Kalman Filter-Based CNN-BiLSTM-ATT Model for Traffic Flow Prediction

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.

Modelling impacts of high-speed rail on urban interaction with social media in China’s mainland

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.

An ethics assessment list for geoinformation ecosystems: revisiting the integrated geospatial information framework of the United Nations

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.