The rapid development of the delivery industry brings convenience to modern society.However,the high rates of crashes and the survival of electric bicycle(e-bike)riders in the delivery industry raise concerns.The prim...The rapid development of the delivery industry brings convenience to modern society.However,the high rates of crashes and the survival of electric bicycle(e-bike)riders in the delivery industry raise concerns.The primary objective of this study is to explore the factors affecting delivery e-bike riders’stressful work pressure and crash involvement in China.Data were collected by a questionnaire survey administered in the city of Ningbo,China.A bivariate ordered probit(BOP)model was developed to simultaneously examine the factors associated with both the working conditions of delivery e-bike riders and their involvement in crashes.The marginal effects for the contributory factors were calculated to quantify their impacts on the outcomes.The results showed that the BOP model can account for commonly unobserved characteristics of the working conditions and crash involvement of delivery e-bike riders.The BOP model results showed that the stressful working conditions of delivery e-bike riders were affected by the number of orders and delivery time and rider age and risky riding behaviors.Delivery rider involvement in crashes was affected by the number of orders,strength of the punishment for traffic violations,and familiarity with traffic regulations.It was also found that stressful working conditions and crash involvement were strongly and positively correlated.The findings of this study can enhance our understanding of the factors that affect the working conditions and delivery rider crash involvement.Based on the results,some suggestions regarding public policy,risky riding behaviors,safety promotion,and stronger corporate governance rules were discussed to increase the targeted safety-related interventions for delivery ebike riders in Ningbo,China.展开更多
Compared to motorized modes,bike-sharing systems(BSSs)are generally recognized as an environmentally friendly mode of transport and mobility.Due to the advantages and benefits of BSSs,they have spread globally in the ...Compared to motorized modes,bike-sharing systems(BSSs)are generally recognized as an environmentally friendly mode of transport and mobility.Due to the advantages and benefits of BSSs,they have spread globally in the past decade.The mapping knowledge domain(MKD)technique is an important tool for bibliometric analysis that can directly reflect the development status and trends in a research field and has been extensively applied in natural science,medical science,engineering and technology,humanities and the social sciences.In this paper,we conduct a systematic analysis of the development trend in bike-sharing studies taken from Web of Science(WoS)Core Collection articles published between 2010 and 2020 using the MKD software tool VOSviewer and Cite Space.The results show that the topics of the cited documents can be divided into three categories:(a)development,operation mode and lessons learned;(b)BSS static rebalancing problem;(c)spatiotemporal characteristics and demand prediction.Next,we conduct document co-citation analysis and keyword co-occurrence analysis to visually explore the research trends in bike-sharing studies.Our results also find that,(a)bicycle rebalancing problem,(b)travel behavioral movements and barrier,(c)impact factors and characteristics of the internal usage demand,(d)innovation and sustainability for BSSs in the future,and(e)built environment and land-use,are the five major research areas and interests for bike-sharing studies.Finally,examining the trends in BSSs studies by identifying keyword bursts allows an on-demand characteristics analysis based on multisource data fusion technology.This study expands the application field of the MKD analysis method and promotes the development of BSSs against a background of new technology innovation.展开更多
For the optimization problem of the cold-chain emergency materials(CEM) distribution routes with multi-demand centers and soft time windows and to solve dispatching materials to medical treatment institutions in vario...For the optimization problem of the cold-chain emergency materials(CEM) distribution routes with multi-demand centers and soft time windows and to solve dispatching materials to medical treatment institutions in various places of the disaster areas under COVID-19, a multi-dimensional robust optimization(MRO) model was proposed, which was solved by a hybrid algorithm combined Pareto genetic algorithm and the improved grey relative analysis(IGRA). The proposed model comprehensively takes into consideration of the cost factors of the cold-chain logistics and robustness of solution with the purpose of minimizing the costs and maximizing robustness. The availability of the proposed approach and hybrid algorithm were thoroughly discussed and qualified through a real-world numerical simulation test case, which was a previous risk area located at Hubei Province. Research results show an average-cost reduction of 4.51% and a robustness increment of 11.69% in addition to consider the urgencies of demand. Consequently, not only the costs can be slightly reduced and the robustness be heightened, but also the blindness of the distribution can be avoided effectively with the demand urgency being considered. Research result indicates that when combining with the specific process of supplies dispatching in the prevention and control, the proposed approach is in a far better agreement in practice, and it could meet the diverse requirements of the emergency scenarios flexibly.展开更多
基金supported by Zhejiang Provincial Philosophy and Social Sciences Planning Project(21NDJC163YB,22NDJC166YB)Natural Science Foundation of China(No.52002282,52272343)Natural Science Foundation of Zhejiang Province(LY21E080010)。
文摘The rapid development of the delivery industry brings convenience to modern society.However,the high rates of crashes and the survival of electric bicycle(e-bike)riders in the delivery industry raise concerns.The primary objective of this study is to explore the factors affecting delivery e-bike riders’stressful work pressure and crash involvement in China.Data were collected by a questionnaire survey administered in the city of Ningbo,China.A bivariate ordered probit(BOP)model was developed to simultaneously examine the factors associated with both the working conditions of delivery e-bike riders and their involvement in crashes.The marginal effects for the contributory factors were calculated to quantify their impacts on the outcomes.The results showed that the BOP model can account for commonly unobserved characteristics of the working conditions and crash involvement of delivery e-bike riders.The BOP model results showed that the stressful working conditions of delivery e-bike riders were affected by the number of orders and delivery time and rider age and risky riding behaviors.Delivery rider involvement in crashes was affected by the number of orders,strength of the punishment for traffic violations,and familiarity with traffic regulations.It was also found that stressful working conditions and crash involvement were strongly and positively correlated.The findings of this study can enhance our understanding of the factors that affect the working conditions and delivery rider crash involvement.Based on the results,some suggestions regarding public policy,risky riding behaviors,safety promotion,and stronger corporate governance rules were discussed to increase the targeted safety-related interventions for delivery ebike riders in Ningbo,China.
基金supported by the National Natural Science Foundation of China(Grant No.52002282,71701046)the Philosophy and Social Science Foundation of Zhejiang Province(21NDJC163YB)
文摘Compared to motorized modes,bike-sharing systems(BSSs)are generally recognized as an environmentally friendly mode of transport and mobility.Due to the advantages and benefits of BSSs,they have spread globally in the past decade.The mapping knowledge domain(MKD)technique is an important tool for bibliometric analysis that can directly reflect the development status and trends in a research field and has been extensively applied in natural science,medical science,engineering and technology,humanities and the social sciences.In this paper,we conduct a systematic analysis of the development trend in bike-sharing studies taken from Web of Science(WoS)Core Collection articles published between 2010 and 2020 using the MKD software tool VOSviewer and Cite Space.The results show that the topics of the cited documents can be divided into three categories:(a)development,operation mode and lessons learned;(b)BSS static rebalancing problem;(c)spatiotemporal characteristics and demand prediction.Next,we conduct document co-citation analysis and keyword co-occurrence analysis to visually explore the research trends in bike-sharing studies.Our results also find that,(a)bicycle rebalancing problem,(b)travel behavioral movements and barrier,(c)impact factors and characteristics of the internal usage demand,(d)innovation and sustainability for BSSs in the future,and(e)built environment and land-use,are the five major research areas and interests for bike-sharing studies.Finally,examining the trends in BSSs studies by identifying keyword bursts allows an on-demand characteristics analysis based on multisource data fusion technology.This study expands the application field of the MKD analysis method and promotes the development of BSSs against a background of new technology innovation.
基金supported by the National Natural Science Foundation of China (No.52062027,71861023 and 52002282)the "Double-first Class" Major Research Programs,Educational Department of Gansu Province (No.GSSYLXM-04)+1 种基金supported by the Natural Science Foundation of Zhejiang Province (LY21E080010)Philosophy and Social Science Foundation of Zhejiang Province (No.21NDJC163YB,22NDJC166YB)。
文摘For the optimization problem of the cold-chain emergency materials(CEM) distribution routes with multi-demand centers and soft time windows and to solve dispatching materials to medical treatment institutions in various places of the disaster areas under COVID-19, a multi-dimensional robust optimization(MRO) model was proposed, which was solved by a hybrid algorithm combined Pareto genetic algorithm and the improved grey relative analysis(IGRA). The proposed model comprehensively takes into consideration of the cost factors of the cold-chain logistics and robustness of solution with the purpose of minimizing the costs and maximizing robustness. The availability of the proposed approach and hybrid algorithm were thoroughly discussed and qualified through a real-world numerical simulation test case, which was a previous risk area located at Hubei Province. Research results show an average-cost reduction of 4.51% and a robustness increment of 11.69% in addition to consider the urgencies of demand. Consequently, not only the costs can be slightly reduced and the robustness be heightened, but also the blindness of the distribution can be avoided effectively with the demand urgency being considered. Research result indicates that when combining with the specific process of supplies dispatching in the prevention and control, the proposed approach is in a far better agreement in practice, and it could meet the diverse requirements of the emergency scenarios flexibly.