Search

Traffic violation is one of the leading causes of traffic crashes. In the context of global aging, it is important to study traffic violations by elderly drivers for improving traffic safety in preparation for a worldwide aging population. In this study, a hybrid approach of Latent Class Analysis (LCA) and XGBoost based SHAP is proposed to identify hidden clusters and to understand the key contributing factors on the severity of traffic violations by elderly drivers, based on the police-reported traffic violation dataset of Beijing (China). First, LCA is applied to segment the dataset into several latent homogeneous clusters, then XGBoost based SHAP is established on each cluster to identify feature contributions and the interaction effects of the key contributing factors on the severity of traffic violations by elderly drivers. Two comparison groups were set up to analyze factors, which are responsible for the different severities of traffic violations. The results show that elderly drivers can be classified into four groups by age, urban or not, license, and season; factors such as less annual number of traffic violations, national & provincial highway, night and winter are key contributing factors for higher severity of traffic violations, which are consistent with common cognition; key contributing factors for all clusters are similar but not identical, for example, more annual number of traffic violations contribute to more severe violation for all clusters except for Cluster 2; some factors which are not key contributing factors may affect the severity of traffic violations when they are combined with other factors, for example, the combination of lower annual number of traffic violations and county & township highway contributes to more severe violation for Cluster 1. These findings can help government to formulate targeted countermeasures to decrease the severity of traffic violations by specific elderly groups and improve road service for the driving population. [ABSTRACT FROM AUTHOR]

How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00–8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance. [ABSTRACT FROM AUTHOR]

With population aging becoming increasingly prominent, it has been recognized as a critical issue for urban planners whether older adults can access fair, high-quality medical services. Yet, few studies have investigated urban medical accessibility at a departmental level for older adults. Taking Beijing as a case study, this study puts forth a novel framework augmented by multisource data to analyze subdistrict-scale accessibility to urban medical departments, with a particular emphasis on older adults' common diseases. Online registration data were collected to indicate the supply capacity of medical services, with the influence of department reputation taken into account when determining the facilities' service range. A hierarchical two-step floating catchment area method was employed to examine accessibility for older adults to the cardiovascular, orthopedic, endocrinology, respiratory, and neurology departments. Subsequently, Gini indices were calculated to evaluate medical accessibility equity across urban spaces. Additionally, multiple linear regression was used to explore the correlations between medical accessibility and potential influencing factors. Results showed that medical accessibility is typically higher in downtown areas and decreases from the urban center to the peripheral areas in Beijing. Cardiovascular, respiratory, and orthopedic patients face relatively greater difficulty in accessing appropriate medical resources. Furthermore, significant inequity in medical accessibility was observed among the subdistricts at different locations, with accessibility inequity in the ecological conservation area being particularly concerning. Additionally, seven factors were found to significantly affect accessibility, including distance to the urban center, density of arterial and collector roads, proportion of residential land use, proportion of public service land use, proportion of transportation land use, and proportion of vehicle ownership. The proposed framework could identify subdistricts with low accessibility for older adult patients to access different medical departments. Therefore, it could potentially support the policy-making process by offering guidelines to adjust the construction plan of the medical system in metropolises and optimize the direction for resource input. [ABSTRACT FROM AUTHOR]

Effective tourist demand forecasting is crucial for company operations and destination management. Furthermore, tourists may plan better personalized multi-attraction itineraries based on demand forecasting to avoid travel peaks and improve the enjoyment of their vacation. This study developed a unique deep learning model called the convolution block attention module (CBAM) that is built on convolutional blocks and attention modules to estimate tourism demand precisely. Then, the passenger flow grid map was extracted from mobile phone signaling data. To forecast the subsequent period of the passenger flow grid map, the CBAM model uses the multi-channel spatial-temporal grid graph that is built by multiple successive passenger flow grid maps. Finally, the forecasted passenger flow grid map was used to derive the tourist demand for multi-attractions for the next period. The analysis of mobile phone signaling data from Beijing and Xiamen using the proposed model reveals that its mean absolute percentage error (MAPE) is 8.11%, which is lower than other benchmark deep learning models. [ABSTRACT FROM AUTHOR]

Increasingly stringent regulations regarding vehicle emissions have contributed to the diversification of vehicle technologies, resulting in the increasing complexity of typical vehicles that make up a fleet. In order to investigate the real gas emissions of different typical vehicles, tests were conducted using a portable emission measurement system (PEMS) in Beijing and emission studies were conducted on eight light-duty passenger vehicles (LDPVs, including light-duty gasoline passenger vehicles and hybrid electric vehicles), eight heavy-duty passenger vehicles (HDPVs), and four light-duty trucks (LDTs). The results show that the emissions of relevant pollutants from LDPV meet the emission standard limits. The emission factors of CO2, CO, NOX, and HC of China IV and China V hybrid electric vehicles (HEVs) are much smaller than the emission standard limits and the emission factors of other vehicles, which have better emission reduction effects. Among LDPV, heavy-duty passenger vehicles (HDPVs), and LDT, the emissions of HDPV and LDT are extremely high. Emission characteristics vary on different types of roads, with the highest emission factors generally occurring on secondary roads. The micro-trip method was used to explore the influence of speed on emission factors. HEV are less sensitive to speed changes and can still maintain a low emission level at low speeds. The average speed and emission factors of HDPV in micro-trip has a strong correlation. [ABSTRACT FROM AUTHOR]

Excess passengers gathering on the urban rail transit platform in a short time brings huge security risks to passengers and the daily operation of urban rail transit. However, the real‐time monitoring method of passenger flows, which relies on manual methods, cannot satisfy the requirement of daily operations at the network level anymore. This study proposes a dynamic data‐driven computation and monitoring method for the number of waiting passengers on platforms to recognize the operational risk in real‐time. For waiting passengers on platforms, the waiting time duration before boarding is also calculated based on a first‐come‐first‐service basis. It can be used to provide real‐time information services to passengers and evaluate service quality. Moreover, the proposed methodology relies solely on the AFC data and the train timetable, which makes it easy to implement in the daily operation of any urban rail transit system. Finally, taking the Beijing rail transit network as a case study, the real‐time number of waiting passengers on each platform, and the time duration passengers should wait before boarding are calculated dynamically. Meanwhile, the spatio‐temporal distribution of passengers' waiting time and waiting passengers are detailly analyzed based on the method of cluster analysis and the complex network theory. [ABSTRACT FROM AUTHOR]

With the development of urbanization and the evolution of urban network systems, multimodal urban transport network (MUTN) systems play a vital role in improving network effects and operational efficiency. However, urban transport networks are easily affected by natural disasters and traffic incidents, which can lead to significant human and economic losses. Accordingly, it is vital to be able to assess the resilience of transport networks in the face of various disruptions. This study, therefore, utilizes complex network theory to analyze the resilience of multimodal urban transport networks, with the resilience accessed based on topological indices. The MUTN in Beijing is selected as a case study for simulation analysis. Based on the road network and subway network, a model MUTN is established, and the Monte Carlo method is used to simulate random attacks. The results show that the MUTN in Beijing has good resilience against disruptions. This study guides the evaluation of the overall resilience of multimodal urban transport networks and will be useful for transportation planners and decision-makers in dealing with emergencies and natural disasters in the future. [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]

More attention should be given to bicycle-motor vehicle (BMV) crashes, as cyclists are at a higher risk of suffering injuries than motor vehicle users in a crash. This study aims to explore the factors influencing the injury severity of bicycle-motor vehicle (BMV) crashes in Beijing (China) and discusses the commonalities and differences between the urban and suburban areas. Information regarding 1,136 crashes between bicycles and motor vehicles were collected using police reported data from 2014 to 2015. A two-stage approach integrating random parameters logit (RP-logit) model and two-step clustering (TSC) algorithm was proposed to investigate the significant influence factors and their combination characteristics. Specifically, the RP-logit model was first used to identify the significant influence factors of urban and suburban areas, and then the TSC algorithm was applied to reveal the combination characteristics of significant influence factors for the fatal crashes. Five factors were found to be statistically significant and had random effects on the injury severity in urban areas, i.e., type of motor vehicle, motor vehicle license ownership, type of bicycle, signal control mode and lighting condition; and seven factors were found to be statistically significant on the injury severity in suburban areas, i.e., type of motor vehicle, motor vehicle license ownership, physical isolation facility, signal control mode, weather, visibility and lighting condition. Based on TSC, the combination of significant factors showed different characteristics for fatal crashes in urban and suburban areas, in which two types of the scene including five factors should be concerned in urban areas while one type of scene containing four factors in suburban areas. The results suggest that different influence factors and individual heterogeneity exist in the RP-logit model for injury severity analysis of BMV crashes in urban and suburban areas. It shows that in urban areas, heavy truck, light truck and bus significantly increase the likelihood of fatal injury than that of suburban areas. These findings can provide valuable reference information for BMV crashes response, such as heavy truck restriction, to facilitate regional safety measures for urban and suburban areas. [ABSTRACT FROM AUTHOR]

The price of anarchy (PoA) is a standard measure for the inefficiency of selfish routing in the static Wardrop traffic model. Empirical studies and a recent analysis reveal a surprising property that the PoA tends to one when the total demand T gets large. These results are extended by a new framework for the limit analysis of the PoA in arbitrary nonatomic congestion games that apply to arbitrary growth patterns of T and all regularly varying cost functions. For routing games with Bureau of Public Road (BPR) cost functions, the convergence follows a power law determined by the degree of the BPR functions, and a related conjecture need not hold. These findings are confirmed by an empirical analysis of traffic in Beijing. We investigate the price of anarchy (PoA) in nonatomic congestion games when the total demand T gets very large. First results in this direction have recently been obtained by Colini-Baldeschi et al. (2016, 2017, 2020) for routing games and show that the PoA converges to one when the growth of the total demand T satisfies certain regularity conditions. We extend their results by developing a new framework for the limit analysis of the PoA that offers strong techniques such as the limit of games and applies to arbitrary growth patterns of T. We show that the PoA converges to one in the limit game regardless of the type of growth of T for a large class of cost functions that contains all polynomials and all regularly varying functions. For routing games with Bureau of Public Road (BPR) cost functions, we show in addition that socially optimal strategy profiles converge to equilibria in the limit game and that the PoA converges to one at a power law with exponent β , where β > 0 is the degree of the BPR functions. However, the precise convergence rate depends crucially on the the growth of T, which shows that a conjecture proposed by O'Hare et al. (2016) need not hold. [ABSTRACT FROM AUTHOR]

With the technological advancement, China's high-speed rail (HSR) has achieved a higher running speed than ever before, which at the same time leads to the intensive competition between air and high-speed rail with trips within 500–1000 km, as well as to the significant changes of travelers' air-to-HSR choice. In order to explore the dynamic adaptation of travelers' choice of high-speed rail (HSR) and air in the case of HSR speed-up, a logit dynamics model of travelers' choice based on evolutionary game was constructed to describe the evolution of travelers' choice. With HSR-air travel time and the ticket price of origin-destination (OD) points of Beijing-Shanghai as the benchmark, the study set up two scenarios of HSR speed-up and compared and analyzed their payoffs in value. On the basis of mathematical simulation, the travelers' "air-to-HSR choice" and the change of competition range under the background of HSR speed-up were both discussed. The result indicated that the HSR speed-up would attract more travelers, and its share rate would rise to 58.31% and 72.73%, respectively, with HSR running speed increasing to 400 and 600 km/h; the marginal utility of HSR speed-up is more obvious, and in the meantime, the increase of HSR running speed extends the intense HSR-air competition range to longer-distance travels. In the case of HSR speed-up, travelers' choice changes as well. The HSR operators themselves should formulate reasonable ticket prices and improve their management. At the same time, the air operators should adjust their operation plan in time. [ABSTRACT FROM AUTHOR]

In the bus network, key bus station failure can interrupt transfer lines, which leads to the low effectiveness of the whole network, especially during peak hours. Thus, identifying key stations in the bus network before the emergency occurs has a great significance to improve the response speed. In this paper, we proposed a new method considering station hybrid influence and passenger flow to identify key stations in the whole bus network. This method aims to measure the influence of bus stations while combining the topological structure of the bus network and dynamic bus stations passenger flow. The influence of bus stations was calculated based on the local structure of the network, which refines from finding the shortest paths with high computational complexity. To evaluate the performance of the method, we used the efficiency of the network and vehicle average speed at the station to examine the accuracy. The results show that the new method can rank the influence of bus stations more accurately and more efficiently than other complex network methods such as degree, H-index, and betweenness. On this basis, the key stations of the bus network of Beijing in China are identified out and the distribution characteristics of the key bus stations are analyzed. [ABSTRACT FROM AUTHOR]

With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing's 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method. [ABSTRACT FROM AUTHOR]

The rapid-developed COVID-19 has been defined as a global emergency by the World Health Organization. Meanwhile, various evidence indicates there is a positive correlation between the transmission and population density, especially in closed and semi-closed space. The urban rail transit, as one of the major mode choices for people to commute in big cities, carries thousands of passengers every day with relatively closed and limited space, which provides favorable conditions for the spread of the virus. If the surrounding area of any station was disrupted under COVID-19, not only the individual line but also the entire urban rail transit network will have the risk to be affected. Therefore, it is necessary to identify and explore the distribution law of key stations during the spreading process of the COVID-19 virus in the urban rail transit network during the COVID-19 pandemic. Based on the spatial distribution of epidemic area and the demand of urban rail transit passengers, we have proposed a construction method of the rail transit network and use the improved shortest path algorithm to determine the route diversity index of each station which indicates its importance in the urban rail transit network. On this basis, we identify the key stations of the Beijing rail transit network to ensure that passengers avoid high-risk stations during the epidemic. The results show that the number of reasonable routes between any two stations is 1 to 5 during the COVID-19 pandemic. Moreover, the routes diversity index of the Beijing rail transit network was 1.235 during the COVID-19 pandemic and 2.2574 in the normal period. According to the reasonable route diversity index, we have identified the key stations of the Beijing rail transit network during the COVID-19, such as Qi-Li-Zhuang station. • A construction method of the urban rail transit network considers the COVID-19. • The improved shortest path algorithm is used to obtain reasonable routes. • Propose a reasonable route diversity index to measure the importance of stations. [ABSTRACT FROM AUTHOR]

Accurate traffic congestion estimation and prediction are critical building blocks for smart trip planning and rerouting decisions in transportation systems. Over the decades, there have been many studies focusing on traffic congestion estimation and prediction with different statistical approaches (e.g., Markov chain) and machine learning models (e.g., clustering, Bayesian networks, and artificial neural networks). However, there is a lack of a unified framework to address the mechanisms of different models and integrate the advantages of different methods through combinations. This paper introduces the FD-Markov-LSTM model, a hybrid interpretable approach that combines the fundamental diagram (FD), Markov chain, and long short-term memory (LSTM). The aim is to estimate and predict traffic states by integrating statistical data in both congested and uncongested scenarios. The FD-Markov-LSTM model leverages the FD to identify hierarchical traffic states and utilizes the Markov process to capture the probabilistic transitions between these states. We employ the LSTM model to further capture the residual time series produced by the Markov chain model (assuming a memoryless property) to enhance the estimation and prediction performance. The proposed model's accuracy in estimating and predicting traffic flow is evaluated using empirical data from three case studies conducted in Beijing and Los Angeles. The results highlight a significant improvement in accuracy compared to classical benchmark models such as the Markov model, ARIMA model, k-Nearest Neighbor model, Random Forest model, and LSTM. Specifically, the FD-Markov-LSTM model achieves reductions of over 39% in mean absolute error, 35% in root mean squared error, and 7.4% in mean absolute percentage error. These results clearly demonstrate that the FD-Markov-LSTM model outperforms the benchmark models, enabling more precise predictions of traffic flow. [ABSTRACT FROM AUTHOR]

Rail transit network (RTN) has become the core of urban public transport system. Because RTN’s topological structure has become complicated with its scale, the phenomenon of overload operation frequently appears at stations or lines, reducing the stability of RTN in operation. There is a need to identify the key stations and to analyze the vulnerability of RTN in depth. This paper quantitatively analyzes statistical topology parameters of Beijing rail transit network (BRTN) based on complex network theory (CNT). The key stations are evaluated by different evaluation indicators, such as node degree, betweenness, and strength. Then, a model is proposed to analyze the cascading failures of weighted BRTN considering loading and redistribution of multi-static passenger flow based on coupled map lattices (CML). Results show that when the external perturbation is larger, the time of all stations failure is earlier once the failure is triggered. Moreover, the perturbation threshold for the cascading failure of weighted BRTN is determined. Transfer regularities of the risk stations in BRTN are researched under different combinations of the topological coupling coefficients and the flow coupling coefficients. Under attack, the threshold of loop line damaged is smaller than a straight line. And, while attacking the loop line, the discrete degree of peak time and all station failure time are higher under different perturbations, meaning that failure is hard to control. This study provides recommendations on developing strategies for RTN design and performance evaluation. Future studies will focus on the coordination of weighted complex public transit networks other than passenger flow redistribution within RTN. [ABSTRACT FROM AUTHOR]

• Estimating nonlinearity and mediation in density effects on three travel decisions. • Developing a quadratic generalized multilevel structural equation model. • Effects of residential density on travel behaviors are significantly curvilinear. • Residential density should lie below around 18,000 persons/km2 in Beijing. • Nonlinearities in the land use-travel links transmit through the mediating effects. We revisit the effectiveness of land use densification as a strategy to promote travel sustainability by investigating the nonlinearity and mediation in the effects of residential densities on home-work distance, car ownership, and driving distance. Using a 2017 travel survey dataset in Beijing, we adopt a quadratic generalized multilevel structural equation model to estimate nonlinear and mediating effects simultaneously. Results suggest that the total effects of densities on home-work distance, car ownership, and driving distance are significantly curvilinear. Over-densification may result in marginal or even countervailing travel consequences. The appropriate level of residential density should lie below a threshold at around 18,000 persons/km2. Moreover, the densification policy in low-density neighborhoods may have a trivial direct effect but a significant indirect effect on driving reduction, through the mediation of other travel decisions. These findings demonstrate the importance of incorporating nonlinearity with mediation to evaluate land use policies, particularly in high-density cities. [ABSTRACT FROM AUTHOR]

With the tide of electrifying urban transportation systems by introducing electric vehicles, the differences between fuel vehicles and electric vehicles in driving styles and strategies to achieve eco-driving have become a burden for efficient operations of urban transportation systems. Most of the previous energy management strategies have sought to achieve system optimisation at a single-vehicle or multi-vehicles level, and failed to consider the vehicle-to-vehicle and vehicle-to-infrastructure effects in a global optimisation manner. Furthermore, as a typical human-in-the-loop cyber–physical system, the mobility behaviours of road users undoubtedly play a vital role in the cooperative and green operations of urban transportation systems. Yet little research has dedicated to develop means to incentivise energy-saving behaviours in transportation systems. Hence, in this paper, we propose a unifying queueing and neural network model to calculate the time and energy efficient course of actions and routes for different types of road users within an urban road network in a real time manner. The lower-level queueing model captures the interactive dynamics of road users and solves the optimal flow ratio at each intersection while the upper-level neural network model further customises desired routes for different types of road users. In addition, an incentive mechanism is proposed to encourage road users to follow the optimal actions via publishing various types of reward-gaining tasks. A case study in a designated area of Beijing shows that the use of the bi-level optimisation algorithm can reduce the average travel time by approximately 20% and decrease the energy consumption by 10% in comparison with the realistic trip data. • An overall energy management framework for urban road networks is built in detail. • A heterogeneous queueing network model is utilised to derive eco-driving strategies. • Random Neural Network is applied to search desired routes for heterogeneous users. • SUMO-based simulation experiment is conducted to validate the performance. • The framework improves time and energy efficiency at the cost of monetary incentives. [ABSTRACT FROM AUTHOR]

The development of electric vehicles (EVs) is expected to play an important role in achieving the emission reduction targets in the transportation sector by many countries and regions, including China's dual carbon goals. A bottleneck to the mass roll-out of EVs is the limited charging facilities. This paper considers the planning of charging facilities for a new developing area in a metropolis, and an optimization model for charging station planning based on the dynamic transportation system is proposed. The proposed model is developed using an objective framework that considers the spatio-temporal characteristics of EV charging demand in order to minimize the overall cost while the constraints from suppliers and drivers are met. The Voronoi diagram is used to determine the final service boundary of each charging station, and the effect is verified in the planning of charge stations for the Yizhuang new town in Beijing. The case study confirms that the proposed method can optimize the charging facilities that fit well with the traffic network conditions. Furthermore, it is shown that the charging demand varies in accordance with the population density and regional functionality in different areas. [ABSTRACT FROM AUTHOR]

As the hub of urban railway transit, metro stations portray the skeleton structure of the public transit network. This study proposes a method of station classification from the dual perspectives of network structure and passenger flow. Each perspective considers the two aspects, one is the characteristics of the node itself, such as degree and the entrance and exit ridership; another considers the characteristics of the influence of other nodes, such as betweenness centrality and passing flow. Among them, the importance index of passing flow is calculated by the PageRank algorithm. According to these characteristics, metro stations are classified by k-means clustering algorithm after dimensionality reduction. The case study is conducted through nearly five million records from 278 stations in Beijing. From the classification results, stations are divided into six categories. Qualitative and quantitative regulations are proposed to reduce the risk of high ridership stations and improve the operation efficiency for few ridership stations. • The PageRank algorithm is used to obtain the key nodes from the perspective of passenger flow. • When selecting characters, the influence of the neighbor nodes on the current node is considered. • After classification, different control methods which can guide the passenger flow towards a balanced direction are proposed. [ABSTRACT FROM AUTHOR]

The urbanization and expansion of megalopolises have led to concerns on traffic, energy crisis and deteriorated green-house gas emissions, and thus the electric vehicles (EVs) are expected to be an essential role in alleviating these problems. In this study, a flexible-possibilist chanced constraints programming (FCCP) model is developed to plan low-carbon energy-transportation systems at the metropolitan scale (METS), which can incorporate multiple uncertainties in both the soft constraints and objective function. By integrating the possibilist programming with fuzzy sets and chanced constraint, the FCCP could tackle multiple complexities such as the combination of vague possibilities, flexibilities and probabilities, hence is superior to conventional approaches. The FCCP model is then applied for the planning METS in Beijing, and solutions are obtained under different satisfactory degrees and confidence levels. The results reveal that: 1) the power demand will be increasingly dependent on the imported power and renewable energy in Beijing; 2) the mass roll-out of EVs will reduce 6.7 million tonnes of CH, 44.7 million tonnes of CO and 1.08 × 105 million tonnes of CO 2 respectively, while the need of battery supply facilities will cost approximately 4 × 109 dollars; 3) the carbon emissions will decrease with the growing number of EVs, the upgraded power supply pattern and the stringent policies. These findings could support decision-makers to plan the METS system when faced with multiple uncertainties. • A flexible-possibilist chanced constraints programming model is developed. • The low-carbon energy-transportation systems at a metropolitan scale is planned. • The EVs and the renewable energies can decrease the carbon mitigation. • The imported and renewable energy power will be more important for Beijing's demand. [ABSTRACT FROM AUTHOR]

• We quantitatively explore the impacts of COVID-19 on the usage of bike sharing. • A novel method is proposed to estimate trip distances and trajectories of bike sharing. • Complex network theory is employed to explore the transformation of user behaviors. • COVID-19 impacts the user behaviors and environmental benefits of bike sharing significantly. The COVID-19 pandemic spreads rapidly around the world, and has given rise to huge impacts on all aspects of human society. This study utilizes big data techniques to analyze the impacts of COVID-19 on the user behaviors and environmental benefits of bike sharing. In this study, a novel method is proposed to calculate the trip distances and trajectories via a python package OSMnx so as to accurately estimate the environmental benefits of bike sharing. In addition, we employ the topological indices arising from complex network theory to quantitatively analyze the transformation of user behavior pattern of bike sharing during the COVID-19 pandemic. The results show that this pandemic has impacted the user behaviors and environmental benefits of bike sharing in Beijing significantly. During the pandemic, the estimated reductions of energy consumption and emissions on 6th Feb decreased to approximately 1 in 17 of those on a normal day, and the environmental benefits at most recovered to 70% of those in normal days. The impacts of COVID-19 on the environmental benefits in different districts are different. Furthermore, the decline of average strength and strength distribution obeying exponential distribution but with different slope rates suggests that people are less likely to take bike sharing to the places where were popular before. The pandemic has also increased the average trip time of bike sharing. Our research may facilitate the understanding of the impacts of COVID-19 pandemic on our society and environment, and also provide clues to adapt to this unprecedented pandemic so as to respond to similar events in the future. [ABSTRACT FROM AUTHOR]