Exploring the impact of incorporating on-demand drivers into a child rideshare platform

Traditional transportation options for children consist of school bus rides and car rides from parents or friends. With an increase in children's involvement in after-school activities and a rise in the number of households with two working parents, people are looking for a better solution to child transportation. Many rideshare companies have already sprung up with a focus to serve this younger population (examples include HopSkipDrive, Zum, and Kango). While these companies provide flexibility by allowing parents to book both pre-scheduled and on-demand rides, they do not provide the necessary reliability. Much like the rideshare giants of today, Uber and Lyft, these child-geared services do not hire their drivers as employees. Instead, the drivers are treated as independent contractors. Contracting drivers means that the driver supply is never guaranteed at any given time since it is up to each driver to accept or deny the ride requests that they receive. This study proposes a driver supply model in which all drivers are hired as employees, as opposed to contractors. Hiring drivers as employees allows for a company to train drivers extensively while on the job, has been proven to result in higher retention levels, and most importantly, enables the company to have clear insight into their supply at any given time. This study aims to identify how a driver pool should be divided (if at all) into two groups: one dedicated to serving pre-scheduled rides and another reserved for serving on-demand rides. Using the Gurobi[superscript TM] optimization solver, we observe expected profits under a variety of scenarios in which we vary the proportion of drivers reserved to handle on-demand requests. Our results show that the optimal proportion of on-demand drivers is dependent on both the total number of requests received and the proportion of on-demand requests received. Results are discussed in detail.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Dieppa, Katherine.

Advisor dc:contributor.advisor

• Carolina Osorio.

• Civil and Environmental Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123219

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123219