Simulation of UC's North Route Shuttle

Shivaram Prakash M08744020 | BANA 6035

Simulation of UC’s North Route Shuttle FINAL PROJECT

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TABLE OF CONTENTS

Introduction 2

Objective 2

Data Collection 2

Modelling in Arena

3

Analysis - Important Facets of the Model

- Scenarios of Operation

- Determining the best scenario

based on statistical significance

4

5

7

Conclusion 8

Appendix 9

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INTRODUCTION

The University of Cincinnati’s Bearcat Transportation System operates shuttles in seven regular

routes every day. The North Route is considered as the busiest of all the routes since most

international students live to the North of the University. Due to heavy demand and long

waiting times, BTS has had to the number of buses during peak times (7 am to 11 am, 4 pm to 8

pm). Hence, simulating this particular route makes sense, so as to try and identify areas of

improvement and increase the efficiency of the system. The shuttle operates from 6 am to

midnight with three different shifts of operation.

OBJECTIVE

To reduce the average waiting time of passengers at all stops and to increase the utilization of

buses (reduce the number of empty Seats).

DATA COLLECTION

Data was collected during different times of the day at the four significant stops of the north

route shuttle viz. The University, Cincinnati Children’s Hospital, Clifton and Dixysmith Avenue.

The Arrival for every half an hour interval was noted and the respective distributions for each

stop was incorporated into the model. Model fitting was done using the input analyzer (Refer

appendix for the plots)

Passenger arrival Time University Cincinnati Children’s Clifton Dixysmith

7.00 – 7.30 am 6 10 10 3

7.30 – 8.00 am 7 8 11 2

8.00 – 8.30 am 8 6 12 3

8.30 – 9.00 am 10 8 10 2

9.00 – 9.30 am 12 6 8 1

9.30 – 10.00 am 13 8 7 2

Average arrival for 30 mins 9.33 7.67 9.67 2.17

Inter-arrival/min 3.214 3.913 3.103 13.884

Since roughly the same number of people take the shuttle during peak time in the evenings, we

can assume the same inter-arrival times for passengers. The people who take the shuttle in the

morning will almost always use the shuttle in the evenings. So it is logical to simulate the model

for one peak duration and assume the same for the other peak duration.

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MODELLING IN ARENA - Model Walkthrough

PART 1:

FIGURE 1 – Passenger arrival at the university

1 - Create modules at each stop simulate passenger arrival at respective stops

2 - An assign module after the create module, assigns the final stop for each passenger

with different discrete probabilities for each stop

3 - A hold module after the assign module, holds all passengers in queue till the arrival

of the bus (when the bus arrives at a stop, it sets a logical variable to ‘0’, thus satisfying

the condition for the hold module – explained in PART 2)

4 – The passengers who board the bus and then get down due to non-availability of

seats, will arrive at this station and continue queueing

5 -The passengers proceed to board the bus

Figure 1 shows the arrival of passenger at the University stop. There will be three similar

models for the remaining three stops

PART 2:

FIGURE 2 – Bus at the University Stop

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Different stops are represented using the station module

1 - The separate module after the station, separates the grouped entity ‘Bus’

(individuals are grouped as a Bus in the previous stop)

2 – The group is separated and the assign module that follows separation, gives the

signal for the passengers to board (Hold at part 1 is released)

3 - A decide module then separate passengers who alight at the stop from the

passengers who continue onward with their journey

4 - The secondary decide module then checks if there are seats left in the bus (the

shuttle services do not allow passengers to stand in the aisle when there are not seats

left)

5 - Upon passing the decide module, another assign module counts the number of

passengers who have boarded the bus (condition for the previous decide module)

6 - Process module – specifies the amount of time the bus waits at a particular stop

(varies from stop to stop)

7 - Batch module – groups all passengers into a bus

8 - Record – For the final statistic – Counting the number of empty seats in the bus at

each iteration

9 - Assign module – Sets the ‘number of passengers’ variable to zero

10 - Route module – Routing the bus to the next station ( Cincinnati Children’s hospital

in this case)

Similar to part 2 for university (as shown in Figure 2), three more parts represent the

other three stops

ANALYSIS

Two important factors namely “The average waiting time of all passengers” and “Number of

Empty Seats in the Bus” form the focus of study. These two factors are calculated as a part of

the simulation and different scenarios are introduced to try and find the best possible condition

for operation.

Important Facets of the model

1. Variables and attributes:

Name Type Function

Alight at Attribute Specifies the alighting point of all passengers

StopFlow1/2/3/4 Variables Logical variables that specifies if the passengers can board

Num Pass at Univ/ Hospital/ Clifton/ Dix

Variables Variables that calculate the number of passengers who have boarded the bus at respective stops

Ppl sent back at Univ/ Hospital/ Clifton/ Dix

Variables Counts the number of people who have got out of the bus because of non-availability of seats

NumSeats Variable Specifies the number of seats in the bus

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2. Travel times between stops:

The time taken between stops was noted for the four stops for one peak hour interval and a

distribution for the travel time was arrived at using the input analyzer. The following

distributions were obtained. (Refer to appendix for distributions)

Stops Time in minutes

University – Cincinnati Children’s Hospital 3.5 + GAMM(2.1, 2.2)

Cincinnati Children’s Hospital - Clifton 1.65 + 1.8 * BETA(1.83, 1.39)

Clifton – Dixysmith Avenue 1.35 + 1.48 * BETA(2.1, 1.7)

Dixysmith Avenue – University 1.06 + ERLA(0.342, 2)

3. Entities:

Entity 1 – Passenger

The create module simulates the arrival of passengers at each stop. Passengers are

represented by pictures as shown below:

Entity 2 – Bus

Once the passengers are batched, they are grouped into another entity named as ‘Bus’.

It is represented by the picture shown below:

Scenarios of Operation:

Scenario 1: One Big Bus

This is the base scenario. A large 26 seater bus runs through the route. Upon running the

simulation for 100 replications of 1 day each, the following statistics were observed.

NumSeats (input variable) 26

Average waiting time of all passengers 38.077 minutes

Average number of empty seats 8.48

BTS

NORTH ROUTE

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Scenario 2: Two Big Buses

The second Scenario employs two big buses in service at the same time. Bus 1 starts at the

University and Bus 2 starts at Clifton.

FIGURE 3 - Change in Part 1

Since there are two buses, an extra decision module is added. This is because, when both buses

arrive at the stop at the same time, there is 50-50 chance that either of the buses will be

chosen. The statistics obtained are as follows




Scenario 3: Two Small Buses

In the third scenario two small buses are used instead of the big ones. The variations in the

Average waiting time and average number of empty seats are noted.




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Determining the best scenario based on statistical significance – PROCESS ANALYZER

The aforementioned three scenarios are specified by changing the value of the control

variable “NumSeats” which determines the size of the bus. The output variables concern

two factors viz. Average waiting time of all passengers, Average number of empty seats

All scenarios are run for 100 replications each and the statistically best scenario is

chosen from the lot.

FIGURE 4. Process Analyzer Results

As seen from Figure 4, the scenarios are run accordingly and the required statistics are

calculated.

The variations of average wait time between the scenario of one bus and the scenario of

two buses are considerably large because, the passengers wait longer at the queue

when the bus is full. When one bus is in operation, more often than not, this is true.

Statistically, the best scenarios for the average wait time of all stops is shown in the

figure below:

Best Scenario for Minimum Average Wait Time

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CONCLUSION

From the simulation, the following conclusions can be drawn.

1. As expected, the minimum average wait time is reduced considerably when there are

two buses in operation

2. When the number of buses is increased without changing the size of the bus, the

utilization of the buses drop (number of empty seats increases)

3. Considering that two buses operate, the following are the results

a. Two big buses – Wait time = 10.821 minutes, Empty Seats = 17.573

b. Two Small buses – Wait time = 12.135 minutes, Empty Seats = 5.630

SUGGESTION

A better utilization is achieved when employing two smaller buses (14 seaters) in operation.

The university should consider switching bigger buses with smaller ones for operation in the

North Route. By compromising on a small decrease in waiting time, fuel costs and operational

costs can be reduced by using smaller buses instead of the bigger ones.

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APPENDIX

INPUT ANALYZER – MODEL FITTING

TRANSFER TIME: UNIVERSITY TO CINCY CHILDREN’S HOSPITAL

TRANSFER TIME: CINCY CHILDREN’S HOSPITAL TO CLIFTON

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TRANSFER TIME: CLIFTON TO DIXYSMITH AVENUE

TRANSFER TIME: DIXYSMITH AVENUE TO UNIVERSITY

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Data & Analytics

Simulation of UC's North Route Shuttle