Typical traffic modeling approaches,such as network-based methods and simulation models,have been shown inadequate for urban-scale studies due to the fidelity issue of models.As a go-around,data-driven models have rec...Typical traffic modeling approaches,such as network-based methods and simulation models,have been shown inadequate for urban-scale studies due to the fidelity issue of models.As a go-around,data-driven models have received increasing attention recently.However,most data-driven methods have been restricted by their data source and cannot be scaled up to manage urban-and regional-scale studies.Regarding this issue,this research proposes a pipeline that collects traffic data from online map vendors to bypass data limitations for large-scale studies.The study consists of two experiments:1)recognizing the dominant traffic patterns of cities and 2)site-specific predictions of typical traffic or the most probable locations of patterns of interests.The experiments were conducted on 32 Swiss cities using traffic data that were collected for a two-month period.The results show that dominant patterns can be extracted from the temporal traffic data,and similar patterns exist not only in various parts of a city but also in different cities.Moreover,the results reveal that a country-level lockdown decreased traffic congestions in regional highways but increased those connections near the city centers and the country borders.展开更多
基金This study was funded by the China Scholarship Council Grant No.201706090254.
文摘Typical traffic modeling approaches,such as network-based methods and simulation models,have been shown inadequate for urban-scale studies due to the fidelity issue of models.As a go-around,data-driven models have received increasing attention recently.However,most data-driven methods have been restricted by their data source and cannot be scaled up to manage urban-and regional-scale studies.Regarding this issue,this research proposes a pipeline that collects traffic data from online map vendors to bypass data limitations for large-scale studies.The study consists of two experiments:1)recognizing the dominant traffic patterns of cities and 2)site-specific predictions of typical traffic or the most probable locations of patterns of interests.The experiments were conducted on 32 Swiss cities using traffic data that were collected for a two-month period.The results show that dominant patterns can be extracted from the temporal traffic data,and similar patterns exist not only in various parts of a city but also in different cities.Moreover,the results reveal that a country-level lockdown decreased traffic congestions in regional highways but increased those connections near the city centers and the country borders.
