(from www.uithoflijn.nl)

## Simulation assignment: the Uithoflijn

### Problem description

In this simulation study, we will consider the Uithoflijn, a new tram line between the central station of Utrecht and de Uithof which will start operating in summer 2018. (see also www.uithoflijn.nl and movie) We will study the operational performance of this line. Important questions are:

• What are feasible frequencies for the Uithoflijn?
• What is the maximum amount of passengers the line can handle?
• How robust is the process?
• What is a good layout at end points? (optional)
• What measures do you recommend to improve the operational performance? (optional)

Important performance measure are:

• Punctuality
• Passenger waiting times

The Uithoflijn consists of 7.9 km of track, with 9 stops. In the following tables, a list of all stops are given. In these tables, some more information is given. Because the tram follows more or less the same route as bus route 12, the corresponding stops are also given. In the last column of each table, the average driving time between each pair of stops is given. Note that this driving time is pure driving time, without the dwell time at the stop.

Stop Corr. stop route 12 Distance(km) Avg. Driving time(sec)
Centraal Station CS Centrumzijde
Vaartsche Rijn Bleekstraat 1.4 134
Galgenwaard Galgenwaard 3.1 243
Kromme Rijn De Kromme Rijn 0.6 59
Heidelberglaan Heidelberglaan 0.4 60
UMC AZU 0.4 86
WKZ 0.6 78
P+R De Uithof 0.6 113
Table 1: Route information Uithoftram Centraal Station to Uithof

Stop Corr. stop route 12 Distance(km) Avg. Driving time(sec)
P+R De Uithof
WKZ 0.6 110
UMC AZU 0.6 78
Heidelberglaan Heidelberglaan 0.4 82
Kromme Rijn De Kromme Rijn 0.8 100
Galgenwaard Galgenwaard 0.6 59
Vaartsche Rijn Bleekstraat 3.1 243
Centraal Station CS Centrumzijde 1.4 135
Table 2: Route information Uithoftram Uithof to Centraal Station

### Data for the Simulation

Here you find files that contains data that you will need for the tram simulation. These files are:
• It may be interesting to consider the following disturbance: at each stop there is a probability of X % that the doors block (X can be 1, 3, 5 or 10). Then the door safety system needs to be reset, which takes 1 minute.
• Another option: introduce a dependency of the dwell time on the number of patients in the tram.

### Workplan

• Work with one other student or individually. There is one opportuinity to obtain additional information from domain expert Marcel van Kooten Niekerk (QBuzz) by means of an interview; see week schedule for the form to make an appointment.
• Interviews will take place with at most five groups simultaneously. Prepare questions for the interview and make an appointment.
• Interview and write minutes of meeting.
• Make sure that you do not miss important characteristics of the process.
• You have to use probability distributions for driving and dwell times of the tram, and for the arrival of passengers that want to enter the tram.
• The simulation has to be implemented in a standard imperative programming language such as Java or C#. You have to fully implement the simulation by yourself.
• You have two types of input models:
• Your own realistic input model which you obtained from the real data of line 12 and the forecast given above.
• The artificial input model derived from our csv files, which is described at the next bullit.
The output results should be described in two different sections of your final report.
• We have a few artificial csv input files which contains a given number of passengers per hour that enter/exit at each stop. Here is a csv file with the format (the numbers may be different on the files that you have to use). The artificial input model is simpler than the realistic model. The numbers are number of passengers per hour. We assume that in the artificial input model, the average number of passengers is one hour is constant in each of the periods 7-9, 9-16, 16-18, 18-21.30. Moreover, in one period, all interarrival times follow the same distribution. This means that some peaks of 15 minutes that occur on the data of line 12 do not occur here. Here the passenger flow is more stable. The test files from April 5, 15.00 and some optional files (if you dare!): Disclaimer: These files are currently being tested by us. Please notify me if you run into trouble

You have to run the simulation for these given data and include the results in your report. NB : you also have to do your own input analysis and define your own set of scenarios. These result in your own set of input files. Of course you have to run these as well and present the results in your report.

• The deliverables consist of (an) implemented simulation model(s) as well as a report. Take care that you perform a decent output analysis. The report has to be between 10 and 20 pages of 11 pt A4 . This excludes pictures but includes tables. A report containing at least:
• Problem description
• Assumptions
• Analysis of the problem (what answers to the posed questions can you give before the simulation by quantitatively analyzing the Uithoflijn)
• Explanation of the models:
• Events and event handlers
• Performance measures
• State
• Input analysis:
• modelling of input data from the given data files.
• choice and motivation for applied probability distributions
• Experiments with your realistic model: set up and results
• Results from the artificial input model.
• Conclusions
• Appendix with minutes of interview meeting
• There is a milestone at which you have to have completed a working implementation of the simulation model (see week schedule). Requirement has been changed to event model and plan for input analysis (see also news)
• The deadline is given in the week schedule.
• You have to put a paper version of your final report in the mailbox of Marjan van den Akker at BBL 5.13 as well as hand in the material through submit.
• You have to give a demonstration of the final version of your program; see week schedule for the form to maken an appointment.
• You have to give a presentation on Thursday April 7.
• Jos Jongerius (Province Utrecht) will give a guest lecture on February 22 and will join the final presentation session.
• Marcel van Kooten Niekerk (Qbuzz) participated in the defintion of the assignment and provided the data.
• Roel van den Broek (COSC student) will be assistant in the evaluation of this assignment.