Uber vs Taxi Usage: the Toronto VfH Case Study Gozde Ozonder, MSc - - PowerPoint PPT Presentation

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Uber vs Taxi Usage: the Toronto VfH Case Study Gozde Ozonder, MSc TMG Workshop Eric J. Miller, PhD Mobility Tools & Services Department of Civil & Mineral Engineering February 5, 2020 University of Toronto The Telegraph, 2015 NL

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TMG Workshop Mobility Tools & Services February 5, 2020

Uber vs Taxi Usage: the Toronto VfH Case Study

Gozde Ozonder, MSc Eric J. Miller, PhD Department of Civil & Mineral Engineering University of Toronto

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The Local, 2019 Pacific Standard, 2019 Global News, 2017 The Telegraph, 2015 NL Times, 2019 The Telegraph, 2014

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Agenda

Findings & Caveats Analysis Results Data & Methods Study Area & Objectives

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Credit: https://canadianobligation.weebly.com/blog/uber- protesting-toronto-taxi-drivers-block-traffic-downtown

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Study Area & Objectives

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Greater Toronto and Hamilton Area (GTHA)

Validating the representativeness of the most recent regional travel survey’s Uber trips Tracking the changes or stabilities in taxi usage between 1996 & 2016 Identifying profiles for Uber and taxi users

(1) (2) (3)

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Data

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Transportation Tomorrow Survey (TTS)

  • ~ 5% of the GTHA
  • Household-based
  • Daily travel diary

1996 TTS 2001 TTS 2006 TTS 2011 TTS 2016 TTS

Credit: http://tts2016.ca/en/index.php Credit: https://www.cnet.com/news/uber-will-reportedly- let-riders-drivers-record-audio-of-trips-for-safety/

Uber Trip Data through the City of Toronto

  • Pick-up/drop-off times & dates
  • Distance traveled
  • Service type

Credit: INRO, 2019

Emme4 Network Assignment

  • Travel

times

+

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  • Trip pattern comparison analysis:
  • Spatiotemporal
  • Trip length ( & )

Fall 2016 Uber & 2016 TTS

  • Taxi time-series analysis:
  • Attributes of taxi users
  • Trip patterns

TTS between 1996 & 2016

  • Subsample comparison analysis:
  • Survey population / trip-making population / Uber users / taxi users
  • Persona analysis:
  • Decision trees

2016 TTS

Analyses

Survey vs Actual Uber Trips Taxi Users & Trips [1996-2016] User Profiles

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Methods

Decision Trees

  • Supervised machine

learning algorithms

  • Use “recursive

partitioning” heuristic

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Homogeneity

  • Determined based
  • n purity/impurity

measures ▪ e.g., entropy, Gini index, Chi square, etc.

Variable Importance

  • Evaluating the impact of

each variable on the

  • utcome
  • Random forests

▪ Permutation importance

predicted class label % of observations in that node predicted probability for “Uber” Avoid overfitting! Feature selection

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Survey vs Actual Uber Trips

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Survey vs Actual Uber Trips (1/2)

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Survey vs Actual Uber Trips (2/2)

10 12 am – 6 am 6 am – 9 am 9 am – 3 pm 3 pm – 7 pm 7 pm – 12 am

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Taxi Users & Trips [1996-2016]

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Taxi Users & Trips [1996-2016] (1/5)

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Taxi Users & Trips [1996-2016] (2/5)

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Taxi Users & Trips [1996-2016] (3/5)

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Taxi Users & Trips [1996-2016] (4/5)

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Taxi Users & Trips [1996-2016] (5/5)

16 0.421 % 0.437 % 0.424 % 0.384 % 0.387 % 2016 Uber Mode Share: 0.23%

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User Profiles

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User Profiles (1/5)

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Uber: Mode = 30 Median = 33 Mean = 35 Taxi: Mode = 40 Median = 51 Mean = 52

Reluctant to change habits? Older people Poor vision? Less tech-savvy? Younger people App-based service? Smartphones? Tech-savvy?

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User Profiles (2/5)

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  • Both: Home & “Other”
  • Uber > Taxi: Work
  • Taxi > Uber: Shopping
  • Taxi > Uber when

0 ≤ Income < $60K

  • Uber > Taxi when

$60K ≤ Income

Home Home Other Other Work Work Shop Shop UBER TAXI

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User Profiles (3/5)

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{11, 12, …, 98} {General Office, Clerical; Manufacturing, Construction, Trades; Professional, Management, Technical; Retail Sales and Service; Unemployed} {“Yes”, “No”} {Downtown Toronto (PD1); Neighboring PDs (PD2-6); Rest of Toronto (PD7-16); Mississauga (PD36); Others}

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User Profiles (4/5)

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UBER USERS TAXI USERS

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User Profiles (5/5)

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Emily: 54 &

  • lder, taxi

user. Leonard: 35 & younger, lives

  • utside the core
  • f the City

(outside PD1-6), but not in Mississauga,

  • wns a driver’s

license, is either unemployed or employed in sector “P” or “M”, Uber user. Kyle: 35 & older, lives in the City or in Mississauga, is either unemployed or works in a sector other than “M”, taxi user. Lorelai: 35 & younger, lives in downtown Toronto, its neighboring PDs, or in Mississauga, Uber user. Nick: 35 &

  • lder, lives
  • utside the City
  • f Toronto but

not in Mississauga, taxi user.

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Findings

  • Representative Uber trips by the latest regional travel survey
  • Strong stability in taxi user profile & trip patterns (20-year period)
  • Different profiles of Uber & taxi users
  • Pivotal Attributes: age & residential location

In general in the GTHA:

  • 54 → taxi
  • 35 , residing in downtown Toronto/surrounding areas/Mississauga → Uber

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Caveats

  • Weekend trip-making

(missing!!!)

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M T W R F S D

  • Behavior of the

visitors to the region (not captured!!!)

  • Relatively early days
  • f Uber in Toronto

Fall 2016

[ ]

2020 2018

Credit: https://leadworklife.com/illustrations/diffusion-of-innovation/

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Thank You! Questions/Comments

gozde.ozonder@mail.utoronto.ca & miller@ecf.utoronto.ca

Credit: https://www.reddit.com/r/BeAmazed/comments/6041sn/aerial_view_of_Toronto