هذه المحاكاه تتطلب شروط وخطوات وهي

هذه المحاكاه تتطلب شروط وخطوات وهي كالاتي

By collecting and analyzing urban freeway traffic data from multiple sources, specific Iowa-based calibration factors for use in VISSIM were developed. In particular, a repeatable methodology for collecting standstill distance and headway/time gap data on urban freeways
was applied to locations throughout the state of Iowa. This collection process relies on
the manual processing of video for standstill distances and individual vehicle data from radar detectors to measure the headways/time gaps. By comparing the data collected from different locations, it was found that standstill distances vary by location and lead-follow vehicle types.
Headways and time gaps were found to be consistent within the same driver population and across different driver populations when the conditions were similar. Both standstill distance and headway/time gap were found to follow fairly dispersed and skewed distributions. Therefore, it is recommended that microsimulation models be modified to include the option for standstill
distance and headway/time gap to follow distributions as well as be set separately for different vehicle classes.

There were two main data collection efforts in this research. The first data collection effort was collecting individual headway/time gap data. In order to collect individual headway data, a number of options were investigated, including manual collection, loop detectors, laser-based collection, video/image processing, and radar-based collection. These options were evaluated with a number of goals in mind for the data collection, including the desire to have timestamped individual vehicle data, especially speed, vehicle class,and lane assignments.
Manual collection was deemed too resource intensive, loop detectors could not be moved to different locations, and no laser-based or video processing options were found to meet the goals of the data collection as well as the selected option.
In the end, it was determined that Wavetronix’s SmartSensor HD side-fired radar detectors best accomplished all of these goals.

By watching a playback of the videos, a counting procedure is done using software that registers each input and the time on the video that it occurred.

النتائج (الإنجليزية) 1:

This simulation requires conditions and steps are as followsBy collecting and analyzing urban freeway traffic data from multiple sources, specific Iowa-based calibration factors for use in VISSIM were developed. In particular, a repeatable methodology for collecting standstill distance and headway/time gap data on urban freeways was applied to locations throughout the state of Iowa. This collection process relies on the manual processing of video for standstill distances and individual vehicle data from radar detectors to measure the headways/time gaps. By comparing the data collected from different locations, it was found that standstill distances vary by location and lead-follow vehicle types. Headways and time gaps were found to be consistent within the same driver population and across different driver populations when the conditions were similar. Both standstill distance and headway/time gap were found to follow fairly dispersed and skewed distributions. Therefore, it is recommended that microsimulation models be modified to include the option for standstill distance and headway/time gap to follow distributions as well as be set separately for different vehicle classes. There were two main data collection efforts in this research. The first data collection effort was collecting individual headway/time gap data. In order to collect individual headway data, a number of options were investigated, including manual collection, loop detectors, laser-based collection, video/image processing, and radar-based collection. These options were evaluated with a number of goals in mind for the data collection, including the desire to have timestamped individual vehicle data, especially speed, vehicle class,and lane assignments.Manual collection was deemed too resource intensive, loop detectors could not be moved to different locations, and no laser-based or video processing options were found to meet the goals of the data collection as well as the selected option.In the end, it was determined that Wavetronix's SmartSensor HD side-fired radar detectors best accomplished all of these goals.By watching a playback of the videos, a counting procedure is done using software that registers each input and the time on the video that it occurred.

النتائج (الإنجليزية) 2:

These simulations require the conditions and steps are as follows

By collecting and analyzing urban freeway traffic data from multiple sources, specific Iowa-based calibration factors for use in VISSIM were developed. Particular with In, a repeatable methodology for collecting standstill and headway distance / time data gap on urban freeways
was applied to locations Throughout the state of Iowa. Relies collection process this on
the manual processing of video for standstill Distances individual and the vehicle data from radar detectors to measure the Headways / time gaps. By vBulletin® comparing the data collected from different locations, it was found That standstill Distances vary by vBulletin® location and lead- . Follow the vehicle types.
Headways and time gaps were found to be consistent within the SAME driver Population and across different driver populations when the conditions were similar Retail . Both standstill distance and headway / time gap were found to follow fairly dispersed and skewed distributions. Therefore, it is recommended That Microsimulation models be modified to include the option for standstill
distance and headway / time gap to . Follow color : as well color : as distributions should be set separately for different classes the vehicle.

Top All were two main data collection Efforts in this research. The first data collection effort was collecting individual headway / time gap data. In order to collect individual headway data, a number of options were investigated, including manual collection, loop detectors, laser-based collection, video / image processing, and radar-based collection. These options were evaluated with a number of Goals in by mind for the data collection, Including the desire to have timestamped individual the vehicle data, Especially speed, the vehicle class, and lane assignments.
Manual collection was Deemed too resource intensive, loop detectors Could not be moved to different locations, and no laser-based versions or video processing options were found to meet the Goals of the data collection color : as well color : as the selected option. with
In the end, it was Determined That Wavetronix's SmartSensor HD side-fired radar detectors best accomplished all of Goals these.

By a watching playback of the videos, a counting procedure is done using software That registers the each sector . input and the time on the video That it occurred.

النتائج (الإنجليزية) 3:

These simulations require conditions and steps are as followsBy collecting and analyzing urban freeway traffic data from multiple sources, specific Iowa-based calibration factors for use in VISSIM were developed. In particular, a repeatable methodology for collecting standstill distance and headway / time gap data on urban freewaysWas applied to locations throughout the state of Iowa. This collection process relies onThe manual processing of video for standstill distances and individual vehicle data from radar detectors to measure the headways / time gaps. By comparing the data collected from different locations, it was found that standstill distances vary by location and lead-follow vehicle types.Headways and time gaps were found to be consistent within the same driver population and across different driver populations when the conditions were similar. Both standstill distance and headway / time gap were found to follow fairly dispersed and skewed distributions. Therefore, it is recommended that microsimulation models be modified to include the option for standstillDistance and headway / time gap to follow distributions as well as be set separately for different vehicle classes.There were two main data collection efforts in this research. The first data collection effort was collecting individual headway / time gap data. In order to collect individual headway data, a number of options were investigated, including manual collection, loop detectors, laser-based collection, video / image processing, and radar-based collection. These options were evaluated with a number of goals in mind for the data collection, including the desire to have timestamped individual vehicle data, especially speed vehicle class, and lane assignments.Manual collection was deemed too resource intensive, loop detectors could not be moved to different locations, and no laser-based or video processing options were found to meet the goals of the data collection as well as the selected option.In the end, it was determined that Wavetronix s SmartSensor HD side-fired radar detectors best accomplished all of these goals.By watching a playback of the videos, a counting procedure is done using software that registers each input and the time on the video that it occurred.