High-Speed Rail(HSR)has increasingly become an important mode of inter-city transportation between large cities.Inter-city interaction facilitated by HSR tends to play a more prominent role in promoting urban and regi...High-Speed Rail(HSR)has increasingly become an important mode of inter-city transportation between large cities.Inter-city interaction facilitated by HSR tends to play a more prominent role in promoting urban and regional economic integration and development.Quantifying the impact of HSR’s interaction on cities and people is therefore crucial for long-term urban and regional development planning and policy making.We develop an evaluation framework using toponym information from social media as a proxy to estimate the dynamics of such impact.This paper adopts two types of spatial information:toponyms from social media posts,and the geographical location information embedded in social media posts.The framework highlights the asymmetric nature of social interaction among cities,and proposes a series of metrics to quantify such impact from multiple perspectives-including interaction strength,spatial decay,and channel effect.The results show that HSRs not only greatly expand the uneven distribution of inter-city connections,but also significantly reshape the interactions that occur along HSR routes through the channel effect.展开更多
To accurately predict traffic flow on the highways,this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism(CNN-BiLSTM-Attention)traffic flow prediction model based ...To accurately predict traffic flow on the highways,this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism(CNN-BiLSTM-Attention)traffic flow prediction model based on Kalman-filtered data processing.Firstly,the original fluctuating data is processed by Kalman filtering,which can reduce the instability of short-term traffic flow prediction due to unexpected accidents.Then the local spatial features of the traffic data during different periods are extracted,dimensionality is reduced through a one-dimensional CNN,and the BiLSTM network is used to analyze the time series information.Finally,the Attention Mechanism assigns feature weights and performs Soft-max regression.The experimental results show that the data processed by Kalman filter is more accurate in predicting the results on the CNN-BiLSTM-Attention model.Compared with the CNN-BiLSTM model,the Root Mean Square Error(RMSE)of the Kal-CNN-BiLSTM-Attention model is reduced by 17.58 and Mean Absolute Error(MAE)by 12.38,and the accuracy of the improved model is almost free from non-working days.To further verify the model’s applicability,the experiments were re-run using two other sets of fluctuating data,and the experimental results again demonstrated the stability of the model.Therefore,the Kal-CNN-BiLSTM-Attention traffic flow prediction model proposed in this paper is more applicable to a broader range of data and has higher accuracy.展开更多
To achieve sustainable development goals,georeferenced data and geographic information systems play a crucial role.Yet,the way in which these data and systems are summoned upon rests on positivist assumptions which ov...To achieve sustainable development goals,georeferenced data and geographic information systems play a crucial role.Yet,the way in which these data and systems are summoned upon rests on positivist assumptions which overlook both epistemological and ethical concerns.This is epitomized by the integrated geospatial information framework(IGIF)of the United Nations,which,from the perspective of sustainable development,aims to provide guidance for the management of geoinformation and related tools,considering these as mirrors of the physical world.In this respect,the article has three main goals.First,it delivers an epistemological and ethical critique of the IGIF,by highlighting its internal tensions.Second,it suggests how the IGIF and similar geoinformation initiatives can benefit from an ethical reflection that allows to conduct georeferenced practices in a fair(er)way.Third,it designs an ethics assessment list for self-evaluating the ethical robustness of geoinformation initiatives as ecosystems.展开更多
With the development of highways,new technologies should be continuously introduced to improve highway traffic safety.Digital twin(DT)has been an emerging field of research in recent years.To develop a digital twin ma...With the development of highways,new technologies should be continuously introduced to improve highway traffic safety.Digital twin(DT)has been an emerging field of research in recent years.To develop a digital twin management system,a data model is essential.In the field of highway operational risk management(HORM),however,the development of data models is still in its infancy.Motivated by the concept of linked data,in this paper,we attempt to propose an information model for HORM.The main achievements of this paper include data architecture,identification and classification code methods,data interaction method,and the developed system.Based on data needs analysis,the highway information model architecture for risk management is defined as five layers:basic highway products,traffic sensors and equipment,traffic rules,traffic flow,and weather.Furthermore,according to the concepts of semantic data,these five layers can be classified into three categories:highway product data,topology data,and sensor data.Although the Industry Foundation Classes(IFC)standard and Brick schema were first proposed and applied in the building domain,some of their entities and relationships can also be applied to highways.To this end,we defined some new classes,a specific ontology,and an integrated framework for HORM.Finally,a case study was carried out.Applying such information model to highways has broad potential.It changes the file-based exchange method to the data-based one,which can promote highway data exchange and applications.The proposed information model could be of great significance for HORM.展开更多
基金This work is supported by the National Natural Science Foundation of China[grant numbers 41801378,42071382].
文摘High-Speed Rail(HSR)has increasingly become an important mode of inter-city transportation between large cities.Inter-city interaction facilitated by HSR tends to play a more prominent role in promoting urban and regional economic integration and development.Quantifying the impact of HSR’s interaction on cities and people is therefore crucial for long-term urban and regional development planning and policy making.We develop an evaluation framework using toponym information from social media as a proxy to estimate the dynamics of such impact.This paper adopts two types of spatial information:toponyms from social media posts,and the geographical location information embedded in social media posts.The framework highlights the asymmetric nature of social interaction among cities,and proposes a series of metrics to quantify such impact from multiple perspectives-including interaction strength,spatial decay,and channel effect.The results show that HSRs not only greatly expand the uneven distribution of inter-city connections,but also significantly reshape the interactions that occur along HSR routes through the channel effect.
基金Supported by Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(No.NJYT23060).
文摘To accurately predict traffic flow on the highways,this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism(CNN-BiLSTM-Attention)traffic flow prediction model based on Kalman-filtered data processing.Firstly,the original fluctuating data is processed by Kalman filtering,which can reduce the instability of short-term traffic flow prediction due to unexpected accidents.Then the local spatial features of the traffic data during different periods are extracted,dimensionality is reduced through a one-dimensional CNN,and the BiLSTM network is used to analyze the time series information.Finally,the Attention Mechanism assigns feature weights and performs Soft-max regression.The experimental results show that the data processed by Kalman filter is more accurate in predicting the results on the CNN-BiLSTM-Attention model.Compared with the CNN-BiLSTM model,the Root Mean Square Error(RMSE)of the Kal-CNN-BiLSTM-Attention model is reduced by 17.58 and Mean Absolute Error(MAE)by 12.38,and the accuracy of the improved model is almost free from non-working days.To further verify the model’s applicability,the experiments were re-run using two other sets of fluctuating data,and the experimental results again demonstrated the stability of the model.Therefore,the Kal-CNN-BiLSTM-Attention traffic flow prediction model proposed in this paper is more applicable to a broader range of data and has higher accuracy.
文摘To achieve sustainable development goals,georeferenced data and geographic information systems play a crucial role.Yet,the way in which these data and systems are summoned upon rests on positivist assumptions which overlook both epistemological and ethical concerns.This is epitomized by the integrated geospatial information framework(IGIF)of the United Nations,which,from the perspective of sustainable development,aims to provide guidance for the management of geoinformation and related tools,considering these as mirrors of the physical world.In this respect,the article has three main goals.First,it delivers an epistemological and ethical critique of the IGIF,by highlighting its internal tensions.Second,it suggests how the IGIF and similar geoinformation initiatives can benefit from an ethical reflection that allows to conduct georeferenced practices in a fair(er)way.Third,it designs an ethics assessment list for self-evaluating the ethical robustness of geoinformation initiatives as ecosystems.
基金supported by the Science and Technology Project of Shandong Provincial Department of Transportation(grant no.2019B32)the Key Science and Technology Projects of the Ministry of Transport of the People’s Republic of China(2019-ZD7-051).
文摘With the development of highways,new technologies should be continuously introduced to improve highway traffic safety.Digital twin(DT)has been an emerging field of research in recent years.To develop a digital twin management system,a data model is essential.In the field of highway operational risk management(HORM),however,the development of data models is still in its infancy.Motivated by the concept of linked data,in this paper,we attempt to propose an information model for HORM.The main achievements of this paper include data architecture,identification and classification code methods,data interaction method,and the developed system.Based on data needs analysis,the highway information model architecture for risk management is defined as five layers:basic highway products,traffic sensors and equipment,traffic rules,traffic flow,and weather.Furthermore,according to the concepts of semantic data,these five layers can be classified into three categories:highway product data,topology data,and sensor data.Although the Industry Foundation Classes(IFC)standard and Brick schema were first proposed and applied in the building domain,some of their entities and relationships can also be applied to highways.To this end,we defined some new classes,a specific ontology,and an integrated framework for HORM.Finally,a case study was carried out.Applying such information model to highways has broad potential.It changes the file-based exchange method to the data-based one,which can promote highway data exchange and applications.The proposed information model could be of great significance for HORM.
