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An Energy-Efficient Ride-Sharing Algorithm Using Distributed Convex Optimization

Year: 2024

Type: article

Abstract: With a rising need for transporting people from one place to another especially within urban spaces, many mobility-on-demand (MoD) systems like Uber, Lyft and the like have burgeoned to provide swift and efficient mobilty services within just a few taps through our smartphone devices. Although carpooling, an inherently fuelefficient concept, has penetrated into this market, commercial ride-sharing systems (e.g., Uber Pool, Lyft Line, etc.) do not perform the vehicle dispatch optimization in a fuel-efficient manner for the rider pick-up and drop-off phases of travel. Moreover, present ride-sharing algorithms have a centralized computing back-bone – making online ride-sharing optimization problems compute heavy, and thereby once again energy inefficient. With the rising level of tailpipe CO2 emissions, it is prudent to focus on fuel efficiency to curb climate impact from warming.

Source: Stanford Digital Repository

DOI: 10.25740/ss423gm7371

Authors: Sakthivelu, Adhithyan, Jayakrishnan, Shashvat

Language: English

Topic: Transportation and Mobility Innovations

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: green

OpenAlex ID: W6889675319

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