Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit
- URL: http://arxiv.org/abs/2601.08753v1
- Date: Tue, 13 Jan 2026 17:30:25 GMT
- Title: Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit
- Authors: Rishav Sen, Amutheezan Sivagnanam, Aron Laszka, Ayan Mukhopadhyay, Abhishek Dubey,
- Abstract summary: Mixed transit fleets consisting of both electric and diesel buses pose significant operational challenges.<n>This paper presents a comprehensive mixed-integer linear programming (MILP) model to address these challenges.<n>We show that our approach can result in significant savings in the operating costs of the mixed transit fleets.
- Score: 11.618218411493478
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. However, the effective management of mixed fleets consisting of both electric and diesel buses poses significant operational challenges. One major challenge is coping with dynamic electricity pricing, where charging costs vary throughout the day. Transit agencies must optimize charging assignments in response to such dynamism while accounting for secondary considerations such as seating constraints. This paper presents a comprehensive mixed-integer linear programming (MILP) model to address these challenges by jointly optimizing charging schedules and trip assignments for mixed (electric and diesel bus) fleets while considering factors such as dynamic electricity pricing, vehicle capacity, and route constraints. We address the potential computational intractability of the MILP formulation, which can arise even with relatively small fleets, by employing a hierarchical approach tailored to the fleet composition. By using real-world data from the city of Chattanooga, Tennessee, USA, we show that our approach can result in significant savings in the operating costs of the mixed transit fleets.
Related papers
- Safe and Sustainable Electric Bus Charging Scheduling with Constrained Hierarchical DRL [43.715336081857394]
Electric Buses (EBs) with renewable energy sources such as photovoltaic (PV) panels is a promising approach to promote sustainable and low-carbon public transportation.<n>We propose a safe Deep Reinforcement Learning framework for solving the EB Charging Scheduling Problem (EBCSP) under multi-source uncertainties.<n>We develop a novel HDRL algorithm, namely Double ActorCritic MultiAgent Proximal Policy Optimization Lagrangian (DACMAPPO-Lagrangian)
arXiv Detail & Related papers (2025-11-25T20:00:02Z) - Smart Routing for EV Charge Point Operators in Mega Cities: Case Study of Istanbul [2.179313476241343]
Inefficient field personnel management can lead to time loss, high operational costs, and resource waste.<n>This study presents an integrated method to optimize the planning of EV charging network maintenance operations.<n>The method was developed in Python and applied to a dataset consisting of 100 EV charging stations in Istanbul.
arXiv Detail & Related papers (2025-09-22T16:40:28Z) - Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers [16.831541035603557]
Vehicle electrification relies on efficient and adaptable charging infrastructure.<n>Fixed-location charging stations often suffer from underutilization or congestion due to fluctuating demand, while mobile chargers offer flexibility by relocating as needed.<n>This paper studies the optimal planning and operation of hybrid charging infrastructures that combine both fixed and mobile chargers within urban road networks.
arXiv Detail & Related papers (2025-06-20T05:51:02Z) - Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach [42.070187224580344]
We introduce continuous-state mean-field control (MFC) and mean-field reinforcement learning (MFRL) models that employ continuous vehicle repositioning actions.<n>MFC and MFRL offer scalable solutions by modeling each vehicle's behavior through interaction with the vehicle distribution, rather than with individual vehicles.<n>Our approach scales to tens of thousands of vehicles, with training times comparable to the decision time of a single linear programming rebalancing.
arXiv Detail & Related papers (2025-03-31T15:00:11Z) - Large Neighborhood Search and Bitmask Dynamic Programming for Wireless Mobile Charging Electric Vehicle Routing Problems in Medical Transportation [5.740535941960799]
We propose the Wireless Mobile Charging Electric Vehicle Problem (WMC-EVRP)<n>This problem enables Medical Transportation Electric Vehicles (MTEVs) to be charged while traveling via Mobile Charging Carts (MCTs)<n>We develop a mathematical model and a tailored meta-heuristic algorithm that combines Bit Mask Dynamic Programming (BDP) and Large Neighborhood Search (LNS)
arXiv Detail & Related papers (2025-03-11T14:11:10Z) - COLA: Cross-city Mobility Transformer for Human Trajectory Simulation [44.157114416533915]
We develop a Cross-city mObiLity trAnsformer (COLA) with a dedicated model-agnostic transfer framework.
COLA divides the Transformer into the private modules for city-specific characteristics and the shared modules for city-universal mobility patterns.
Our implemented cross-city baselines have demonstrated its superiority and effectiveness.
arXiv Detail & Related papers (2024-03-04T07:45:29Z) - Fair collaborative vehicle routing: A deep multi-agent reinforcement
learning approach [49.00137468773683]
Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other.
Traditional game theoretic solution concepts are expensive to calculate as the characteristic function scales exponentially with the number of agents.
We propose to model this problem as a coalitional bargaining game solved using deep multi-agent reinforcement learning.
arXiv Detail & Related papers (2023-10-26T15:42:29Z) - Federated Reinforcement Learning for Real-Time Electric Vehicle Charging
and Discharging Control [42.17503767317918]
This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments.
A horizontal federated reinforcement learning (HFRL)-based method is proposed to fit various users' behaviors and dynamic environments.
Simulation results illustrate that the proposed real-time EV charging/discharging control strategy can perform well among various factors.
arXiv Detail & Related papers (2022-10-04T08:22:46Z) - Intelligent Electric Vehicle Charging Recommendation Based on
Multi-Agent Reinforcement Learning [42.31586065609373]
Electric Vehicle (EV) has become a choice in the modern transportation system due to its environmental and energy sustainability.
In many cities, EV drivers often fail to find the proper spots for charging, because of the limited charging infrastructures and the largely unbalanced charging demands.
We propose a framework, named Multi-Agent Spatiotemporal-temporal ment Learning (MasterReinforce), for intelligently recommending public charging stations.
arXiv Detail & Related papers (2021-02-15T06:23:59Z) - Electric Vehicle Charging Infrastructure Planning: A Scalable
Computational Framework [5.572792035859953]
The optimal charging infrastructure planning problem over a large geospatial area is challenging due to the increasing network sizes of the transportation system and the electric grid.
This paper focuses on the demonstration of a scalable computational framework for the electric vehicle charging infrastructure planning over the tightly integrated transportation and electric grid networks.
arXiv Detail & Related papers (2020-11-17T16:48:07Z) - Efficient algorithms for electric vehicles' min-max routing problem [4.640835690336652]
An increase in greenhouse gases emission from the transportation sector has led companies and the government to elevate and support the production of electric vehicles (EV)
With recent developments in urbanization and e-commerce, transportation companies are replacing their conventional fleet with EVs to strengthen the efforts for sustainable and environment-friendly operations.
deploying a fleet of EVs asks for efficient routing and recharging strategies to alleviate their limited range and mitigate the battery degradation rate.
arXiv Detail & Related papers (2020-08-07T18:45:26Z) - Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route
Service [7.2775693810940565]
We present an integer program for optimal assignment and scheduling, and we propose meta-heuristic algorithms for larger networks.
For Chattanooga, the proposed algorithms can save $145,635 in energy costs and 576.7 metric tons of CO2 emission annually.
arXiv Detail & Related papers (2020-04-10T17:45:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.