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Shipping by the crowd - empirical analysis of operations and behavior

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Empirical analysis of crowd-sourced freight deliveries

Presenter: Amanda Stathopoulos, Assistant Professor of Civil and Environmental Engineering, Northwestern University

This seminar presents results from empirical analysis of crowd-sourced freight deliveries in the US. Crowd-sourced deliveries build on the idea that citizens deliver goods, ideally along planned travel routes. Crowdshipping has a potential to match highly fragmented transport capacities with vastly diverse demand for urban freight deliveries, temporally, spatially and in real-time. This is typically achieved through platforms that connect carriers with consumers in need of deliveries. A third-party broker, who operates the platform, provides match-making, analysis and customer services between demand and supply. The main advantage of crowdshipping is the reduced need for fixed facilities, such as cars or warehouses, to run operations. The main obstacles are trust, liability issues, and ensuring a critical mass of couriers and customers. Despite the growth in operations, there is still a poor understanding of the performance, functionality and acceptability of these new delivery methods. The seminar presents results analyzing the performance in the early stages of operation of crowdshipping. Based on real operational data from 2 years across the US the performance is examined with an emphasis on the specificity of crowdshipping, namely related to delivery variability and the temporal matching dynamics. Based on additional survey experiments the behavior of the main agents in the system is modeled with an emphasis on revealing acceptance and priorities of both occasional drivers and senders. The research derives from a Partnership-for-Innovation (PFI) project funded by the NSF where a Chicago based research team (NU, UIC) is evaluating the capabilities of CROwd-sourced Urban Delivery (CROUD) in collaboration with a crowd-shipper technology firm.

About Amanda: Amanda’s research focuses on developing new methodologies to collect data and specify mathematical models to account for broad and realistic choice behaviour in the transport setting (for instance social determinants, environmental concern, user experience, simplified decision rules). These richer layers of user motivations is an area of primary relevance in improving understanding and prediction of travel behavior. For a range of current transportation challenges such as promoting transit ridership growth, moving towards alternative fuels, or getting companies to adopt better practices in delivering goods, there is increasing recognition of the need to build adequate tools to account for decision complexity on the user side to match with effective decision support.

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Shipping by the crowd - empirical analysis of operations and behavior

  1. 1. 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
  2. 2. 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
  3. 3. Behavior Consumer Ecological Technology DemographicRegulation 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
  4. 4. 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
  5. 5. 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)
  6. 6. 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
  7. 7. 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
  8. 8. 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    
  9. 9. 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  
  10. 10. 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
  11. 11. A.  Stathopoulos     Func:oning  of  system:  what  is  the  delivery   performance?   Part  1:  deliverability     Indep. Variables Coef. t-stat istic exp(Coef.) constant 0.5199 2.90 1.682 total_distanceX100m -0.0463 -4.14 0.955 sender_business_binary (base private) 0.9856 6.80 2.679 size_smallpackage (base large+long) 0.4514 4.26 1.571 carrier_age25_34 (base missing + 18_24) 0.4776 2.89 1.612 carrier_age35over (base missing + 18_24) 0.7801 4.71 2.182 category_perishable (base all other) 0.8242 3.79 2.280 region_southernUS (base all other) 0.3796 3.25 1.462 Summary statistics Log likelihood at constants -5425.849 Log likelihood of model -1525.081 McFadden's pseudo r-squared 0.724 Nagelkerke pseudo r-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 (πi)= β 'xi
  12. 12. A.  Stathopoulos   Part  1:  deliverability     0.8 0.9 0 250 500 750 1000 total_distance PredictedProb as.factor(c 0 1 Condition total distance sender status package size carrier age goods category region probability of delivery 1: least advantageous Average private large 25_34 all other rest 0.708 2 Average private large over35 all other South USA 0.828 3 Average business small 25_34 perishable rest 0.959 4: most advantageous Average business small over35 perishable South USA 0.979 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)
  13. 13. 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 -202468 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
  14. 14. A.  Stathopoulos   0 50 100 150 0.00.20.40.60.81.0 Hours: Published to delivery - cumulative SurvivalProbability 0 20 40 60 80 100 120 140 0.00.20.40.60.81.0 Hours: Pickup to delivery - cumulative SurvivalProbability   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
  15. 15. A.  Stathopoulos   dura:on  models  for  crowdshipping   0 50 100 150 0.00.20.40.60.81.0 Hours: Published to delivery − cumulative DeliveryProbability s1_small s2_medium s3_large s4_xlarge s5_xlarge−long Smaller package delivered earlier (red) 0 50 100 150 0.00.20.40.60.81.0 Hours: Published to delivery − cumulative DeliveryProbability 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour of posting has an impact 0 50 100 150 0.00.20.40.60.81.0 Hours: Published to delivery − cumulative DeliveryProbability 10miles 11−50miles 50p_miles Longer distance -> slower delivery (purple)
  16. 16. 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
  17. 17. 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. (base rest) Day: mo_sat Day: tu_we_th Hour: 11_13 Hour: 14_18 Lognormal exp(beta) Weibull exp(beta) ExponenKal exp(beta) Slower(longersurvival)Quickerdelivery
  18. 18. A.  Stathopoulos   Part  1:  :me-­‐un:l-­‐delivery   0 10 20 30 40 50 60 70 0.00150.00200.0025 Exponential Minutes since published HazardRate Exponential 0 1000 2000 3000 4000 5000 0.00100.00150.00200.00250.00300.0035 Log−normal Minutes since published HazardRate Log-normal 0 1000 2000 3000 4000 5000 0.00100.00150.00200.00250.0030 Weibull Minutes since published HazardRate 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?
  19. 19. 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
  20. 20. 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
  21. 21. A.  Stathopoulos     Discrete  choice  experiments  to  study:   Part  2:  behavior   Experimental Design1 The experimental design required many variables to make the choices relevant. Research2 has shown response quality likely increases when most important information is included (27). To3 give the respondents a general sense of the current situation, the SP experiment included changing4 three main variables for the current situation setting. These setting variables varied across5 questions, but held steady across the alternatives in a given question. The variables included the6 purpose for travel (3 levels), whether it is a workday (2 levels), and time of day (3 levels). These7 variables were chosen based on research by Paleti et al. that showed VOT varies as a function of8 the individual daily activity pattern, and the schedule for that day (28). This resulted in 189 combinations, before adding any alternative variables. After careful consideration of the10 combinations, three did not make logical sense, and were removed. The three settings included a11 work trip on a non-work day. It technically is possible for that scenario to exist, but uncommon12 for most responders and could be confusing. That left 15 “main” combinations. However, the13 combinations increased when considering the length (5 levels, 10 to 120 minutes) and travel time14 variability (3 levels, 5% to 60% of original travel time) of the current planned trip. This increased15 the number of possible settings to 225. To see an example of a setting, consider Figure 1, in the16 “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
  22. 22. A.  Stathopoulos   Part  2:  behavior   questions, but held steady across the alternatives in a given question. The variables included the purpose for travel (3 levels), whether it is a workday (2 levels), and time of day (3 levels). These variables were chosen based on research by Paleti et al. that showed VOT varies as a function of the individual daily activity pattern, and the schedule for that day (28). This resulted in 18 combinations, before adding any alternative variables. After careful consideration of the combinations, three did not make logical sense, and were removed. The three settings included a work trip on a non-work day. It technically is possible for that scenario to exist, but uncommon for most responders and could be confusing. That left 15 “main” combinations. However, the combinations increased when considering the length (5 levels, 10 to 120 minutes) and travel time variability (3 levels, 5% to 60% of original travel time) of the current planned trip. This increased the number of possible settings to 225. To see an example of a setting, consider Figure 1, in the “Current Planned Trip Information” section in the center of the figure. FIGURE 1 Example of stated preference experiment web interface. 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
  23. 23. 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 Mixed Logit ML with Attitudinal Coeff. Rob T-stat Coeff. Rob T-Stat Coeff. Rob T-Stat Intercept for Status Quo -0.197 -2.72 -0.649 -1.60 1.250 3.10 Attributes Travel Time -0.314 -13.58 Profit Earned 0.085 13.17 Random Parameter Standard Deviation for Status Quo* -2.02 -9.59 -1.450 -9.16 Travel Time ≤ 45 min -0.0687 -9.04 -0.069 -9.04 Travel Time > 45 min -0.0317 -9.16 -0.032 -9.14 Profit Earned ≤ $4 0.742 7.74 0.728 7.81 Profit Earned > $4 & ≤ $18 0.161 8.15 0.160 8.12 Profit Earned > $18 0.0510 5.32 0.051 5.32 Alternative level features Original Trip Time is 10 min -0.757 -3.36 -0.767 -3.36 Income is < $35K or ≥ $90K 1.67 4.04 Original Trip is Leisure -0.770 -3.67 -0.749 -3.57 Holds Graduate Degree 1.47 3.45 0.806 2.41 Original Trip Evening * Male -0.608 -2.00 -0.662 -2.21 Reason is Never Would Work 4.62 4.67 2.240 2.35 Min.Expected to Earn ≥ $18/hr. 2.270 5.40 Min.Expected to Earn < $8/hr. -1.150 -2.50 Would not mind extra time in car to make money: low 4.390 8.35 I have enough free time: high -2.090 -3.79 I have enough free time: medium -1.010 -3.00 I use my time well in the car: high -1.790 -3.61 I work well with others: low -6.430 -2.85 Model Fit Statistics Baseline Mixed Logit ML with Attitudinal Observations (Individuals) 1430 1430 (143) 1430 (143) Null Log Likelihood -1982.40 -1982.40 -1982.40 Constants Only Log Likelihood -1971.43 -1971.43 -1971.43 Final Log Likelihood -1849.95 -1510.98 -1481.27 Rho2 0.067 0.238 0.253 adj. Rho2 0.065 0.231 0.243 Number of Draws 1000 1000
  24. 24. A.  Stathopoulos   the  crowdshipping  driver:  WTW   Decreasing  returns   •  Piece-­‐wise  linear  spec.   •  Bigger  drop  for  profit  than  for   added  trip  :me   •  Most  willingness  for  20-­‐40min   commute  &  4-­‐8$  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   Utilityvariation 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’      
  25. 25. 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
  26. 26. 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 Results Stated Preference QuestionnaireLimle  experience   How  translate  into   experiment?  
  27. 27. A.  Stathopoulos   0% 10% 20% 30% 40% 50% 60% 70% 80% CSN1 - No Trust CSN2 - Share of Private Informa@on CSN3 - No Professional Carriers CSN4 - Delivery Condi@ons Worries CSN5 - Complicated System CSN6 - Less Efficient Percentage of Agreement on Nega@ve Statements Global Male Female Low Income Medium Income High Income Shipping Experience No Shipping Experience Crowdshipping Experience No Crowdshipping Experience Age: 15 to 24 Age: 25 to 34 Age: 35 to 44 Age: 45 to 54 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  
  28. 28. 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, …
  29. 29. A.  Stathopoulos   MODELS MNL Short Distance MNL Medium Distance EC Medium Distance MNL Long Distance EC Long Distance df 16 16 17 14 15 Final Log-L -2158.594 -1339.481 -1326.282 -1812.330 -1768.260 Rho-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 -18.10 Time (h) -0.0720 -3.88 -0.0146 -2.56 -0.0209 -2.68 - - - - Expert Driver [occasional] 0.294 3.42 - - - - 0.831 9.43 1.22 10.47 Experienced Driver (n shipm.) 0.830 5.86 1.26 5.17 1.62 5.23 1.78 12.49 2.29 11.88 4.5 Star Rating [4 stars] 0.643 5.79 - - - - - - - - 5 Star Rating [4 stars] 0.914 7.65 0.479 3.60 0.506 2.86 - - - - 4.5&5 Star Rating [4 stars] - - - - - - 1.02 10.84 1.33 11.61 Delivery Cond (Day) [driver sets] - - - - - - 0.411 4.11 0.469 3.80 Pick-Up Cond. (Day) [driver sets] 0.815 10.05 0.376 2.32 0.778 3.35 0.899 10.34 1.14 10.03 Pick-Up Cond (Time) [driver sets] 0.466 5.39 0.430 3.32 0.511 2.62 0.465 4.95 0.811 6.78 Male - - - - -0.357 -2.40 -1.44 -1.99 15-24-Year-Old Male 0.769 1.99 - - - - - - - - 25-34-Year-Old Male 0.644 3.94 1.57 3.01 - - - - 45-54-Year-Old Male -0.594 -1.99 -0.985 -2.24 -2.23 -2.04 - - - - 55-Year-Old and Over - - 0.653 3.44 1.59 2.74 0.632 2.34 2.40 1.99 Low Income - - 0.638 3.36 1.52 2.68 - - - - Medium Income - - 0.522 2.77 1.22 2.39 - - - - Experience shipm. 0.377 2.35 - - - - - - - - Crowdshipping STD - - - - -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   AttributesAcceptancefactors 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  
  30. 30. 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
  31. 31. 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?
  32. 32. 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  
  33. 33. 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)'.

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