The Experts below are selected from a list of 71937 Experts worldwide ranked by ideXlab platform
Sandeep Menneni - One of the best experts on this subject based on the ideXlab platform.
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replication of work zone capacity values in a simulation model
Transportation Research Record, 2009Co-Authors: Indrajit Chatterjee, Praveen Edara, Sandeep MenneniAbstract:Evaluating the traffic impacts of work zones is vital for any transportation agency to plan and schedule work activity. Traffic impacts can be estimated by using microscopic simulation models. One challenge in using these software models is obtaining the desired work zone capacity values, which tend to vary from state to state. Thus, the Default Parameter values in the model that are suitable for normal traffic conditions are unsuitable for work zone conditions, let alone for conditions specific to particular states. Although a few studies have been conducted on Parameter selection to obtain desired capacity values, none of them have provided a convenient look-up table (or chart) for the Parameter values that will replicate field-observed capacities. Without such provision it has not been possible for state agencies to use any of the research recommendations. This study provides the practitioner a simple method for choosing appropriate values of driving behavior Parameters in the VISSIM microsimulation model to match the desired field capacity for work zones operating in a typical early-merge system. The two most significant car-following Parameters and one lane-changing Parameter were selected and varied to obtain different work zone capacity values. CC1 is the desired time headway, CC2 is the longitudinal following threshold during a following process, and the safety distance reduction factor is representative of lane-changing aggressiveness. It has been verified that the recommended Parameter values not only produce the desired capacities but also create traffic conditions consistent with traffic flow theory.
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replication of work zone capacity values in a simulation model
Transportation Research Record, 2009Co-Authors: Indrajit Chatterjee, Praveen Edara, Sandeep MenneniAbstract:Evaluating the traffic impacts of work zones is vital for any transportation agency to plan and schedule work activity. Traffic impacts can be estimated by using microscopic simulation models. One challenge in using these software models is obtaining the desired work zone capacity values, which tend to vary from state to state. Thus, the Default Parameter values in the model that are suitable for normal traffic conditions are unsuitable for work zone conditions, let alone for conditions specific to particular states. Although a few studies have been conducted on Parameter selection to obtain desired capacity values, none of them have provided a convenient look-up table (or chart) for the Parameter values that will replicate field-observed capacities. Without such provision it has not been possible for state agencies to use any of the research recommendations. This study provides the practitioner a simple method for choosing appropriate values of driving behavior Parameters in the VISSIM microsimulation model to match the desired field capacity for work zones operating in a typical early-merge system. The two most significant car-following Parameters and one lane-changing Parameter were selected and varied to obtain different work zone capacity values. CC1 is the desired time headway, CC2 is the longitudinal following threshold during a following process, and the safety distance reduction factor is representative of lane-changing aggressiveness. It has been verified that the recommended Parameter values not only produce the desired capacities but also create traffic conditions consistent with traffic flow theory.
Indrajit Chatterjee - One of the best experts on this subject based on the ideXlab platform.
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replication of work zone capacity values in a simulation model
Transportation Research Record, 2009Co-Authors: Indrajit Chatterjee, Praveen Edara, Sandeep MenneniAbstract:Evaluating the traffic impacts of work zones is vital for any transportation agency to plan and schedule work activity. Traffic impacts can be estimated by using microscopic simulation models. One challenge in using these software models is obtaining the desired work zone capacity values, which tend to vary from state to state. Thus, the Default Parameter values in the model that are suitable for normal traffic conditions are unsuitable for work zone conditions, let alone for conditions specific to particular states. Although a few studies have been conducted on Parameter selection to obtain desired capacity values, none of them have provided a convenient look-up table (or chart) for the Parameter values that will replicate field-observed capacities. Without such provision it has not been possible for state agencies to use any of the research recommendations. This study provides the practitioner a simple method for choosing appropriate values of driving behavior Parameters in the VISSIM microsimulation model to match the desired field capacity for work zones operating in a typical early-merge system. The two most significant car-following Parameters and one lane-changing Parameter were selected and varied to obtain different work zone capacity values. CC1 is the desired time headway, CC2 is the longitudinal following threshold during a following process, and the safety distance reduction factor is representative of lane-changing aggressiveness. It has been verified that the recommended Parameter values not only produce the desired capacities but also create traffic conditions consistent with traffic flow theory.
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replication of work zone capacity values in a simulation model
Transportation Research Record, 2009Co-Authors: Indrajit Chatterjee, Praveen Edara, Sandeep MenneniAbstract:Evaluating the traffic impacts of work zones is vital for any transportation agency to plan and schedule work activity. Traffic impacts can be estimated by using microscopic simulation models. One challenge in using these software models is obtaining the desired work zone capacity values, which tend to vary from state to state. Thus, the Default Parameter values in the model that are suitable for normal traffic conditions are unsuitable for work zone conditions, let alone for conditions specific to particular states. Although a few studies have been conducted on Parameter selection to obtain desired capacity values, none of them have provided a convenient look-up table (or chart) for the Parameter values that will replicate field-observed capacities. Without such provision it has not been possible for state agencies to use any of the research recommendations. This study provides the practitioner a simple method for choosing appropriate values of driving behavior Parameters in the VISSIM microsimulation model to match the desired field capacity for work zones operating in a typical early-merge system. The two most significant car-following Parameters and one lane-changing Parameter were selected and varied to obtain different work zone capacity values. CC1 is the desired time headway, CC2 is the longitudinal following threshold during a following process, and the safety distance reduction factor is representative of lane-changing aggressiveness. It has been verified that the recommended Parameter values not only produce the desired capacities but also create traffic conditions consistent with traffic flow theory.
Praveen Edara - One of the best experts on this subject based on the ideXlab platform.
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replication of work zone capacity values in a simulation model
Transportation Research Record, 2009Co-Authors: Indrajit Chatterjee, Praveen Edara, Sandeep MenneniAbstract:Evaluating the traffic impacts of work zones is vital for any transportation agency to plan and schedule work activity. Traffic impacts can be estimated by using microscopic simulation models. One challenge in using these software models is obtaining the desired work zone capacity values, which tend to vary from state to state. Thus, the Default Parameter values in the model that are suitable for normal traffic conditions are unsuitable for work zone conditions, let alone for conditions specific to particular states. Although a few studies have been conducted on Parameter selection to obtain desired capacity values, none of them have provided a convenient look-up table (or chart) for the Parameter values that will replicate field-observed capacities. Without such provision it has not been possible for state agencies to use any of the research recommendations. This study provides the practitioner a simple method for choosing appropriate values of driving behavior Parameters in the VISSIM microsimulation model to match the desired field capacity for work zones operating in a typical early-merge system. The two most significant car-following Parameters and one lane-changing Parameter were selected and varied to obtain different work zone capacity values. CC1 is the desired time headway, CC2 is the longitudinal following threshold during a following process, and the safety distance reduction factor is representative of lane-changing aggressiveness. It has been verified that the recommended Parameter values not only produce the desired capacities but also create traffic conditions consistent with traffic flow theory.
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replication of work zone capacity values in a simulation model
Transportation Research Record, 2009Co-Authors: Indrajit Chatterjee, Praveen Edara, Sandeep MenneniAbstract:Evaluating the traffic impacts of work zones is vital for any transportation agency to plan and schedule work activity. Traffic impacts can be estimated by using microscopic simulation models. One challenge in using these software models is obtaining the desired work zone capacity values, which tend to vary from state to state. Thus, the Default Parameter values in the model that are suitable for normal traffic conditions are unsuitable for work zone conditions, let alone for conditions specific to particular states. Although a few studies have been conducted on Parameter selection to obtain desired capacity values, none of them have provided a convenient look-up table (or chart) for the Parameter values that will replicate field-observed capacities. Without such provision it has not been possible for state agencies to use any of the research recommendations. This study provides the practitioner a simple method for choosing appropriate values of driving behavior Parameters in the VISSIM microsimulation model to match the desired field capacity for work zones operating in a typical early-merge system. The two most significant car-following Parameters and one lane-changing Parameter were selected and varied to obtain different work zone capacity values. CC1 is the desired time headway, CC2 is the longitudinal following threshold during a following process, and the safety distance reduction factor is representative of lane-changing aggressiveness. It has been verified that the recommended Parameter values not only produce the desired capacities but also create traffic conditions consistent with traffic flow theory.
Francis A. Drummond - One of the best experts on this subject based on the ideXlab platform.
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grid set match an agent based simulation model predicts fruit set for the lowbush blueberry vaccinium angustifolium agroecosystem
Ecological Modelling, 2017Co-Authors: Alex W Bajcz, David E Hiebeler, Francis A. DrummondAbstract:Abstract Fruit set, the proportion of flowers whose ovaries successfully bear fruit, is the product of dozens of processes, many of which are difficult to study and manipulate in situ . To establish which of these processes are most important in the lowbush blueberry ( Vaccinium angustifolium Aiton; Ericaceae) agroecosystem, an agent-based simulation model, Grid-Set-Match , with temporal and spatial elements was constructed using the R software package. Pollination ecology data from this system were used to Parameterize the model. Then, results from 1,000 iterations of the model were compared to field fruit set data from a survey of 162 lowbush blueberry fields from 1993 to 2015 as well as from a field study conducted from 2013 to 2015. Predicted fruit set for an average lowbush blueberry field was 0.366 according to Grid-Set-Match . Based on currently available field data, this estimate appears realistic, although it may be a slight underestimate in part because variability in pollinator densities between real fields is higher than the model accounted for. A multiple regression model indicated that, across a sample of clones created by Grid-Set-Match under Default Parameter settings, fruit set declined significantly as the average genetic load of a clone’s neighbors increased and as self-pollination rate increased, with the latter effect being ∼20% stronger than the former. As such, future research should be directed towards understanding and better managing factors that may influence rates of self- vs. cross-pollination in this system, such as pollen carryover, intra-hive pollen transfer, and pollinator flight distances between consecutively visited flowers.
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grid set match an agent based simulation model predicts fruit set for the lowbush blueberry vaccinium angustifolium agroecosystem
Ecological Modelling, 2017Co-Authors: Alex W Bajcz, David E Hiebeler, Francis A. DrummondAbstract:Abstract Fruit set, the proportion of flowers whose ovaries successfully bear fruit, is the product of dozens of processes, many of which are difficult to study and manipulate in situ . To establish which of these processes are most important in the lowbush blueberry ( Vaccinium angustifolium Aiton; Ericaceae) agroecosystem, an agent-based simulation model, Grid-Set-Match , with temporal and spatial elements was constructed using the R software package. Pollination ecology data from this system were used to Parameterize the model. Then, results from 1,000 iterations of the model were compared to field fruit set data from a survey of 162 lowbush blueberry fields from 1993 to 2015 as well as from a field study conducted from 2013 to 2015. Predicted fruit set for an average lowbush blueberry field was 0.366 according to Grid-Set-Match . Based on currently available field data, this estimate appears realistic, although it may be a slight underestimate in part because variability in pollinator densities between real fields is higher than the model accounted for. A multiple regression model indicated that, across a sample of clones created by Grid-Set-Match under Default Parameter settings, fruit set declined significantly as the average genetic load of a clone’s neighbors increased and as self-pollination rate increased, with the latter effect being ∼20% stronger than the former. As such, future research should be directed towards understanding and better managing factors that may influence rates of self- vs. cross-pollination in this system, such as pollen carryover, intra-hive pollen transfer, and pollinator flight distances between consecutively visited flowers.
Gregory F. Cooper - One of the best experts on this subject based on the ideXlab platform.
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A Bayesian Network Scoring Metric That Is Based On Globally Uniform Parameter Priors
arXiv: Artificial Intelligence, 2012Co-Authors: Mehmet Kayaalp, Gregory F. CooperAbstract:We introduce a new Bayesian network (BN) scoring metric called the Global Uniform (GU) metric. This metric is based on a particular type of Default Parameter prior. Such priors may be useful when a BN developer is not willing or able to specify domain-specific Parameter priors. The GU Parameter prior specifies that every prior joint probability distribution P consistent with a BN structure S is considered to be equally likely. Distribution P is consistent with S if P includes just the set of independence relations defined by S. We show that the GU metric addresses some undesirable behavior of the BDeu and K2 Bayesian network scoring metrics, which also use particular forms of Default Parameter priors. A closed form formula for computing GU for special classes of BNs is derived. Efficiently computing GU for an arbitrary BN remains an open problem.
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UAI - A bayesian network scoring metric that is based on globally uniform Parameter priors
2002Co-Authors: Mehmet Kayaalp, Gregory F. CooperAbstract:We introduce a new Bayesian network (BN) scoring metric called the Global Uniform (GU) metric. This metric is based on a particular type of Default Parameter prior. Such priors may be useful when a BN developer is not willing or able to specify domain-specific Parameter priors. The GU Parameter prior specifies that every prior joint probability distribution P consistent with a BN structure S is considered to be equally likely. Distribution P is consistent with S if P includes just the set of independence relations defined by S. We show that the GU metric addresses some undesirable behavior of the BDeu and K2 Bayesian network scoring metrics, which also use particular forms of Default Parameter priors. A closed form formula for computing GU for special classes of BNs is derived. Efficiently computing GU for an arbitrary BN remains an open problem.
