Showing papers on "Fleet management published in 2016"

Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions

Abstract: There are natural synergies between shared autonomous vehicle (AV) fleets and electric vehicle (EV) technology, since fleets of AVs resolve the practical limitations of today’s non-autonomous EVs, including traveler range anxiety, access to charging infrastructure, and charging time management. Fleet-managed AVs relieve such concerns, managing range and charging activities based on real-time trip demand and established charging-station locations, as demonstrated in this paper. This work explores the management of a fleet of shared autonomous electric vehicles (SAEVs) in a regional, discrete-time, agent-based model. The simulation examines the operation of SAEVs under various vehicle range and charging infrastructure scenarios in a gridded city modeled roughly after the densities of Austin, Texas. Results based on 2009 NHTS trip distance and time-of-day distributions indicate that fleet size is sensitive to battery recharge time and vehicle range, with each 80-mile range SAEV replacing 3.7 privately owned vehicles and each 200-mile range SAEV replacing 5.5 privately owned vehicles, under Level II (240-volt AC) charging. With Level III 480-volt DC fast-charging infrastructure in place, these ratios rise to 5.4 vehicles for the 80-mile range SAEV and 6.8 vehicles for the 200-mile range SAEV. SAEVs can serve 96–98% of trip requests with average wait times between 7 and 10 minutes per trip. However, due to the need to travel while “empty” for charging and passenger pick-up, SAEV fleets are predicted to generate an additional 7.1–14.0% of travel miles. Financial analysis suggests that the combined cost of charging infrastructure, vehicle capital and maintenance, electricity, insurance, and registration for a fleet of SAEVs ranges from $0.42 to $0.49 per occupied mile traveled, which implies SAEV service can be offered at the equivalent per-mile cost of private vehicle ownership for low-mileage households, and thus be competitive with current manually-driven carsharing services and significantly cheaper than on-demand driver-operated transportation services. When Austin-specific trip patterns (with more concentrated trip origins and destinations) are introduced in a final case study, the simulation predicts a decrease in fleet “empty” vehicle-miles (down to 3–4% of all SAEV travel) and average wait times (ranging from 2 to 4 minutes per trip), with each SAEV replacing 5–9 privately owned vehicles.

Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects

Abstract: Electric vehicles can become integral parts of a smart grid, since they are capable of providing valuable services to power systems other than just consuming power. On the transmission system level, electric vehicles are regarded as an important means of balancing the intermittent renewable energy resources such as wind power. This is because electric vehicles can be used to absorb the energy during the period of high electricity penetration and feed the electricity back into the grid when the demand is high or in situations of insufficient electricity generation. However, on the distribution system level, the extra loads created by the increasing number of electric vehicles may have adverse impacts on grid. These factors bring new challenges to the power system operators. To coordinate the interests and solve the conflicts, electric vehicle fleet operators are proposed both by academics and industries. This paper presents a review and classification of methods for smart charging (including power to vehicle and vehicle-to-grid) of electric vehicles for fleet operators. The study firstly presents service relationships between fleet operators and other four actors in smart grids; then, modeling of battery dynamics and driving patterns of electric vehicles, charging and communications standards are introduced; after that, three control strategies and their commonly used algorithms are described; finally, conclusion and recommendations are made.

Operations of Shared Autonomous Vehicle Fleet for Austin, Texas, Market

Abstract: The emergence of automated vehicles holds great promise for the future of transportation. Although commercial sales of fully self-driving vehicles will not commence for several more years, once these sales are possible a new transportation mode for personal travel promises to arrive. This new mode is the shared autonomous (or fully automated) vehicle (SAV), combining features of short-term, on-demand rentals with self-driving capabilities: in essence, a driverless taxi. This investigation examined the potential implications of the SAV at a low level of market penetration (1.3% of regional trips) by simulating a fleet of SAVs serving travelers in the 12-mi by 24-mi regional core of Austin, Texas. The simulation used a sample of trips from the region’s planning model to generate demand across traffic analysis zones and a 32,272-link network. Trips called on the vehicles in 5-min departure time windows, with link-level travel times varying by hour of day based on MATSIM’s dynamic traffic assignment simulatio...

Management of a Shared Autonomous Electric Vehicle Fleet: Implications of Pricing Schemes:

Abstract: The market potential of a fleet of shared autonomous electric vehicles (SAEVs) is explored by using a multinomial logit mode choice model in an agent-based framework and different fare settings. The mode share of SAEVs in the simulated midsize city (modeled roughly after Austin, Texas) is predicted to lie between 14% and 39% when the SAEVs compete with privately owned, manually driven vehicles and city bus service. The underlying assumptions are that SAEVs are priced between $0.75/mi and $1.00/mi, which delivers significant net revenues to the fleet owner–operator under all modeled scenarios; that they have an 80-mi range and that Level 2 charging infrastructure is available; and that automation costs are up to $25,000 per vehicle. Various dynamic pricing schemes for SAEV fares indicate that specific fleet metrics can be improved with targeted strategies. For example, pricing strategies that attempt to balance available SAEV supply with anticipated trip demand can decrease average wait times by 19% to 23%...

The impact of depot location, fleet composition and routing on emissions in city logistics

Abstract: This paper investigates the combined impact of depot location, fleet composition and routing decisions on vehicle emissions in city logistics. We consider a city in which goods need to be delivered from a depot to customers located in nested zones characterized by different speed limits. The objective is to minimize the total depot, vehicle and routing cost, where the latter can be defined with respect to the cost of fuel consumption and CO 2 emissions. A new powerful adaptive large neighborhood search metaheuristic is developed and successfully applied to a large pool of new benchmark instances. Extensive analyses are performed to empirically assess the effect of various problem parameters, such as depot cost and location, customer distribution and heterogeneous vehicles on key performance indicators, including fuel consumption, emissions and operational costs. Several managerial insights are presented.

Cyber–Physical Control of Road Freight Transport

Abstract: Freight transportation is of outmost importance in our society and is continuously increasing. At the same time, transporting goods on roads accounts for about 26% of the total energy consumption and 18% of all greenhouse gas emissions in the European Union. Despite the influence the transportation system has on our energy consumption and the environment, road transportation is mainly done by individual long-haulage trucks with no real-time coordination or global optimization. In this paper, we review how modern information and communication technology supports a cyber–physical transportation system architecture with an integrated logistic system coordinating fleets of trucks traveling together in vehicle platoons. From the reduced air drag, platooning trucks traveling close together can save about 10% of their fuel consumption. Utilizing road grade information and vehicle-to-vehicle communication, a safe and fuel-optimized cooperative look-ahead control strategy is implemented on top of the existing cruise controller. By optimizing the interaction between vehicles and platoons of vehicles, it is shown that significant improvements can be achieved. An integrated transport planning and vehicle routing in the fleet management system allows both small and large fleet owners to benefit from the collaboration. A realistic case study with 200 heavy-duty vehicles performing transportation tasks in Sweden is described. Simulations show overall fuel savings at more than 5% thanks to coordinated platoon planning. It is also illustrated how well the proposed cooperative look-ahead controller for heavy-duty vehicle platoons manages to optimize the velocity profiles of the vehicles over a hilly segment of the considered road network.

Shared-Vehicle Mobility-on-Demand Systems: A Fleet Operator's Guide to Rebalancing Empty Vehicles

Abstract: The authors consider the operation of automated mobility-on-demand systems, whereby users share access to a fleet of self-driving vehicles. In these systems, rebalancing, the process by which the supply of empty vehicles is periodically realigned with the demand for transport, is carried out by a fleet operator. Where much of the rebalancing literature skews to the theoretical or simulation-based, the authors consider shared mobility systems from the perspective of the fleet operator. The authors test, via simulations, how key performance metrics vary as a function of fleet size and rebalancing policy using rental data from car2go, a free-floating carsharing service operating in markets across Europe and North America. Results reveal that rebalancing can dramatically reduce the number of customer walk aways, even for relatively small fleet sizes. A framework is provided to assess what fleet size is a appropriate for a city factoring in the cost of vehicles, customer walk aways, and the added expense of moving empty vehicles.

Smart e-bike monitoring system: real-time open source and open hardware GPS assistance and sensor data for electrically-assisted bicycles

Abstract: The smart e-bike monitoring system (SEMS) is a platform for the real-time acquisition of usage data from electrically-assisted bikes (also called pedelecs or e-bikes). It is autonomous (runs off the bike battery), replicable (open source and open hardware), scalable (different fleet sizes) and modular (sensors can be added), so it can be used for further research and development. The system monitors location (global positioning system), rider control data (level of assistance) and other custom sensor input in real time. The SEMS data feeds an online interface for data analysis, for riders to view their own data and for sharing on social media. The basic system can be replicated by other researchers and can be extended with modules to explore various issues in e-bike research. The source code and hardware design are publicly available, under the General Public License, for non-commercial use. SEMS was implemented on 30 bikes and collected data during 10 months of real-word trials in the UK. This study details the design and implementation of the hardware and software, discusses the system use and explores features for future design iterations. The SEMS turns singular e-bikes into a networked fleet and is an example of the internet of things in the cycling context.

Managing a fleet of autonomous electric vehicles for on-demand transportation and ancillary services to electrical grid

Abstract: A computer-implemented method for managing a fleet of electric vehicles includes a fleet management computing system selecting an optimal vehicle fleet size and a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area and (ii) historical traffic flow though the geographic area during one or more time periods of interest. The fleet management computing system collects transportation demand data from a plurality of users comprising requests for transportation to locations within the geographic area and uses (i) the optimal vehicle fleet size, (ii) the plurality of discharging parking lot locations, and (iii) the transportation demand data to select routing information for each of a plurality of electric vehicles. Then, the fleet management computing system routes each respective autonomous vehicle according to its respective routing information.

Uncertainty in Fleet Renewal: A Case from Maritime Transportation

Abstract: This paper addresses the fleet renewal problem and particularly the treatment of uncertainty in the maritime case. A stochastic programming model for the maritime fleet renewal problem is presented. The main contribution is that of assessing whether or not better decisions can be achieved by using stochastic programming rather than employing a deterministic model and using average data. Elements increasing the relevance of uncertainty are also investigated. Tests performed on the case of Wallenius Wilhelmsen Logistics, a major liner shipping company, show that solutions to the model we present perform noticeably better than solutions obtained using average values.

Mixed bus fleet management strategy for minimizing overall and emissions external costs

Abstract: Diesel buses add substantially to air pollution. To mitigate this problem, more and more clean-energy buses are introduced. Among them, electric bus has been recognized as the cleanest with lower emissions. But the deployment of electric bus is limited by its short travel distance and long charging time. In this paper, based on the approach of remaining life additional benefit-cost (RLABC), we propose an approach called new life additional benefit-cost (NLABC) to solve the mixed bus fleet management (MBFM) problem. An integer program is developed based on the NLABC analysis for maximizing the total net benefit of early replacement, where both the optimal fleet size and composition under budget constraints can be determined. Arguably, the routing problem is a major issue to be tackled due to the range limitations and operating costs of electric buses in the MBFM problem. Hence, we include the routing problem associated with bus services coordination among multiple routes in this formulation. Two routing methods are proposed to solve the recharging problem to study the tradeoff between accuracy and efficiency. Four types of buses, including electric bus, compressed natural gas bus, hybrid-diesel bus, and diesel bus, are considered, while accounting for their different operating costs, external costs of emissions, and purchase costs. To illustrate the approach, we apply the formulation to some transit lines in Hong Kong. The results show that vehicle routing with bus service coordination and mixed fleet optimization are important considerations for managing the bus fleet; both of which can produce considerable benefits.

Configurable monitor and parts management system

Abstract: A monitoring and maintenance system that utilizes imperial and theoretical data to compare parts, vehicles, users, regions, wear intensity indexes over time and tracking information to provide a sophisticated data collection system for heavy-duty equipment or rental equipment. This tracking is designed to better the specifications, designs, training, preventative maintenance, and replacement wear understanding of fleet management.

Teleoperation of On-Road Vehicles via Immersive Telepresence Using Off-the-shelf Components

Abstract: The quality of visual information and response time are crucial aspects of any modern teleoperation system. This is especially true for operation of on-road vehicles, which must function in highly dynamic, unforgiving environments. In this work we demonstrate that a suitable teleoperation system can be exclusively composed of low-cost off-the-shelf components yet still meet the high performance demands of remotely driving a car on the road. The user is given immersive situational awareness through an on-board head-mounted display linked to an actuated stereoscopic camera, thereby maintaining depth perception and intuitive camera control. Communication speeds are evaluated over various wireless connection types, and a usability study shows that the system allows for advanced driving maneuvers while remotely controlled. 3G and 4G data networks are demonstrated to provide adequate bandwidth for the task given proper data compression, thus expanding the potential range for teleoperation. Applications for such a system are further discussed, extending to fleet management and autonomous vehicle safety measures.

Fuel consumption prediction of fleet vehicles using Machine Learning: A comparative study

Abstract: Ability to model and predict the fuel consumption is vital in enhancing fuel economy of vehicles and preventing fraudulent activities in fleet management. Fuel consumption of a vehicle depends on several internal factors such as distance, load, vehicle characteristics, and driver behavior, as well as external factors such as road conditions, traffic, and weather. However, not all these factors may be measured or available for the fuel consumption analysis. We consider a case where only a subset of the aforementioned factors is available as a multi-variate time series from a long distance, public bus. Hence, the challenge is to model and/or predict the fuel consumption only with the available data, while still indirectly capturing as much as influences from other internal and external factors. Machine Learning (ML) is suitable in such analysis, as the model can be developed by learning the patterns in data. In this paper, we compare the predictive ability of three ML techniques in predicting the fuel consumption of the bus, given all available parameters as a time series. Based on the analysis, it can be concluded that the random forest technique produces a more accurate prediction compared to both the gradient boosting and neural networks.

Robust planning and disruption management in roll-on roll-off liner shipping

Abstract: This paper presents different strategies for handling disruptions in fleet deployment in roll-on roll-off liner shipping, which basically consists of assigning a fleet of vessels to predefined voyages at minimum cost. A new mathematical model of the problem is presented, including a set of robust planning strategies, such as adding slack and rewarding early arrivals. To solve real-life instances a rolling horizon heuristic is proposed. A computational study, where we also propose some recovery planning strategies, is conducted, and simulation results show that adding robustness significantly reduces the actual cost of the plan and the total delays of the voyages.

Dynamic Snow Plow Fleet Management Under Uncertain Demand and Service Disruption

Abstract: It is sometimes challenging to plan winter maintenance operations in advance because snow storms are stochastic with respect to, e.g., start time, duration, impact area, and severity. In addition, maintenance trucks may not be readily available at all times due to stochastic service disruptions. A stochastic dynamic fleet management model is developed to assign available trucks to cover uncertain snow plowing demand. The objective is to simultaneously minimize the cost for truck deadheading and repositioning, as well as to maximize the benefits (i.e., level of service) of plowing. The problem is formulated into a dynamic programming model and solved using an approximate dynamic programming algorithm. Piecewise linear functional approximations are used to estimate the value function of system states (i.e., snow plow trucks location over time). We apply our model and solution approach to a snow plow operation scenario for Lake County, Illinois. Numerical results show that the proposed algorithm can solve the problem effectively and outperforms a rolling-horizon heuristic solution.

Potential Users of Electric Mobility in Commercial Transport – Identification and Recommendations☆

Abstract: Commercial transport is seen as early adopter of electric mobility. But there is lack of knowledge regarding the use of battery electric vehicles for commercial transportation and potential user groups. We outline a reliable and cost effective methodology to identify vehicles that can be substituted by battery electric vehicles in corporate fleets – technologically and economically efficient. We analyzed statistical data to identify economic sectors that might suit for electric mobility and conducted an online survey with fleet managers of these sectors to gain knowledge about driving patterns and their attitude towards the use of BEV. Furthermore we conducted a GPS data tracking of selected corporate fleets to proof substitution potentials. The analysis was done in Austria and Germany. For fleet management systems designed for mixed and electric fleets we outline a framework and explain algorithmic concepts. Finally, we derive recommendations for stakeholders such as policy makers, vehicle manufacturers, service providers and corporate fleet operators. The statistical analyses show that highest potentials for battery electric vehicles are according to NACE nomenclature in Wholesale and retail trade, Service and Human health sector. The survey revealed that driving range of battery electric vehicles already comply daily mileage requirements with a high extent within Germany nursing companies and pharmacies. Furthermore, the attitude of the interrogated fleet managers towards the use of BEV is mostly positive but detailed knowledge about BEV and driving patterns of the own vehicles is lacking. Finally, the GPS data tracking could proof high potentials for BEV in these economic sectors.

Carrier collaboration with endogenous fleets and load factors when networks are complementary

Abstract: This paper analyzes the effect of carrier collaboration on fleet capacity, fleet structures in terms of the number and the size of vehicles, and load factors. The model features complementary networks, scheduling, price elastic demands, and demand uncertainty. For the case of a given number of vehicles, the analysis shows that carrier collaboration increases vehicle sizes (thus, fleet capacity) if marginal seat costs are low while fleet capacity remains unchanged if marginal seat costs are high. If both vehicle sizes and vehicle numbers can be varied, then collaboration will always increase vehicle numbers and fleet capacity, while the effects on vehicle sizes and, thus, also load factors, are ambiguous and therewith hard to predict. Numerical simulations indicate that collaboration increases expected load factors also when the number of vehicles is endogenous.

The smart bus for a smart city — A real-time implementation

Abstract: The need for a real-time public transport information system is growing steadily. People want to plan their city commutes and do not like waiting for long hours, nor take a long route to reach their destination. The proposed hardware solution in this paper computes the shortest path to reach the destination in real time and gives that information to the bus driver. Artificial Neural Networks (ANN) is used to give an accurate estimate of the arrival time (ETA) to the commuter by means of an application. ETA to the next stop is communicated to the commuter using the MQTT (Message Queuing Telemetry Transport) protocol, by the hardware mounted on the bus. The proposed solution also adds a fleet management console to the administrators, making them manage and monitor the fleet of buses in real time. The prototype thus developed makes sure the commuting in cities is pleasant, and hassle free.

Reclaim Street Space! – Exploit the European Potential of Car Sharing

Abstract: Sustainable Urban Mobility Plans (SUMPs) are a core element of the European Commission's Urban Mobility Package. A bundle of measures combines infrastructure; clean fuels and vehicles; and soft measures to promote walking, cycling and public transport use. Looking at the problems of cities world-wide, one crucial aspect not (yet) adequately addressed is increasing car-ownership, leading to over-consumption of space in cities creating both congestion and parking problems. The City of Bremen's SUMP, which received the 2014 CIVITAS Award and the 2015 SUMP Award, integrates car sharing as a strategic element to reduce car ownership. The ambitious Car Sharing Action Plan from 2009, the first municipal thematic plan on car sharing, set a target of 20,000 car sharing users by 2020 and, more important, the replacement of about 6,000 private cars through the service of car sharing. Annual user surveys in Bremen show that every car sharing car takes 15 private cars off the road. This figure includes only cars that car sharers report giving up after becoming car sharers and does not include car purchases that were avoided by users who may have bought a car if no option had been available. Station-based car sharing, with its wide variety of vehicles and the reliability of pre-reservation (but also the option of spontaneous bookings) has a much higher impact on car ownership than does free-floating car sharing. In its 2010 ‘momorandum’, the European Intelligent Energy Europe (IEE) project memo Car Sharing estimated that European cities could be unburdened of the parking needs of 600,000 cars – end-to-end a row from London to Athens – if other cities applied policies similar to those in Bremen (and also Switzerland). The potential is huge to improve traffic, the environment and quality of life in European cities. This practitioner's report presents municipal policies and activities undertaken in Bremen (1) to exploit the potential of car sharing (e.g. providing mobility hubs and on-street car sharing stations; integration in new urban developments; multi-modal integration; optimising fleet management, information and awareness), (2) to develop quality requirements and (3) to prepare for future developments (e.g. autonomous transport systems). It also clarifies the different roles of station-based and free-floating car sharing and their potential for different types of cities and towns. The presentation will show – from the perspective of a municipality – how car sharing can be integrated into both advanced sustainable urban mobility planning and into more efficient urban developments. New urban developments with integrated car sharing, bike sharing and high quality public transport do not need as many car parking spaces as conventional developments, creating potential for reducing costs and improving the quality of urban space.

Utilizing Telematics Data to Support Effective Equipment Fleet-Management Decisions: Utilization Rate and Hazard Functions

Abstract: Contractors and equipment rental companies have started to acknowledge and use the telematics technology as a reliable solution for timely collection of their equipment fleet data. Telematics is the integration of wireless communications, vehicle monitoring systems, and location devices to provide real-time spatial and performance data of the fleet machines. Despite the large amount of real-time equipment data made available by telematics, fleet managers still try to identify ways to use such data to make informed fleet-management decisions. This paper presents the development of novel telematics-based computational methodologies to support two major equipment fleet management tasks: fleet use assessment and equipment health monitoring. First, a description of the telematics system and data used are presented. Second, a computational algorithm is proposed to quantify the fleet-wide equipment used, based on basic telematics data. Third, a health-monitoring framework is developed to estimate equipme...

Fleet Service Generation—Challenges in Corporate Asset Management

Abstract: Product manufacturers often have access to information concerning maintenance and operation of their products in customer sites. By analysing data from wider product installation base, manufacturers would be able to have a better understanding of their product’s life cycle, than any asset owner. Also this extensive body of information that is available to the manufacturer could help in finding out critical development needs. Thus product manufacturers could support asset owners and end-users by providing knowledge based fleet level services for decision making related e.g. to maintenance and operations tasks. In practise development and provision of knowledge intensive fleet services by product manufacturer is not that straightforward. There are e.g. technical and ownership related barriers on data transfer which may retard development of fleet data based services. In the paper we will discuss from the different point of views current barriers and opportunities of knowledge intensive fleet services supporting end-users’ asset management processes focusing on corporate level of companies.