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ENGG1001 GTFS Data Analysis
Creation date：2024-04-09 16:36:01
Belonging category：计算机computer science
GTFS Data Analysis

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GTFS Data Analysis

ENGG1001 Assignment 2

1 Getting started
To start, download gtfs.zip from Blackboard and extract the contents. The gtfs.zip folder
contains all the necessary files to start this assignment. You will be required to implement
your assignment in a file called a2.py This is the only file you should upload.
Note: The functionality of your assignment will be marked by automated tests. This
means that the output of each function, as well as the output of your overall program, must
be exact to the specifications outlined below in the Implementation section.
2 Data description
The General Transit Feed Specification (GTFS) defines a common format for public trans-
portation schedules and associated geographic information. GTFS feeds let public transport
agencies publish their transit data and developers write applications that consume that data
in an interoperable way.
This assignment will require the following files and the corresponding specific attributes
from the GTFS;
• stops.txt: Stops where vehicles pick up or drop off riders. Also defines stations and
station entrances.
– stop id: Identifies a stop.
– stop name: Name of the stop.
– stop lat: Latitude of the stop location.
– stop lon: Longitude of the stop location.
– parent station: It contains the name of the parent station. For example, UQ
Lakes has several platforms (from A to E) where each is identified as a specific
stop, the parent station enclosing all is ’place UQLAKE’.
• stop times.txt: Times that a vehicle arrives at and departs from stops for each trip.
– trip id: Identifies a trip.
1
– arrival time: Arrival time at a specific stop for a specific trip on a route. Often,
arrival and departure times at a stop will be the same. For times occurring after
midnight on the service day, the values are greater than 24:00:00 in HH:MM:SS
local time for the day on which the trip schedule begins.
– departure time: Departure time from a specific stop for a specific trip on a route.
– stop id: Identifies a stop.
• trips.txt: Trips for each route. A trip is a sequence of two or more stops that occur
during a specific time period.
– service id: Uniquely identifies a set of dates when service is available for one or
more routes. ID referencing calendar.service id.
– trip id: Identifies a trip.
• calendar.txt: Service dates specified using a weekly schedule with start and end
dates.
– service id: Uniquely identifies a set of dates when service is available for one or
more routes.
– monday: Indicates whether the service operates on all Mondays in the date range
specified by the start date and end date fields. 1 - Service is available for all
Mondays in the date range, 0 - Service is not available for Mondays in the date
range.
– tuesday to sunday: Functions in the same way as monday except applies to other
days from tuesday to sunday.
– start date: Start service day for the service interval.
– end date: End service day for the service interval.
3 Implementation
Within this section, the following function will be developed;
• arriving buses(stop name, start time, end time, date, interval length):
This function returns a list containing the number of buses arriving at stop name be-
tween start time and end time on date with aggregation intervals of interval length.
Denote ta the arrival time of a bus at stop name, the first value in the returned list
should be the number of buses satisfying the following conditions; start time ≤ ta <
start time+ interval length, the second value in the list should be the number of
buses where start time+interval length ≤ ta < start time+2*interval length,
etc.
– stop name: str
– start time: str in the HH:MM format
2
– end time: str in the HH:MM format
– interval length: int in minutes
– date: str date in the format ’YYYYmmdd’. You can assume the provided
date exists in the available date range in calendar.txt. If not, the result should
be an empty list.
You must write the following functions as part of your implementation. You are encour-
aged to add your own additional functions if they are beneficial to your solution.
• read data() -> df, df, df, df: This function reads the csv files and returns four
data frames named trips, stops, stop times, calendar in accordance with the
file names. Date and time columns in the relevant data sets should be converted to
datetime objects. Note that arrival time and departure time in stop times can
be greater than 24:00:00 (night time within a service day can take values between
24:00:00 and 29:59:59); rows with this type of entries should be removed.
Example:
>>> trips, stops, stop times, calendar = read data()
trips.size, stops.size, stop times.size, calendar.size
(694288, 140250, 18963581, 1550)
• find service(calendar, date) -> list: This functions returns a list containing
the service ids operating on the date. The returned service ids should satisfy
two conditions; (i) date must be between start date and end date, (ii) service must
operate on the day of the week for the given date.
• create subsets(stops, stop times, trips, calendar, stop name, date) -> df,
df: As a first step, this function (i) finds the stop id(s) corresponding to the stop name
in the data frame stops, (ii) creates a subset, named stoptime subset, of the data
frame stop times consisting of the rows which contain the previously chosen stop id(s),
and (iii) creates a subset, named trip subset, of the data frame trips consisting
of the rows which contain the same trip ids as stoptime subset. At this stage,
stoptime subset and trip subset would include all the observations related to stop name
without taking into account the operation date.
Second, this function should identify the service id(s) operating on the date through
the function find service described above, and create further subsets of trip subset
and stoptime subset (with the same names) accounting for the available service id(s)
on the date and the corresponding trip id(s), respectively. Note that if stop name
does not exist in stops[’stop name’], stops[’parent station’] must be searched.
Hint: empty command from Pandas can be useful to determine whether the resulting
series or data frame is empty.
Example:
>>> stoptime subset, trip subset = create subsets(stops, stop times, trips,
calendar, ’place UQLAKE’, ’20201116’)
stoptime subset.size
4893
3
• calculate counts(start time, end time, interval, stoptime subset) -> list:
This functions considers the data frame stoptime subset, and returns a list containing
the number of vehicles between start time and end time with aggregation intervals of
interval. Mathematically speaking, denote ta the arrival time of a bus at stop name,
the first value in the returned list should be the number of buses satisfying the follow-
ing conditions; start time ≤ ta < start time+ interval length, the second value
in the list should be the number of buses where start time+interval length ≤ ta <
start time+2*interval length, etc.
Example:
>>> calculate counts(’06:00’, ’09:00’, 15, stoptime subset)
[3, 3, 4, 7, 8, 12, 12, 13, 13, 16, 13, 13]
4 Example Output
>>> start time = ’06:00’
end time = ’09:00’
stop name = ’place UQLAKE’
day = ’20201116’
interval = 30
counts = arriving buses(stop name, start time, end time, day, interval)
counts
6, 11, 20, 25, 29, 26
5 Marking Criteria
5.1 Functionality Assessment
The functionality will be marked out of 12. Your assignment will be put through a series
of tests and your functionality mark will be proportional to the number of tests you pass.
If, say, there are 25 functionality tests and you pass 20 of them, then your functionality
mark will be 20/25 * 12. You will be given the functionality tests before the due date
for the assignment so that you can gain a good idea of the correctness of your assignment
yourself before submitting. You should, however, make sure that your program meets all the
specifications given in the assignment. That will ensure that your code passes all the tests.
Note: Functionality tests are automated and so string outputs need to exactly match what
is expected.
5.2 Code Style Assessment
The style of your assignment will be assessed by one of the tutors, and you will be marked
according to the style rubric provided with the assignment. The style mark will be out of 3.
4
6 Assignment Submission
You must submit your completed assignment electronically through Blackboard. The only
file you submit should be a single Python file called a2.py (use this name – all lower case).
This should be uploaded to Blackboard>Assessment>Assignment 2. You may submit your
assignment multiple times before the deadline – only the last submission will be marked.
Late submission of the assignment will not be accepted. In the event of exceptional
personal or medical circumstances that prevent you from handing in the assignment on
time, you may submit a request for an extension. See the course profile for details of how to
apply for an extension.

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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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