Transportation accessibility has been treated as an important means of reducing the urban-rural income disparity.However,only a few studies have examined the effects of different types of transportation accessibility ...Transportation accessibility has been treated as an important means of reducing the urban-rural income disparity.However,only a few studies have examined the effects of different types of transportation accessibility on urban-rural income disparity and their spatial heterogeneity.Based on data from 285 prefecture-level(and above)Chinese cities in 2000,2005,2010,2015,and 2020,this study uses spatial econometric models to examine how highway accessibility and railway accessibility influence the urban-rural income disparity and to identify their spatial heterogeneity.The result reveals that highway accessibility and railway accessibility have‘coreperiphery’ring-like circle structures.The urban-rural income disparity exhibits strong spatial clustering effects.Both highway accessibility and railway accessibility are negatively associated with urban-rural income disparity,and the former having a greater effect size.Moreover,there is a substitution effect between highway accessibility and railway accessibility in the whole sample.Furthermore,these associations differ in geographic regions.In the central region,highway accessibility is more important in reducing the urban-rural income disparity,but its effect is weakened with the increase of railway accessibility.In the western region,railway accessibility has a larger effect on narrowing the urban-rural income disparity,and this effect is strengthened by the increase of highway accessibility.We conclude that improving transportation accessibility is conducive to reducing the urban-rural income disparity but its effect is spatial heterogenetic.Highways and railways should be developed in a coordinated manner to promote an integrated transport network system.展开更多
Transportation infrastructure is crucial to China’s economic growth because it substantially contributes to the holistic development of rural primary,secondary,and tertiary industries.This study innovatively examines...Transportation infrastructure is crucial to China’s economic growth because it substantially contributes to the holistic development of rural primary,secondary,and tertiary industries.This study innovatively examines transportation infrastructure and urbanization levels to explore,both theoretically and empirically,their relationship with the holistic development of primary,secondary,and tertiary industries in rural China,and the mediating role of urbanization on this relationship.We employed fixed-effects models,the entropy weight approach,mixed regression,and generalized method of moments to analyze the data of 30 provinces across China from 2013 to 2020.The results indicate that the construction of transportation infrastructure directly fosters the collective advancement of such industries in rural areas and that urbanization partially mediates the transportation infrastructure-rural industry integration relationship.However,the western region shows disparities in the integrated development of these sectors.Further analysis reveals that foreign investments amplify the positive influence of transportation infrastructure on rural industry integration.Essentially,the enhancement of rural transportation infrastructure,promotion of urbanization,implementation of strategic planning,and strengthening of support mechanisms are crucial aspects in the comprehensive development of rural industries and the achievement of rural revitalization in China.展开更多
This study reviewed the urban passenger transportation(UPT)development of seven typical cities in China from 2000 to 2014,estimated the UPT CO2emission,analyzed the structure,and discussed the main factors of UPT CO,e...This study reviewed the urban passenger transportation(UPT)development of seven typical cities in China from 2000 to 2014,estimated the UPT CO2emission,analyzed the structure,and discussed the main factors of UPT CO,emission.Results showed that increases of GDP,population,and UPT scale of the cities have speeded up.The most significant development of UPT is that the growth of private vehicles is greatly faster than that of public transportation.The total and per-capita UPT CO2 emissions both increased.The share of private vehicles emission to total UPT CO2emission has increased,with the share in range of 65%-88%in 2014,exponentially leading to the increases of total and per-capita UPT CO2 emission.Although UPT CO2 emission structure with more share of public transportation would slow down the UPT CO2emission increase,private vehicle CO2 emission is recognized as the dominated driving factor.Contributions of driving factors,such as GDP,population,private vehicle CO2 emissions,to UPT CO2 emission are different among the cities.Private vehicle CO2 emission.is the dominated factor for UPT CO2emission in Beijing and Taiyuan.Besides private vehicle CO2emission,GDP also plays an important role in UPT CO2emissions of Chengdu,Shanghai,Guangzhou,and Urumqi.Contributions of private vehicle CO2 emission and GDP to UPT CO2 emission are almost same in Xi'an.展开更多
The objective of this study was to assess the condition of the road network within the district with a view to find out if and how they affect the transportation costs. Four sets of primary and secondary data on the d...The objective of this study was to assess the condition of the road network within the district with a view to find out if and how they affect the transportation costs. Four sets of primary and secondary data on the district’s road surface types, road condition mix, cost of transportation of farm produce and humans on the road network were collected, collated and subjected to statistical analysis using a Completely Randomized Design. The results indicated that the road conditions had high significant effects on the transportation costs of both human and agricultural produce. The road network of the district consists of 21 roads with a total length of 176.6 kilometers out of which 8 were classified as poor, 7 as fair and only 6 as good. The highest agricultural produce transportation cost of 1.46 per tonne-kilometer was obtained from 3 of the poor roads while the lowest cost of 0.86 per tonne-kilometer was obtained from only 1 of the 6 good roads. The highest passenger transportation cost of 0.3 per passenger kilometer was obtained for 1 of the 8 poor roads while the lowest cost of 0.1 per passenger kilometer was obtained for all the 6 good roads and 4 of the fair roads. In conclusion, transportation cost of passengers on the poor and fair roads was 2 - 3 times as high as the cost of transportation on the good roads. Transportation cost of agricultural produce on the poor roads was 70% higher than it was on the good roads.展开更多
城市轨道交通起讫点(origin-destination,OD)客流短时预测在智能交通系统中意义重大,它为交通管控策略实施以及出行者出行选择提供了重要的决策依据。卷积神经网络被广泛用于交通数据空间相关性提取,但其平移不变性与局部敏感性导致该...城市轨道交通起讫点(origin-destination,OD)客流短时预测在智能交通系统中意义重大,它为交通管控策略实施以及出行者出行选择提供了重要的决策依据。卷积神经网络被广泛用于交通数据空间相关性提取,但其平移不变性与局部敏感性导致该方法更重视局部特征而忽视全局特征。本研究构建了基于注意力机制的异构数据特征提取机模型(heterogeneous data feature extraction machine,HDFEM)以实现OD矩阵空间相关性的全局感知。该模型从时空特征和用地属性特征出发,构造异构数据OD时空张量与地理信息张量,依托模型张量编码层对异构数据张量进行分割与编码,通过注意力机制连接各张量块特征,提取OD矩阵中各个部分间的空间相关性。该方法不仅实现了异构数据与OD客流数据的融合,还兼顾了卷积神经网络模型未能处理的OD矩阵远距离特征,进而帮助模型更全面地学习OD客流的空间特征。对于OD时序特性,该模型使用了长短时记忆网络来处理。在杭州地铁自动售检票系统(auto fare collection,AFC)数据集上的实验结果表明:HDFEM模型相对于基于卷积神经网络的预测模型,其均方误差、平均绝对误差与标准均方根误差分别下降了4.1%,2.5%,2%,验证了全局OD特征感知对于城市轨道交通OD客流预测的重要性。展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42371214,42101184)Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(No.22CGA27)Funded Projects for the Academic Leaders and Academic Backbone,Shaanxi Normal University(No.18QNGG013)。
文摘Transportation accessibility has been treated as an important means of reducing the urban-rural income disparity.However,only a few studies have examined the effects of different types of transportation accessibility on urban-rural income disparity and their spatial heterogeneity.Based on data from 285 prefecture-level(and above)Chinese cities in 2000,2005,2010,2015,and 2020,this study uses spatial econometric models to examine how highway accessibility and railway accessibility influence the urban-rural income disparity and to identify their spatial heterogeneity.The result reveals that highway accessibility and railway accessibility have‘coreperiphery’ring-like circle structures.The urban-rural income disparity exhibits strong spatial clustering effects.Both highway accessibility and railway accessibility are negatively associated with urban-rural income disparity,and the former having a greater effect size.Moreover,there is a substitution effect between highway accessibility and railway accessibility in the whole sample.Furthermore,these associations differ in geographic regions.In the central region,highway accessibility is more important in reducing the urban-rural income disparity,but its effect is weakened with the increase of railway accessibility.In the western region,railway accessibility has a larger effect on narrowing the urban-rural income disparity,and this effect is strengthened by the increase of highway accessibility.We conclude that improving transportation accessibility is conducive to reducing the urban-rural income disparity but its effect is spatial heterogenetic.Highways and railways should be developed in a coordinated manner to promote an integrated transport network system.
基金supported by 2023 Chongqing Education Commission Humanities and Social Sciences Research Planning Project[Grant No.23SKGH090]2023−2024 Higher Education Science Research Project of Chongqing Higher Education Association[Grant No.cqgj23037C].
文摘Transportation infrastructure is crucial to China’s economic growth because it substantially contributes to the holistic development of rural primary,secondary,and tertiary industries.This study innovatively examines transportation infrastructure and urbanization levels to explore,both theoretically and empirically,their relationship with the holistic development of primary,secondary,and tertiary industries in rural China,and the mediating role of urbanization on this relationship.We employed fixed-effects models,the entropy weight approach,mixed regression,and generalized method of moments to analyze the data of 30 provinces across China from 2013 to 2020.The results indicate that the construction of transportation infrastructure directly fosters the collective advancement of such industries in rural areas and that urbanization partially mediates the transportation infrastructure-rural industry integration relationship.However,the western region shows disparities in the integrated development of these sectors.Further analysis reveals that foreign investments amplify the positive influence of transportation infrastructure on rural industry integration.Essentially,the enhancement of rural transportation infrastructure,promotion of urbanization,implementation of strategic planning,and strengthening of support mechanisms are crucial aspects in the comprehensive development of rural industries and the achievement of rural revitalization in China.
基金the National Natural Science Foundation of China(41301033).
文摘This study reviewed the urban passenger transportation(UPT)development of seven typical cities in China from 2000 to 2014,estimated the UPT CO2emission,analyzed the structure,and discussed the main factors of UPT CO,emission.Results showed that increases of GDP,population,and UPT scale of the cities have speeded up.The most significant development of UPT is that the growth of private vehicles is greatly faster than that of public transportation.The total and per-capita UPT CO2 emissions both increased.The share of private vehicles emission to total UPT CO2emission has increased,with the share in range of 65%-88%in 2014,exponentially leading to the increases of total and per-capita UPT CO2 emission.Although UPT CO2 emission structure with more share of public transportation would slow down the UPT CO2emission increase,private vehicle CO2 emission is recognized as the dominated driving factor.Contributions of driving factors,such as GDP,population,private vehicle CO2 emissions,to UPT CO2 emission are different among the cities.Private vehicle CO2 emission.is the dominated factor for UPT CO2emission in Beijing and Taiyuan.Besides private vehicle CO2emission,GDP also plays an important role in UPT CO2emissions of Chengdu,Shanghai,Guangzhou,and Urumqi.Contributions of private vehicle CO2 emission and GDP to UPT CO2 emission are almost same in Xi'an.
文摘The objective of this study was to assess the condition of the road network within the district with a view to find out if and how they affect the transportation costs. Four sets of primary and secondary data on the district’s road surface types, road condition mix, cost of transportation of farm produce and humans on the road network were collected, collated and subjected to statistical analysis using a Completely Randomized Design. The results indicated that the road conditions had high significant effects on the transportation costs of both human and agricultural produce. The road network of the district consists of 21 roads with a total length of 176.6 kilometers out of which 8 were classified as poor, 7 as fair and only 6 as good. The highest agricultural produce transportation cost of 1.46 per tonne-kilometer was obtained from 3 of the poor roads while the lowest cost of 0.86 per tonne-kilometer was obtained from only 1 of the 6 good roads. The highest passenger transportation cost of 0.3 per passenger kilometer was obtained for 1 of the 8 poor roads while the lowest cost of 0.1 per passenger kilometer was obtained for all the 6 good roads and 4 of the fair roads. In conclusion, transportation cost of passengers on the poor and fair roads was 2 - 3 times as high as the cost of transportation on the good roads. Transportation cost of agricultural produce on the poor roads was 70% higher than it was on the good roads.
文摘城市轨道交通起讫点(origin-destination,OD)客流短时预测在智能交通系统中意义重大,它为交通管控策略实施以及出行者出行选择提供了重要的决策依据。卷积神经网络被广泛用于交通数据空间相关性提取,但其平移不变性与局部敏感性导致该方法更重视局部特征而忽视全局特征。本研究构建了基于注意力机制的异构数据特征提取机模型(heterogeneous data feature extraction machine,HDFEM)以实现OD矩阵空间相关性的全局感知。该模型从时空特征和用地属性特征出发,构造异构数据OD时空张量与地理信息张量,依托模型张量编码层对异构数据张量进行分割与编码,通过注意力机制连接各张量块特征,提取OD矩阵中各个部分间的空间相关性。该方法不仅实现了异构数据与OD客流数据的融合,还兼顾了卷积神经网络模型未能处理的OD矩阵远距离特征,进而帮助模型更全面地学习OD客流的空间特征。对于OD时序特性,该模型使用了长短时记忆网络来处理。在杭州地铁自动售检票系统(auto fare collection,AFC)数据集上的实验结果表明:HDFEM模型相对于基于卷积神经网络的预测模型,其均方误差、平均绝对误差与标准均方根误差分别下降了4.1%,2.5%,2%,验证了全局OD特征感知对于城市轨道交通OD客流预测的重要性。
