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Deep Learning for Prediction of Bus Arrival Time in Public Transportation
Published in Turan Paksoy, Çiğdem Koçhan, Sadia Samar Ali, Logistics 4.0, 2020
Faruk Serin, Suleyman Mete, Muhammet Gul, Erkan Celik
Public transportation network mainly consists of route, stop, and bus. A line between two sequential stops on a route is defined as a segment. A bus travel time on a segment is calculated using automatic vehicle location data as in (8) where tvb is bus, v, arriving time at beginning-station, b, of segment s; tve is bus, v, arriving time at end-station, e, of segment s. Travel time of all buses on segment, s, are arranged sequentially as time series as in (9). Finally, series are rearranged according to time window as in Table 1 (time window =3).
Global Navigation Satellite Systems (GNSS)
Published in Leonid Nadolinets, Eugene Levin, Daulet Akhmedov, Surveying Instruments and Technology, 2017
Leonid Nadolinets, Eugene Levin, Daulet Akhmedov
The established fields for GNSS usage are surveying, shipping, and aviation. However, satellite navigation is currently enjoying a surge in demand for location-based services (LBS) and systems for the automobile industry. Applications such as automatic vehicle location (AVL) and the management of vehicle fleets also appear to be on the rise. In addition, GNSS is increasingly being utilized in communications technology. For example, the precise GNSS time signal is used to synchronize telecommunications networks around the world. Since 2001, the U.S. Federal Communications Commission (FCC) has required that when Americans call 911 in an emergency that their position be automatically determined to within approximately 125 m. This law, known as E-911 (Enhanced 911), necessitates that mobile telephones be upgraded with this new technology.
The Importance of Public Transportation
Published in Aaron Steinfeld, Jordana L. Maisel, Edward Steinfeld, Accessible Public Transportation, 2017
Aaron Steinfeld, Edward Steinfeld
Many transit agencies are now actively pursuing novel methods for improving situational awareness through adoption of new information technology. For example, increasing numbers of transit agencies are acquiring automatic vehicle location (AVL) systems to provide better service. These systems allow the providers to track vehicles in real time and make adjustments rapidly, but, they also establish a potential to deliver real-time arrival estimates. Providing real-time estimates alone can increase ridership on some routes as much as 40 percent (Casey, 2003) and close to two percent system-wide (Brakewood, Mcfarlane, & Watkins, 2015). Such data is particularly important to people with disabilities. This group is generally more vulnerable to exposure in severe climates, often have medical needs that require timely attention, and have heightened concern about security risks while waiting at stops. The value of situational awareness is demonstrated by research findings for riders without disabilities. For example, use of a real-time arrival information system improves perceptions of security (Ferris, Watkins, & Borning, 2010a). Unfortunately, real-time arrival systems are expensive and often beyond the reach of cash-strapped agencies. Another important information need for riders is knowledge about system component maintenance problems. Few agencies provide riders such information and it is generally limited to elevator and escalator status. Another type of information that can benefit riders, even before they leave their origin for a stop, is information about whether the next vehicle is too full to board or find a seat. “Fullness” data is extremely rare in transit systems.
A ripple effect in prehospital stroke patient care
Published in International Journal of Production Research, 2021
Brandon W. Lee, Jiho Yoon, Seung Jun Lee
The centralised system enables EMS agencies to effectively manage the utilisation of ambulance capacity within their coverage area, which is essential for minimising the severity of a patient's condition in the prehospital stages. Some EMS agencies in developed countries use a Computer Aided Dispatch (CAD) system, coupled with Automatic Vehicle Location (AVL), which allows a dispatcher to keep track of the ambulance locations and their availability for a new patient (Lim, Mamat, and Braunl 2011; NHTSA 2020). The centralised system of our analytical models mentioned in Section 4.3 is essentially equivalent to the AVL system, since the dispatcher has knowledge about the availability of all ALS and BLS units in the coverage area when he or she decides how to assign the most appropriate ambulance unit for a patient. For the purpose of prehospital stroke patient care, our model supports the EMS agencies' direction of implementing the CAD with AVL capabilities.