Goto

Collaborating Authors

fleet management


Microsoft's hybrid platform, Azure Stack Hub, gets management, machine learning updates

ZDNet

The cloud computing race in 2020 will have a definite multi-cloud spin. Here's a look at how the cloud leaders stack up, the hybrid market, and the SaaS players that run your company as well as their latest strategic moves. There hasn't been a whole lot of news lately about Microsoft's private/hybrid-computing platform, the product formerly known as Azure Stack and, more recently, as Azure Stack Hub. But, at Build 2020 this week, Microsoft officials are talking up some of the new features coming soon to Azure Stack Hub in preview form. Microsoft is making available in private preview Azure Stack Hub Fleet Management.


How AI Is Transforming Fleet Safety

#artificialintelligence

AI-integrated software is a sophisticated system made up of several devices and applications such as predictive data analysis and machine learning systems, HD cameras and sensors, communication and display systems. AI-based fleet management platform Driveri, currently deployed in fleets across the country, is a combination of all of these components. Before understanding how each of these parts combines to create a fleet management powerhouse, it is important to know what each one does. Cameras ensure that video data can be captured, analyzed and accessed at any time leading to a better study of driver behavior, road conditions or hazards. This is significant because it creates a future of fleet management where human error is reduced across the transport cycle.


How AI fleet Management Will Shape the Future of Transportation

#artificialintelligence

There are many opinions about how Artificial Intelligence (AI) is going to change the world with expectations about its capabilities for now and in the future. AI simply refers to intelligence displayed by machines in contrast to that displayed by humans. Although humans are intelligent, they cannot be programmed to exceed their current capabilities in the same way a machine can. This has led to the creation of smart machines that handle tasks otherwise difficult for humans to handle efficiently. Artificial intelligence is gradually becoming a constant presence in many technological applications.


SUPAID: A Rule mining based method for automatic rollout decision aid for supervisors in fleet management systems

arXiv.org Artificial Intelligence

The decision to rollout a vehicle is critical to fleet management companies as wrong decisions can lead to additional cost of maintenance and failures during journey. With the availability of large amount of data and advancement of machine learning techniques, the rollout decisions of a supervisor can be effectively automated and the mistakes in decisions made by the supervisor learnt. In this paper, we propose a novel learning algorithm SUPAID which under a natural 'one-way efficiency' assumption on the supervisor, uses a rule mining approach to rank the vehicles based on their roll-out feasibility thus helping prevent the supervisor from makingerroneous decisions. Our experimental results on real data from a public transit agency from a city in U.S show that the proposed method SUPAID can result in significant cost savings.


Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem

arXiv.org Artificial Intelligence

--Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace. Handcrafting heuristic solutions that account for the dynamics in these resource allocation problems is difficult, and may be better handled by an end-to-end machine learning method. Previous works have explored machine learning methods to the problem from a high-level perspective, where the learning method is responsible for either repositioning the drivers or dispatching orders, and as a further simplification, the drivers are considered independent agents maximizing their own reward functions. In this paper we present a deep reinforcement learning approach for tackling the full fleet management and dispatching problems. In addition to treating the drivers as individual agents, we consider the problem from a system-centric perspective, where a central fleet management agent is responsible for decision-making for all drivers. I NTRODUCTION The order dispatching and fleet management system at a ride-sharing company must make decisions both for assigning available drivers to nearby passengers (hereby called orders) and for repositioning drivers who have no nearby orders. These decisions have short-term effects on the revenue generated by the drivers and driver availability. In the long term they change the distribution of drivers across the city, which in turn has a critical impact on how well future orders can be served. Provident algorithmic solutions, which account for the short term and long-term consequences of their decisions can improve the quality of service of the ride-sharing platforms and are thus an important area of research. Recent works [1], [2] have successfully applied Deep Reinforcement Learning (RL) techniques to dispatching problems, such as the Traveling Salesman Problem (TSP) and the more general V ehicle Routing Problem (VRP) [3], however they have primarily focused on static ( i. e. those where all orders are known up front) and/or single-driver dispatching problems. In contrast to these problems, the fleet management and order dispatching problem ride-sharing platforms face has multiple drivers and dynamically changing supply and demand conditions. We refer to this dynamic dispatching and fleet management problem as the Multi-Driver V ehicle Dispatching and Repositioning Problem (MDVDRP). VRPs and other problems similar to the MDVDRP are studied in the field of combinatorial optimization. Exactly solving instances of these problems at the scale of real-world environment is computationally intractable [4].


Fleet Management and mitigating risks from common road accidents

#artificialintelligence

The high frequency of road accidents makes driver safety one of the biggest challenges facing Fleet Management each day. In the US alone, 6 million car accidents every year happen every year, with more than 40,000 motor vehicle accident-related deaths in 2017. Several factors come into play when looking at the cause of traffic accidents. It could be the weather, changing road conditions, or the fault of other road users such as another driver or pedestrian. Apart from the risks posed by accidents to drivers, companies face significant losses when such accidents and traffic violations occur.


How AI fleet Management Will Shape the Future of Transportation

#artificialintelligence

The future of transportation looks more promising than ever due to the exciting applications of AI in fleet management. Unpredictable road conditions, operational costs, and driver retention problems could easily become obsolete as fleets move to AI-based systems. Every stakeholder stands to benefit a lot from the efficiency and reliability of this technology because of a reduction in costs, accidents, driver turnover, and other problems which could reflect on the pricing of fleet services. It could also ensure that other road users remain safe.


How Artificial Intelligence Technology is changing Transportation

#artificialintelligence

There are many opinions about how Artificial Intelligence (AI) is going to change the world with expectations about its capabilities for now and in the future. AI simply refers to intelligence displayed by machines in contrast to that displayed by humans. Although humans are intelligent, they cannot be programmed to exceed their current capabilities in the same way a machine can. This has led to the creation of smart machines that handle tasks otherwise difficult for humans to handle efficiently. Artificial intelligence is gradually becoming a constant presence in many technological applications.


How AI fleet Management Will Shape the Future of Transportation » Data Is Utopia

#artificialintelligence

There are many opinions about how Artificial Intelligence (AI) is going to change the world with expectations about its capabilities for now and in the future. AI simply refers to intelligence displayed by machines in contrast to that displayed by humans. Although humans are intelligent, they cannot be programmed to exceed their current capabilities in the same way a machine can. This has led to the creation of smart machines that handle tasks otherwise difficult for humans to handle efficiently. Artificial intelligence is gradually becoming a constant presence in many technological applications.


Our New Website Concargo

#artificialintelligence

"This article originally appeared on netradyne.com" There are many opinions about how Artificial Intelligence (AI) is going to change the world with expectations about its capabilities for now and in the future. AI simply refers to intelligence displayed by machines in contrast to that displayed by humans. Although humans are intelligent, they cannot be programmed to exceed their current capabilities in the same way a machine can. This has led to the creation of smart machines that handle tasks otherwise difficult for humans to handle efficiently.