A Real-Time Energy and Cost Efficient Vehicle Route Assignment Neural Recommender System

TL;DR

This paper introduces a neural network-based recommender system for real-time vehicle route assignment that optimizes energy use and costs, aiding fleet management and vehicle technology selection.

Contribution

It develops a novel machine learning approach for estimating vehicle energy consumption over routes and integrates it into a real-time assignment system for fleet optimization.

Findings

Efficiently estimates energy consumption with minimal route info.

Provides top-k vehicle recommendations for trips.

Supports deployment in transportation simulation tools.

Abstract

This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-$k$ vehicles star ranking system, and (2) engage in more general assignment problems where $n$ vehicles need to be deployed over $m \leq n$ trips. This new assignment system has been deployed and integrated into the POLARIS Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium