Last Srinath First Krishna - Transportation Research...
Transcript of Last Srinath First Krishna - Transportation Research...
Graduate Research Award Program Application: 2010-2011 Page 2 of 11
t1
Application Checklist and Application Packaqe Gover Sheet
Applicant's name: Ravulaparthy Srinath Krishna Dale _0511712010_Last First Middle
A completed application checklist and application package cover sheet
Personal information of applicant
Qualifications of applicant
Research project proposal
Reference letter #1
Reference letter #2
Research advisor form
Unofficialtranscripts from:Arizona State University and UC Santa BarbaraThe Otficialtranscripts have been ordered and will be sent separately,
Writing Sample
(The writing sample should be submitted with the application. An appropriate writingsample might be a previous publication, a paper written for a class assignment, aresearch project report, or similar example of professional writing. lf papers were co-authored, the role of the applicant must be clearly described. writing samples may notbe longer than 25 pages.)
Graduate Research Award Program Application: 2010-2011 Page 3 of 11
APPLICATION FORM
GRADUATE RESEARCH PROGRAM ON PUBLIC-SECTOR AVIATION ISSUES
Sponsored By: Administered By:FederalAviat¡on Administration Airport Cooperative Research ProgramU.S. Department of Transportat¡on Transportation Research Board, National Academies
PART I- PERSONAL INFORMATION OF APPLICANT
(Please Type)
1. Full legal name:Ravulaoarthv Srinath KrishnaLast First Middle Former name (if any)
2.Dateofbirth:-01/09/1984Placeofbirth:-Hyderabad,lndia
3. Citizenship:_lndia
4. Gender: [X]Male [ ]Female
5. Ethnicity (optional) :
American lndian or Alaskan Native: origin in any of the original peoples of North AmericaBlack: origin in any of the black racial groupsHispanic: Mexican, Puerto Rican, Central or South America, or other Spanish culture or origin,regardless of race
X I Asian or Pacific lslander: origin in any of the original peoples of the Far East, Southeast Asia,or the pacific lslands. lncludes China, Japan, Korea, the Philippine lslands, Samoa, and thelndian Subcontinent
lWhite: origin in any of the original peoples of Europe, North Africa, or the Middle East
6. Mailing address: 1832 Ellsion Hall, Department of Geography, UC Santa Barbara, Santa BarbaracA 931 06
7. Permanent address: C-319, Dattasai Apartments RTC X Roads, Hyderabad 500020, AndhraPradesh, lndia
8. Telephone numbers - Mailing:805-455-381727674243
Permanent:91-40-
9. Email address: [email protected]
10, College or University currently enrolled at: University of California, Santa Barbara
11. Major Field: GeographyDegree objective: [ ] Maste/s [X ] DoctorateExpected month and year of graduation: _051201
12. Names of two people from whom you are requesting reference letters.a. Dr. Kostas Gouliasb. MichaelGorton
13. Name and title of faculty research advisor for this project:Dr. Kostas Goulias, Professor
-a_
Graduate Research Award Program Application: 2010-2011 Page 4 of 11
PART II _ QUALIFICATIONS OF APPLICANT
(Please Type)
14. Education: ln reverse chronological order, list colleges or universities attended.
15. Professional Experience. ln reverse chronologicalorder,list professional experience, includingsummer and term{ime work.
16. Awards, honors, and publications: List fellowships, scholarships, and other academic and/orprofessional positions, held since entering college or university.
College / University
Please explain any interruption(s) of training, illness, etc.)
Name of Employer Location Dates Nature of Work
Arizona State University Tempe, AZ 01/2006 - 05/2006 Teaching Assistant:Teaching undergraduatecourse in "TestingMaterials for Construction"
Arizona State University Tempe, AZ 05/2006 - 01/2008 Research Assistant:Research in traffic safety,statistics, travel behaviorand traffic enoineerino
HDR Engineering lnc. Phoenix, AZ 01/2008 - 06/2009 Transportalion Planner:Worked on airtransporlation planning,travel demand modeling,and traff ic enoineerino
UC Santa Barbara Santa Barbara, CA 08/2009 - till dale Research Assistant:Researching on travelbehavior, demographicsimulation
Award, Honor, or publication Date(s) Description
ffashington.S., Ravulaparthy.S et al )1/20093ayesian lmputaÌion Method in Revealed)reference Survevs. oresented at TRB 2009.
Graduate Research Award Program Application: 2010-2011 Page 5 of 11
17. Describe your career goals and how this research will contribute to achieving those goals (you
may add page(s) if necessary):
A large majority of Metropolitan Planning Organizations (MPO's) consider airports in a simplified mannerby treating them as special generators as part of their regional travel demand models, As a result regionalmodels usually provide little help in analyzing policies involving changes or improvements to the airportsin terms of service and ground transportation access. The dynamic changes in the demographics of airtravelers is an important component in regional air travel demand modeling.
My research would focus on building a demographic spatial micro-simulator with detailedinformation at person and household level. The new techniques being carried out in panel surveys andactivity-based surveys help in the design of such a simulator. The research on socio-demographicforecasting in travel demand modeling has been progressing slowly which does not provide sufficienttools for policy makers. Within these issues, my research addresses the need for a socio-demographicmicro-simulator tool which can forecast social, economic and demographic data more precisely at personand household level, while integrating changes in land-use over time and accounting for migrationpatterns in the region.
Furthermore, thís research also gives me an opportunity to collaborate with experts in other fieldsin academia, who would provide valuable input which eventually would lead to a successfulinterdisciplinary collaboration in solving problems in aviation and air travel demand modeling. Thisresearch project also provides opportunities for undergraduates with flair of research in this field.
After completing my PhD, I intend to pursue a career in academia and research, as academicsform the right platform in communicating my research. This would provide me with an opportunity of beingat the forefront in this vast expanding field of travel demand modeling and travel behavior. I am especiallyexcited for being part of the transportation community, when people from both industry and academia areadvocating in a paradigm shift from the current transportation planning practices primarily focused onsustainable transportation systems in making the world a better place to live.
Graduate Research Award Program Applicationl 2010-2011 Page 6 of 11
I atfirm that the information
Signature of the Applicant:
is true and complete to the best of my knowle{ge.
D,-" !{]ftf øro[NOTE: This applÍcation Ís not complete wìthout a signature.]
Graduate Research Award Program Application: 2010-2011 Page 7 of 11
APPLICATION FORM
GRADUATE RESEARCH AWARD PROGRAM ON PUBLIC-SECTOR AVIATION ISSUES
Sponsored By: Administered By:Federal Aviation Administration Airport Cooperative Research ProgramU.S. Department of Transportat¡on Transportation Research Board, National Academies
PART III -RESEARCH PROJECT PROPOSAL
(Please Type)
Name:_RavulaparthySrinathKrishna Dale_0511712010Last First Middle
Title of Research Projecl:
Dynamic Social and Demographic Microsimulation for Airport Travel Demand
ln 500 words or less, describe the proposed research project. lnclude project objectives,methodology, and expected outcomes. Also indicate how this research work could benefit theaviation community, and contribute to your career goals.
[Please attach additional sheets as needed.]
ACRP Graduate Research Program Application : 2010 -201 I Srinath K Ravulaparthy
Research Project Proposal
A large majority of Metropolitan Planning Organizations (MPO's) consider airports in a
simplified manner by treating them as special generators as part. of their regional travel demand
models. Specific features like the composition of the air travelers' demographics, travel behavior
and their attitudes towards current airport facilities and accessibility are rarely analyzed or
explicitly modeled. As a result regional models usually provide little help in analyzing policies
involving changes or improvements to the airports in terms of service and ground transportation
access. Having worked as a Transportation Planner in a private industry and interacting'with the
MPO's has made me realize the scope and need for such research and development in the field oftravel demand forecasting with special emphasis on air travel.
Innovative methods in travel demand forecasting have laid foundations to capture and
predict travel behavior more reaiistically than ever before. One of the important input
components for any travel demand modeling process are the social, economic and demographic
data at the person and household level. Moreover, the forecasts from these new travel demand
models are highly sensitive to input information provided. The research on socio-demographic
forecasting in travel demand modeling has been progressing slowly which does not provide
sufficient tools for policy makers. Within these issues, ffiy research addresses the need for a
socio-demographic micro-simulator tool which can forecast social, economic and demographic
data more precisely at person and household level, while integrating changes in land-use over
time and accounting for migration pattems in the region.
My research would focus on building a demographic spatial micro-simulator with
detailed information at person and household level. The new techniques being carried out inpanel surveys and activity-based surveys help in the design of such a simulator. This research
would conduct an agent based simulation, where the agent (an individual) is evolved over time
(human life cycle evolution) and the various decision making processes that are undertaken
during these periods are also simulated in both time and space. Accounting for such fine detail
would provide potential opportunities in improving airport related services and ground access
transportation. The prediction in land-use changes over time is also an important factor that is
considered in the simulator, which can dictate future improvements surrounding the airport
services in providing sustainable transportation opportunities. Incorporating immigration into
this simulator would provide the changes in regional travel needs and spatial patterns that would
be important as these would be serving as potential air travelers, who have higher propensity to
return to their of origin for family and vacation visits. The simulation of such fine detail is
possible with advances in micro-analytic simulation models and related programming languages.
Thus, for a successful implementation of this research it would be very helpful to have
external funding. This funding would definitely encourage in successful implementation of this
ACRP Graduate Research Program Application:2010-2011 Srinath K Ravulaparthy
research, which would ultimately contribute to the field of Transportation Planning and Travel
Demand Modeling.
Thank you for considering my application.
Srinath KRavulaparthy Date: 05llll20l0
UNIVERSITY OF CALIFORNIA Sarìta Barbara
Y. DAVIS .lR\¡¡M. LæANGELES ' MEÌ.CED ' Rñ:ERSIDE. SÀNDIEGO . S¡{N¡ffi-*CISC-o SANTA B.ARBARA . SANTA CRÜZ
DEPARn¿ENT oF GEoGRA?HY -ì61I Ellison HallSanta Bar'Þara, CA 9310ó-4060Phoûe: (805) 893-36ó3Fax: (805) 893-7782http ;//iv$l&'. geog,ucsb.edu
N4ay I4,20I0
Airport Cooperative Research Program
Dear Colleagues:
It is with grêat enthusiasm that I am writing this letter of recommendation for one of my current
Ph.D. students Srinath Rawlaparthy. Srinath is uniquely talented and extremely energetic in his
approach to research. He continues his graduate studies at the Ph.D. level after receiving an MS
in engineering from Arizona State University and very good studies in engineering from India.
Srinath decided to come back to graduate school after a short period of consulting practice giving
up the comfort of a well paying job for the hard life of a graduate student. At UCSB Srinath is
taking during this year a series of courses in Econometrics and he is excelling in every aspect.
He is also an inspiration for other graduate students with his phenomenally positive attitude and
genuine curiosþ. Of course these are the traits that brought him here at UCSB as the
recommendation letters from his past mentors testiff and make him one of the best students I
could ever recommend for his airport related research on developing a new forecasting model
system.
Srinath's research is on the development of a demographic spatial micro-simulator with
detailed information at person and household levels. He is using agent based simulation to
develop personal histories of change using software. This software contains statistical models oflof3
individuals and their households that are based on travel surveys. We expect these demographic
simulators to become a standard tool for regional travel demand forecasting after the hard
fundamental research is complete but certainly within the next three to four years. Before
moving these methods to widespread practice, a variety of design options need to be considered,
tested, and validated. Particularly important for this proposed research is the travel behavior
aspect of long distance travel and the propensity of households and individuals to travel long
distances by air. As we see in other forecasting models for travel behavior dynamic
microsimulation of households and individuals is becoming the premier tool and aviation should
not be left behind in this new and exciting development. The research work to do all this
requires excellent background in statistics and econometrics, programming skills, and deep
understanding of the social and demographic processes that are reflected in the simulator. In
addition, understanding oftravel demand and its determinants is equally important.
Srinath has many of these skills and the talent to creatively develop new methods and
techniques. As an MS student at ArizonaState, Srinath worked on discrete choice models using
Bayesian statistics. He is combining that background with other economehic methods to
develop the models needed in the microsimulator and in a very short time he already produced
many useful results. This is anamazingproductivity and takes very smart individuals to make
something like this happen in such a short time. Knowledge, creativity, energetic enthusiasm,
and skills of this type are of paramount importance as we move to 'ogreener" policies at every
jurisdictional level and as we prepare for the new transportation legislation. In fact, in
California we already experience the impact of "greener" legislation and many planning agencies
are required to use better modeling and simulation tools. I believe Srinath's work will be at the
center stage of this development and will become a uniquely qualifìed leader.
There is more to say about Srinath. In the past few months working in a project with
other graduate students, Srinath gave proof of his ability to work with other students that is
2of 3
admirable. He is motivated by other students but he also motivates them to think harder, faster,
and better leading by the example of an amazing work ethos and contagious positive energy.
I am convinced Srinath is uniquely qualified to receive a research grant from ACRP for
many reasons. First, he has completed a truly impressive anay of preparatory work in India,
Arizona State, and he continues doing this at UCSB with outstanding performance and
enthusiasm. Second, Srinath has a consistent record of excellence in everything he does. Third,
he is extremely dedicated and through his resourcefulness made already substantial progress
towards his goal. Fourth, Srinath has the right attitude to achieve great results and is willing to
make the necessary sacrifices to achieve academic excellence. Fifth, the methods he proposes to
use to predict airport demand is at the highest levels of excellence and is feasible within the time
frame proposed.
Srinath is matching perfectly the purpose of this competition as his previous
achievements demonstrate making him a truly outstanding candidate. The ACRP grant will
complete his portfolio of accomplishments and enable him to continue unobstructed this
challenging course to complete his Ph.D. I am convinced we will all be very proud to have
Srinath as a colleague in transportation for many years tô come.
Konstadinos G. Goulias, Ph.D., Professor, University of California Santa Barbara
3of3
()N1: (.()Ml'r\NY |,ll,rtt.¡' \'olttt itttt'
May 14,2010
To Whorn It May Concern:
I am pleased to recommend Srinath Ravulaparthy for the Airport Cooperative Research Program
Graduate Research Program Award in Aviations Issues. I was Srinath's mentor during his tenure
at HDR Fngineering, ¡ic., in Phoenix, Arizona, and workeC closel¡, rryith hirn cn the develoPrrent
of Arizona's first statewide travel demand model.
I first became acq¡ainte{ with Srinath when we studied together under Dr. Ram Pendyala at
Arizona State University. He is a serious student of travel behavior forecasting. I was impressed
with his aptitude and curiosity and invited him to join our travel demand modeling team at HDR
upon his graduation.
As Srinath came on board, HDR was beginning a six-month effort to develop an Arizona
statewide travel demand model to support an Arizona Department of Transportation planning
framework study with a}}S}vision. ihe pace of work was furious but Srinath's cheerfulness and
diligence helpeá carry us through challenging elements of model development' Srinath evaluated
,u.i"y clata, àevelopéA trip genération and attraction models, and worked to develop the
statewide model socioeconomic database.
In addition to applying his skills in modeling travel behav-ior, Srinath never hesitated to jump into
unfamiliar waters. He-set to work willingly on airpoft traffic engineering projects and other traffic
impact study projects making him an asset to the office'
It was a great pleasure to work with Srinath. I cannot imagine a better recipient for this award. I
enthusiastically recommend him to you.
Sincerelv./1
ru/"/ r&r-Michael E. Gorton, AICPSenior Transportation Planner
5CI"'r\n¡rslvERSÄRY. ,.j, i,t ,::i,, ¡ t: ., ..'1' t ''.:
. ..:; r I
32û0 East Camclhack lÌoad
surte 350
Phrrr:nix. AZ 85018-23 I 1
Phone: i602) 522'77110
fãx 16021527-:11Ð1
v.¡¡''v.hdrinc.corl
HDR Engineeting, lnc.
Graduate Research Award Program Application: 2010-2011 Page 9 of 10
APPLICATION FORM
GRADUATE RESEARCH AWARD PROGRAM ON PUBLIC-SECTOR AVIATION ISSUES
Sponsored By. Administered BY.
FederalAviation Administration Airport Cooperative Research ProgramU.S. Department of Transportation Transportation Research Board, National Academies
PART V - FACULTY RESEARCH ADVISOR
To be completed bv the applicant:
NOTE: ln order to enrich the educational experience gained from your proposed research proiect, it isnecessary for you to request a faculty member from your university who is familiar with your researchproject to act as a research advisor to you during the course of the proiect. Please provide the followinginformation and ask the faculty member to complete the form. You should submit it with your application.The researÇh advisor may also be a reference respondent.
Applicant's Nam e: _Bavu laparthy Srinath-Date: May 1 4, 2010Last First Middle
Title of Proposed Research Project:
Dynamic Social and Demographic Microsimulation for Airport Travel Demand
To be completed by the facultv research advisorNOTE: The research product of research award recipients can be considerably enhanced if a facultymember at the applicant's university acts as an advisor to the applicant during the conduct of theresearch. Therefore, each applicant is required to designate such an advisor who will be available to
him/her throughout the course of the research project to provide advice as it progresses. When researchpapers by award winners are published by the Transportation Hesearch Board, the faculty member will beidentified as the research advisor.
Faculty Research Advisor's Name: Konstadinos G GouliasTitle: Professor_Department: GeographyUniversity: University of California Santa BarbaraMailing Address: 1832 Ellison Hall, Department of Geography, UCSB, Santa Barbara CA 93106Email: [email protected] Phone: 805-308-2837
1. Have you examined the applicant's proposed research plan? Yes -X No
2. Do you consider the applicant's research plan reasonable? Yes -X- No ,-lf no, please comment.
Graduate Research Award Program Application: 2010-2011 Page 10 of 10
PART V: Page 2
3. Do you believe that this applicant can complete the proposed research within the time frame
indicated? Yes -X-
No
-.
lf no, please comment'
4. Will the applicant receive academic credit for this work? Yes X- No
-.
lf yes, please
indicate the nature of this academic credit. [Note: Receiving academic credit in no way counts
against the applicant.l
-He will receive credit for independent research Geog 598
5. Please indicate briefly how you plan to monitor and advise on the work of the applicant on thisproject.
_We will meet individually every week lor 2-3 hours and he will also share his research in our
weekly meetings with the entire research group. ln addition, we willjointly define research milestones
and a detailed schedule of deliverables.
6. I am willing to be the research advisor to the applicant if the applicant receives this research
award. Yes -X- No -,;Z-;4- -'- -2 -'\Signature: -=t-- '*--u-Ç-C Date:
--May 14,2010
Name: SrinathKrishnaRavulaparthyStudent lD: 100090¿1402
Print'Date; O31O2J2010External DegreesJawaharlal Nehru Technological Un¡vers¡tyBachelorofTechnology 06/01/2005
Degrees Awarded
Degree: l\4aster ofsdenæConfer Date: 05/08/2008Degree GPA: 3.63Plan: Civil and Environmental Eng¡neering
fra A, Fulton Schooì of Eng¡neer¡ng
Beginning of Graduate Record
2006 Spr¡ng
Course SegÊrjplig! Attemoted Earned
CEE 515.M Properties ofconcrete 3.000 3.000CEE 580.M Pract¡cum 3.000 3.000CEE 598-M Special Top¡æ 3.000 3-000Course Top¡c: ST:Sustainable Trans SysCEE 598-M Spec¡al Top¡cs 3.000 3.000Course Top¡c: ST:Sustainable Urban Engrg¡,ilCEE598 GRADEB+ TOA- EFF010307
Cum GPA; 3.63 Cum Totals 36.000 98.001
Page 1 of 1
2007 Summer 1
Attemoled Earned Grade Po¡nts
1.000 1.000 Y 0.000
Atlempted Earned Po¡nts
1.000 1.000 0.000
37.000 37.000 98.001
2007 Summer 2
AttomÞted Earned Grade Points1-000 1.000 Y 0.000
Attemoted Earned Points1.000 1.000 0.000
38.000 38.000 98.001
2007 Fall
Attempted Earned Grade Po¡nts
3.000 0.000 x 0.000
6.000 6.000 Y 0.0006.000 6.000 Y 0.000
Attsmpted Earnsd
'12.000 12.000
50.000 50.000
Points
0.000
98-001
Arizona State UniversityUnofficial Transcript
Course Descr¡ot¡on
CEE 592-M Rêsêarch
Têrm GPA:
Cum GPA;
0.00 Term Totals
3.ô3 Cum Totals
Grade
BY
Points9.0000.000
1'1.001
'12.000
Po¡nts
32.001
32.001
Points0.0009.999
9.000
12.000
Points30.999
63.000
Po¡nts
35.001
Gourse Descr¡pt¡Òn
CEË 592.M Rêseãrch
Term GPA: 0.00 TermTotals
Cum GPA: 3.63 Cum Totals
Course Descriotion
CEE 573 TransportationOperations
CEE 580 PracticumCEE 592 Rêsêa¡chThes¡s; The Feasibility of Bayesian lmputation ofNon-Chosen Attrìbute Values ¡n Revealed PreferenceChoice Surueys
Term GPA: 3.56
Cum GPA: 3.56
Attemotêd Earned
Term Totals 12.000 12.000
Cum Totals 12.000 12.000
2006 Fall
Descr¡plion Attempted Earned
Research 3.000 3.000Spec¡al Topi6 3.000 3.000ST:St¿t & Eæn ¡/odelin CeSpecial Topiæ 3.000 3.000ST;Dâtabâse l\rgmntConferênce and 3.000 3.000Workshop
CourseTopic: Cw:FundamentlsGiscience
Course
cEE 592-McEE 598-MCourse Topic:csE 598.MCourse Topic:GPH 594-M
Grade
Bt
B
Term GPA: 0.00 TermTotals
Cum GPA; 3.63 Cum Totals
END OF TRANSCRIPT
Term GPA:
Cum GPA:
course Descr¡ption
CEE 590.M Read¡ng andConference
CEE 598.M Special Top¡GCEE 599-M ThesisPUP 598.M Special Topic
Attempted Eamed
Term Totals 12.000 '12.000
Cum Totals 24.000 24.000
Attêmptêd Earned
Term Totals 12.000 12.000
2007 Spr¡ng
Attempted Earned Grade Points3.000 3.000 A 12.000
A- 11.001Y 0.000A 12.000
3.44
3.50
3.000 3.0003.000 3.0003_000 3.000
Term GPA:
5/17/2010
Univers ity of California, Santa Barbara
Unofficial Transcript
Srinath RawlaparthyPerm Number:3844974
Fall2009
course Grade
ECON 2414-ECONOMETRICS A+GEOG 2ol-SEMINAR GEOGRAPHY S
GEOG 2BBKG-SPECIAL TOPIC GEOG APSTAT 274-TIME SERIES B+
Quarter Total (Grad) GPA 3.76
Cumulative Total (Grad) GPA 3.76
Wìnter 2010
course Grade
ECON 241B-ECONOMETRICS AGEOG 2OOB-INTRO GEOG RESRCH AGEOG 2o1-SEMINAR GEOGRAPHY S
GEOG 211B-TRANSPORT MOD&SIM A+
Quarter Total (Grad)Cumulative Total (Grad)
GPA 4.OO
GPA 3.88
Spring 2010
Course
ECO N 241C-ECONO METRICSGEOG 2OOC-INTRO GEOG RESEARCH
GEOG 201-SEMINAR GEOGRAPHY
GEOG 21IA-TRANSP PLAN & MOD
Unofficial Transcript - Printable Version
¿$ erint this çindo'rr
Col I e ge/Objective/tt/lai orL&S/ PHD/ GEOG
r'..!] close this r+indoç
511712010 2:03:25 PM
Deoree Status Deoree Quarter
EnrlCd
L324322t52580994L251
EnrlCd
13565224672247561598
Grade EnrlCd
133916394r2267357741
Att CompUnit Un¡t4.0 4.02.0 2.04.0 4.04.0 4.0
14,0 L4.0
t4.0 14,0
Att CompUn¡t Unit4.0 4.04.0 4.02.0 2.05.0 5.0
15,0 15.029.0 29.0
Att CompUnit Unit4.0 0,02.0 0.02.0 0.05,0 0,0
i|f; Po¡nts Additionalrnfo
4.0 16.0 00.0 0,004.0 16.004.O 13.2 0
72.O 45.20
12.o 45.20
äî Po¡nts Add¡tionalrnfo
4.O 16.0 04.0 16,000,0 0.005.0 20.00
13.0 52,0025.0 97.20
ïtî Po¡nts Additional rnro
0,0 0.000.0 0.000.0 0,000.0 0.00
Transfer Work Undergraduate Ïotal: 0.0
UC & Transfer Work Undergraduate Total: 0.0
UC Work Graduate Ïotal: 29.0
,..ucsb.edu/.../UnofficialTranscri ptPrinta... 1/L
PSTAT 274 - Time Series
Final Project
Vehicle Miles Traveled in California: A Time Series Analysis
December 7,2009
Submitted by:
Srinath Ravulapanhy
Department of Geography
UC Santa Barbara
Summary and ConclusionsThe increasing motorized travel trend in California over the past years is measured in billionsVehicle Miles Traveled (VMT). This data was obtained from Caltrans measured over 20 years
from January 1989 through December 2008, the data suggests an increasing trend with a strong
seasonal effect. This data can be analyzed in the domain of time series, which accounts for both
the increasing trend and seasonal effects observed in the data (see Figure i).
As the data is measured monthly with an increasing trend, this trend is eliminated by differencingthe original VMT time series at lag 1. There is a strong seasonal component presence which isevident from the ACF plot for the differenced series (see Figure 5), where ACF decays slowlyfor every 12'h lag. Differencing further at lag 12 eliminates the seasonal component, but PACF ofthis series suggests a strong spike at lag 12 (see Figure 9), which indicates the presence of an
Autoregressive (AR) component. In order to account for this strong seasonal effect and
increasing trend, SARIMA models were chosen to fit the original data series.
Potential SARIMA models were compared based on the model goodness of fit statistics like,AICC, log-likelihood, model variance, and model AIC values (refer to Appendix-A for model
outputs). From the potential models compared, SARIMA (2, 1,0) x (2, 1,0) (12) represents a
better model fit for the data, with lowest AICC being -274.33 and maximum log-likelihood value
of 141.27. Standard residual analysis like: ACF and PACF plots, test statistics like Portmanteau
and Ljung-Box tests also suggest that the residuals follow a white noise sequence.
The forecasting of the VMT data is pivotal in regional planning process and policy analysis
which helps planners make informed decisions. This forecasting process is undertaken from the
SARIMA model fit to the data series. The forecasted data (refer to Appendix-A for forecasted
values) for the next 12 months (see Figure 11) are within the confidence intervals, suggesting the
model estimated is a good fit to the current data series.
The VMT time series data can also be analyzed using spectral analysis expressed as a linear
combination of uncorrelated white noise sequences plus the deterministic trend component,
which is composed of sine and cosine waves of different frequencies. There is a strong seasonal
component evident from the periodogram (see Figure 13), with an estimated period of 12
months, which is the time required for one complete cycle.
The increasing trend in the time series data is fitted by the linear combination of sine and cosine
waves for time r plus a third degree polynomial (refer to Appendix-A for model outputs), for the
frequencies IlI2 and 3/12 which are predominant for the estimated periodogram (see Figure 13).
The residuals from the model fit have a strong seasonal component as observed from ACF and
PACF plots (see Figure 16 and Figure 17), and this indicates a presence of AR and MAcomponents.
Potential SARIMA models were compffed based on the goodness of fit statistics. SARIMA (3,
0, 0 x (2, 0, 1) (12) represents a better model fit with an AICC of -250.23 and log-likelihood of
133.11(refer to Appendix-A for model outputs). Standard residual analysis is also conducted for
the model residuals as fitted from the SARIMA model. The tests are performed based on the
residual ACF ptot (see Figure 20), test statistics like Portmanteau and Ljung-Box tests and
cumulative periodogram (see Figure 19) of the fitted residuals, which suggest that the residuals
follow a white noise sequence.
Time Series Analysisl,.L Introduction:
Over the past years, there has been an increase in motorized travel in the state of California, and
this trend can be studied using the measure, Vehicle Miles Traveled (VMT) which is estimated
for motorists traveling on California state highways. This data was obtained from California
Department of Transportation (Caltrans), and provides an insight about this incremental trend.
Since, the data is observed over time; it can be analyzed using appropriate time series methods,
which involve: data analysis, model fitting, and forecasting.
l.2DataDescription:
VMT data from January 1989 through December 2008 was obtained from Caltrans, Traffic Data
Branch. VMT is a measure to indicate the total motorized travel on California state highways.
VMT measured in billion vehicle miles traveled is calculated according to the equation [1] given
as follows, where 7C denotes the traffic counts recorded along each roadway section and LM
represents the total number of lane miles for each roadway section.
VMT : TC * LM t1l
This data can be represented as a time series plot shown in Figure 1; time series model can be fitto this dataset which can explain the affects of time on motorized travel like: seasonality effects
and increasing trends in travel; the modeling also helps in forecasting the VMT data into the
future. This modeling effort is necessary to understand and explicitly capture dynamics of the
travel over time, as this forecasted VMT data is useful in regional planning and policy
applications, which helps planners to make informed decisions'
L.3 Data Analysis:
Based on the plot from Figure 1, it can be said that the data represents a seasonality effect with
an increasing trend. Typically, we would prefer to have a stationary time series with constant
variance, absence of any trend and seasonality effects. Sample Auto Correlation Function (ACF)
and Partial Auto Conelation Function (PACF) for the data are also plotted as in Figure 2 and
Figure 3 respectively.
Upon close observation, there is a strong seasonal component effect at 12th Lag for sample PACF.
Thus, based on these plots it would be appropriate to transform the data to achieve the
aforementioned requirements: stationary time series, elimination of trend, and seasonality
effects. Therefore, this can be achieved by differencing the original time series at first lag, whichresults in the elimination of a trend. Figure 4 shows this data series along with sample ACF and
PACF for the differenced time series in Figure 5 and Figure 6 respectively.
As seen from the above plots, sample ACF slowly decays at every l}'h lag, and a spike in sample
PACF at l2th lag, which indicates a seasonality effect with periodicity of 12. Thus, in order to
stabilize this effect and eliminate seasonality, the series is again differenced allag 12. Figure 7,
shows the time series plot for this difference, with corresponding ACF and PACF plots inFigure-8 and Figure-9 respectively. Sample ACF and PACF plots still indicate the presence ofseasonality; therefore, we need to explicitly model this effect through Seasonal Autoregressive
Integrated Moving Average (SARIMA) models.
L.4 Preliminary Model Identifïcation:
The VMT time series data indicates the presence of strong seasonal component, and this can be
evidently seen from the above sample ACF and PACF plots. Based on these plots, the time series
can be model with Autoregressive Moving Average (ARMA) of order p and q respectively
denoted as ARMA(p, q). As seen from the sample ACF and PACF plots for the differenced timeseries at lag 12, there seems to be a presence of both MA and AR components leading to an
ARMA model which may seem plausible for this non-seasonal component. Similarly, from the
differenced time series at first lag, the sample ACF decays slowly at every l}th lag,but the PACF
slowly decays after l't Iag, this indicates modeling the seasonal component with an
autoregressive (AR) of order 1 or either 2.
L.5 Model Fit:
Seasonal ARIMA models are estimated, with the integration component being "1", since we
differenced once to eliminate the trend in the data and the second difference at lag 12
corresponds reduced the effect of seasonality. SARIMA models are generally represented as
SARIMA (p, d, q) x (P, D, Q) þeasonality), wherc p is order of the AR component; d number ofdifferences applied, and q is the order of MA for the non-seasonal component, and P is the order
of AR component; D is the number of differences applied, and Q is the order of MA for the
seasonal component, with a periodicity as observed from the seasonality effect. Seasonality forthe VMT data series is 12.
Thus, the general form of SARIMA models that can be formulated is represented as in equation
[2], which accounts for both the trend and the seasonality effects for an observed time series
process. Let Xt correspond to the original time series, then according to equation [2], SARIMAmodel as described with AR component as a functioî ç(B) and seasonal AR component as a
function of Ø(Ê); similarly, with a MA component as a function 0(B) and seasonal MA
component as a function @(B') for seasonality s, with B represented as a back shift operator,
where Ê(X,) - Xn,.In the equation below, Z is white noise, where Zt-WNP,ær)'
ç(B)ø(Bs)Yt = 0(B)@(B')Z' l4
where Yt = (!.- B)dQ - B)DXt
Potential models are fit to the original VMT time series data, in R-software using maximum
likelihood estimation (MLE). The potential models that were compared are Model-l: SARIMA
(2, l, O) x (1, 1, 0) (12); Model-2: SARIMA (2, 1,0) x (2, L,0) (12); Model-3: SARMA (3, 1, 0)
x (1, 1, 0) (12) and Model-4: SARIMA (3, i, 0) x (2,1,0) (12) . The model outputs along with
estimated coefficients and goodness of fit statistics are provided in Appendix-A.
L.6 Residual Analysis:
Test for residuals are performed for the fitted models, to check that the residuals resemble a
white noise with constant vadance, and are IID. Residual plots along with Ljung-Box statistic
probabilities are plotted for residuals of Model-2 are shown in Figure-l0 respectively. Based on
the plots it can be said that, the residuals for the model resemble an iid white noise process, with
negligible correlation structure. Necessary statistical tests are also performed on residuals for the
fitted models.
Portmanteau Test: This statistic is represented al Qo which is given by equation [3], large value
of the statistic suggests that sample ACF of the data are too large for the data to be an iid
sequence and this statistics is 12 distributed with h degrees of freedom. Therefore, if Qo> f at
a=0.05 we reject the null hypothesis, that they represent an iid sequence'
t3lQp = nll o'(Ð
Therefore, this statistic is computed for h = 15 for all the four models in R-software, with values
for Model-l being23.74 at probability (p) = 0.069; Model-2 being23.63 at p = 0.071, Model-3
being22.51at p = 0.095, and Model-4 being 22.9 atP = 0.086
Ljung-Box Z¿sf: This statistic is an enhanced version of Portmanteau test, and is given as in
equation t3l. Simila¡ to that of Portmanteau test, a large value of the statistic suggests that
sample ACF of the data arctoo large for the data to be an iid sequence, and this statistic is also fdistributed with h degrees of freedom. Thus, we accept the null hypothesis, that they represent an
iid sequence if Qø< t' at o=0.05.
Similarly, this statistic is also calculated at lag h=15, with values of the probabilities for the
models being: Model-l is p - 0.052, Model-2 is p = 9'954, Model-3 is p = 0'073 and Model-4 is
p = 0.066 Therefore, based on the residual plots and statistical tests it can be said that the
residuals from the model fits are an iid sequence with white noise distribution.
L.7 Model Selection:
Based on the aforementioned discussion with reference to fitted models and residual analysis, allthe models are compared with each other for final model selection on the basis of goodness of fitmeasures which are described as follows:
AICC value: AICC value for each model fit is calculated based on the original data. The
parameters that determine the AICC value are: order of the ARMA(p, q) model,length ofthe time series, and the loglikelihood function as obtained from the model output. The
criteria, is the model with lowest AICC value is preferred over the other, which indicates
a parsimonious model. Thus, the calculated AICC values are, Model-l is -273.38, Model-2 is -274.33, Model-3 is -271.85 and Model- is -272.49
Model AIC value: Model AIC value is obtained from the model output, and similarly, the
model with least AIC value is preferred over the others. The model AIC values are,
Model-1 is -271.51, Model-2 is -272.54, Model-3 is -270.06 and Model-4 is -270.8
LogJikelihood value: Model log-likelihood function as obtained from the model output is
also used for the model selection. The model with a high logJikelihood value is selected
over the other models. Thus, the log-likelihood values are, Model-l is 139.76, Model-2 isI41.27, Model-3 is 140.03 and Model-4 ís 14I.4
Estimated rnodel variance: The estimated model variance as obtained from the fittedmodels, the one with the lowest is preferred over the other models. The estimated modelva¡iances are, Model-1 is 0.01699, Model-2 is 0.01675, Model-3 is 0.01695 and Model-4is 0.01672
Therefore, based on these above model selection criterions, the model that best describes the
VMT time series dataset is Model-2 which is of the form: SARIMA (2, I,0) x (2,1, 0) (12) . krgeneral the SARIMA models are given in the following form with a non-seasonal and seasonal
component explaining the time series process.
The confidence intervals for Model-2 coefficients g and (Þ as shown in Appendix-A is calculated
as g+1.96*SE and (Þ+1.96SE, where SE is the model standard error. These confidence intervals
are given by upper boind and lower bound as shown in Table 1.
Table 1 Conlidence Intervals for the SARIMA model coefficients
Coefficients(<p, o)
Upper bound(<o+1.96*SE. O+ 1.96SE)
Lower bound(<p-1.96*SE, O-1.9658)
<o r = -0.581 -0.454 -0.707
o r = -0.309 -0.184 -0.434Qt = -0.278 -0.t43 -0.4t4(Þr = -0.135 0.016 -0.288
1,.8 Model Forecasting:
The VMT time series data is used in forecasting into the future with the fitted model. The data is
forecasted for the îext12 intervals, which indicates the next 12 months of the time series data.
Figure 11 illustrates the forecasting indicated by red line, and bounded by 95Vo prediction
intervals indicated in blue dotted lines. Based on the forecasts, it can be said that the predictions
are within the confidence intervals, indicating that Model-2 is a good model fit better explaining
the VMT time series phenomenon. The forecasted values for the next 12 months are provided in
Appendix-A along with the standard effors.
Spectral Analysis2.1 Introduction:
,In spectral analysis, the approach is based on the assumption that the time series can be
represented as a linear combination of uncorrelated noise series plus the deterministic trend
component. The deterministic component in spectral analysis, assumes that the time series is best
regarded as a sum of periodic sine and cosine waves of different frequencies or periods. The
general linear combination form can be represented as in equation [4], for k = n/2, and f¡, = An,
for fr = l, 2,...,nn where n is length of the time series.
Xt= a.o+\i!2t@¡rcos2ttf¡rt *bvsínLrf¡rt) + 4 t4l
2.2DataAnalysis:
VMT time series data plot as observed from Figure 1, a corresponding spectral analysis can be
performed based on spectrum of the data. A cycle is defined a sine or cosine wave over a defined
interval l0,2nl; for example xt = sin(2nl't) for frequency / over time I The function of the
formf -1, is called period, which is defined as the length of time required for one full cycle. This
frequency can be identified as from the periodo grarn Py(f¡,) given by equation [5] which can be
generated for a given time series process Xr. The periodogram for the YMT time series is shown
as plotted in Figure 12, this periodogtam is further smoothened to better represent the
frequencies observed in the data as observed from Figure 13.
Px(fù =|@ln+bll tsl
where, ãu = |nül'=r. X ¡c o sLn f¡rt and ß y = |LT=, x rs ínLn f¡rt
The height of the periodogram shows the relative strength cosine-sine pairs at various
frequencies in the overall behavior ofthe series. As observed from Figure 13, it can be said that
there exists strong peaks at frequencies Il!2,2112,,.,6112, with dominant peaks at 1'll2 and 3ll2
contributing significantly to the periodogram. Thus, from this it can be said that the period of this
time series is 12, which means, that it takes 12 months to complete one cycle. Furthermore, the
cumulative periodogram is also plotted in Figure 14 to examine if the data is white noise. Based
on Figure 14, we reject the hypothesis, that the observed data is not white noise as the
cumulative periodogram falls outside the Kolmogorov-Smirnov confidence intervals.
2.3 Model Estimation for trend in data:
A linear model is fit to the VMT time series data to eliminate the linear trend and account for the
periodic effects as observed from the data, a linear model of the following form as represented inequation [6] is fitted to the data.
Xt: &o* at* þt'+yf + arcosLnflt*b6in2rf1t* ø2cosLnf2t+brsinLtfrt+Y, [6]
In equation 16l, Y, are the residuals of the model fit, and the coefficients estimated are significant
as obtained from model output. The model ouþut is provided as in Appendix-A This linear
model eliminates the trend as observed to the original VMT data, with an R-square of 0.974.
2.4 Model fit to the Residuals:
Residual analysis on Y, suggests that they follow SARIMA (p, d, q) x (P, D, Q) þeasonality)process, with a seasonal component of 72. This can be observed as from Figure 15 which plots
the residuals, along with ACF and PACF plotted in Figure 16 and Figure 17 respectively.
Furthermore, the cumulative periodogram of the residuals suggests that they are not white noise,
which needs to be modeled.
Thus, based on the ACF and PACF plots of the residuals (I) from the linear model as
represented in equation [6], it can be said that ACF decays for every l2'h lag and a peak at the
l2th lagfor PACF, suggests both AR and MA component with strong seasonality of 12. Potential
models where tested based on these assumptions against various model comparison statistics as
discussed in the previous section. Therefore, a SARMA (3, 0, 0 x (2,0, l) (12) was fit to the
residuals (f). The goodness of fit statistics for the fïtted model is: log-likelihood of 133.11, withan AICC of -253.81, model AIC of -250.23 and model variance is 0.01575, along with parameter
estimates having low standard errors. The model output for the fitted model is provided as inAppendix-A
2.5 Residual Analysis from SARIMA mode fit:
Analysis of residuals is conducted as obtained from the SARIMA model fit. Figure 18 plots the
residuals of the model fit, with Figure 20 showing the ACF along with the standard residuals
from the model. As seen from Figure 20, Ljung-Box statistic probabilities suggest that the
residuals follow a white noise process; this fact is further strengthened from Portmanteau test,
which accepts the hypothesis that the residuals follow an iid white noise sequence as, Qois 20.60
at probability 0.\49, which is less than f at s=0.05; furthermore, the Ljung-Box statistic with
Qø = 28.102 at probability 0.107, which is less than f at a=0.05, also suggests that the residuals
follow an iid white noise sequence. From Figure 19 which illustrates the cumulative periodogram
of the model fit residuals, it can be concluded that they follow a white noise process, as they lie
within the Kolmogorov-Smirnov confidence intervals.
References:
1. Brockwell.P., Davis.R., (2002) Introd.uction to Time Series Fareeasting. Springer, New
York.2. Califomia Department of Transportation, Traffi.c Data Branch, httçllir¿ffrc-
counts.dot.câ.govl
3. Cowpertwait. P,, Metcalfe. 4., (2009). Intoductory Time Series wíth R. Springer' New
Yorlc4. Cryer. J., Chan. K., (2008) Time Series Anølysis with Applications in R. Springer, New
York.5. 'Walter.Z., Nenadic.O ., Time Series AnøIytis with R - Pørt I.
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