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The Current Status of Ground Medical Transport
Published in John W. Overton, Eileen Frazer, Safety and Quality in Medical Transport Systems, 2019
The reality remains that the vast majority of ground vehicle medical transport is not for life-threatening conditions, some 97 percent of transports are routine non-life-threatening transports. Also, emergency-response driving has a high risk of crashes that cause injury and fatality, with the majority of those fatalities and injuries affecting the general public, not the EMS personnel or patients, unlike air transport, as described above (De Graeve et al. 2003). The recently developed American National Standards Institute/American Society of Safety Engineers Z15.1 Fleet Safety Standard (ANSI 2006 now updated and is 2012 version) is currently the only nationally approved formal safety standard in the U.S.A. that is now applicable to the safety management of ground EMS vehicle fleets. Specific ambulance vehicle standards are a developing issue, lagging behind other passenger vehicle safety standards (Levick 2008b). Fleet management safety issues such as driver selection, performance-monitoring, hours of service and impaired driver identification and management are key to optimizing fleet performance safety. It is likely that the implementation of this ANSI Z.15 standard will hopefully provide more emphasis on EMS vehicle and fleet safety generally, enhance the data collected regarding EMS vehicle safety, and assist in bringing EMS vehicle safety more in line with state-of-the-art fleet management safety practices (as is outlined in Chapter 13).
The Internet of Things Applications
Published in Ravi Ramakrishnan, Loveleen Gaur, Internet of Things, 2019
Ravi Ramakrishnan, Loveleen Gaur
Currently, the biggest application of the IoT is in logistics fleet tracking. Vehicle fleet tracking systems use GPS technology to track the locations of the vehicles in real time. The vehicle locations and routers data can be aggregated and analyzed for detecting hurdles such as traffic congestions on routes, assignments and generation of alternative routes, and supply chain optimization. Earlier research has proposed a system that can analyze messages sent from the vehicles to identify unexpected incidents and discrepancies between actual and planned data, so that remedial actions can be taken (Moeinfar et al., 2012). An extension to this is shipment monitoring solutions for transportation systems that allow the monitoring of the conditions inside containers; for example, containers carrying fresh food produce can be monitored to prevent spoilage of food. The IoT-based shipment monitoring systems use sensors such as temperature, pressure, and humidity, for instance, to monitor the conditions inside the containers and send the data to the cloud, where it can be analyzed to detect food spoilage (Bahga & Madisetti, 2014). Previous research has proposed a cloud-based framework for real-time fresh food supply tracking and monitoring. Another earlier research deals with Container Integrity and Condition Monitoring using a Vibration Sensor Tag (Bukkapatnam et al., 2012) where a system was proposed that can monitor the vibration patterns of a container and its contents to reveal information related to its operating environment and integrity during transport, handling, and storage.
Other technology aspects
Published in Hanky Sjafrie, Introduction to Self-Driving Vehicle Technology, 2019
At the end of the chapter, we discussed how back-end systems can be used to assist SDVs. Live map updates can be used both to ensure that SDVs are aware of any changes to road layouts (be they temporary or permanent), and to provide dynamic high-definition maps with centimeter accuracy for highly accurate localization. Fleet management systems allow operators of large vehicle fleets to perform tasks like dynamic scheduling, vehicle tracking, and can even be used to monitor the mechanical health of the fleet. Finally, SOTA allows manufacturers and operators to update a vehicle's software remotely. For SDVs, running the latest software could be critical, especially given the pace of software development, and the likely improvements in performance that might bring.
Work, labour and mobility: opening up a dialogue between mobilities and political economy through mobile work
Published in Mobilities, 2023
Then there is an array of devices which achieve control-at-a-distance either by directly limiting the speed of a body and/or by inscription, recording the speed of movement through a trace. There are devices which govern the maximum speed which any unit can achieve. These place physical limits on engines, for example by setting a maximum speed of a type of train, coach or bus or ship, or the ‘governing’ of trucks in distribution fleets, typically to 90kph (56 mph). Governing speed (literally limiting the capacity of an engine) is how operators seek to achieve maximum efficiencies from units in an industry where fuel is one of the major operating costs. Then there are the devices which relate to compliance with regulatory working hours. Chief of these is the tachograph, which records truck drivers’ driving and rest time through the working day and week. The original tachograph is a classic example of what Bruno Latour calls an inscription device – it’s a piece of paper into which a trace is inscribed of the movement, and speed, of a vehicle in any given day. Contemporary tachographs are digital, but their purpose is the same; to regulate drivers and monitor their working time. More recently, the advent of tracking technologies in real time (GPS) has allowed operators to know exactly where any individual unit in a given fleet is at any one point in time. This is the means to keeping track of mobile assets in geographical space.
The safety ladder: developing an evidence-based safety management strategy for small road transport companies
Published in Transport Reviews, 2018
Tor-Olav Nævestad, Beate Elvebakk, Ross Owen Phillips
The seven studies of fleet management technology and organisational follow-up of and feedback on driving style indicate positive outcomes: safer driving and/or fewer accidents. These interventions seem to rely on a combination of driver self-monitoring by means of technology and management monitoring and support. The main methodological challenges of these eight studies are that the drivers’ driving style may be influenced by the fact that their behaviours are recorded in the study period, and that some of the studies lack control groups or pre-periods with the equipment fitted to evaluate the importance of this mechanism. Hickman and Hanowski (2011), Wouters & Bos (2000) and Toledo, Musicant, and Lotant (2008) provide examples of relatively robust designs.
Random forest models for motorcycle accident prediction using naturalistic driving based big data
Published in International Journal of Injury Control and Safety Promotion, 2023
Fatma Outay, Muhammad Adnan, Uneb Gazder, Syed Fazal Abbas Baqueri, Hammad Hussain Awan
One of the key distinguishing factors of this study is the use of big trajectory data & its processing to define mobility, acceleration-based events & aggressive overtaking features for utilizing them in the prediction of a motorcycle accident in the near future. Because of the use of big trajectory data, employing a machine learning algorithm in the form of random forest is also natural as the capability of RF in handling different types of variables & modelling complex nonlinear relationships makes it a promising method. Additionally, the problem of accident prediction formulated in this study also has significant importance for motorcycle insurance companies & smart startups that manage the fleet of motorcycles. The model developed in this study can be used to devise a range of insurance policies (pricing & possible benefits) that have their premise on dynamic changes in motorcycle driving behaviour to attract a larger customer base. Fleet managers based on the developed model results can identify risk-seeking drivers & developed programs/intervention to nurture their driving behaviour in the right direction. In the short run, the future work will involve further processing of the trajectory data to obtain more mobility features that are relevant to space & time. Individual mobility network (IMN) presented in (Guidotti & Nanni, 2020) and features derived from these networks can be the next possible candidate. In the long run, an automation pipeline, where such trajectory data is collected in real-time, model improvement & monitoring risky driving behaviour can provide an alert mechanism to timely inform such risk-seeking motorcyclists of the danger they may cause to themselves & others.