Macroscopic and Microscopic Simulation for the Evaluation of People Mover Systems. Dr.-Ing. Peter Mott Sven Beller PTV AG, Karlsruhe, Germany

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1 Macroscopic and Microscopic Simulation for the Evaluation of People Mover Systems Dr.-Ing. Peter Mott Sven Beller PTV AG, Karlsruhe, Germany 1. Introduction This paper is intended to show how a comprehensive transportation planning system can be used to analyse the impact of a People Mover or Personal Rapid Transit (PRT) system on traffic and transportation operations. Both systems use comparatively small driverless vehicles which are controlled automatically. Based on two examples we demonstrate the benefits of macroscopic planning and microscopic simulation of public transport systems: In the first example the aim is to choose a system that enables the provision of efficient transport services in a high density area such as a new development in West Bay area, Doha, Qatar. An underground system will connect this area with the other parts of the city. A people mover system is planned to be integrated into the multi-modal transport system in order to serve the high density area. According to information on existing and future land use, traffic demand is generated by office buildings, hotels, a congress centre and a shopping mall in this area. Due to difficult climate conditions, the footpaths between these origins of traffic and the people mover stations have to be as short as possible. An important aspect here is to find out how the smaller vehicles of the people mover system can handle the expected flow emerging from the high-capacity underground trains. The second example models and analyses a demand-responsive transport system (DRT) which means that vehicles are not assigned to fixed routes and fixed times. DRT matches the service more closely to the customers' needs: passengers request a journey as soon as they arrive at the pick-up point and the vehicles will then take them to the desired destination on the shortest possible route without any intermediate stops. Small vehicles along with a simple but effective empty vehicle management provide a highly available transport service. In most situations there are no waiting times for the passengers. Other transport systems may also be integrated to form a comprehensive, city-wide, multi-modal transport model for further analysis even including private transport modes such as cars and bikes. For clarity, the other systems are not shown here.

2 Both examples are modelled and analysed with the transportation planning software suite PTV Vision. The macroscopic software tool VISUM is used for the first application and the microscopic application VISSIM is used for the second one. Among others, these systems provide the following options: Detailed representation of public transport services with line routes, detailed timetables and vehicle types allocated to each line route. Demand modelling defined in terms of space and time: zone or station based with a passenger volume distributed by time according to variable intervals, e.g. 15 or 30 seconds, one or several hours. The macroscopic analysis is based on assignment methods which take both travel demand distribution in terms of time and capacity of individual public transport systems into account. Modelling the interaction between vehicles and passengers as part of a multi-modal microscopic simulation. Calculation of a wide range of performance indicators from the passenger's and the operator's point of view, such as travel time and waiting time, number of transfers, passenger volume and volume capacity ratio per link etc. Option to export a macroscopic model from VISUM for further (microscopic) analysis to VISSIM, hence providing an almost seamless top-down workflow. 2. The Macroscopic View The goal in the first example is to assess whether the transport system can cope with the predicted passenger volumes, in particular regarding the transfer between the metro and the people mover system. The traffic volumes are based on estimated passenger flows during the morning peak period including a temporal distribution with 30-min intervals. The system is defined by stations, routes, the service frequency and the vehicle size. This distribution display is parameterised so that changes can be modelled easily. Figure 1 shows the layout of a north-south metro line (M1) operating every 10 minutes and two people mover lines (PM Red and PM Blue) that depart every 5 minutes. This service covers a total of six stations.

3 Figure 1: Layout of a Metro and People Mover network The travel demand is based on the assumption that there are major passenger flows from the other parts of the city during the morning peak, lasting about two hours. Additional demand arises from the passengers travelling within the area only. Figure 2 illustrates the Origin-Destination matrix based on the network; Figure 3 shows the relative distribution of the demand over time. Figure 2: Layout of a Metro and People Mover network, superposed by the structure of the origin-destination matrix

4 Figure 3: Distribution of passenger demand during the morning peak period After defining the timetable and the vehicle types including the total number of seats, it is possible to automatically calculate the line route capacity and the volume capacity ratio for each link and time interval. In this particular case, the volume capacity ratio per time interval is an important factor. An even volume assignment during the morning peak period does not reveal any problems (see Figure 4). Figure 4: Volume capacity ratio using even volume assignment during morning peak However, the analysis which is based on time intervals shows the situation to be quite different: as expected, the load factor soars and the travel demand is too high on some routes to accommodate the planned number of passengers, while there is sufficient capacity on alternative routes (see Figure 5).

5 Figure 5: Volume capacity ratio per time interval to identify transport capacity bottlenecks on network sections The inclusion of the volume capacity ratio in the so-called timetable-based assignment leads to a completely different result: the impedance takes into account an additional penalty, depending on the volume capacity ratio per link and time interval. If the volume capacity ratio per route section exceeds the defined values the impedance on this route is increased. Hence other connections - if available become more attractive to passengers. This approach can be interpreted as controlled guiding of passenger flows to less busy routes. Hence it is possible to determine the amount of passengers that can still be managed and to analyse the preferred services in a more realistic and reliable manner. Based on the assignment results and the calculation of performance indicators, the planning program calculates the following indicators used for system evaluation: Passenger volume and capacity utilisation per route, link and time interval, waiting time per stop and travel time from origin to destination Performance like no. of vehicles required, vehicle kilometres etc. Estimation of operating costs and expected fare revenues. System analysis based on macroscopic assignment provides more than only the "big picture" and allows identifying potential bottlenecks in the system over time. Even capacity restraints in public transport assignment can be taken into account. For a more detailed assessment of the various processes determining the overall system performance the microscopic simulation comes into play. It can also cope with situations where the demand significantly exceeds the capacity.

6 3. The Microscopic View In contrast to macroscopic models, microscopic models simulate the movement and interaction of individual entities. The software package VISSIM is part of the PTV Vision software suite as well and provides microscopic simulation methods for assessing and solving a wide range of transportation problems. The heart of VISSIM includes scientifically approved models for car-following, lane-changing and pedestrian movements. Simulations include road users, public transport and pedestrians and their interactions with each other. All is based on an integrated network model of roads and rails along with their control methods as well as the pedestrian infrastructure to connect with them. This multi-modal network is then used by cars, HGVs, buses, trams and trains as well as cyclists and pedestrians - only to name a few. During the simulation a wide selection of evaluations are available for online and offline analysis. Another special characteristic is an animated visualisation in 2D or 3D which offers an instant comprehension of the simulated traffic situation and also fills the gap between the technical expertise and a non-technical audience Line-Based Operation Line-based operation is the typical application of public transport in VISSIM. The essential input data for the microsimulation model includes Road/rail infrastructure: tracks/roads (with length, width, gradient), branches and merges (switches/junctions), stations (with platforms), depots (if required) Operational data: line allocation (based on routes on the network), departure times (timetable/frequency), vehicle type and capacity, depot capacity Passenger volumes: either the desired route relations (origin-destination matrix) or the linespecific passenger volumes at individual stations, and their temporal distribution. If passenger volumes are not available dwell time distributions may be used instead. Looking at the first example mentioned above, a detailed representation of the processes at the transfer station is useful in particular, if the number of passengers currently waiting at the station exceeds the capacity of the next vehicle. This may happen as a large number of passengers alight from a metro train at the interchange and the next people mover vehicles arriving cannot handle all of them instantly. In such a situation, the microscopic system

7 allows passengers to enter the vehicle until its capacity is reached. The remaining passengers wait for the next vehicle. The network used in the first example is exported from the macroscopic model to the microscopic simulation tool VISSIM. Some further adjustments ensure that all data is supplied in order to run the simulation. That means that the first example encompasses both macroscopic simulation of the total system and microscopic simulation of the processes at the transfer station in order to precisely calculate the number of passengers affected by the delay and their waiting times at the transfer points Demand-Based Operation A major benefit of a PRT system is the "freedom of travel", i.e. that vehicles don't travel on fixed routes with a timetable, but serve the passengers individually by providing an on-demand service. In contrast to line-based operation, this challenges the use of standard microscopic simulation software as different methods and control operations are required. For these purposes VISSIM offers an application programming interface (API) which allows expanding its simulation capabilities beyond what comes directly out of the box. The API offers a wide range of options on how to implement various external control strategies: It ranges from handing over parameters up to total control of vehicle movement and PRT system operation. This approach also allows for applying complex control strategies modelled by an external system. The second example is a case study which shows such a demand-based operation of a PRT system. It covers an area of 500m by 350m and includes 4.7 km of guideways, 17 branches/switches, 7 stations and 1 depot (see Figure 6 and Figure 7). The main objective of such a model is to analyse and visualise the PRT operation for variations in temporal and spatial demand as well as to assess the impact of different control strategies. Since the operation of such a demand-based model is much more complex than a line- and timetable based model, some additional information is required in order to get a realistic model: Structural data: Number of vehicles the station can hold, station type, number of berths for boarding and alighting at each station Operational data: Dynamic route assignment (depending on the destination), empty vehicle redistribution, demand-based pre-allocation to stations, re-allocation during empty journey, recharging Passenger volumes: max. and min. group sizes and their distribution

8 Figure 6: Structure of the model for the simulation of a demand-responsive PRT system with vehicles, stations, switches and the depot (2D view) Figure 7: Structure of the model for the simulation of a demand-responsive PRT system with vehicles, stations, switches and the depot (3D view)

9 The vehicle assignment and main system control is done by scripting through the VISSIM API. The script language used for this example is Visual Basic for Applications (VBA) using Microsoft Excel as front end. Here control buttons for loading the network, running the simulation and changing some parameters such as the total number of vehicles in the system are provided. During the simulation, in addition to the visual animation, several program indicators are shown in Excel for each station in order to ensure system integrity and to help trouble-shooting. One indicator shows for example the trip demand at each station and how many vehicles are available to meet that demand. The vehicle movements and interactions are done automatically in VISSIM with no need for scripting. It also includes conflict handling, e.g. at guideway merges. The principle of the simulated process is as follows: Initially all PRT vehicles (pods) reside in the depot. Then some vehicles are assigned to each station according to the parameter setting of how many free vehicles should ideally be available at each station. Passengers who arrive at a station enter their desired destination at the service terminal and request a pickup. Typically, vehicles are available at each station so that there are no waiting times. If not, a free vehicle is sent to the station. Figure 8: Excel front end to control the simulation

10 The selected vehicle receives the information of the passenger s desired destination. The passenger transport starts immediately after boarding. At each guideway branch the vehicle receives the direction information according to its destination so that the vehicle takes the optimum route to its destination. During the trip, the vehicle reacts to the other vehicles in the network by considering speeds and necessary time gaps between the vehicles. As the vehicle arrives at the destination it proceeds to the drop-off location for passengers to alight. The empty vehicle can then be rescheduled: If there are less empty vehicles than desired, it will stay at this station. If there are enough vehicles, the first vehicle in the queue will return to the depot empty. It can be re-allocated during the trip if a new request for a ride has been submitted from a station along the route or if a station is in need for more empty vehicles. The time required for boarding and alighting and the departure times result from the station layout, the number of passengers, operation processes, walking speeds of pedestrians and their service time at the terminal, where they enter their destination. If there are always enough vehicles available and passengers don't arrive in large numbers at the same time then there is virtually no delay for them to start travelling.

11 Figure 9: Layout of a station in 2D view. Passengers alight on the left and board on the right side of the station. The optional window for each vehicle shows technical data such as current speed during the simulation. The station layout will become a major factor as soon as large groups of passengers arrive at a station in order to depart and/or if many vehicles arrive here at short intervals. Then a station with multiple berths and/or a sawtooth layout has the advantage of parallel boarding/alighting. All these parameters can be varied and combined in several scenarios to be tested. Consequently, several simulation runs are executed evaluating the proposed scenarios. During each run user-configurable data is collected which is used to compare and evaluate the scenarios. Typical performance indicators are: From the passenger's point of view: waiting time at the station and journey time from the starting point to the destination. From the operator's point of view: number of required vehicles, vehicle kilometres, empty vehicle movements, dwell time at the depot As an example, the average waiting time at stations has been evaluated to see the impact of the number of vehicles available in the system as well as the impact of empty vehicle redistribution. These scenarios were simulated:

12 Scenario 1: 30 vehicles in total, 3 free vehicles at each station Scenario 2: 40 vehicles in total, 1 free vehicle at each station Scenario 3: 40 vehicles in total, 3 free vehicles at each station Figure 10 shows the average waiting time at each station. The simulated total passenger demand was chosen to be below capacity. The differences in time within each scenario result mainly from the different demands at each station. Most passengers depart from station no. 6 whereas most passengers arrive at station no. 1. From the results it is clearly visible that looking only at the total number of vehicles is not enough to assess a PRT system. The empty car management is equally important which can be seen especially for the stations with the highest demand (4, 6 and 7) where the negative impact is much higher for scenario 2. The extra 10 vehicles for scenario 3 help to further reduce the waiting time compared to scenario 1, but even with the situation shown in scenario 1 the system can operate well - for some stations (2, 5 and 1) there is virtually no difference in waiting time as compared to scenario 3. Figure 10: Average waiting time per station for three scenarios. The time needed for passenger interaction at the terminal is not included here. One conclusion of this simulation could be that 25% of the money for rolling stock could be saved if the empty car management is done well. Other examples for microsimulation assessments could include longer simulation periods (e.g. an entire day), consider the battery status of the

13 vehicles, predict station demand by learning from history, include sawtoothshaped stations versus in-line stations and of course different control strategies for vehicle assignment and empty vehicle re-destribution. When all the simulation work is completed, the animation may be recorded as a 3D movie so that the results can be visualised and presented, also to a nontechnical audience. Figure 11: 3D movie recording of the simulation nearby station 3 Figure 12: 3D movie recording of boarding passengers at station 6

14 Multi-Modal Modal Operation Another interesting application for simulation is a multi-modal transport model. It offers the entire range of real-world transport: private traffic such as car and bike, public transport such as trains, trams, buses and PRT and all linkage between the different modes, even taking into account movements of pedestrians. One way of assessing the multi-modal networks is to export a demand model from VISUM and import it into the microsimulation tool VISSIM. Questions to be addressed could be: What are the average transfer times between a train arrival and the PRT, bus and tram system? What would be the total journey time depending on the combination of public transport modes? How are the effects of vehicles arriving at "rendezvous" type stations? Will the capacity be sufficient to allow transfer without substantial delays caused by crowding? A multi-modal network takes care of all the inter-dependencies between the traffic modes and hence provides a single model for complex assessments. Therefore it is essential that each mode of traffic and even more important that all interactions between them are modeled realistically. Here the VISSIM microsimulation provides a profound concept for handling all the traffic modes including their interactions. As for a PRT system, this kind of interaction rarely occurs as it usually travels on a guideway which is separated from all other traffic. However, for systems that share the same space with other traffic (such as trams or busses) all relevant interaction can be modeled and hence the resulting transport model is very accurate. 4. Conclusion The impact of People Mover and PRT systems on traffic can and should be analysed at a macroscopic and microscopic level. The planning suite PTV Vision incorporates the two systems VISUM (macroscopic) and VISSIM (microscopic). An interface between the two systems allows the export of relevant data from the macroscopic to the microscopic model. This reduces the time and cost required for data supply if both systems are used for a specific analysis. Users of the PTV Vision suite benefit also from its particularly wide range of features designed to model a variety of public transport applications. At the

15 same time, it integrates all relevant modes of transport for multi-modal analysis. The impact of the overall system and its individual components can therefore be analysed and evaluated from the passenger's and operator's point of view. Application programming interfaces (API) make the software fit for the future by providing expansion slots to include handling of non-standard tasks. Wrapping it all together, the macroscopic approach is ideal for large networks and line- and timetable-based systems as it allows analysis of service, demand and capacity utilisation differentiated according to time. The microscopic analysis is an excellent method for providing a detailed view on the situation, in particular with regard to operation at or above capacity limits, demand-dependent control methods and differentiated analysis of traffic and transportation operations. It also provides a 3D visualization. In addition to standard tasks in traffic and transport, the presented transportation planning software also proofs to be a capable and valuable tool for a wide range of assessments for both people mover and PRT systems. Applying such a tool contributes to an efficient, straight-forward planning process and helps cutting investment cost by providing a solid base for financial decisions.

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