SPOILER ALERT!
A Mesoscopic Simulation Model For Airport Curbside Management
newmarket to toronto airport
Let and denote the average waiting time of passengers and drivers of vehicle type up to time , respectively. The inputs for the vehicle mode share, passengers per vehicle, and commercial vehicle travel times were estimated using data from the Pearson International Airport Master Plan. What may not have been expected is the decrease in the number of taxis departing, although this is explained by the increase in travel time for commercial vehicles. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. A system with a utilization ratio close to 1 indicates a saturated system while a utilization ratio of 0 indicates an empty system. This results in fewer taxis cycling through the system. On the other hand, the passenger wait time for taxis and limos increased significantly. The average wait time for passengers and vehicles, shown in (7) and (8) and denoted by and , respectively, divides the total wait time by the number of passengers or vehicles.
The mesoscopic approach to curbside modelling has the potential to provide airports with a means of evaluating their curbside operations by replicating the aggregate flow of vehicles at the curbside while tracking individual passengers and vehicles at the pickup area. As expected, allowing vehicles to double park reduces the utilization ratio because some vehicles use the roadway to park instead of the parking spaces. In addition, the double parked vehicles can prevent the adjacent parked vehicle from leaving. Let be the average dwell time of vehicle type up to time where for taxis, for limos, and for passenger vehicles. The model is multidimensional because it simultaneously represents all key players at the curbside including taxis, limos, and passenger vehicles. As a result, the model concept could be a valuable tool for planners to evaluate the traffic demand at the airport curbside, to assess different scenarios that could be applied to manage demand, or to help planners identify and address other problems, such as the layout of the curbside. The results of both the base case and the increased demand scenario are presented in Table 4 and tests of hypothesis are performed to assess which of the MOEs are significantly changed.
A 100% increase in passenger demand further increases the average passenger wait time to 236 seconds. With an even likely higher arrival rate to the point that the curbside system fails, the number of queuing passengers and the average passenger wait time for private vehicles increase significantly. The next steps for the model would be to address some of the limitations, particularly the matching of passengers to private vehicles entering the system as well as refining and improving other processes. The output shows the vehicles moving through the system along with the performance measures which are also updated at each time step. A graphical user interface (GUI) provides an animation of the CA model over time. Applying the scenario analysis to the model demonstrates that the model is capable of analyzing different policy scenarios to determine the potential impact of the policy application. For planners, the model provides the advantages of both micro- and macrosimulation models, as it is much simpler than existing microscopic simulation models, but provides a large number of performance measures that allow for substantial analysis of the curbside. The most important factor affecting the measurements is the CVHA supply running out much sooner and vehicles returning to the airport at a slower rate which is consistent with poor weather observations at Pearson Airport.
On the other hand, a lower ratio for private vehicles is more desirable, indicating that vehicles will not have trouble locating a parking space. The results from reducing the maximum dwell time, reported in Table 7, show a significant decrease in the average dwell time for private vehicles from 89 seconds to 75 seconds. This would be expected to have an impact on the private vehicle dwell times, the number of vehicles circulating, and the private vehicle waiting time. Let and denote the total waiting time of passengers and drivers of vehicle type up to time , respectively. Passenger waiting time is the average time that passengers wait at the pickup area for a vehicle of their desired mode of transportation. The dwell time of a vehicle is the total time parked at the pickup area, including the waiting time prior to passenger arrival and loading time. The ratio can be expressed as the number of occupied spaces divided by the total number of spaces.
Let and denote the average waiting time of passengers and drivers of vehicle type up to time , respectively. The inputs for the vehicle mode share, passengers per vehicle, and commercial vehicle travel times were estimated using data from the Pearson International Airport Master Plan. What may not have been expected is the decrease in the number of taxis departing, although this is explained by the increase in travel time for commercial vehicles. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. A system with a utilization ratio close to 1 indicates a saturated system while a utilization ratio of 0 indicates an empty system. This results in fewer taxis cycling through the system. On the other hand, the passenger wait time for taxis and limos increased significantly. The average wait time for passengers and vehicles, shown in (7) and (8) and denoted by and , respectively, divides the total wait time by the number of passengers or vehicles.
The mesoscopic approach to curbside modelling has the potential to provide airports with a means of evaluating their curbside operations by replicating the aggregate flow of vehicles at the curbside while tracking individual passengers and vehicles at the pickup area. As expected, allowing vehicles to double park reduces the utilization ratio because some vehicles use the roadway to park instead of the parking spaces. In addition, the double parked vehicles can prevent the adjacent parked vehicle from leaving. Let be the average dwell time of vehicle type up to time where for taxis, for limos, and for passenger vehicles. The model is multidimensional because it simultaneously represents all key players at the curbside including taxis, limos, and passenger vehicles. As a result, the model concept could be a valuable tool for planners to evaluate the traffic demand at the airport curbside, to assess different scenarios that could be applied to manage demand, or to help planners identify and address other problems, such as the layout of the curbside. The results of both the base case and the increased demand scenario are presented in Table 4 and tests of hypothesis are performed to assess which of the MOEs are significantly changed.
A 100% increase in passenger demand further increases the average passenger wait time to 236 seconds. With an even likely higher arrival rate to the point that the curbside system fails, the number of queuing passengers and the average passenger wait time for private vehicles increase significantly. The next steps for the model would be to address some of the limitations, particularly the matching of passengers to private vehicles entering the system as well as refining and improving other processes. The output shows the vehicles moving through the system along with the performance measures which are also updated at each time step. A graphical user interface (GUI) provides an animation of the CA model over time. Applying the scenario analysis to the model demonstrates that the model is capable of analyzing different policy scenarios to determine the potential impact of the policy application. For planners, the model provides the advantages of both micro- and macrosimulation models, as it is much simpler than existing microscopic simulation models, but provides a large number of performance measures that allow for substantial analysis of the curbside. The most important factor affecting the measurements is the CVHA supply running out much sooner and vehicles returning to the airport at a slower rate which is consistent with poor weather observations at Pearson Airport.
On the other hand, a lower ratio for private vehicles is more desirable, indicating that vehicles will not have trouble locating a parking space. The results from reducing the maximum dwell time, reported in Table 7, show a significant decrease in the average dwell time for private vehicles from 89 seconds to 75 seconds. This would be expected to have an impact on the private vehicle dwell times, the number of vehicles circulating, and the private vehicle waiting time. Let and denote the total waiting time of passengers and drivers of vehicle type up to time , respectively. Passenger waiting time is the average time that passengers wait at the pickup area for a vehicle of their desired mode of transportation. The dwell time of a vehicle is the total time parked at the pickup area, including the waiting time prior to passenger arrival and loading time. The ratio can be expressed as the number of occupied spaces divided by the total number of spaces.
SPOILER ALERT!
A Mesoscopic Simulation Model For Airport Curbside Management
airport limo taxi toronto
Let and denote the average waiting time of passengers and drivers of vehicle type up to time , respectively. The inputs for the vehicle mode share, passengers per vehicle, and commercial vehicle travel times were estimated using data from the Pearson International Airport Master Plan. What may not have been expected is the decrease in the number of taxis departing, although this is explained by the increase in travel time for commercial vehicles. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. A system with a utilization ratio close to 1 indicates a saturated system while a utilization ratio of 0 indicates an empty system. This results in fewer taxis cycling through the system. On the other hand, the passenger wait time for taxis and limos increased significantly. The average wait time for passengers and vehicles, shown in (7) and (8) and denoted by and , respectively, divides the total wait time by the number of passengers or vehicles.
The mesoscopic approach to curbside modelling has the potential to provide airports with a means of evaluating their curbside operations by replicating the aggregate flow of vehicles at the curbside while tracking individual passengers and vehicles at the pickup area. As expected, allowing vehicles to double park reduces the utilization ratio because some vehicles use the roadway to park instead of the parking spaces. In addition, the double parked vehicles can prevent the adjacent parked vehicle from leaving. Let be the average dwell time of vehicle type up to time where for taxis, for limos, and for passenger vehicles. The model is multidimensional because it simultaneously represents all key players at the curbside including taxis, limos, and passenger vehicles. As a result, the model concept could be a valuable tool for planners to evaluate the traffic demand at the airport curbside, to assess different scenarios that could be applied to manage demand, or to help planners identify and address other problems, such as the layout of the curbside. The results of both the base case and the increased demand scenario are presented in Table 4 and tests of hypothesis are performed to assess which of the MOEs are significantly changed.
A 100% increase in passenger demand further increases the average passenger wait time to 236 seconds. With an even likely higher arrival rate to the point that the curbside system fails, the number of queuing passengers and the average passenger wait time for private vehicles increase significantly. The next steps for the model would be to address some of the limitations, particularly the matching of passengers to private vehicles entering the system as well as refining and improving other processes. The output shows the vehicles moving through the system along with the performance measures which are also updated at each time step. A graphical user interface (GUI) provides an animation of the CA model over time. Applying the scenario analysis to the model demonstrates that the model is capable of analyzing different policy scenarios to determine the potential impact of the policy application. For planners, the model provides the advantages of both micro- and macrosimulation models, as it is much simpler than existing microscopic simulation models, but provides a large number of performance measures that allow for substantial analysis of the curbside. The most important factor affecting the measurements is the CVHA supply running out much sooner and vehicles returning to the airport at a slower rate which is consistent with poor weather observations at Pearson Airport.
On the other hand, a lower ratio for private vehicles is more desirable, indicating that vehicles will not have trouble locating a parking space. The results from reducing the maximum dwell time, reported in Table 7, show a significant decrease in the average dwell time for private vehicles from 89 seconds to 75 seconds. This would be expected to have an impact on the private vehicle dwell times, the number of vehicles circulating, and the private vehicle waiting time. Let and denote the total waiting time of passengers and drivers of vehicle type up to time , respectively. Passenger waiting time is the average time that passengers wait at the pickup area for a vehicle of their desired mode of transportation. The dwell time of a vehicle is the total time parked at the pickup area, including the waiting time prior to passenger arrival and loading time. The ratio can be expressed as the number of occupied spaces divided by the total number of spaces.
Let and denote the average waiting time of passengers and drivers of vehicle type up to time , respectively. The inputs for the vehicle mode share, passengers per vehicle, and commercial vehicle travel times were estimated using data from the Pearson International Airport Master Plan. What may not have been expected is the decrease in the number of taxis departing, although this is explained by the increase in travel time for commercial vehicles. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. A system with a utilization ratio close to 1 indicates a saturated system while a utilization ratio of 0 indicates an empty system. This results in fewer taxis cycling through the system. On the other hand, the passenger wait time for taxis and limos increased significantly. The average wait time for passengers and vehicles, shown in (7) and (8) and denoted by and , respectively, divides the total wait time by the number of passengers or vehicles.
The mesoscopic approach to curbside modelling has the potential to provide airports with a means of evaluating their curbside operations by replicating the aggregate flow of vehicles at the curbside while tracking individual passengers and vehicles at the pickup area. As expected, allowing vehicles to double park reduces the utilization ratio because some vehicles use the roadway to park instead of the parking spaces. In addition, the double parked vehicles can prevent the adjacent parked vehicle from leaving. Let be the average dwell time of vehicle type up to time where for taxis, for limos, and for passenger vehicles. The model is multidimensional because it simultaneously represents all key players at the curbside including taxis, limos, and passenger vehicles. As a result, the model concept could be a valuable tool for planners to evaluate the traffic demand at the airport curbside, to assess different scenarios that could be applied to manage demand, or to help planners identify and address other problems, such as the layout of the curbside. The results of both the base case and the increased demand scenario are presented in Table 4 and tests of hypothesis are performed to assess which of the MOEs are significantly changed.
A 100% increase in passenger demand further increases the average passenger wait time to 236 seconds. With an even likely higher arrival rate to the point that the curbside system fails, the number of queuing passengers and the average passenger wait time for private vehicles increase significantly. The next steps for the model would be to address some of the limitations, particularly the matching of passengers to private vehicles entering the system as well as refining and improving other processes. The output shows the vehicles moving through the system along with the performance measures which are also updated at each time step. A graphical user interface (GUI) provides an animation of the CA model over time. Applying the scenario analysis to the model demonstrates that the model is capable of analyzing different policy scenarios to determine the potential impact of the policy application. For planners, the model provides the advantages of both micro- and macrosimulation models, as it is much simpler than existing microscopic simulation models, but provides a large number of performance measures that allow for substantial analysis of the curbside. The most important factor affecting the measurements is the CVHA supply running out much sooner and vehicles returning to the airport at a slower rate which is consistent with poor weather observations at Pearson Airport.
On the other hand, a lower ratio for private vehicles is more desirable, indicating that vehicles will not have trouble locating a parking space. The results from reducing the maximum dwell time, reported in Table 7, show a significant decrease in the average dwell time for private vehicles from 89 seconds to 75 seconds. This would be expected to have an impact on the private vehicle dwell times, the number of vehicles circulating, and the private vehicle waiting time. Let and denote the total waiting time of passengers and drivers of vehicle type up to time , respectively. Passenger waiting time is the average time that passengers wait at the pickup area for a vehicle of their desired mode of transportation. The dwell time of a vehicle is the total time parked at the pickup area, including the waiting time prior to passenger arrival and loading time. The ratio can be expressed as the number of occupied spaces divided by the total number of spaces.
SPOILER ALERT!
A Mesoscopic Simulation Model For Airport Curbside Management
limo cidery tours
Let and denote the average waiting time of passengers and drivers of vehicle type up to time , respectively. The inputs for the vehicle mode share, passengers per vehicle, and commercial vehicle travel times were estimated using data from the Pearson International Airport Master Plan. What may not have been expected is the decrease in the number of taxis departing, although this is explained by the increase in travel time for commercial vehicles. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. A system with a utilization ratio close to 1 indicates a saturated system while a utilization ratio of 0 indicates an empty system. This results in fewer taxis cycling through the system. On the other hand, the passenger wait time for taxis and limos increased significantly. The average wait time for passengers and vehicles, shown in (7) and (8) and denoted by and , respectively, divides the total wait time by the number of passengers or vehicles.
The mesoscopic approach to curbside modelling has the potential to provide airports with a means of evaluating their curbside operations by replicating the aggregate flow of vehicles at the curbside while tracking individual passengers and vehicles at the pickup area. As expected, allowing vehicles to double park reduces the utilization ratio because some vehicles use the roadway to park instead of the parking spaces. In addition, the double parked vehicles can prevent the adjacent parked vehicle from leaving. Let be the average dwell time of vehicle type up to time where for taxis, for limos, and for passenger vehicles. The model is multidimensional because it simultaneously represents all key players at the curbside including taxis, limos, and passenger vehicles. As a result, the model concept could be a valuable tool for planners to evaluate the traffic demand at the airport curbside, to assess different scenarios that could be applied to manage demand, or to help planners identify and address other problems, such as the layout of the curbside. The results of both the base case and the increased demand scenario are presented in Table 4 and tests of hypothesis are performed to assess which of the MOEs are significantly changed.
A 100% increase in passenger demand further increases the average passenger wait time to 236 seconds. With an even likely higher arrival rate to the point that the curbside system fails, the number of queuing passengers and the average passenger wait time for private vehicles increase significantly. The next steps for the model would be to address some of the limitations, particularly the matching of passengers to private vehicles entering the system as well as refining and improving other processes. The output shows the vehicles moving through the system along with the performance measures which are also updated at each time step. A graphical user interface (GUI) provides an animation of the CA model over time. Applying the scenario analysis to the model demonstrates that the model is capable of analyzing different policy scenarios to determine the potential impact of the policy application. For planners, the model provides the advantages of both micro- and macrosimulation models, as it is much simpler than existing microscopic simulation models, but provides a large number of performance measures that allow for substantial analysis of the curbside. The most important factor affecting the measurements is the CVHA supply running out much sooner and vehicles returning to the airport at a slower rate which is consistent with poor weather observations at Pearson Airport.
On the other hand, a lower ratio for private vehicles is more desirable, indicating that vehicles will not have trouble locating a parking space. The results from reducing the maximum dwell time, reported in Table 7, show a significant decrease in the average dwell time for private vehicles from 89 seconds to 75 seconds. This would be expected to have an impact on the private vehicle dwell times, the number of vehicles circulating, and the private vehicle waiting time. Let and denote the total waiting time of passengers and drivers of vehicle type up to time , respectively. Passenger waiting time is the average time that passengers wait at the pickup area for a vehicle of their desired mode of transportation. The dwell time of a vehicle is the total time parked at the pickup area, including the waiting time prior to passenger arrival and loading time. The ratio can be expressed as the number of occupied spaces divided by the total number of spaces.
Let and denote the average waiting time of passengers and drivers of vehicle type up to time , respectively. The inputs for the vehicle mode share, passengers per vehicle, and commercial vehicle travel times were estimated using data from the Pearson International Airport Master Plan. What may not have been expected is the decrease in the number of taxis departing, although this is explained by the increase in travel time for commercial vehicles. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. A system with a utilization ratio close to 1 indicates a saturated system while a utilization ratio of 0 indicates an empty system. This results in fewer taxis cycling through the system. On the other hand, the passenger wait time for taxis and limos increased significantly. The average wait time for passengers and vehicles, shown in (7) and (8) and denoted by and , respectively, divides the total wait time by the number of passengers or vehicles.
The mesoscopic approach to curbside modelling has the potential to provide airports with a means of evaluating their curbside operations by replicating the aggregate flow of vehicles at the curbside while tracking individual passengers and vehicles at the pickup area. As expected, allowing vehicles to double park reduces the utilization ratio because some vehicles use the roadway to park instead of the parking spaces. In addition, the double parked vehicles can prevent the adjacent parked vehicle from leaving. Let be the average dwell time of vehicle type up to time where for taxis, for limos, and for passenger vehicles. The model is multidimensional because it simultaneously represents all key players at the curbside including taxis, limos, and passenger vehicles. As a result, the model concept could be a valuable tool for planners to evaluate the traffic demand at the airport curbside, to assess different scenarios that could be applied to manage demand, or to help planners identify and address other problems, such as the layout of the curbside. The results of both the base case and the increased demand scenario are presented in Table 4 and tests of hypothesis are performed to assess which of the MOEs are significantly changed.
A 100% increase in passenger demand further increases the average passenger wait time to 236 seconds. With an even likely higher arrival rate to the point that the curbside system fails, the number of queuing passengers and the average passenger wait time for private vehicles increase significantly. The next steps for the model would be to address some of the limitations, particularly the matching of passengers to private vehicles entering the system as well as refining and improving other processes. The output shows the vehicles moving through the system along with the performance measures which are also updated at each time step. A graphical user interface (GUI) provides an animation of the CA model over time. Applying the scenario analysis to the model demonstrates that the model is capable of analyzing different policy scenarios to determine the potential impact of the policy application. For planners, the model provides the advantages of both micro- and macrosimulation models, as it is much simpler than existing microscopic simulation models, but provides a large number of performance measures that allow for substantial analysis of the curbside. The most important factor affecting the measurements is the CVHA supply running out much sooner and vehicles returning to the airport at a slower rate which is consistent with poor weather observations at Pearson Airport.
On the other hand, a lower ratio for private vehicles is more desirable, indicating that vehicles will not have trouble locating a parking space. The results from reducing the maximum dwell time, reported in Table 7, show a significant decrease in the average dwell time for private vehicles from 89 seconds to 75 seconds. This would be expected to have an impact on the private vehicle dwell times, the number of vehicles circulating, and the private vehicle waiting time. Let and denote the total waiting time of passengers and drivers of vehicle type up to time , respectively. Passenger waiting time is the average time that passengers wait at the pickup area for a vehicle of their desired mode of transportation. The dwell time of a vehicle is the total time parked at the pickup area, including the waiting time prior to passenger arrival and loading time. The ratio can be expressed as the number of occupied spaces divided by the total number of spaces.
A Mesoscopic Simulation Model For Airport Curbside Management
mississauga to airport
Let and denote the average waiting time of passengers and drivers of vehicle type up to time , respectively. The inputs for the vehicle mode share, passengers per vehicle, and commercial vehicle travel times were estimated using data from the Pearson International Airport Master Plan. What may not have been expected is the decrease in the number of taxis departing, although this is explained by the increase in travel time for commercial vehicles. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. A system with a utilization ratio close to 1 indicates a saturated system while a utilization ratio of 0 indicates an empty system. This results in fewer taxis cycling through the system. On the other hand, the passenger wait time for taxis and limos increased significantly. The average wait time for passengers and vehicles, shown in (7) and (8) and denoted by and , respectively, divides the total wait time by the number of passengers or vehicles.
The mesoscopic approach to curbside modelling has the potential to provide airports with a means of evaluating their curbside operations by replicating the aggregate flow of vehicles at the curbside while tracking individual passengers and vehicles at the pickup area. As expected, allowing vehicles to double park reduces the utilization ratio because some vehicles use the roadway to park instead of the parking spaces. In addition, the double parked vehicles can prevent the adjacent parked vehicle from leaving. Let be the average dwell time of vehicle type up to time where for taxis, for limos, and for passenger vehicles. The model is multidimensional because it simultaneously represents all key players at the curbside including taxis, limos, and passenger vehicles. As a result, the model concept could be a valuable tool for planners to evaluate the traffic demand at the airport curbside, to assess different scenarios that could be applied to manage demand, or to help planners identify and address other problems, such as the layout of the curbside. The results of both the base case and the increased demand scenario are presented in Table 4 and tests of hypothesis are performed to assess which of the MOEs are significantly changed.
A 100% increase in passenger demand further increases the average passenger wait time to 236 seconds. With an even likely higher arrival rate to the point that the curbside system fails, the number of queuing passengers and the average passenger wait time for private vehicles increase significantly. The next steps for the model would be to address some of the limitations, particularly the matching of passengers to private vehicles entering the system as well as refining and improving other processes. The output shows the vehicles moving through the system along with the performance measures which are also updated at each time step. A graphical user interface (GUI) provides an animation of the CA model over time. Applying the scenario analysis to the model demonstrates that the model is capable of analyzing different policy scenarios to determine the potential impact of the policy application. For planners, the model provides the advantages of both micro- and macrosimulation models, as it is much simpler than existing microscopic simulation models, but provides a large number of performance measures that allow for substantial analysis of the curbside. The most important factor affecting the measurements is the CVHA supply running out much sooner and vehicles returning to the airport at a slower rate which is consistent with poor weather observations at Pearson Airport.
On the other hand, a lower ratio for private vehicles is more desirable, indicating that vehicles will not have trouble locating a parking space. The results from reducing the maximum dwell time, reported in Table 7, show a significant decrease in the average dwell time for private vehicles from 89 seconds to 75 seconds. This would be expected to have an impact on the private vehicle dwell times, the number of vehicles circulating, and the private vehicle waiting time. Let and denote the total waiting time of passengers and drivers of vehicle type up to time , respectively. Passenger waiting time is the average time that passengers wait at the pickup area for a vehicle of their desired mode of transportation. The dwell time of a vehicle is the total time parked at the pickup area, including the waiting time prior to passenger arrival and loading time. The ratio can be expressed as the number of occupied spaces divided by the total number of spaces.
Let and denote the average waiting time of passengers and drivers of vehicle type up to time , respectively. The inputs for the vehicle mode share, passengers per vehicle, and commercial vehicle travel times were estimated using data from the Pearson International Airport Master Plan. What may not have been expected is the decrease in the number of taxis departing, although this is explained by the increase in travel time for commercial vehicles. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. A system with a utilization ratio close to 1 indicates a saturated system while a utilization ratio of 0 indicates an empty system. This results in fewer taxis cycling through the system. On the other hand, the passenger wait time for taxis and limos increased significantly. The average wait time for passengers and vehicles, shown in (7) and (8) and denoted by and , respectively, divides the total wait time by the number of passengers or vehicles.
The mesoscopic approach to curbside modelling has the potential to provide airports with a means of evaluating their curbside operations by replicating the aggregate flow of vehicles at the curbside while tracking individual passengers and vehicles at the pickup area. As expected, allowing vehicles to double park reduces the utilization ratio because some vehicles use the roadway to park instead of the parking spaces. In addition, the double parked vehicles can prevent the adjacent parked vehicle from leaving. Let be the average dwell time of vehicle type up to time where for taxis, for limos, and for passenger vehicles. The model is multidimensional because it simultaneously represents all key players at the curbside including taxis, limos, and passenger vehicles. As a result, the model concept could be a valuable tool for planners to evaluate the traffic demand at the airport curbside, to assess different scenarios that could be applied to manage demand, or to help planners identify and address other problems, such as the layout of the curbside. The results of both the base case and the increased demand scenario are presented in Table 4 and tests of hypothesis are performed to assess which of the MOEs are significantly changed.
A 100% increase in passenger demand further increases the average passenger wait time to 236 seconds. With an even likely higher arrival rate to the point that the curbside system fails, the number of queuing passengers and the average passenger wait time for private vehicles increase significantly. The next steps for the model would be to address some of the limitations, particularly the matching of passengers to private vehicles entering the system as well as refining and improving other processes. The output shows the vehicles moving through the system along with the performance measures which are also updated at each time step. A graphical user interface (GUI) provides an animation of the CA model over time. Applying the scenario analysis to the model demonstrates that the model is capable of analyzing different policy scenarios to determine the potential impact of the policy application. For planners, the model provides the advantages of both micro- and macrosimulation models, as it is much simpler than existing microscopic simulation models, but provides a large number of performance measures that allow for substantial analysis of the curbside. The most important factor affecting the measurements is the CVHA supply running out much sooner and vehicles returning to the airport at a slower rate which is consistent with poor weather observations at Pearson Airport.
On the other hand, a lower ratio for private vehicles is more desirable, indicating that vehicles will not have trouble locating a parking space. The results from reducing the maximum dwell time, reported in Table 7, show a significant decrease in the average dwell time for private vehicles from 89 seconds to 75 seconds. This would be expected to have an impact on the private vehicle dwell times, the number of vehicles circulating, and the private vehicle waiting time. Let and denote the total waiting time of passengers and drivers of vehicle type up to time , respectively. Passenger waiting time is the average time that passengers wait at the pickup area for a vehicle of their desired mode of transportation. The dwell time of a vehicle is the total time parked at the pickup area, including the waiting time prior to passenger arrival and loading time. The ratio can be expressed as the number of occupied spaces divided by the total number of spaces.
