Amutheezan Sivagnanam, Afiya Ayman, Michael Wilbur, Philip Pugliese, Abhishek Dubey, Aron Laszka
Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21) 2021
Public transit agencies operating mixed fleets of electric and internal combustion vehicles face complex decisions about vehicle-to-route assignment that significantly impact operational costs and environmental impact. This work is significant because it provides a principled optimization framework that considers the heterogeneous energy consumption of different vehicle types. The approach enables data-driven decisions about fleet electrification strategies.
This paper presents a Markov decision process formulation of dynamic resource allocation for mixed-fleet public transit systems with electric and hybrid vehicles. The framework optimizes vehicle assignments to routes and charging schedules to minimize energy consumption and emissions while meeting service requirements. The approach addresses the complexity of managing heterogeneous vehicle types with different energy characteristics.
The optimization framework was evaluated on real transit data from Chattanooga and demonstrated substantial energy savings compared to baseline approaches. The greedy algorithm and simulated annealing heuristics successfully solved large-scale instances while achieving near-optimal solutions. The analysis revealed that optimal vehicle assignment can reduce energy consumption by up to 145,635 kwh and CO2 emissions by up to 576.7 tons.
@inproceedings{aaai21,
author = {Sivagnanam, Amutheezan and Ayman, Afiya and Wilbur, Michael and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21)},
title = {Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service},
year = {2021},
acceptance = {21.4},
abstract = {Affordable public transit services are crucial for communities since they enable residents to access employment, education, and other services. Unfortunately, transit services that provide wide coverage tend to suffer from relatively low utilization, which results in high fuel usage per passenger per mile, leading to high operating costs and environmental impact. Electric vehicles (EVs) can reduce energy costs and environmental impact, but most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging, while serving an existing fixed-route transit schedule. We present an integer program for optimal assignment and scheduling, and we propose polynomial-time heuristic and meta-heuristic algorithms for larger networks. We evaluate our algorithms on the public transit service of Chattanooga, TN using operational data collected from transit vehicles. Our results show that the proposed algorithms are scalable and can reduce energy use and, hence, environmental impact and operational costs. For Chattanooga, the proposed algorithms can save \$145,635 in energy costs and 576.7 metric tons of CO2 emission annually.},
contribution = {colab},
tag = {ai4cps,transit},
keywords = {transit optimization, electric vehicles, energy optimization, mixed fleet, operational planning}
}