A.  Stathopoulos  

Shipping  by  the  crowd:  empirical  analysis  of  opera:ons  and  behavior    

Amanda  Stathopoulos,  CEE  Northwestern  University  

Choice Modelling Centre Seminar, Institute for Transport Studies at the University of Leeds Thursday 10th November 2016

A.  Stathopoulos  

Outline  

1. The concept Crowd-sourced delivery project - Capacity saver? Freight side-liner?

3. Models of crowdshipping Examine operations & Behavior of on-the-go shipping

4. First insights and outlook

2. Literature Drawing on related sectors/issues

Behavior

Consumer

Ecological

Technology

Demographic

Regulation

Economic

Trends & Challenges for urban delivery

54% urban, 12% mega-cities

E-commerce B2C 1.9 trl E-logistics ‘logsumer’

Speedy e-tailer delivery Multiple channels

Sharing economy -  Collaborative

consumption -  Collaborative

business

Quality of life Competing objectives

Real time big data + analytics Automotive tech. advances UAV. IoT for freight

Insurance Regulate new technology/models

A.  Stathopoulos  

Research  ra:onale  

•  Crowd-sourced, on-the-go goods delivery

•  Limited understanding of disruptive models

Technology - Device enabled

Sharing - Culture of collective ownership

Enabled by: Operating in the context of: Growing expectations •  Personalization •  Transparency •  Speed/cost

Growing pressures •  E-commerce •  Single parcel

Sharing economy

crowdshipping

A.  Stathopoulos  

Northwestern  +  UIC    research  

Designing a new system – interdisciplinary challenge

Crowd-shipping

Behavior New agents: needs, preferences, aspirations?

Operations City-logistics routing, collaborative delivery, consolidation

Economics Design pricing, Bidding, Incentives

Computation Predictive analytics, Big data analysis, Integrated system

Legal? Insurance, labor

Stathopoulos (NU)

Nie, Lin (NU+UIC)

NSF Partnership for innovation CROUD project Schumer (NU)

Wolfson (UIC)

A.  Stathopoulos  

A  crowd-­‐based  delivery  system  

Sender/ Customers •  Models; B2B,

B2C?

Carrier •  Commuter •  Dedicated non-

professional •  Professional

Definition: “Crowdshipping” delivery of goods by non-professional tapping into existing travel routes

Exchange – Revenue •  Fixed or Negotiated,

bidding

Need – Generate task – Match with driver – Negotiate price – Deliver – Reception -- Rating

Value system Company goal ranging from •  efficiency to •  community oriented

A.  Stathopoulos  

Research  challenge  

Sender/ Customers •  Models; B2B,

B2C?

Carrier •  Commuter •  Dedicated non-

professional •  Professional

Research to date on crowdshipping:

Exchange – Revenue •  Fixed or Negotiated,

bidding

q  Unclear what value customers give to price, speed, tailoring, access specific to crowdshipping

Obstacles revealed q  Trust in new

setting q  Critical mass

and hen-and-egg problems

q  Unclear motivation for drivers

Rogues & Montreuil 2014 BCG surveys

A.  Stathopoulos  

Research  challenge  

Urban logistics

Connected research

q  Models of parcel pickup behaviour (Collins 2015)

q  Collaborative city logistics (Chowdhury 2016)

Peer-based eco.

q  Varying motivations (Bellotti et al. 2015)

Sharing transport

q  Age and education relate to on-demand rides (Rayle et al., 2016, Shaheen et al., 2016)

q  Attributes relevant in ride sourcing (Agatz 2012, Furuhata 2013)

q  Urban location and transit use separate ‘sharers’ (Clewlow 2016)

Industry

q  Didi Kuadi 250mln users in China (includes express)

q  GrabTaxi 9mln downloads

Urban  delivery;  like  ride-­‐hailing  

dynamic  organiza:on  of  delivery,  efficiency  

Long  distance;  Like  carpoolign,  

slower  organiza:on,  community-­‐based    

A.  Stathopoulos  

Research  ra:onale  

•  This research empirically examines 2 parts of this problem

Part  1:  Opera:onal  performance  of  on-­‐demand  delivery  

Delivery rate •  Logistic regression of delivery •  Performance variation?

Chain-of-event dynamics •  Hazard models of duration •  Performance variation?

Part  2:  Behavior  analysis  

Customer choice of delivery •  Acceptance of crowd-delivery •  Preference by context and

heterogeneity

Driver choice of shipment •  Willingness to work •  Value of time and preference

heterogeneity

Delivery  not  guaranteed   Dynamics  unknown  

Behavior  unexplored  Critical  mass,  acceptance  

A.  Stathopoulos  

The  data  

•  Collaboration with crowdshipping startup in the US

Bubble plot: users who have published on platform (size # publ, darkness = date of enrolling)

Ca. 250’000 enrolled in system (majority are drivers) Operations for about 2 years •  Working with posted (12’000) and delivery instances

•  Varying type of goods and distances •  Around 40 variables including time-stamp, location, delivery features

•  Little know about users/drivers

A.  Stathopoulos  

  Func:oning  of  system:  what  is  the  delivery  performance?  

Part  1:  deliverability    

Indep.Variables Coef.t-statistic exp(Coef.)

constant 0.5199 2.90 1.682total_distanceX100m -0.0463 -4.14 0.955sender_business_binary(baseprivate) 0.9856 6.80 2.679size_smallpackage(baselarge+long) 0.4514 4.26 1.571carrier_age25_34(basemissing+18_24) 0.4776 2.89 1.612carrier_age35over(basemissing+18_24) 0.7801 4.71 2.182category_perishable(baseallother) 0.8242 3.79 2.280region_southernUS(baseallother) 0.3796 3.25 1.462 Summarystatistics Loglikelihoodatconstants -5425.849 Loglikelihoodofmodel -1525.081 McFadden'spseudor-squared 0.724 Nagelkerkepseudor-squared 0.896 AIC 3068.2

1

Binary  logistic  regression  model  of  delivery   Lower delivery prob. •  Each extra 100 miles

reduces odds of delivery by 5%

Increase prob. •  Small package 1.6

times the odds •  Perishable good have

2.3 times the delivery odds

Y= if the posted object was delivered

logit (π i) = β ' xi

A.  Stathopoulos  

Part  1:  deliverability    

0.8

0.9

0 250 500 750 1000total_distance

PredictedProb

as.factor(category_perish)0

1

Conditiontotal

distancesenderstatus

packagesize

carrierage

goodscategory region

probabilityofdelivery

1:leastadvantageous Average private large 25_34 allother rest 0.708

2 Average private large over35 allotherSouthUSA 0.828

3 Average business small 25_34 perishable rest 0.9594:mostadvantageous Average business small over35 perishable

SouthUSA 0.979

1

Applying deliverability model on specific contexts reveals systematic variation

4 scenarios Delivery probability

ranges from 0.71 to 0.98 depending on scenario

Probability of delivery success for scenario 2 (red) and 3 (blue) by varying distance (10-1000 miles)

A.  Stathopoulos  

  Time-­‐of-­‐event  models    Survival  analysis  on  delivery  (prob.  to  survive  past  :me  t  undelivered)    Different  events  and  explanatory  factors  

Part  1b:  dura:on  models  

1 2 3 4 5 6 7

-20

24

68

Dur. publish-deliver

weekday published

log(duration)

posted  to  acceptance   75%   90%   95%   99%  hours   4   49   123   574  days   0.2   2.0   5.1   23.9  

posted  to  pickup   75%   90%   95%   99%  hours   22   113   207   652  days   0.9   4.7   8.6   27.2  

posted  to  delivery   75%   90%   95%   99%  hours   25   127   240   765  days   1.0   5.3   10.0   31.9  

pickup  to  delivery   75%   90%   95%   99%  hours   2   14   30   146  days   0.1   0.6   1.3   6.1  

Performance in real data

From a consumer posting –driver accepting a delivery – source of delay

Coordinating pickup, initial delay

A.  Stathopoulos  

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0

Hours: Published to delivery - cumulative

Sur

viva

l Pro

babi

lity

0 20 40 60 80 100 120 140

0.0

0.2

0.4

0.6

0.8

1.0

Hours: Pickup to delivery - cumulative

Sur

viva

l Pro

babi

lity

  Connec:ng  peers  –  dynamics  of  pos:ng?  

Part  1b:  dura:on  models  

From hour of posting, uneven dynamics in first 2

days

KM  plot  Posted  -­‐>  delivered  

KM  plot  Pickup  -­‐>  delivered  

Non-parametric Duration models reveal timing of delays

Once picked up: The delivery is satisfied

quickly

A.  Stathopoulos  

dura:on  models  for  crowdshipping  

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0

Hours: Published to delivery − cumulative

Del

ivery

Pro

babi

lity

s1_smalls2_mediums3_larges4_xlarges5_xlarge−long

Smaller package delivered earlier (red)

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0

Hours: Published to delivery − cumulative

Del

ivery

Pro

babi

lity

012345678

01234567891011121314151617181920212223

Hour of posting has an impact

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0

Hours: Published to delivery − cumulative

Del

ivery

Pro

babi

lity

10miles11−50miles50p_miles

Longer distance -> slower delivery (purple)

A.  Stathopoulos  

dura:on  models  for  crowdshipping  

Hazard form

Exponential Constant

Time variation

inc if p>1, dec if p<1, const.p=1

inc then dec

Weibull

Lognormal Not prop. hazard

Focus on parametric models •  Give structure (shape) to the hazard function •  Hazard can be used for forecasting •  Many functions to try; estimate with standard

likelihood methods

Prop. hazard

Prop. hazard

Ratio of hazards

A.  Stathopoulos  

dura:on  models  for  crowdshipping  

Prop.Hazard  duration  modes   Delivery Risk over the 250 hours: •  For delivery ⇨ 50 miles 1/3 of the

speed •  For perishable good ⇨ 2-3 times

faster

•  Evidence of non-monotonic hazard rate (lognormal model best fit)

    Exponent.   Weibull   Lognormal       exp(beta)   exp(beta)   exp(beta)  (Intercept)   0.00*   0.00*   0.00*  Pack.size.med   0.81*   0.81*   0.78*  Pack.size.large   0.80*   0.79*   0.69*  Pack.size.xlarge   0.54*   0.51*   0.46*  Pack.size.xlarge-­‐long   0.72*   0.70*   0.57*  Distance_11-­‐50m   0.97   0.95   0.80*  Distance_50p_m   0.33*   0.31*   0.27*  Perish.  (base  rest)   3.46*   3.30*   2.36*  Day:  mo_sat   1.53*   1.48*   1.24*  Day:  tu_we_th   2.03*   1.84*   1.26*  Hour:  11_13   1.46*   1.48*   1.39*  Hour:  14_18   1.88*   1.97*   1.90*  Shape  param.       0.77*   0.78*  

df   11   12   12  

LL  final  model   -­‐39199.4   -­‐38778.1   -­‐38283.9  

AIC   78420.8   77580.2   76591.8  

Obs. 5158, * sig at p= 0.99

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00

Pack.size.med

Pack.size.large

Pack.size.xlarge

Pack.size.xlarge-long

Distance_11-50m

Distance_50p_m

Perish.(baserest)

Day:mo_sat

Day:tu_we_th

Hour:11_13

Hour:14_18

Lognormalexp(beta) Weibullexp(beta) ExponenKalexp(beta)

Slo

wer

(lon

ger s

urvi

val)

Qui

cker

del

iver

y

A.  Stathopoulos  

Part  1:  :me-­‐un:l-­‐delivery  

0 10 20 30 40 50 60 70

0.00

150.

0020

0.00

25

Exponential

Minutes since published

Haz

ard

Rat

e

Exponential

0 1000 2000 3000 4000 5000

0.00

100.

0015

0.00

200.

0025

0.00

300.

0035

Log−normal

Minutes since published

Haz

ard

Rat

e

Log-normal 0 1000 2000 3000 4000 5000

0.00

100.

0015

0.00

200.

0025

0.00

30

Weibull

Minutes since published

Haz

ard

Rat

e

Weibull

Different implied ‘failure patterns’

•  Lognormal (best fit) is nonmonotonic

•  Chance of delivery not just decreasing but also…

•  Odd increase in the first 2 hours after posing

•  Less careful? Novelty draw?

A.  Stathopoulos  

Summary:  real  opera:ons  

•  Analysis of delivery performance •  Varies significantly over space, by

good shipped, shipment distance

•  Duration models reveal critical stages between posting and delivery

•  Systematic differences by covariates

•  Critical elements: not guaranteed to find match or be delivered in reasonable time

Key  findings  

Useful  for  

Issues  /  Forward  looking  

•  Identifying inefficiencies •  E.g. delay posting until

more drivers attentive •  Improve matching assistance

on platform

•  More advanced model that joins stages of delivery •  Account for repeat delivery (efficiency inc?) •  Better rationale for non-monotonic delivery pattern

But… Platform design and shared data does not allow: q  Price/performance trade-off analysis -> price is

‘engineered’ and does not vary

q  Transactions are available but cannot run choice model -> rating and other features not recorded at time-of-choice -> choices deterministic

q  Nearly no personal / motivational data to study acceptance

q  Sensitive nature of data; non-disclosure

A.  Stathopoulos  

  Discrete  choice  experiments  to  study:  

Part  2:  behavior  Miller, Stathopoulos, and Nie 6

Experimental Design 1 The experimental design required many variables to make the choices relevant. Research 2

has shown response quality likely increases when most important information is included (27). To 3 give the respondents a general sense of the current situation, the SP experiment included changing 4 three main variables for the current situation setting. These setting variables varied across 5 questions, but held steady across the alternatives in a given question. The variables included the 6 purpose for travel (3 levels), whether it is a workday (2 levels), and time of day (3 levels). These 7 variables were chosen based on research by Paleti et al. that showed VOT varies as a function of 8 the individual daily activity pattern, and the schedule for that day (28). This resulted in 18 9 combinations, before adding any alternative variables. After careful consideration of the 10 combinations, three did not make logical sense, and were removed. The three settings included a 11 work trip on a non-work day. It technically is possible for that scenario to exist, but uncommon 12 for most responders and could be confusing. That left 15 “main” combinations. However, the 13 combinations increased when considering the length (5 levels, 10 to 120 minutes) and travel time 14 variability (3 levels, 5% to 60% of original travel time) of the current planned trip. This increased 15 the number of possible settings to 225. To see an example of a setting, consider Figure 1, in the 16 “Current Planned Trip Information” section in the center of the figure. 17

18

19 FIGURE 1 Example of stated preference experiment web interface. 20

21

A. Commuter willingness to take on delivery B. Sender preference over (crowd)drivers

A.  Stathopoulos  

Part  2:  behavior  

Miller, Stathopoulos, and Nie 6

Experimental Design 1 The experimental design required many variables to make the choices relevant. Research 2

has shown response quality likely increases when most important information is included (27). To 3 give the respondents a general sense of the current situation, the SP experiment included changing 4 three main variables for the current situation setting. These setting variables varied across 5 questions, but held steady across the alternatives in a given question. The variables included the 6 purpose for travel (3 levels), whether it is a workday (2 levels), and time of day (3 levels). These 7 variables were chosen based on research by Paleti et al. that showed VOT varies as a function of 8 the individual daily activity pattern, and the schedule for that day (28). This resulted in 18 9 combinations, before adding any alternative variables. After careful consideration of the 10 combinations, three did not make logical sense, and were removed. The three settings included a 11 work trip on a non-work day. It technically is possible for that scenario to exist, but uncommon 12 for most responders and could be confusing. That left 15 “main” combinations. However, the 13 combinations increased when considering the length (5 levels, 10 to 120 minutes) and travel time 14 variability (3 levels, 5% to 60% of original travel time) of the current planned trip. This increased 15 the number of possible settings to 225. To see an example of a setting, consider Figure 1, in the 16 “Current Planned Trip Information” section in the center of the figure. 17

18

19 FIGURE 1 Example of stated preference experiment web interface. 20

21

Commuter willingness to take on delivery

Online survey designed as a ‘game’ to analyze willingness-to-work as crowdshipper during a commute Trade detour for profit: Willingness-to-work •  Presented different context+

Day + Timing (18 comb) •  Time detour, variability and

profit •  Included indicators for: sharing factor, income discontent, life and work balance, new endeavors

A.  Stathopoulos  

the  crowdshipping  driver  Main findings

More  likely  to  do  shipment  •  Short  commute,  leisure  trip,  low  

earning  •  Agtudes;  like  :me  in  car,  don’t  

work  well  with  others,  low  earnings  expecta:on,  have  free  :me  

Less  likely  to  accept  crowdshipment  •  Graduate,  lowest+highest  income,  

female  in  evening  travel  •  Agtudes;  high  earning  expecta:on  

No  support  for:  •  Desire  to  try  new  things,  be  their  own  

boss,  awareness  of  crowdshipping,  living  paycheck  to  paycheck    

Baseline MixedLogit

MLwithAttitudinal

Coeff.

RobT-stat Coeff.

RobT-Stat Coeff.

RobT-Stat

InterceptforStatusQuo -0.197 -2.72 -0.649 -1.60 1.250 3.10

Attributes

TravelTime -0.314 -13.58 ProfitEarned 0.085 13.17 RandomParameterStandard

DeviationforStatusQuo*

-2.02 -9.59 -1.450 -9.16

TravelTime≤45min

-0.0687 -9.04 -0.069 -9.04TravelTime>45min

-0.0317 -9.16 -0.032 -9.14

ProfitEarned≤$4

0.742 7.74 0.728 7.81ProfitEarned>$4&≤$18

0.161 8.15 0.160 8.12

ProfitEarned>$18

0.0510 5.32 0.051 5.32

Alternativelevelfeatures

OriginalTripTimeis10min

-0.757 -3.36 -0.767 -3.36Incomeis<$35Kor≥$90K 1.67 4.04 OriginalTripisLeisure

-0.770 -3.67 -0.749 -3.57

HoldsGraduateDegree

1.47 3.45 0.806 2.41OriginalTripEvening*Male

-0.608 -2.00 -0.662 -2.21

ReasonisNeverWouldWork

4.62 4.67 2.240 2.35Min.ExpectedtoEarn≥$18/hr.

2.270 5.40

Min.ExpectedtoEarn<$8/hr.

-1.150 -2.50Wouldnotmindextratimeincartomakemoney:low

4.390 8.35

Ihaveenoughfreetime:high

-2.090 -3.79Ihaveenoughfreetime:medium

-1.010 -3.00

Iusemytimewellinthecar:high

-1.790 -3.61

Iworkwellwithothers:low

-6.430 -2.85

ModelFitStatistics Baseline MixedLogitMLwith

Attitudinal Observations(Individuals) 1430 1430(143) 1430(143) NullLogLikelihood -1982.40 -1982.40 -1982.40 ConstantsOnlyLogLikelihood -1971.43 -1971.43 -1971.43 FinalLogLikelihood -1849.95 -1510.98 -1481.27 Rho2 0.067 0.238 0.253 adj.Rho2 0.065 0.231 0.243 NumberofDraws 1000 10001

A.  Stathopoulos  

the  crowdshipping  driver:  WTW  

Decreasing  returns  •  Piece-­‐wise  linear  spec.  •  Bigger  drop  for  profit  than  for  

added  trip  :me  •  Most  willingness  for  20-­‐40min  

commute  &  4-­‐8$  profit  range  

0  1  2  3  4  5  6  7  8  

0   10   20   30   40   50  -­‐8  -­‐7  -­‐6  -­‐5  -­‐4  -­‐3  -­‐2  -­‐1  0  

0   40   80   120  

Util

ity v

aria

tion

Profit earned [$] Time detour [min]

    Baseline   Mixed  Logit   ML  with  Am.  adj.  Rho2   0.065   0.231   0.243  Value  of  Time  (WTW)  ($/hr.)   $22.16          

Median  WTW  ($/hr.)       $18.71   $18.86  

Willingness-to-work estimates: required pay-off

0%

5%

10%

15%

20%

25%

30%

10 25 45 75 120 Travel Time of Chosen Alternative (min)

Difficult  to  compensate  driver:  high  ‘required  compensa:on’      

A.  Stathopoulos  

Summary:  poten:al  drivers  

•  Decreasing marginal impact of detour and profit

•  WTW for short trips •  Personal: U-shaped acceptance curve

income •  Setting: purpose [leisure], timing

[eve*male] •  Motivations: earnings expectations,

sharing attitude, time-use

Key  findings  Useful  for  

Issues  /  Forward  looking  

•  Defining the detour and profit compensation that works for driver and minimizes impact

•  Design collaborative delivery schemes

•  Design incentives for recruitment and retention

•  Should base experiment on drivers real experience •  Attention to interaction between driver – public •  Longer trips, deeper analysis of motivations

A.  Stathopoulos  

Part  2:  behavior  General acceptance of crowdshipping

Find general acceptance crowdshipping option for parcel or personal objects Trade traditional shipping guarantees for new service? •  Focus on context •  Selection of attributes •  Identify other drivers of the

decision •  Altruistic/community oriented •  Self-centered reasons

ResultsStated Preference QuestionnaireLimle  experience  

How  translate  into  experiment?  

A.  Stathopoulos  

0%10%20%30%40%50%60%70%80%

CSN1-NoTrust

CSN2-ShareofPrivateInforma@on

CSN3-NoProfessionalCarriers

CSN4-DeliveryCondi@onsWorries

CSN5-ComplicatedSystem

CSN6-LessEfficient

PercentageofAgreementonNega@veStatements

Global

Male

Female

LowIncome

MediumIncome

HighIncome

ShippingExperience

NoShippingExperience

CrowdshippingExperience

NoCrowdshippingExperience

Age:15to24

Age:25to34

Age:35to44

Age:45to54

the  crowdshipping  sender  

Focus-­‐groups  to  iden:fy  amributes  (3  groups)  •  Tradi:onal  (:me  +  cost)  •  Flexibility  and  control  over  delivery  condi:ons  •  Driver  creden:al  

A.  Stathopoulos  

The  sender  Sender willingness to try crowdshipping

Shipping scenario (587 resp) Context A short distance (5 m) for a large package (size of a television); A medium distance (100 m) for a medium package (size of a backpack); A long distance (1,400 miles) for an extra-large package (size of a mattress). Attributes •  Traditional (time + cost) •  Flexibility and control over

delivery conditions •  Driver credential Follows up with 3 options: use traditional service, not ship at all,

…

A.  Stathopoulos  

MODELS MNLShortDistance

MNLMediumDistance

ECMediumDistance

MNLLongDistance

ECLongDistance

df 16 16 17 14 15FinalLog-L -2158.594 -1339.481 -1326.282 -1812.330 -1768.260Rho-Square 0.244 0.531 0.535 0.365 0.380

Value R t-test Value R t-test Value R t-test Value R t-test Value R t-test

Cost($) -0.114 -10.01 -0.126 -11.09 -0.174 -9.12 -0.0202 -21.92 -0.0251 -18.10Time(h) -0.0720 -3.88 -0.0146 -2.56 -0.0209 -2.68 - - - -ExpertDriver[occasional] 0.294 3.42 - - - - 0.831 9.43 1.22 10.47ExperiencedDriver(nshipm.) 0.830 5.86 1.26 5.17 1.62 5.23 1.78 12.49 2.29 11.884.5StarRating[4stars] 0.643 5.79 - - - - - - - -5StarRating[4stars] 0.914 7.65 0.479 3.60 0.506 2.86 - - - -4.5&5StarRating[4stars] - - - - - - 1.02 10.84 1.33 11.61DeliveryCond(Day)[driversets] - - - - - - 0.411 4.11 0.469 3.80Pick-UpCond.(Day)[driversets] 0.815 10.05 0.376 2.32 0.778 3.35 0.899 10.34 1.14 10.03Pick-UpCond(Time)[driversets] 0.466 5.39 0.430 3.32 0.511 2.62 0.465 4.95 0.811 6.78Male - - - - -0.357 -2.40 -1.44 -1.9915-24-Year-OldMale 0.769 1.99 - - - - - - - -25-34-Year-OldMale 0.644 3.94 1.57 3.01 - - - -45-54-Year-OldMale -0.594 -1.99 -0.985 -2.24 -2.23 -2.04 - - - -55-Year-OldandOver - - 0.653 3.44 1.59 2.74 0.632 2.34 2.40 1.99LowIncome - - 0.638 3.36 1.52 2.68 - - - -MediumIncome - - 0.522 2.77 1.22 2.39 - - - -Experienceshipm. 0.377 2.35 - - - - - - - -CrowdshippingSTD - - - - -3.96 -3.66 - - -7.55 -3.39

The  sender  

Big  changes  for  distance  context  •  Time  mamers  less  for  long  distance  •  Exper:se  mamers  more/  stars  less  •  Different  age  and  income  effects  

Attr

ibut

es

Acc

epta

nce

fact

ors

Urban  delivery  •  Experience  -­‐>  ra:ng  bonus  •  Strong  :me  sensi:vity  •  Don’t  care  about  delivery  •  Accñ:  Young/male,  used  to  

shipping  100  mile  delivery  •  Ra:ng  less  impact  •  Less  impact  of  pick-­‐up  

arrangements  •  Accñ:  low  inc,  higher  age  

Long  distance  •  Time  insig./  cost  low  •  Delivery  control  mamers  •  Accñ:  female,  older  

A.  Stathopoulos  

Summary  of  findings  

•  We looked at 2 sides of crowdshipping

Part  1:  Opera:onal  performance  of  on-­‐demand  delivery  

Delivery rate •  Identified impacts (distance) •  Scenario application reveals

wide variation •  Reveals performance gaps

Time-to-delivery •  Identified impacts (time-of-posting) •  Rate goes down over time •  Unstable dynamics with peer-

matching

Part  2:  Behavior  analysis  

Sender choice of shipper •  Identified factors that drive

choice of crowdshipment •  Acceptance depends on age

and income •  Context changes preference

Driver choice of shipment •  Only small detour from commute

accepted •  Identified motivations (solitary,

like time in car, earnings) •  Non-linear profit sensitivity makes

difficult compensation

A.  Stathopoulos  

Next  steps  

•  Deliverability models: improve with non-linearity and spatial variation

•  Time-to-delivery: improve duration analysis with multi-stage models

•  Sender and driver behavior models Ø  Run extended experiments with real crowdshippers Ø  Analyze motivations for participation (from pecuniary to

community) Ø  Begin to design systems that will have joint acceptance

•  Broader analysis; do these systems increase welfare, efficiency, sustainability?

A.  Stathopoulos  

Next  steps  

Which  companies  and  service  models  will  remain?  

Permanence  

How  do  sharing  systems  sustain  when  mo:va:ons  are  disconnected?  (local)  Behaviors  are  self-­‐oriented  /  demands  professional  service?  

Mo:va:ons  

Effec:ve  impact?  Difficult  to  isolate  the  ‘detour’  and  induced  driving  

Impact  Which  model  framework  will  allow  to  build  fleet  and  customer  base  in  tandem  

Cri:cal  mass  

Analysis  can  help  design  bidding  plauorm,  opera:ons  (consolid/collab),  behavior  incen:ves  and  mining  of  the  travel  data  of  app-­‐users  

Mul:ple  perspec:ves    

Driver:  low  tolerance  for  devia:on  Goal:  build  system  around  actual  travel,  mine  user  data  

Non-­‐delivery,  transac:on  problems  Goal:  design  incen:ves  /  business  models  

Goal:  Defining  the  latent  constructs  and  impact    

Dis-­‐harmony  in  sender-­‐driver  rela:on  Goal:  design  incen:ves  /  business  models  

A.  Stathopoulos  

Ques:ons?  

Behavior  analysis  papers    Miller,  Stathopoulos,  and  Nie  ‘Crowdsourced  Urban  Package  Delivery:  modelling  traveler  Willingness  to  Work  as  Crowdshippers’  (awai:ng  publica:on  decision  in  TRR  2017)    Punel,  Stathopoulos  ‘Exploratory  analysis  of  crowdsourced  delivery  service  through  a  stated  preference  experiment’  (to  be  presented  in  TRB  2017)    

For more discussion a-stathopoulos@northwestern.edu Thanks to: US National Science Foundation Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) Grant No. 1534138 'Enhancing Intelligence of Crowdsourced Urban Delivery (CROUD)'.