Showing papers on "Fleet management published in 2014"

Vehicle customization and personalization activities

Abstract: Systems and methods are provided that are directed to vehicle pre-acquisition activities, vehicle post-acquisition modification, customization and/or personalization, fleet management, including proactive fleet monitoring fleet services and/or vehicle, vehicle market and related analysis.

HUMAN factors in intelligent transportation systems

Abstract: The Intelligent Transportation System (ITS) Program is a cooperative effort by government, private industry, and academia to apply advanced technology to the task of resolving the problems of surface transportation. The objective is to improve travel efficiency and mobility, enhance safety, conserve energy, provide economic benefits, and protect the environment. The current demand for mobility has exceeded the available capacity of the roadway system. Because the highway system cannot be expanded, except in minor ways, the available capacity must be used more efficiently to handle the increased demand. ITS applies advanced information processing, communication, sensing, and computer control technologies to the problems of surface transportation. Considerable research and development efforts will be required to produce these new technologies and to convert technologies developed in the defense and space programs to solve surface transportation problems. ITS has been subdivided into six interlocking technology areas. This book addresses human factors concerns for four of these areas: * Advanced Traveler Information Systems are a variety of systems that provide real time, in-vehicle information to drivers regarding navigation and route guidance, motorist services, roadway signing, and hazard warnings. * Advanced Vehicle Control Systems refer to systems that aid drivers in controlling their vehicle particularly in emergency situations and ultimately taking over some or all of the driving tasks. * Commercial Vehicle Operations address the application of ITS technologies to the special needs of commercial roadway vehicles including automated vehicle identification, location, weigh-in-motion, clearance sensing, and record keeping. * Advanced Traffic Management Systems monitor, control and manage traffic on streets and highways to reduce congestion using vehicle route diversion, automated signal timing, changeable message signs, and priority control systems. Two technical areas are not specifically addressed in individual chapters, but many aspects of them are covered in associated chapters: * Advanced Rural Transportation Systems include systems that apply ITS technologies to the special needs of rural systems and include emergency notification and response, vehicle location, and traveler information. * Advanced Public Transportation Systems enhance the effectiveness, attractiveness and economics of public transportation and include fleet management, automated fare collection, and real-time information systems.

Factors influencing fleet manager adoption of electric vehicles

Abstract: Research has identified several reasons why fleet managers are good candidates to be electric vehicle (EV) early adopters such as their intense usage and high automobile purchase rates. This expectation is supported by a recent study which found that to 2013, governments and private companies were responsible for a majority of global EV purchases. Using content analysis of fleet manager interviews and pilot project reports, this study investigated 14 US and Dutch organizations that adopted EVs from 2010 to 2013 to determine which factors influenced their purchase decisions. In addition, it also analyzed the reasons why these same firms did or did not expand their EV fleets. Fleet managers identified testing new technologies as being the overarching driver of their initial adoption of EVs. Organizations also noted several influential but secondary factors including lowering their environmental impact, government grants, and improving the organization’s public image. For organizations that decided to expand their EV fleets, the primary motivating rationales were firm-specific, including pursuing first-mover advantage, specialized operational capabilities, or a compelling business model.

Applying online fleet driver assessment to help identify, target and reduce occupational road safety risks

Abstract: This paper focuses on the application of online fleet driver assessment tools to help identify, target and reduce occupational road safety risks. It covers the principles of risk assessment, the uses of online tools, predictive validity analysis, intervention allocations and some suggested next steps.

Integration of conventional and flexible bus services with timed transfers

Abstract: Conventional bus services, which have fixed routes and fixed service schedules, and flexible bus services, which provide doorstep services, have different advantages and disadvantages, with conventional services being generally preferable at high demand densities and flexible services being preferable at low densities. By efficiently integrating conventional and flexible services and thus matching service type to various regions, the total cost of transit services may be significantly reduced, especially in regions with substantial demand variations over time and space. Additionally, transit passengers must often transfer among routes because it is prohibitively expensive to provide direct routes for among all origin–destination pairs in large networks. Coordinating vehicle arrivals at transfer terminals can greatly reduce the transfer times of passengers. In this paper, probabilistic optimization models, which are proposed to deal with stochastic variability in travel times and wait times, are formulated for integrating and coordinating bus transit services for one terminal and multiple local regions. Solutions for decision variables, which include the selected service type for particular regions, the vehicle size, the number of zones, headways, fleet, and slack times, are found here with analytic optimization or numerical methods. The proposed models generate either common headway or integer-ratio headway solutions for timed transfer coordination based on the given demand. A genetic algorithm is proposed as a solution method and tested with numerical examples.

Public transport vehicle scheduling featuring multiple vehicle types

Abstract: Vehicle scheduling is a crucial step of the public transport planning process because it results in the number of vehicles required, thus it is directly related to fixed cost and labor cost. It is desirable, therefore, to minimize the number of vehicles used and operational cost. This paper proposes a new methodology for the multiple vehicle types vehicle scheduling problem (MVT-VSP). The methodology is based on a minimum-cost network flow model utilizing sets of Pareto-optimal timetables for individual bus lines. Given a fixed fleet size the suggested methodology also allows a selection of the optimal timetable. The method developed enables to stipulate the use of a particular vehicle type for a trip or to allow for a substitution either by a larger vehicle or a combination of smaller vehicles with the same or higher total capacity. Moreover, a variation of the method portrayed makes it possible to construct sub-optimal timetables given a reduction of the vehicle-scheduling cost. It is demonstrated that a substitution of vehicles is beneficial and can lead to significant cost reductions in the range of more than 27%. The suggested methodology is applied to a real-life case study in Auckland, New Zealand, and the results show improvements of greater than 15% in terms of the cost of fleet compared with vehicle schedules that are provided by standard models.

Measurement Techniques for Online Battery State of Health Estimation in Vehicle-to-Grid Applications

Abstract: The evolution of the power grid calls for a smarter infrastructure, capable of integrating different energy sources and all the different kind of users of the grid. In addition, it should be able to dynamically adapt to fluctuations in loads and sources. In the Smart Grid scenario, energy storage systems (ESSs) play a fundamental role, allowing for decoupling production and usage times. Lithium ion batteries are among the most promising technologies in ESSs. They will allow for a full exploitation of renewable energy sources, can be used to shape load curves and constitute the energy reserve in battery electric vehicles. In addition, if such vehicles are plugged into the power grid, they can act as support electricity storage, realizing the so-called vehicle-to-grid (V2G) paradigm. In all these applications, the reliability of the battery system must be guaranteed. Therefore, measurement techniques to assess the state of health of batteries are needed. In this paper, with particular reference to V2G applications, two fast techniques to compute an index representing the state of health of a Li-ion battery are introduced. The first one relies on fuzzy logic, while the second is based on a neural network. In both cases, an early characterization of batteries in the same family of the ones to be monitored is needed. Compared with experimental data, the techniques exhibit good performances without the need for great processing power. Their performances are adequate for the intended V2G fleet management purpose.

A Strategic Repositioning Algorithm for Bicycle-Sharing Schemes

Abstract: The presence of enough bicycles and free docking points to satisfy user demands in stations is a known operational issue in bicycle-sharing schemes. Empty and full stations in such systems are equally undesirable and disruptive for the operation of the network, since the former turn away potential users while the latter could not be used to terminate bicycle journeys. Repositioning practices have been used in the past to address this situation with partial success. This paper introduces a new planning approach for such activities, addressing both routing and assignment aspects of bicycle repositioning using a fleet of carrier vehicles. A case study on bicycle usage patterns from a large bicycle-sharing scheme is carried out; examples are also provided that demonstrate the behaviour of the algorithm. Implementation concerns and means to improve computational performance are also discussed.

Dynamic Coordination in Fleet Management Systems: Toward Smart Cyber Fleets

Abstract: Fleet management systems are commonly used to coordinate mobility and delivery services in a broad variety of domains. However, their traditional top-down control architecture becomes a bottleneck in open and dynamic environments, where scalability, proactiveness, and autonomy are becoming key factors for their success. Here, the authors present an abstract event-based architecture for fleet management systems that supports tailoring dynamic control regimes for coordinating fleet vehicles, and illustrate it for the case of medical emergency management. Then, they go one step ahead in the transition toward automatic or driverless fleets, by conceiving fleet management systems in terms of cyber-physical systems, and putting forward the notion of cyber fleets.

Integrated motion planning and coordination for industrial vehicles

Abstract: A growing interest in the industrial sector for autonomous ground vehicles has prompted significant investment in fleet management systems. Such systems need to accommodate on-line externally imposed temporal and spatial requirements, and to adhere to them even in the presence of contingencies. Moreover, a fleet management system should ensure correctness, i.e., refuse to commit to requirements that cannot be satisfied. We present an approach to obtain sets of alternative execution patterns (called trajectory envelopes) which provide these guarantees. The approach relies on a constraint-based representation shared among multiple solvers, each of which progressively refines trajectory envelopes following a least commitment principle.

Machine learning methods for vehicle predictive maintenance using off-board and on-board data

Abstract: Vehicle uptime is getting increasingly important as the transport solutions become more complex and the transport industry seeks new ways of being competitive. Traditional Fleet Management Systems ...

Smart monitoring system and method of operation

Abstract: Systems and methods are described that can provide a fleet management solution for a fleet of power equipment and other power equipment machines. The fleet management solution can include receiving a wireless signal corresponding to a power equipment machine according to a wireless protocol. The fleet management solution can also include determining a status information corresponding to the power equipment machine based on the wireless signal. The fleet management solution can also include performing an action related to the power equipment machine based on the corresponding status information.

An Incremental Map-Matching Algorithm Based on Hidden Markov Model

Abstract: Map-matching algorithms aim at establishing a vehicle location on a road segment based on positioning data from a variety of sensors: GPS receivers, WiFi or cellular radios They are integral part of various Intelligent Transportation Systems (ITS) including fleet management, vehicle tracking, navigation services, traffic monitoring and congestion detection Our work was motivated by an idea of developing an algorithm that can be both utilized for tracking individual vehicles and for monitoring traffic in real-time We propose a new incremental map-matching algorithm that constructs of a sequence of Hidden-Markov Models (HMMs) Starting from an initial HMM, the next models are developed by alternating operations: expansion and contraction In the later, the map-matched trace is output We discuss results of initial experiments conducted for 20 GPS traces, which to test algorithm robustness, were modified by introduction of noise and/or downsampled

Data-Driven Lead-Acid Battery Prognostics Using Random Survival Forests

Abstract: : Problems with starter batteries in heavy-duty trucks can cause costly unplanned stops along the road. Frequent battery changes can increase availability but is expensive and sometimes not necessary since battery degradation is highly dependent on the particular vehicle usage and ambient conditions. The main contribution of this work is a case-study where prognostic information on remaining useful life of lead-acid batteries in individual Scania heavy-duty trucks is computed. A data driven approach using random survival forests is proposed where the prognostic algorithm has access to fleet management data including 291 variables from 33603 vehicles from 5 different European markets. The data is a mix of numerical values such as temperatures and pressures, together with histograms and categorical data such as battery mount point. Implementation aspects are discussed such as how to include histogram data and how to reduce the computational complexity by reducing the number of variables. Finally, battery lifetime predictions are computed and evaluated on recorded data from Scanias fleet-management system.

Vehicle Procurement Policy for Humanitarian Development Programs

Abstract: This article aims to identify optimal vehicle procurement policies for organizations engaged in humanitarian development programs and to derive general insights on the characteristics of these policies. Toward that end, we follow an inductive approach. First, we study the operations of the International Committee of the Red Cross (ICRC) in three representative countries: Sudan, Afghanistan, and Ethiopia. Using a linear programming (LP) model primed with field data provided by the ICRC, we calculate the optimal vehicle fleet size and compare it with the policies actually implemented. Second, drawing from results of the LP model, we develop a stylized quadratic control model and use it to characterize the general structure of the optimal policy under different demand scenarios and operational constraints. After demonstrating that the results of the control model are consistent with those of the LP model in the specific context analyzed, we discuss the optimal policies and the applicability of the former as a practical tool for strategic asset planning.

A stochastic programming formulation for strategic fleet renewal in shipping

Abstract: Shipping companies repeatedly face the problem of adjusting their vessel fleet to meet uncertain future transportation demands and compensating for aging vessels In this paper, a new multi-stage stochastic programming formulation for strategic fleet renewal in shipping is proposed The new formulation explicitly handles uncertainty in parameters such as future demand, freight rates and vessel prices Extensive computational tests are performed, comparing different discretizations of the uncertain variables and different lengths of the planning horizon It is shown that significantly better results are obtained when considering the uncertainty of future parameters, compared to using expected values

A study on the vehicle size and transfer policy for car rental problems

Abstract: This study addresses the problems of fleet size and vehicle transfer for car-rental companies with multiple branches. The problem is formulated as a constrained mixed-integer programming model within an environment of dynamic demand. We develop a hybrid heuristic approach to solve this problem, using quantitative results to demonstrate the robustness and effectiveness of the proposed approach. We apply the proposed model to the analysis of a case involving a car-rental company in Taiwan. Sensitivity analyses are also conducted to investigate the effects of various system parameters on the computational results.

System for detecting the operational status of a vehicle using a handheld communication device

Abstract: A system which utilizes the on-board capabilities of handheld communication devices, such as smartphones, tablet computers and the like, to detect the operational status of a vehicle, such as the engine being ON, the engine idling, the vehicle moving, etc. The detected operational state may be desirable for monitoring operation of the vehicle, such as fleet management systems, wherein the duration and location of idling are of particular interest. The detected operational state may also be useful for controlling functionality on the handheld communication device, such as disabling texting or other manually operated functions when the vehicle is in motion.

Development of Vehicle Fleet Composition Model System for Implementation in Activity-Based Travel Model:

Abstract: The development of a vehicle fleet composition and utilization model system that may be incorporated into a larger activity-based travel demand model is described. It is of interest and importance to model household vehicle fleet composition and utilization behavior because the energy and environmental impacts of personal travel are dependent not only on the number of vehicles but also on the mix of vehicles that a household owns and the extent to which different vehicles are used. A vehicle composition (fleet mix) and utilization model system was developed for integration into the activity-based travel demand model that was being developed for the greater Phoenix metropolitan area in Arizona. At the heart of the vehicle fleet mix model system is a multiple discrete continuous extreme value model capable of simulating vehicle ownership and use patterns of households. Vehicle choices are defined by a combination of vehicle body type and age category and the model system is capable of predicting vehicle com...

An optimal aircraft fleet management decision model under uncertainty

Abstract: SUMMARY Decision planning for an efficient fleet management is crucial for airlines to ensure a profit while maintaining a good level of service. Fleet management involves acquisition and leasing of aircraft to meet travelers' demand. Accordingly, the methods used in modeling travelers' demand are crucial as they could affect the robustness and accuracy of the solutions. Compared with most of the existing studies that consider deterministic demand, this study proposes a new methodology to find optimal solutions for a fleet management decision model by considering stochastic demand. The proposed methodology comes in threefold. First, a five-step modeling framework, which is incorporated with a stochastic demand index (SDI), is proposed to capture the occurrence of uncertain events that could affect the travelers' demand. Second, a probabilistic dynamic programming model is developed to optimize the fleet management model. Third, a probable phenomenon indicator is defined to capture the targeted level of service that could be achieved satisfactorily by the airlines under uncertainty. An illustrative case study is presented to evaluate the applicability of the proposed methodology. The results show that it is viable to provide optimal solutions for the aircraft fleet management model. Copyright © 2013 John Wiley & Sons, Ltd.

Image Representations on a Budget: Traffic Scene Classification in a Restricted Bandwidth Scenario

Abstract: Modern fleet management systems typically monitor the status of hundreds of vehicles by relying on GPS and other simple sensors. Such systems experience significant problems in cases of GPS glitches as well as in areas without GPS coverage. Additionally, when the tracked vehicle is stationary, they cannot discriminate between traffic jams, service stations, parking lots, serious accidents and other interesting scenarios. We propose to alleviate these problems by augmenting the GPS information with a short descriptor of an image captured by an on-board camera. The descriptor allows the server to recognize various scene types by image classification and to subsequently implement suitable business policies. Due to restricted bandwidth we focus on finding a compact image representation that would still allow reliable classification. We therefore consider several state-of-the-art descriptors under tight representation budgets of 512, 256, 128 and 64 components, and evaluate classification performance on a novel image dataset specifically crafted for fleet management applications. Experimental results indicate fair performance even with very short descriptor sizes and encourage further research in the field.

Major equipment life cycle cost analysis

Abstract: Equipment life-cycle cost analysis (LCCA) is typically used as one component of the equipment fleet management process and allows the fleet manager to make equipment repair, replacement, and retention decisions on the basis of a given piece of equipment’s economic life. The objective of this research is to develop a robust method that permits equipment fleet managers to maximize the cost effectiveness of the fleet by optimizing the overall lifecycle value of each piece in the fleet. Minneapolis Public Works Fleet Services Division (MPWFSD) equipment fleet data was utilized in developing the proposed stochastic LCCA model for public agency’s fleet. The research compared output using actual data from current software to the output from the new stochastic LCCA method using equipment deterioration curves and probabilistic input variables for capital costs, fuel, and other operating costs to demonstrate enhanced ability to optimize fleet management decisions. The interest rate was found to have a greater impact on economic life output than fuel prices for a dump truck. The fuel volatility did impact the life-cycle costs when applying the stochastic confidence levels. Based on Monte Carlo simulation sensitivity analysis results, the time factor and engine factor were found to be the most sensitive input variables to the LCCA model. This leads to the conclusion that when deciding to replace a piece of equipment, engine efficiency should be a high priority due to the costs associated with the time factor, engine factor, and its subsequent annual usage.

Designing vehicle tracking system - an open source approach

Abstract: Vehicle tracking system has been extensively used for fleet management, asset tracking, surveillance, stolen vehicle recovery and many more. Advances in modern technologies like ubiquitous computing, Internet of Things (IoT) coupled with the availability of the economical hardware building blocks is creating a new wave of vehicular tracking systems. In this work we present VERTIGUO (VEhiculaR TrackInG Using Opensource approach), a GPS,GSM and GPRS technology based vehicular tracking system, that is accurate, robust, flexible, economical and feature rich. Unlike the traditional COTS (Commercial Of The Shelf) vehicular tracking system, that are closed and confined to smartphone and PC’s, our vehicular tracking system infrastructure is open sourced and is available for the research fraternity to test, experiment and add more features. The hardware (HW) is developed by leveraging our expertize on open source HW platform. The software (SW) infrastructure can track the vehicles through a web interface on smartphones and PC or through an SMS on normal GSM based feature phones. This application will work for all mobile phones provided they are on a GSM network. In this paper we describe our system architecture, prototype and results obtained in our field trials.