Instant Social Ride-Sharing

Győző Gidófalvi*

Uppsala University, Dept. of Information Technology

Gergely Herényi

motoros.hu: Online Hungarian Forum for Mobility and Transport

Torben Bach Pedersen*Aalborg University, Dept of Computer Science

November 19th, 2008

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Outline

Ride-sharing background Instant Social Ride-Sharing System / Service

• Process and system components• Mobile demo application

Dual-objective grouping of trips into ride-shares • Detour measure between offer and request• Social connection between participants• Calculating a ride-share assignment• Modes of operation

Simulated data sets Evaluation of transport effectiveness Conclusions and future work

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Ride-Sharing Background

Congestion, parking, and pollution are increasing in most cities

A potential solution is ride-sharing (carpooling)• Encouraged by measures:

exclusive lanes, parking places, and reduced road tolls for carpools• But wide-spread adoption is hindered by barriers:

Lack of effective mechanisms for scheduling/coordinating ride-shares Safety risks Social discomfort in sharing private spaces Imbalance of costs and benefits among parties

Current commercial products / system for ride-sharing:• nuRide, Carpoolworld, liftShare, eRideShare, etc…• Post and search trips• Manually construct / negotiate regular ride-shares• Trust managed through a self-regulatory user-rating

Existing systems do not effectively eliminate barriers!

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Instant Social Ride-Sharing System / Service

Idea: Share a ride with a friend or a friend of a friend! • Exploit exponentially growing popularity of social

networking and the voluntary sharing of personal information online to eliminate the social barrier to ride-sharing

• Utilize mobile technologies (communication, computing and positioning) to allow instant ride-sharing, automatic scheduling and to help coordinate ride-shares

Question: Are there enough ride-sharing possibilities between friends to allow for effective transport?

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SRSS Process and System Components

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SRSS Mobile Demo Application

Web demo:

http://www.motoros.hu/SRSS/SRSS.html

Java mobile application:

http://www.motoros.hu/SRSS/SRSS.jad

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Dual-Objective Grouping of Trips into Ride-Shares

Minimize the extent of the “detour” that offering parties must make in order to serve requests

Maximize the amount of social connections amongst participants of ride–shares

Expected to increase the social comfort level and trust among ride-share participants

Leads to increased user acceptance and adoption of ride–sharing

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Detour Measure Between Offer and Request

Break displacements between origin and destination of an offer and a request into perpendicular offset components

Relative detour : fraction of the weighted average of the offset components and the shared distance

Detour measure is calculated using an SQL function

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Social Connection Between Participants

Number of relatively short paths between two users indicates the strength of the social connection, ssc

ssc values between pairs of connected users are pre-calculated and stored in an RDBMS using a self-join

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Calculating a Ride-Share Assignment

1. Calculate candidate matches between active offers and valid requests such that:1. The users of the offer and the request are socially

connected2. The offering user has to make a detour that is less than a

maximum threshold: max_det3. The seat supply matches seat demand

2. Derive a single match score that is linearly related to the required detour and inversely related to the ssc between the users

3. Iteratively, in a greedy fashion assign requests to offers such that: 1. As many as possible of requests are assigned to an offer2. The total match score of the assignment is minimized

The Ride-Share Assignment algorithm is effectively implemented as a stored procedure in a RDBMS

Refer to the paper for implementation details

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Modes of Operation

Requests can be assigned to stationary or to mobile (already departed) offers

Offers can depart, in an eager fashion, immediately after the first assignment, or can wait, in a lazy fashion, for more assignments

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Simulated Data Sets

Transportation data set:• ST-ACTS: Trip = locations of two consecutive activities• 1.74 million, on average 2.9 km long trips of 550 thousand prospective

users for the course of a day

Social network data set:• People tend to make social connections with other people that they

frequently interact with in physical space (friends at home, colleagues)• A user is assigned12 random friends, and 12 home and 12 work

friends that are live and work close to the user social network with 11 million links

SRSS user data set:• Users join SRSS by invitation in the order of the number of received

invitations and the utility the user sees in joining

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Evaluation of Transport Effectiveness

Transport effectiveness for varying number of users and under various parameter settings (max_det and max_wt) are evaluated according to three measures: resource-effectiveness, time-effectiveness, and reliability.

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Evaluation of Transport Effectiveness (cont.)

For 60,000 users (10% of the population): • AVO-level is raised from 1 to 1.61 • Users wait 2.4 minutes for a ride–share • Offers have to make a detour that is 8% of the shared distance• 91% of the requests can be served

Corresponding savings during the course of a single workday in a city like Copenhagen:• 32% of the normal transport cost, specifically:

176,000 vehicle–kilometers 14,000 liters of fuel 32.7 tonnes of CO2–emissions

The proposed SRSS is clearly effective!

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Conclusions and Future Work

Conclusions• Developed an instant Social Ride-Sharing

System/Service (SRSS) along with a mobile demo application

• Experiments on simulated data sets show that the proposed SRSS can provide highly effective transportation and is feasible

Future work• Road network distance based detour measure• Highly scalable implementation of SRSS using a data

stream management system• Alternative system architecture possibilities: distributed