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CIVL2700/9700 – Transport Systems
创建日期：2024-04-09 15:50:26
标签： CIVL2700
Transport Systems

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CIVL2700/9700 – Transport Systems 1

ACADEMIC HONESTY
• Students are always encouraged to help each other with studying, however copying
solutions from anyone, where you have little or no academic input is not acceptable. Any
form of copying is not acceptable.

NOTES
• This group project (evaluated only based on the final report) is worth 40% (5%+35%) of the
total mark.
• Each group (3 to 4 students) should submit only one report. That is, one of the group
members should submit the report electronically through Canvas as a single PDF file.
• Late penalties for both preliminary and final reports apply (5% for each day). No
technological excuses will be accepted. Submission information is listed in the unit of study
outline.
• In this assignment, you will conduct 3 simplified transport engineering mini projects. The
assignment main objectives are: (i) allowing you to experience real-world transport systems
analysis work in a simulated environment, (ii) reinforcing and applying the theories,
concepts, and materials presented in lectures, (iii) improving your data analysis and
programming skills required in different Engineering specializations including transport
engineering, and (iv) improving your teamwork skills to deliver a collaborative project.
• It is expected that every group coordinate to work towards the on-time delivery of the
report. You can use tutorial Q&A Zoom sessions to ask your questions.
• The final deliverable of the assignment is a comprehensive but concise and focused report
(in PDF format). For this assignment, each group is a transport consulting firm and the UoS
(i.e. CIVL2700/9700) is the valuable client. Therefore, high quality reports and timely
submissions are required. Please provide a cover page, an executive summary, a
comprehensive introduction describing the objectives of the report. A table of contents with
major section headings should be included. The body of the report should include major
section headings as appropriate. All calculations should be included in the report, and all
figures and tables should be neatly prepared and clearly labeled. Most importantly, you
should include a conclusion/discussion that expresses what you learned from the
assignment experience. Since the assignment is simple case studies, the
conclusion/discussion portion should also include your perspectives relating the tasks to the
field of transport engineering. References and/or appendices can be included as
appropriate.
• You can be creative with appendices. Note that the quality of report is very crucial,
specifically the presentation of results (e.g. axes labels, axes limit consistency, legends,
captions, etc.) and your discussion on the results (e.g. trends, relationships, patterns, etc.).

School of Civil Engineering

CIVL2700/9700 – Transport Systems 2
• This assignment requires the use of a spreadsheet software (e.g., MS Excel), a geographic
visualization software (like QGIS), and a programming language (e.g., MATLAB, Python, etc.).
Other materials specific to individual tasks is noted in the task descriptions.
• There is no unique answer to the tasks. However, there are certain calculations, conclusions,
and statements that are wrong, irrelevant, or not well-supported by observations. Please
avoid stating them.
• Every task is presented in a few steps. These steps are only to guide you through the tasks.
The report shouldn’t be structured according to the steps. Instead, it should have a
continuous and cohesive structure within each task focusing on the motivation and purpose
of steps.
• A general guideline for the Preliminary report is to include the results of the first 3 steps of
Task 1, the first 2 steps of Task 2, and the first 2 steps of Task 3.
• Evaluation is based on Calculations/Accuracy, Diagrams, Presentation, Completeness, and
Discussion.




School of Civil Engineering

CIVL2700/9700 – Transport Systems 3
Task 1 (30%)
Traffic Data Analysis: Loop Detectors Data and the Fundamental Diagram

Required Material:
A Spreadsheet Software (Excel)
Data files: CIVL2700-task1-data.xls (available on Canvas)

Introduction:
In this first task, you will investigate a set of traffic data collected from inductive loop detectors
located on a motorway. The aim is to estimate some common traffic state variables (e.g. flow,
density, occupancy, speed) and plot the relationships between them. Moreover, you need to
estimate the Fundamental Diagram for different time and space scales (aggregation) and
identify how this scaling changes the results. You need to also compare different estimators of
average flow and density and comment on the results.

Data description:
The data file contains inductive loop detector readings (aggregated over 5-minute intervals)
for two weeks in November 2007 for 25 successive loop detector stations (identified by the
VDS number) on a particular freeway (I-880 N) in Alameda County, California, US. Find the loop
detector information in the last sheet. The loop detector data are in the form of counts and
occupancies (per lane and averaged over all lanes). The data also contain a value called
“Observed” which designates the percentage of time that the detector was working properly.

Step 1:
Choose a series of 4 successive loop detectors for one weekday (Make sure that you don’t pick
a weekend, and you should choose detectors that are working properly for longer periods of
time). Copy and save this file in your personal directory. As you know, traffic flow (volume) in
traffic engineering is typically expressed in terms of vehicles per hour. Convert each of flow
data in the file to an equivalent value expressed in vehicles per hour.

Step 2:
Choose one of the detectors. Plot time-series of flow, 5-minutes values from step 1, for (i)
individual lanes and (ii) aggregated for all lanes. Then, estimate the average flow, q, of vehicles
during each hour for the whole day for (i) individual lanes and (ii) aggregated for all lanes.

Step 3:
Repeat Step 2 for the other 3 detector stations. Compare the results. Do the patterns for 5-
minute vs. 1-hour and 1 lane vs. all lanes look similar? Discuss and elaborate.

Step 4:
Convert the average occupancy (o) values to density (k) by using the formula:
= ! + "

School of Civil Engineering

CIVL2700/9700 – Transport Systems 4
Note that occupancy is expressed in the dataset as a decimal rather than a percentage. Find
the detector length, ", in the information sheet (in meters). Try two different values for vehicle
length ! ,6.0 m and 7.0 m.). Which value of ! seems more correct?

Step 5:
Produce a scatter plot of 5-minute flows (expressed in vph from step 1) as a function of the
density (calculated in step 4). Plot data for (i) individual lanes (ii) all lanes together. Comment
on what your data reveals. Are there any patterns to the relationship between flow and
density? Discuss.

Step 6:
Repeat Step 5 for the other 3 detector stations. Compare the results. Do the patterns for 1 lane
vs. all lanes look similar? Discuss.

Step 7:
Compute the average speed (v) for each 5-minute period using the following formula:
=

Plot speed as a function of density for (i) one individual lane (ii) all lanes together (for only one
detector station). Are there any patterns to the relationships? Do the maximum values seem
to make sense? Is the area congested for some period of day? Comment.

Step 8:
In this step, you need to come up with a simple idea how to present the FD (flow vs. density)
for all the 4 detectors (opposed to 4 FDs for each detector). In other words, you need to
propose an aggregation method to spatially aggregate the flow and density data across the 4
detectors. Present the results, then justify and discuss your proposed method.


School of Civil Engineering

CIVL2700/9700 – Transport Systems 5
Task 2 (35%)
Assessment of Traffic on a Road

Required Material:
A Programming Software (e.g. Matlab)

Introduction:
In this task, you will model a road as a queueing system. The aim is to investigate the effect of
different aspects of modelling (e.g. modelling assumptions, deterministic vs. stochastic arrival
and departures) on the analysis of the system and study a few performance measures of the
system (e.g. average travel time). To this end, you have to develop an event-based queueing
simulation (i.e. a type of model) of traffic on a single road (e.g. one street). A single road is
inherently a single queue that has one state variable: the number of waiting vehicles. The
events of a single queue are: arrival, service or departure, and termination. The model should
keep track of each event by recording a time-sorted list of events.
We would like to see the effect of deterministic and stochastic arrivals and departures on the
queueing dynamics such as number of waiting vehicles and average travel time of vehicles. For
the deterministic process, you can use a uniform process while for stochastic process, you can
use Poisson process.

Poisson distribution:
Siméon-Denis Poisson (1781–1840) was a French mathematician. The Poisson distribution is
frequently used to model (or simulate) events, where events are occurring at random time
points assuming events occur continuously and independently of one another. A typical usage
is to model arrivals of vehicles (or customers) in a queue. The PDF, expected value, and variance
of a Poisson distribution are as follows: Pr( = ) = #$ %! E[] = Var() =
Step 1:
Arrival modelling.
For deterministic arrival with rate [veh/sec] until time T, a new vehicles arrives every 1/,
that is the times of arrival events are = {0, &$ , '$ , … }.
For stochastic arrival implement a homogenous Poisson process as follows. is the rate at
which vehicles enter the road from outside the system. The outcome of this step is the arrival
event list.
Simulation of event times of a Poisson process with rate until time T is:
1. = 0, = 0.
2. Draw ~(0,1).
3. = − ln() ⁄ .

School of Civil Engineering

CIVL2700/9700 – Transport Systems 6
4. If > , STOP.
5. = + 1, % = .
6. Go to line 2.
Note that (0,1) denotes a uniform distribution between 0 and 1.

Step 2:
Departure modelling.
For deterministic departure with service rate [veh/sec] the service time is 1/.
For stochastic departure with service rate implement an exponential number generator
1. Draw ~(0,1).
2. ()*!+,) = − ln() /.

Step 3:
Assume the road has the length of L [m] and the free flow speed is V [m/s]. Readily, the free
flow travel time is - = ⁄ . In addition, assume the service rate (road capacity) is .
Complete the event-based simulation as:
Once a vehicle enters the road at time t
• the time it joins the queue is t+t0
• at t+t0 increase queue by one
• if queue size is exactly one
o service time for vehicle is tservice ~ exponential ()
o create departure event at time + - + tservice
Once a vehicle departures the road at time t
• reduce queue size by one
• if the queue on road is still larger than zero
o service time for next vehicle is tservice ~ exponential ()
o create departure event at time + tservice

Step 4:
Collect all important and useful statistics such as the arrival time and the departure time of
each vehicle. With this you can construct the input-output curves and estimate the number of
vehicles on the road, total travel time, and average travel time. Note that the indicators under
interest are random variables. So running the simulator provides one realization of these
random variables. Hence a large number of realizations must be drawn to obtain a meaningful
distribution. It is not unusual to have performance measures with complex distribution that is
multi-modal and asymmetric. Therefore, the mean may not always be sufficient to describe
the random variable. Thoroughly compare the outputs of the model as D/M/1; M/D/1; and
M/M/1 with = 300, = 15, = 0.4, = 0.5. The arrival duration is 15 minute. Run the
model until all the arrived vehicles depart.


School of Civil Engineering

CIVL2700/9700 – Transport Systems 7
Step 5:
Assume an intersection with two approaches (each with one lane) and a two-phase traffic
signal (for the sake of simplicity assume no yellow time and no all-red time). During the red
phase there is no departure while during the green phase the departure follows the
deterministic process with service rate . Run Step 4, assuming D/D/1 for both roads with =300, = 15, = 0.2, = 0.5. The arrival duration is 15 minutes. Run the model until all the
arrived vehicles depart. Consider
i. C=60 [sec], G=40 [sec]
ii. C=60 [sec], G=30 [sec]
iii. C=60 [sec], G=20 [sec]
iv. C=90 [sec], G=60 [sec]
v. C=90 [sec], G=45 [sec]
vi. C=90 [sec], G=30 [sec].



School of Civil Engineering

CIVL2700/9700 – Transport Systems 8
Task 3 (35%)
Public Transport Data Analysis: Analyzing Bus Trips in Sydney

Required Material:
A Spreadsheet Software (Excel)
A geographical data visualization software (e.g. QGIS)
A programming language suitable for working with big datasets (Python, R, or MATLAB)
Data files: Bus occupancy data of Sydney available at https://opendata.transport.nsw.gov.au/
The required data has been uploaded to Canvas

Introduction:
In this task, you will investigate the delay of bus stops on a number of popular routes and
analyze the data from a traffic engineering point of view. You will visualize bus routes using the
GPS locations provided in the datasets, perform data analysis on the timetable (expected) and
actual arrival time of buses across three weeks in 2016 and 2017. The aim is to identify the hot
spots of public transport including routes and stops, and to make professional suggestions to
improve the operation of the on-road public transport system.

Data description:
The data contains the qualitative occupancy, GPS location, planned and actual arrival of every
bus on every route of the Sydney road network over three weeks. You are encouraged to
carefully read the data specifications provided by TfNSW (uploaded to Canvas). You should
download the CSV file of a single day in order to understand and visualise it, as well as the
whole three week dataset (which is large enough that you cannot open it in Excel). The analysis
should be done on the whole dataset.

Step 1:
Randomly select four high frequency routes that go through the Ultimo and Surry Hills suburbs.
Each route should have at least 15 stops. Use the one-day dataset to do this task, and then
visualize the data on a geographic map of Sydney. You can use QGIS to this aim, and a short
introduction to this task will be given to you in class.

Step 2:
Write a program to calculate the hourly bus delays at each stop of each route during weekdays
for the three weeks of data. This program should be written in a language that can handle large
datasets, such as Pandas library in Python, R, or MATLAB. You should find a way to deal with
missing data in the actual arrival times column.

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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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