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Adapting Automated People Mover Capacity to Real-Time Demand Via Model-Based Predictive Control

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Adapting Automated People Mover Capacity to Real-Time Demand Via Model-Based Predictive Control Adapting Automated People Mover Capacity to Real-Time Demand via Model-Based Predictive Control M.P. (Mark) van Doorne BSc. December 2015 Adapting Automated People Mover Capacity to Real-Time Demand via Model-Based Predictive Control by M.P. van Doorne BSc. to obtain the degree of Master Master of Science in Transport, Infrastructure & Logistics at the Delft University of Technology. to be defended publicly on Wednesday December 2nd, 2015 at 14:00 PM. Graduation Committee TU Delft Prof. dr. ir. G. Lodewijks TU Delft (3ME) dr.ir. W.W.A. Beelaerts van Blokland TU Delft (3ME) dr.ir. G. Homem de Almeida Correia TU Delft (CiTG) Supervision NACO Royal HaskoningDHV ir. T. van Vrijaldenhoven Airport Planning & Building Design ir. P.H. Ringersma Airport Planning & Building Design drs. ing. R. Lunstroo Airport Planning & Building Design Executive Summary This report contains the findings of a research on the control of an Automated People Mover (APM) system. APMs are combined passenger transit systems that are an important asset for large airports to support intra-terminal passenger movements and/or provide inter-terminal tran- sit. While APMs were first introduced at airports in the early '70s, the physical and operational system characteristics have not changed much since. The problem is that the control system of any APM attempts to replicate a predefined schedule by constantly measuring parts of the system (i.e. trains) and take actions to diminish any errors. These schedules are already defined in the design phase of a system and should cope with a peak demand throughout the operational life cycle. Such a design approach inevitably results in that there will be a gap between the actual demand and the available capacity. Especially in airports with large demand fluctuations, this will e.g. incur large over-capacities during down periods. The objective of this research is to analyse the current fixed schedule control of an APM system and design a more intelligent control logic that adapts the capacity available to the real-time demand. This research will not only address the technical aspect to obtain such an Adaptive Control System (ACS), but will also consider the change in the system state compared to a conventional system. Which system state is better or worse depends on the requirements set by the system owner (i.e. airport). These requirements will be a trade-off between aspects that concern the economic and sustainable impact of the system and the passenger experience. This is summarised in a research objective: Determine the feasibility of adapting Automated People Mover capacity to real-time demand, by taking economic, sustainable, passenger comfort and implementation aspects into consideration. To transform the objective into an actual design, a stepwise approach is taken in which first a conceptual outline is made of the ACS, after which a detailed design is tested and evaluated. To determine the generality of the ACS, it is applied to two test case (proposed) APM systems at Amsterdam Schiphol International Airport (AMS) and Shenzhen Ba'oan International Airport (SZX). Thereby, a set of (Key) Performance Indicators (KPIs) is formulated to quantitatively support any conclusion on the performance of an ACS, by measuring: Passenger experience: platform dwell time, platform & train Level of Service (LoS); System cost: capital cost & operational Cost; External effect: CO2 pollution. Conceptual Design of an Adaptive Control System The technological development of Communication Based Train Control (CBTC) for APMs allows for a system that changes states according to a schedule. The goal of the ACS is to replace the currently used fixed schedule with a flexible schedule that adapts to real-time demand. It is therefore proposed that the available CBTC technology that currently controls train movements is combined with an hierarchically higher controller that can change the system capacity in terms of train frequency and train capacity. iii A known example of a system that already has such a higher level controller is Personal Rapid Transit (PRT), which allocates small vehicles (pods) to a station with demand. However, the problem with the controller type used for this system, is that it reacts to demand initiated by a passenger with a push of a button. This is fine for a system in which a high number of vehicles is available to keep the reaction time low, but will be problematic in a typical APM system with few trains that need more time to anticipate. The result is that passengers have to wait uncomfortably long and instead some form of proactive control is required to activate a train in time. It is therefore chosen to design an Adaptive Control System (ACS) by means of Model-based Predictive Control (MPC) method. An MPC calculates a set of future actions that as a com- bination satisfies an overall objective based on passenger demand forecast models. The prime objective of the MPC is to minimise the difference between the system capacity for the next n time steps and the demand forecast for that same period n. This demand is determined by aircraft movements that induce a high and narrow arrival distribution and a lower but broader departure distribution. While demand characteristics can roughly be calculated based on historical data and airport forecasts, it is preferred to place a sensor at an appropriate distance before the platform such that the minimum forecast period is met. The capacity can either be changed by running more/less trains or increase/decrease the amount of cars per train. The complete control structure is summarised in Figure2. Figure 2: Adaptive Control System The MPC can adjust train scheduling and train composition based on changes in demand. The primary action of the controller is to add a 1-car train to the schedule in response to the initial creation of demand, such that the first passenger has to wait a maximum acceptable waiting time (based on system owner's requirement). If the demand for a scheduled train exceeds the available capacity, there are two approaches to increase capacity further; an additional train can be scheduled before or after the first train and effectively increase frequency, or the train can be extended by an extra car. If in either approach a maximum is reached (no more trains able to be scheduled or no more cars to be added), the other approach should be used to further iv Graduation Thesis expand capacity. This results in two concept ACS alternatives which can be tested and evaluated together with a reference alternative: Reference Case: a representation of a conventional fixed schedule operation; ACS 1 - Frequency: an ACS that favours changing train frequency over train capacity; ACS 2 - Capacity: an ACS that favours changing train capacity over train frequency. Table 1: Summary of Simulation Results AMS KPI Passenger Experience Cost (daily) External PI Wait(s) LoS Plat LoS Train Capital Operation kg CO2 Reference Case 90.611 A D $1,314 $560 9,424 ACS 1: Frequency 95.33 A D $2,012 $350 5,874 ACS 2: Capacity 94.18 A D $1,136 $346 5,806 1during daytime Table 2: Summary of Simulation Results SZX KPI Passenger Experience Cost (daily) External PI Wait(s) LoS Plat LoS Train Capital Operation kg CO2 Reference Case 92.31 C D $3,504 $4,454 110,751 ACS 1: Frequency 80 D D $3,107 $1,678 41,714 ACS 2: Capacity 80.42 D D $2,888 $1,757 43,674 Detailed Design of an Adaptive Control System The conceptual design of the ACS is converted into detailed designs for proposed APM systems at Amsterdam Schiphol International Airport (AMS) and Shenzhen Bao'an International Airport (SZX). The simulation results, summarised in the tables1 and2, show a comparable result on the (Key) Performance Indicators (KPIs) for the two ACS alternatives in terms of dwell time, Level- of-Service (LoS), operational costs and CO2 pollution. Capital costs are however consistently lower in ACS alternative 2 (capacity), as fewer cars have to be acquired. This difference is caused by the distinctive decision logic of ACS alternative 1 (frequency), which can add an extra earlier train to decrease the waiting time of passengers. A mismatch can occur between the demand forecast and capacity availability when this train is added but passengers miss it and an extra car in the following train is required to compensate. The expected lower waiting time that should result from the logic is thereby insignificant with comparable average dwell times for the ACS alternatives. The dwell time results of the ACS alternatives also show a distinctive difference between the two test cases, in which the results are better for SZX than AMS. This is the result of the ACS decision logic that makes a primary decision to schedule a 1-car train when demand is created and then it waits as long as possible to optimally fill that train (180 seconds). Any further adjustments to the schedule or train composition are possible when the demand surpasses the capacity of the first scheduled 1-car train. This does however only sporadically occur in the low demand system at AMS and is more frequent in the control of the SZX system, hence resulting in better results of the ACS in the latter test case. v The results of the ACS alternatives for the KPI passenger experience are on par with the reference case. The dwell time is similar for AMS and lower for SZX. However the ACS does come with a higher platform utilization, which results in a drop in level of service for a singular platform in the SZX test case (from LoS C to Los D, on a ranking from A=best to F=worst).
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