Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersectio...Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersections,a dynamic data-driven flow prediction model was developed.The model consists of two prediction components based on the signal states(red or green) for each movement at an upstream intersection.The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted.With an online turning proportion estimation method,along with the predicted travel times,the anticipated vehicle arrivals can be forecasted at the downstream intersection.The model performance was tested at a set of two signalized intersections located in the city of Gainesville,Florida,USA,using the CORSIM microscopic simulation package.Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%-20%,and show a normal distribution.It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.展开更多
Recent research has revealed that human exposure to air pollutants such as CO, NO_x, and particulates can lead to respiratory diseases, especially among school-age children. Towards understanding such health impacts, ...Recent research has revealed that human exposure to air pollutants such as CO, NO_x, and particulates can lead to respiratory diseases, especially among school-age children. Towards understanding such health impacts, this work estimates local-scale vehicular emissions and concentrations near a highway traffic network, where a school zone is located in. In the case study, VISSIM traffic micro-simulation is used to estimate the source of vehicular emissions at each roadway segment. The local-scale emission sources are then used as inputs to the California line source dispersion model(CALINE4) to estimate concentrations across the study area. To justify the local-scale emissions modeling approach, the simulation experiment is conducted under various traffic conditions. Different meteorological conditions are considered for emission dispersion. The work reveals that emission concentrations are usually higher at locations closer to the congested segments, freeway ramps and major arterial intersections. Compared to the macroscopic estimation(i.e. using network-average emission factors), the results show significantly different emission patterns when the local-scale emission modeling approach is used. In particular, it is found that the macroscopic approach over-estimates emission concentrations at freeways and under-estimations are observed at arterials and local streets. The results of the study can be used to compare to the US environmental protection agency(EPA) standards or any other air quality standard to further identify health risk in a fine-grained manner.展开更多
基金Project(71101109) supported by the National Natural Science Foundation of China
文摘Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersections,a dynamic data-driven flow prediction model was developed.The model consists of two prediction components based on the signal states(red or green) for each movement at an upstream intersection.The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted.With an online turning proportion estimation method,along with the predicted travel times,the anticipated vehicle arrivals can be forecasted at the downstream intersection.The model performance was tested at a set of two signalized intersections located in the city of Gainesville,Florida,USA,using the CORSIM microscopic simulation package.Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%-20%,and show a normal distribution.It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.
文摘Recent research has revealed that human exposure to air pollutants such as CO, NO_x, and particulates can lead to respiratory diseases, especially among school-age children. Towards understanding such health impacts, this work estimates local-scale vehicular emissions and concentrations near a highway traffic network, where a school zone is located in. In the case study, VISSIM traffic micro-simulation is used to estimate the source of vehicular emissions at each roadway segment. The local-scale emission sources are then used as inputs to the California line source dispersion model(CALINE4) to estimate concentrations across the study area. To justify the local-scale emissions modeling approach, the simulation experiment is conducted under various traffic conditions. Different meteorological conditions are considered for emission dispersion. The work reveals that emission concentrations are usually higher at locations closer to the congested segments, freeway ramps and major arterial intersections. Compared to the macroscopic estimation(i.e. using network-average emission factors), the results show significantly different emission patterns when the local-scale emission modeling approach is used. In particular, it is found that the macroscopic approach over-estimates emission concentrations at freeways and under-estimations are observed at arterials and local streets. The results of the study can be used to compare to the US environmental protection agency(EPA) standards or any other air quality standard to further identify health risk in a fine-grained manner.
