Artificial intelligence (AI) is increasingly gaining traction in the fleet management sector for streamlining operations and increasing efficiency. AI can assist in fleet monitoring, prioritizing driver safety without compromising efficiency and cost, and making timely decisions. This article discusses the importance and applications of AI in fleet management.
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Importance of AI in Fleet Management
Currently, delays due to unexpected road conditions or weather, unnecessary or ineffective repairs, siloed data preventing fleet maintenance optimization, and staff shortages are the major challenges faced by fleet management companies, which are increasing the operational costs of the fleet while reducing operational efficiency.
AI-driven fleet management can effectively overcome these challenges by empowering human decision-making, integrating fleet operations, accelerating vehicle repairs, and optimizing fleet maintenance operations. Telematics devices, such as global positioning system (GPS) trackers, sensors, and dash cams, installed in vehicles can provide information about the vehicle's status, including data on its fuel consumption, location, speed, and movement. AI can analyze substantial amounts of data generated from telematics devices to predict vehicle maintenance requirements and identify vehicle use patterns.
Specifically, AI can identify trends and patterns that are extremely difficult to detect through manual analysis. AI can reduce operating costs and optimize fuel efficiency by providing real-time insights and feedback to fleet managers and drivers, ensuring efficient fuel management and minimizing the impact of emissions on the environment.
Moreover, AI can also perform other tasks, such as route optimization to maximize fleet productivity, coaching drivers to develop better driving habits, and automating and monitoring the vehicle maintenance schedule of the entire fleet, with the development of the Internet of Things (IoT).
AI-driven fleet maintenance solutions can benefit fleet managers significantly by reducing the time required for paperwork and manual tasks. Modern fleet management software, such as Samsara and Verizon, utilizes AI for fleet management task automation and assists fleet management companies in optimizing their fleet. Thus, fleet management companies can gain a competitive edge over their competitors by investing in AI to realize more effective and efficient fleet management.
AI Applications in Fleet Management
Integrated Fleet Operations: The implementation of an integrated AI system can eliminate data silos and ensure seamless access to data required across various areas of fleet operations, including planning, monitoring, and maintenance, which improve operational agility by providing the necessary information to the personnel to make good decisions in real-time. Additionally, an integrated AI system can provide crucial insights into all aspects of fleet operations to enable end-to-end optimization and planning.
Vehicle Route Planning: Route planning for a fleet of vehicles can be extremely challenging, specifically when managing a large fleet of vehicles, as several factors, including vehicle availability, weather conditions, and traffic patterns, must be considered to ensure that the vehicles operate on time and efficiently.
In specific scenarios, fleet managers are also required to consider the personal preferences of their customers and dispatch drivers who are familiar with those customers. AI-powered solutions can streamline route planning by analyzing real-time data collected from different sources, such as traffic and weather alerts and GPS trackers, to optimize routes automatically for maximum efficiency.
Thus, adopting an AI-powered fleet management system allows fleet management companies to easily adjust and plan routes, resulting in fewer delays, higher profits, and greater customer satisfaction.
Safety Management of Drivers: Although driver safety remains a top priority for several fleet managers, improving and monitoring driver behavior while ensuring the highest efficiency in other fleet operations is very challenging.
AI-powered telematics devices, such as speed sensors and dash cams, can automatically detect, record, and alert fleet managers about unsafe driving behaviors, such as harsh braking, distracted driving, and speeding. Additionally, these devices can send in-vehicle audio prompts in real-time to drivers to enable them to make safer driving decisions.
AI also assists fleet managers in providing targeted coaching and training to drivers. Specifically, AI can score the performance of each driver, identify the areas where individual drivers require improvement, and assist fleet managers in creating personalized coaching plans for upskilling their drivers by analyzing the driver behavior data.
Analysis of Dashcam Video: Dashcams allow fleet managers to protect drivers from false incident claims, ensure compliance with all relevant safety regulations, and monitor the behavior of drivers. However, reviewing several hours of dashcam footage can be time-consuming and tedious.
AI-powered dash cam software can assist managers with dash cam video analysis by flagging and categorizing unsafe driving behavior footage automatically, allowing fleet managers to easily review the driver performance and offering them insight into the kind of training they must provide to their drivers.
Additionally, simplified footage review allows managers to find and share important video clips with the insurance provider or police more easily during an incident, accelerating the claims process and increasing accuracy while determining liability or fault.
Real-Time Analytics: Real-time analytics, which involves analyzing and collecting real-time vehicle data, is critical to ensure streamlined fleet operation. AI fleet management systems can detect potential issues in vehicles and vehicle misuse and immediately send alerts to fleet managers to enable them to take corrective actions through real-time analytics, which ensure 24/7 fleet safety, lower fuel costs, and decrease risky accidents.
Maintenance of Vehicles: AI algorithms eliminate the need to rely on scheduled maintenance or wait for the occurrence of breakdown signs for vehicle maintenance by identifying small wear and tear signs in vehicles before they transform into bigger and more expensive problems through the analysis of real-time data generated from vehicle sensors.
For instance, AI-powered fleet management software can use data from the vehicle’s on-board diagnostics (OBDII) to alert about the smallest issues and recommend specific actions immediately, such as changing engine oil or replacing worn brake pads, before these problems result in significant damage to the vehicle, which prevents breakdowns and reduces accident risk.
Additionally, AI can automate the entire fleet’s maintenance scheduling and record-keeping, eliminating the hassles of paperwork and ensuring proper maintenance operations scheduling.
Recent Studies
Inefficient supply-demand matching has been a major issue for ride-sharing platforms. The rapidly growing mobile network services can potentially address the supply-demand gap by effectively dispatching vehicles. In a study recently published in the journal IEEE Access, researchers proposed a QRewriter-Dueling Deep Q-Network (QRewriter-DDQN) algorithm to dispatch several available vehicles in advance to locations with high demand for serving more orders.
The QRewriter-DDQN algorithm was factorized into a QRewriter module and a DDQN module, which were parameterized using Q-table with reinforcement learning (RL) methods and neural networks, respectively. Additionally, the DDQN module utilized the Kullback-Leibler (KL) distribution distance between demand/orders and supply/available vehicles as excitation to capture the complex dynamic supply-demand variations.
Subsequently, the QRewriter module learned to improve the DDQN dispatching policy using the effective and streamlined Q-table in RL. Higher space for performance improvement of the DDQN dispatching policy could be obtained by aggregating the QRewriter state into a low-dimension meta state.
Researchers designed a simulator to train the QRewriter-DDQN and test its performance. Experiment results demonstrated that the QRewriter-DDQN algorithm outperformed state-of-the-art algorithms such as multi-agent DDQN and other baselines based on order response rate.
In another recent study published in the journal IEEE Transactions on Intelligent Transportation Systems, researchers proposed malfunction classifications for trucks, which is a novel idea for smart fleet management systems. In the proposed cooperative intelligent transportation (C-ITS), the developed neural network used the data from truck fleets to select the trucks that require service. The applied heuristic algorithm utilized the classification outputs to select the most relevant results from the results returned from the deep neural network classifier.
The composed system gained additional efficiency as the proposed process was multithreaded. Researchers evaluated the developed solution on the Scania Truck data collection. The implemented deep learning (DL) model attained more than 98% accuracy and an above 95% recall, which displayed the validity of the concept for further development.
References and Further Reading
W, Zhang., Q, Wang., J, Li., C, Xu. (2020). Dynamic Fleet Management With Rewriting Deep Reinforcement Learning. IEEE Access, 8, 143333-143341. https://doi.org/10.1109/ACCESS.2020.3014076.
Ke, Q., Siłka, J., Wieczorek, M., Bai, Z., Woźniak, M. (2022). Deep Neural Network Heuristic Hierarchization for Cooperative Intelligent Transportation Fleet Management. IEEE Transactions on Intelligent Transportation Systems, 23, 9, 16752-16762. https://doi.org/10.1109/TITS.2022.3195605.
Barrometro, P. (2023). The Role of AI in Fleet Management [Online] (Accessed on 22 October 2023)
AI-DRIVEN FLEET MANAGEMENT [Online] Available at https://social-innovation.hitachi/en-us/think-ahead/transportation/ai-fleet-management/ (Accessed on 22 October 2023)