The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phon...The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phone data,for instance,are found to be a useful data source for extracting diurnal human mobility patterns and for understanding urban dynamics.While previous studies often use call detail record(CDR)data,this study deploys aggregated network-driven mobile phone data that may reveal human mobility patterns more comprehensively and can mitigate some of the privacy concerns raised by mobile phone data usage.We first propose an analytical framework for characterizing and classifying urban areas based on their temporal activity patterns extracted from mobile phone data.Specifically,urban areas’diurnal spatiotemporal signatures of human mobility patterns are obtained through longitudinal mobile phone data.Urban areas are then classified based on the obtained signatures.The classification provides insights into city planning and development.Using the proposed framework,a case study was implemented in the city of Wuhu,China to understand its urban dynamics.The empirical study suggests that human activities in the city of Wuhu are highly concentrated at the Traffic Analysis Zone(TAZ)level.This large portion of local activities suggests that development and planning strategies that are different from those used by metropolitan Chinese cities should be applied in the city of Wuhu.This article concludes with discussions on several common challenges associated with using network-driven mobile phone data,which should be addressed in future studies.展开更多
A growing number of international studies have highlighted that ambient air pollution exposures are related to different health outcomes. To do so, researchers need to estimate exposure levels to air pollution through...A growing number of international studies have highlighted that ambient air pollution exposures are related to different health outcomes. To do so, researchers need to estimate exposure levels to air pollution throughout everyday life. In the literature, the most commonly used estimate is based on home address only or taking into account, in addition, the work address. However, several studies have shown the importance of daily mobility in the estimate of exposure to air pollutants. In this context, we developed an R procedure that estimates individual exposures combining home addresses, several important places, and itineraries of the principal mobility during a week. It supplies researchers a useful tool to calculate individual daily exposition to air pollutants weighting by the time spent at each of the most frequented locations (work, shopping, residential address, etc.) and while commuting. This task requires the efficient calculation of travel time matrices or the examination of multimodal transport routes. This procedure is freely available from the Equit’Area project website: (https://www.equitarea.org). This procedure is structured in three parts: the first part is to create a network, the second allows to estimate main itineraries of the daily mobility and the last one tries to reconstitute the level of air pollution exposure. One main advantage of the tool is that the procedure can be used with different spatial scales and for any air pollutant.展开更多
Metro is an important form of public transport in Shanghai.Based on the metro card data,we conduct the cluster analysis of Shanghai metro stations according to the pattern of passenger flow changing with time.Then the...Metro is an important form of public transport in Shanghai.Based on the metro card data,we conduct the cluster analysis of Shanghai metro stations according to the pattern of passenger flow changing with time.Then the characteristics of travel time and surrounding land use are investigated for different types of stations to explore the relationship between urban land-use characteristics and travel activities reflected by passenger flow at metro stations.It is found that the passenger flow pattern of metro stations is closely related to the location conditions of stations and its surrounding land-use patterns.Based on various characteristics,285 metro stations are classified into four types,including residential-oriented stations,employmentoriented stations,employment-residence-oriented stations,and integrated functionaloriented stations,reflecting the interaction between spontaneous travel behavior and urban land-use characteristics and providing a reference for optimizing the urban functional structure and the spatial allocation of facilities.展开更多
Over the last decade, the popularity of Transportation Network Companies (TNCs) as a mode of travel has been increasing at a steady pace. This trend <span style="font-family:Verdana;">highlights the im...Over the last decade, the popularity of Transportation Network Companies (TNCs) as a mode of travel has been increasing at a steady pace. This trend <span style="font-family:Verdana;">highlights the importance of identifying the determinants that influence transportati</span><span style="font-family:Verdana;">on users to adopt TNCs as a preferred mode choice and the impacts of su</span><span style="font-family:Verdana;">ch preferences on their travel patterns and transportation network o</span><span style="font-family:Verdana;">peration. This paper reports on a recent study undertaken in Birmingham, AL aiming at understanding and documenting the factors that influence transportation users to select TNCs (such as Uber/Lyft) for completing typical day trips. In </span><span style="font-family:Verdana;">doing so, a travel diary questionnaire survey was developed in accordance with</span> <span style="font-family:Verdana;">the Institute of Transportation Engineers (ITE) Manual on Transportation Engineering Studies using the Qualtrics Research Core platform. The que</span><span style="font-family:Verdana;">stionnair</span><span style="font-family:Verdana;">e was used to survey over 450 transportation users in the Birmingham Metro area. The survey participants provided detailed trip information for a </span><span style="font-family:Verdana;">typical 24-hr day along with demographic data and travel preference informatio</span><span style="font-family:Verdana;">n. The survey responses provide high-resolution micro-level indicators </span><span style="font-family:Verdana;">of travel preferences and behaviors in a TNC-served area, which is a much-needed </span><span style="font-family:Verdana;">type of information for researchers and transportation planning agencies.</span>展开更多
COVID-19 has upended the whole world. Due to travel restrictions by governments and increased perceived risks of the disease, therehave been significant changes in social activities and travel patterns. This paper inv...COVID-19 has upended the whole world. Due to travel restrictions by governments and increased perceived risks of the disease, therehave been significant changes in social activities and travel patterns. This paper investigates the effects of COVID-19 on changes toindividuals’ travel patterns, particularly for travel purposes. An online questionnaire survey was conducted in China, which incorporatesquestions about individuals’ sociodemographic and travel characteristics in three different periods of COVID-19 (i.e. before theoutbreak, at the peak and after the peak;the peak here refers to the peak of the pandemic in China, between the end of January and1 May, 2020). The results show that trip frequency decreased sharply from the outbreak until the peak, and drastically increased afterthe peak. Nevertheless, the data fromthis study suggests that it has not fully recovered to the level before the outbreak. Subsequently,a series of random parameters bivariate Probit models for changes in travel patterns were estimated with personal characteristics.The findings demonstrate that during the peak of the pandemic, residents who did not live in more developed cities reached lowfrequencytravel patterns more quickly. For travel purposes, residents of Wuhan, China resumed travelling for work, entertainmentand buy necessities at a much higher rate than other cities. After the peak, students’ travel for work, entertainment and to buy necessitiesrecovered significantly faster than for other occupations. The findings would be helpful for establishing effective policies tocontrol individual travel and minimize disease spread in a possible future pandemic.展开更多
Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure.Although conventional algorithms for periodic frequent pattern detection have...Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure.Although conventional algorithms for periodic frequent pattern detection have numerous applications,there is still little research on periodic frequent pattern detection of individual passengers in the metro.The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network,which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data.This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes(PFPTS).This discovered pattern can automatically capture the features of the temporal dimension(morning and evening peak hours,week)and the spatial dimension(entering and leaving stations).The corresponding complete mining algorithm with the PFPTS-tree structure has been developed.To evaluate the performance of PFPTS-tree,several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network.The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.展开更多
Periodic pattern mining is of great significance for understanding passenger travel behav-ior,but the previous works mainly focused on the trajectory data and the dimension of the spot/point.Besides,many uncertain fac...Periodic pattern mining is of great significance for understanding passenger travel behav-ior,but the previous works mainly focused on the trajectory data and the dimension of the spot/point.Besides,many uncertain factors(severe weather,traffic accident,etc.)may interfere with discovering original and accurate periodic travel patterns.This paper pro-poses a novel type of travel pattern called motif periodic frequent pattern(MPFP),which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics considering,uncertain disturbances.We also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data(SCD),and apply the real long-time-span experimental data from a large-scale metro system is applied.Results show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of“week”.Only the top 10 types of all 4624 types account for about 95%of all motifs and the top 5 types constitute about 90%,and the MPFP of the top 3 types of motifs account for nearly 80%of all periodic patterns,in which Mono-MPFP and 2-MPFP are the main ones.The relatively stable time range of MPFP is three months,and the threshold for the optimal uncertain disturbance factor should be set at 5%.Additionally,several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.展开更多
The traffic of overloaded trucks is a critical problem in highways.It affects pavement performance life,reduces the service life of bridges,and has a negative impact on road safety,average speed and level of service.T...The traffic of overloaded trucks is a critical problem in highways.It affects pavement performance life,reduces the service life of bridges,and has a negative impact on road safety,average speed and level of service.There are several practices to prevent the truck overloading issue,i.e.,enforcement activities to verify the truck’s compliance with the legal weight limits.This paper investigates the development of a method that uses available weigh-in-motion(WIM)data to identify overloaded truck weight and travel patterns.The proposed approach is based on regression trees method,a simple and easily understandable analytic tool used to build prediction models from a large set of data.An overall analysis of the overloaded truck regression tree model shows that the most important variable to classify and predict overloading is the truck type.Regarding the axle overloading,the most significant variable is the time of the day(most of the overloaded trucks travel at late night or early morning).The regression tree results can be used to optimize the efficiency of administration activities by planning truck enforcement operations based on the more critical scenarios.Also,the results improve the knowledge about the load characteristics of trucks,which can lead to more effective pavement management systems and more assertive pavement structure designs.展开更多
The outbreak and spreading of the COVID-19 pandemic have had a significant impact on transportation system.By analyzing the impact of the pandemic on the transportation system,the impact of the pandemic on the social ...The outbreak and spreading of the COVID-19 pandemic have had a significant impact on transportation system.By analyzing the impact of the pandemic on the transportation system,the impact of the pandemic on the social economy can be reflected to a certain extent,and the effect of anti-pandemic policy implementation can also be evaluated.In addition,the analysis results are expected to provide support for policy optimization.Currently,most of the relevant studies analyze the impact of the pandemic on the overall transportation system from the macro perspective,while few studies quantitatively analyze the impact of the pandemic on individual spatiotemporal travel behavior.Based on the license plate recognition(LPR)data,this paper analyzes the spatiotemporal travel patterns of travelers in each stage of the pandemic progress,quantifies the change of travelers'spatiotemporal behaviors,and analyzes the adjustment of travelers'behaviors under the influence of the pandemic.There are three different behavior adjustment strategies under the influence of the pandemic,and the behavior adjustment is related to the individual's past travel habits.The paper quantitatively assesses the impact of the COVID-19 pandemic on individual travel behavior.And the method proposed in this paper can be used to quantitatively assess the impact of any long-term emergency on individual micro travel behavior.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.41571146)China Postdoctoral Science Foundation(No.2019M651784)。
文摘The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phone data,for instance,are found to be a useful data source for extracting diurnal human mobility patterns and for understanding urban dynamics.While previous studies often use call detail record(CDR)data,this study deploys aggregated network-driven mobile phone data that may reveal human mobility patterns more comprehensively and can mitigate some of the privacy concerns raised by mobile phone data usage.We first propose an analytical framework for characterizing and classifying urban areas based on their temporal activity patterns extracted from mobile phone data.Specifically,urban areas’diurnal spatiotemporal signatures of human mobility patterns are obtained through longitudinal mobile phone data.Urban areas are then classified based on the obtained signatures.The classification provides insights into city planning and development.Using the proposed framework,a case study was implemented in the city of Wuhu,China to understand its urban dynamics.The empirical study suggests that human activities in the city of Wuhu are highly concentrated at the Traffic Analysis Zone(TAZ)level.This large portion of local activities suggests that development and planning strategies that are different from those used by metropolitan Chinese cities should be applied in the city of Wuhu.This article concludes with discussions on several common challenges associated with using network-driven mobile phone data,which should be addressed in future studies.
文摘A growing number of international studies have highlighted that ambient air pollution exposures are related to different health outcomes. To do so, researchers need to estimate exposure levels to air pollution throughout everyday life. In the literature, the most commonly used estimate is based on home address only or taking into account, in addition, the work address. However, several studies have shown the importance of daily mobility in the estimate of exposure to air pollutants. In this context, we developed an R procedure that estimates individual exposures combining home addresses, several important places, and itineraries of the principal mobility during a week. It supplies researchers a useful tool to calculate individual daily exposition to air pollutants weighting by the time spent at each of the most frequented locations (work, shopping, residential address, etc.) and while commuting. This task requires the efficient calculation of travel time matrices or the examination of multimodal transport routes. This procedure is freely available from the Equit’Area project website: (https://www.equitarea.org). This procedure is structured in three parts: the first part is to create a network, the second allows to estimate main itineraries of the daily mobility and the last one tries to reconstitute the level of air pollution exposure. One main advantage of the tool is that the procedure can be used with different spatial scales and for any air pollutant.
文摘Metro is an important form of public transport in Shanghai.Based on the metro card data,we conduct the cluster analysis of Shanghai metro stations according to the pattern of passenger flow changing with time.Then the characteristics of travel time and surrounding land use are investigated for different types of stations to explore the relationship between urban land-use characteristics and travel activities reflected by passenger flow at metro stations.It is found that the passenger flow pattern of metro stations is closely related to the location conditions of stations and its surrounding land-use patterns.Based on various characteristics,285 metro stations are classified into four types,including residential-oriented stations,employmentoriented stations,employment-residence-oriented stations,and integrated functionaloriented stations,reflecting the interaction between spontaneous travel behavior and urban land-use characteristics and providing a reference for optimizing the urban functional structure and the spatial allocation of facilities.
文摘Over the last decade, the popularity of Transportation Network Companies (TNCs) as a mode of travel has been increasing at a steady pace. This trend <span style="font-family:Verdana;">highlights the importance of identifying the determinants that influence transportati</span><span style="font-family:Verdana;">on users to adopt TNCs as a preferred mode choice and the impacts of su</span><span style="font-family:Verdana;">ch preferences on their travel patterns and transportation network o</span><span style="font-family:Verdana;">peration. This paper reports on a recent study undertaken in Birmingham, AL aiming at understanding and documenting the factors that influence transportation users to select TNCs (such as Uber/Lyft) for completing typical day trips. In </span><span style="font-family:Verdana;">doing so, a travel diary questionnaire survey was developed in accordance with</span> <span style="font-family:Verdana;">the Institute of Transportation Engineers (ITE) Manual on Transportation Engineering Studies using the Qualtrics Research Core platform. The que</span><span style="font-family:Verdana;">stionnair</span><span style="font-family:Verdana;">e was used to survey over 450 transportation users in the Birmingham Metro area. The survey participants provided detailed trip information for a </span><span style="font-family:Verdana;">typical 24-hr day along with demographic data and travel preference informatio</span><span style="font-family:Verdana;">n. The survey responses provide high-resolution micro-level indicators </span><span style="font-family:Verdana;">of travel preferences and behaviors in a TNC-served area, which is a much-needed </span><span style="font-family:Verdana;">type of information for researchers and transportation planning agencies.</span>
基金National Key R&D Program of China(Grant No.2020YFB1600400)Innovation-Driven Project of Central South University(Grant No.2020CX013).
文摘COVID-19 has upended the whole world. Due to travel restrictions by governments and increased perceived risks of the disease, therehave been significant changes in social activities and travel patterns. This paper investigates the effects of COVID-19 on changes toindividuals’ travel patterns, particularly for travel purposes. An online questionnaire survey was conducted in China, which incorporatesquestions about individuals’ sociodemographic and travel characteristics in three different periods of COVID-19 (i.e. before theoutbreak, at the peak and after the peak;the peak here refers to the peak of the pandemic in China, between the end of January and1 May, 2020). The results show that trip frequency decreased sharply from the outbreak until the peak, and drastically increased afterthe peak. Nevertheless, the data fromthis study suggests that it has not fully recovered to the level before the outbreak. Subsequently,a series of random parameters bivariate Probit models for changes in travel patterns were estimated with personal characteristics.The findings demonstrate that during the peak of the pandemic, residents who did not live in more developed cities reached lowfrequencytravel patterns more quickly. For travel purposes, residents of Wuhan, China resumed travelling for work, entertainmentand buy necessities at a much higher rate than other cities. After the peak, students’ travel for work, entertainment and to buy necessitiesrecovered significantly faster than for other occupations. The findings would be helpful for establishing effective policies tocontrol individual travel and minimize disease spread in a possible future pandemic.
基金supported by the National Natural Science Foundation of China(Grant No.52102382)the Shanghai Science and Technology Committee(Grant No.20DZ1203201)+1 种基金the Fundamental Research Funds for the Central Universities(2022-5-YB-04)the Shanghai Shentong Metro Group Co.,Ltd.(Grant Nos.JSKY21R005-1-WT-21064 and JS-KY21R005-2).
文摘Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure.Although conventional algorithms for periodic frequent pattern detection have numerous applications,there is still little research on periodic frequent pattern detection of individual passengers in the metro.The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network,which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data.This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes(PFPTS).This discovered pattern can automatically capture the features of the temporal dimension(morning and evening peak hours,week)and the spatial dimension(entering and leaving stations).The corresponding complete mining algorithm with the PFPTS-tree structure has been developed.To evaluate the performance of PFPTS-tree,several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network.The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.
基金supported by the National Natural Science Foundation of China(No.52372332)the Fundamental Research Funds for the Central Universities of China(No.2022-5-YB-04)the Shanghai Shentong Metro Group Co.,Ltd.(Nos.JSKY21R005-1-WT-21064,and JS-KY22R033-2).
文摘Periodic pattern mining is of great significance for understanding passenger travel behav-ior,but the previous works mainly focused on the trajectory data and the dimension of the spot/point.Besides,many uncertain factors(severe weather,traffic accident,etc.)may interfere with discovering original and accurate periodic travel patterns.This paper pro-poses a novel type of travel pattern called motif periodic frequent pattern(MPFP),which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics considering,uncertain disturbances.We also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data(SCD),and apply the real long-time-span experimental data from a large-scale metro system is applied.Results show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of“week”.Only the top 10 types of all 4624 types account for about 95%of all motifs and the top 5 types constitute about 90%,and the MPFP of the top 3 types of motifs account for nearly 80%of all periodic patterns,in which Mono-MPFP and 2-MPFP are the main ones.The relatively stable time range of MPFP is three months,and the threshold for the optimal uncertain disturbance factor should be set at 5%.Additionally,several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.
基金The authors thank the Arteris S.A.(Autopista Fernao Dias and Centro de Desenvolvimento Tecnologico),ANTT(Agencia Nacional de Transportes Terrestres),and CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)for supporting this research.
文摘The traffic of overloaded trucks is a critical problem in highways.It affects pavement performance life,reduces the service life of bridges,and has a negative impact on road safety,average speed and level of service.There are several practices to prevent the truck overloading issue,i.e.,enforcement activities to verify the truck’s compliance with the legal weight limits.This paper investigates the development of a method that uses available weigh-in-motion(WIM)data to identify overloaded truck weight and travel patterns.The proposed approach is based on regression trees method,a simple and easily understandable analytic tool used to build prediction models from a large set of data.An overall analysis of the overloaded truck regression tree model shows that the most important variable to classify and predict overloading is the truck type.Regarding the axle overloading,the most significant variable is the time of the day(most of the overloaded trucks travel at late night or early morning).The regression tree results can be used to optimize the efficiency of administration activities by planning truck enforcement operations based on the more critical scenarios.Also,the results improve the knowledge about the load characteristics of trucks,which can lead to more effective pavement management systems and more assertive pavement structure designs.
基金supported by“Pioneer”and“Leading Goose”R&D Program of Zhejiang(2022C01042)the National Natural Science Foundation of China(Grant No.92046011)+1 种基金Center for Balance Architecture Zhejiang UniversityAlibaba-Zhejiang University Joint Research Institute of Frontier Technologies.
文摘The outbreak and spreading of the COVID-19 pandemic have had a significant impact on transportation system.By analyzing the impact of the pandemic on the transportation system,the impact of the pandemic on the social economy can be reflected to a certain extent,and the effect of anti-pandemic policy implementation can also be evaluated.In addition,the analysis results are expected to provide support for policy optimization.Currently,most of the relevant studies analyze the impact of the pandemic on the overall transportation system from the macro perspective,while few studies quantitatively analyze the impact of the pandemic on individual spatiotemporal travel behavior.Based on the license plate recognition(LPR)data,this paper analyzes the spatiotemporal travel patterns of travelers in each stage of the pandemic progress,quantifies the change of travelers'spatiotemporal behaviors,and analyzes the adjustment of travelers'behaviors under the influence of the pandemic.There are three different behavior adjustment strategies under the influence of the pandemic,and the behavior adjustment is related to the individual's past travel habits.The paper quantitatively assesses the impact of the COVID-19 pandemic on individual travel behavior.And the method proposed in this paper can be used to quantitatively assess the impact of any long-term emergency on individual micro travel behavior.
