponential random graph model (ERGM) show that the two interconnected forces, namely, globalization and decentralization, have tremendous impacts on the evolution of the Chinese urban network. Specifically, the uneven decentralization has a complex effect on the evolution of China's urban network in terms of a polarization-trickle-down-repolarization process. And the integration of inland China into the process of globalization has a significant effect on the connectivity of Chinese cities and causes a significant rise of central and western cities within the Chinese urban network. • China's urban network demonstrates a diamond structure anchored by four core cities. • Chinese cities are interacting more closely and frequently over time. • The impact of decentralization leads to an evolutionary process of polarization-trickle-down-repolarization. • The rising of central and western cities is closely related to the integration into the process of globalization. [ABSTRACT FROM AUTHOR]

More and more people in mega cities are choosing to travel by public transportation due to its convenience and punctuality. It is widely acknowledged that there may be some potential associations between passengers. Their travel behavior may be working together, shopping together, or even some abnormal behaviors, such as stealing or begging. Thus, analyzing association between passengers is very important for management departments. It is very helpful to make operational plans, provide better services to passengers and ensure public transport safety. In order to quickly explore the association between passengers, we propose a multi-view interactive exploration method that provides five interactive views passenger 3D travel trajectory view, passenger travel time pixel matrix view, passenger origin-destination chord view, passenger travel vehicle bubble chart view and passenger 2D travel trajectory view. It can explore the associated passengers from multiple aspects such as travel trajectory, travel area, travel time, and vehicles used for travel. Using Beijing public transportation data, the experimental results verified that our method can effectively explore the association between passengers and deduce the relationship. [ABSTRACT FROM AUTHOR]

Optimizing to increase the utilization ratio of regenerative braking energy reduces energy consumption, and can be done without increasing the deviation of train running time in one circle. The latter entails that the train timetable is upheld, which guarantees that the demand for passenger transport services is met and the quality of services in the urban rail transit system is maintained. This study proposes a multi-objective optimization model for urban railways with timetable optimization to minimize the total energy consumption of trains while maximizing the quality of service. To this end, we apply the principles and ideas of calculus to reduce the power of the velocity in the train energy consumption model. This greatly simplifies the complexity of the optimization model. Then, considering the conflicting requirements of decision-makers, weight factors are added to the objective functions to reflect decision-makers' preferences for energy-saving and the quality of service. We adopt the nondominated sorting genetic algorithm-II (NSGA-II) to solve the proposed model. A practical case study of the Yizhuang urban railway line in Beijing is conducted to verify the effectiveness of the proposed model and evaluate the advantages of the optimal energy saving timetable (OEST) in comparison to the optimal quality of service timetable (OQOST). The results showed that the OEST reduced total energy consumption by 8.72% but increased the deviation of trains running time in one circle by 728 s. The total energy consumption was reduced by 6.09%, but there was no increase in the deviation of train running time in one circle with the OQOST. [ABSTRACT FROM AUTHOR]

Existing studies on the selection of railway travel modes almost always compare high‐speed trains and regular‐speed trains. In this study, three high‐speed trains (Revival, Harmony, and Electric multiple unites (EMU)) on the Beijing–Shanghai line, and the first‐ and second‐class seats on these three trains are selected as research objects to analyse the travel selection behaviour of Chinese high‐speed train passengers. Three methods are selected to study this behaviour: the Support Vector Machine, Nested Logit model, and Multiple Logit model. The results of these three models are calibrated using LIBSVM software and STATA software and show that age, funding source, income level, and purpose of travel are major factors that affect Chinese railway passengers' choices on high‐speed railways. Finally, it is shown that the Support Vector Machine is the most accurate of these three methods, followed by the Nested Logit model. The results of this study can complement existing research on the travel selection behaviour of Chinese railway passengers and has important implications for enabling China's high‐speed rail operators to adjust their passenger transportation products. [ABSTRACT FROM AUTHOR]