Dynamic Ride-sharing Systems: a Case Study in Metro Atlanta

Niels Agatz

Joint work withAlan Erera, Martin Savelsbergh, Xing Wang

ARCFebruary 26, Atlanta

Motivation

ObservationsCongestion is a major issue in urban areas

around the worldCongestion leads to

• Loss of productivity• Wasted fuel• Pollution

Private car occupancies are low• Average of 1.8 for leisure trips• Average of 1.1 for commuting trips

Motivation

OpportunityEffective use of empty car seats can

• Reduce congestion• Reduce fuel consumption• Reduce pollution• Increase productivity

Enabling technology exists• GPS-enabled mobile devices• Mobile-to-mobile communication• Route guidance technology

Dynamic Ride-Sharing

Outline

Introduction to dynamic ride-sharing A dynamic ride-share setting Solving the problem Simulation Metro Atlanta Simulation Results

1. Announcement of trips

2. Matching of drivers and riders

3. Execution of identified trips

4. Sharing of trip costs

Dynamic ride-sharing: basics

Assumption: if no trip is identified, drivers and riders

use their own car to reach destination

Dynamic ride-sharing: features

Dynamic: established on short-notice Non-recurring trips: different from carpooling Automated matching: different from online notice-

boards Independent drivers: different from taxis Cost-sharing: variable trip related expenses Prearranged: different from spontaneous, casual

ride-sharing

Dynamic ride-sharing: providers

IPhone applications: Carticipate (Aug. ‘08) Avego (Dec. ‘08)

Google Android applications: Piggyback (May ‘08)

Web-based: Zebigo, PickupPal, Nuride, Rideshark, Ridecell,

Goloco, Zimride, …, etc.

What is in it for participants?

Save costs by sharing fuel expenses and tolls Save time through use of HOV-lanes Save the planet

What is in it for providers?

Private: take a cut of the participant’s cost savings

Public (society): decrease external costs of transportation: emissions, pollution, and traffic congestion

Objectives

Participants: ↓ travel costs Private ride-share provider: ↓ total travel costs

↑ cut Society: ↓ pollution and traffic congestion

Objectives are aligned: ↓ system-wide vehicle miles, win-win-win

Ride-share Setting

Trip Announcement: Trips between origin - destination Role: driver, rider or flexible Participant wants to save $ At most: a single driver - a single rider No Transfers or multi-passenger trips

Announcement: Time Information

Announcement time

Departure time

Latest arrival time

direct travel timeflexibility

time window for matching

Lead-time

EarliestLatest

Relative to duration of trip?

Round Trips

Most trips in practice are round trips Announcement round trips: jointly or

separately? Rider may want assurance return ride before

departure Return trip alternatives?

System Dynamics

New announcements continuously enter and leave the system!

People leave becauseride-share was found Announcement expired

Effectively Handling Dynamics

Run optimization at specific time intervals; After a given number of announcements.

Optimization Engine: Input: trip announcements Output: driver-rider matches

Commit tentative match as-late-as-possible! shortly before latest departure of ride-share

Effectively Dealing with Dynamics

Effectively Dealing with Dynamics

Run 1

Expired

Effectively Dealing with Dynamics

Run 2

Ride-share finalized

Simulation Environment

Based on traffic model data from ARC

Ride-share Characteristics

Focus: Home-based Work, SOV Ride-share participation rate (bc 2%) Driver – Rider ratio (bc 1:1) Announcement lead-time (abs) (bc 30 min) Time flexibility (abs) (bc 20 min)

Announcement Generator

Randomly generate trip announcements based on o-d trip data:2024 Traffic Analysis Zones (TAZs)# work-related round trips per day: 2.96 million~ 87% single occupancy trips# O-D pairs: 2.90 millionmax # trips per O-D pair: 881min # trips per O-D pair: 0.01

Generate time information:

1. Home-work Draw latest departure: 7:30am, sd. 1h Calculate: earl. departure, lat. arrival,

announcement

2. Work-home Draw time at work: 8 hour, sd. 30 min Add time at work to announcement times

Announcement Generator cont’d

Ride-share Scenarios

SEPAR: separate announcements for round trip, no guaranteed ride back

JOINT: joint announcement for round trip, guaranteed ride back (not necessarily with same driver!)

BOUND: all daily trips known upfront (no dynamics)

Simulation Results

SUCCESS OUTWARD

(riders)

SUCCESS

RETURN

(riders)

∆ VHM ∆ TRIPS

SEPAR 76.4% 96.7% -28.7% -37.6%

JOINT 68.2% 100% -21.3% -33.7%

BOUND 75.0% 100% -24.0% -37.6%

-8.2! -0.6% of total trips

Sensitivity

Increase/ decrease participation rate

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90

SUCCES RATE

VH

M S

AV

ING

S

1%

2%

5%

More + better matches!

Sensitivity cont’d

Flexible roles → success rate ↑ ↑ time flexibility → success rate ↑ response time ↓ → success rate ↓ ↑ lead-time → success rate ↑

Cost savings

Total daily system-wide savings: $53,000 20% cut Ride-share provider: $10,600 Viable business model?

Avg. savings pp matched: $1.4 (20%) Enough incentive to participate?

Input: $0.55 per mile (AAA 2007)

Environmental benefits

System-wide vehicle-miles: -105,519 Vehicle Trips: -15,400

Emissions Hydrocarbons: -650 lbs/ day CO: -4000 lbs / day Oxides of Nitrogen: -323 lbs/ day

When can we find ride-shares?

0%

10%

20%

30%

40%

50%

60%

70%

80%

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

departure time

succ

ess

rateDensity is good

What if we offer awards?

Subsidize ride-share by a fixed award per match

20.2

20.4

20.6

20.8

21.0

21.2

21.4

68.2 76.3 78.9

Success Rate

VH

M s

avin

gs

No award

Award = $0.55

Award = $1.10

$9,500 $19,000

+8% +2.5%

Under development

Study system performance over time Key observations:

New participants try ride-sharing Dissatisfied participants may stop announcing

trips

Inspired by innovation diffusion models: Inflow new users dependent on size of user pool! Outflow depends on cumulative success rate

Outlook

More realistic travel time settingsTime-dependent travel times

Behavioral modeling Match acceptanceCancelationsNo-showsChoices…

Thank You