Table 2 - uploaded by Abdullah Kurkcu
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![Figure 2-Smartphone Brand Market Shares [31]](profile/Abdullah-Kurkcu/publication/320532133/figure/fig2/AS:551555549982720@1508512498996/Smartphone-Brand-Market-Shares-31_Q320.jpg)
This paper developed a methodology to relate passenger data collected by Wi-Fi and Bluetooth sensors to scheduled bus departures for the purpose of studying passenger arrival behavior. One major advantage of using these sensors is their simplicity and cost. For stations at which AFC systems are not available, wireless sensors can easily be deployed...
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This paper represents the utilization of Field Pro-grammable Gate Arrays (FPGA) chip for Bluetooth controlled land vehicle. Bluetooth controlled land vehicle is developed utilizing VHDL and acknowledged in Basys-3 FPGA using Xilinx Web-Pack Vivado 18.1. The navigation and control of the vehicle is executed remotely through an Android cell phone whi...
Precise indoor positioning systems (IPSs) are key to perform a set of tasks more efficiently during aircraft production, operation and maintenance. For instance, IPSs can overcome the tedious task of configuring (wireless) sensor nodes in an aircraft cabin. Although various solutions based on technologies of established consumer goods, e.g., Blueto...
This research proposes a novel scalable Discrete Cosine transform (DCT) processor. It is based on a shared-resource enhanced Coordinate Rotation Digital Computer (CORDIC) unit, in a modified Loeffler architecture. All micro-rotation operations have moved to the last stages, and are implemented as one unified block in an overlapped form to reduce th...
Citations
... It is built using ultrasonic sensors connected to a Raspberry Pi (RPi) to measure the distance between the bicycle and lateral obstacles, especially moving vehicles. RPi is a single board computer using a Linux based operating system, and it is widely used in research projects in the literature (Miha [13], Dozza et al. [14], Ambrož [13], Kurkcu and Ozbay [15], and Kurkcu, Ozbay [16]). Fig. 1 shows how the bicycle is usually positioned on the road and a representation of ultrasonic sensor measurements. ...
The number of bicycle riders in New York City (NYC) has been increasing steadily in the past few years. These numbers include private and shared bicycles. The NYC bicycle network has been expanded to accommodate the needs of the increasing number of riders. Although the new infrastructure has reduced the number of cyclists killed or seriously injured (KSI) in some areas, in other areas similar improvements were not observed. A data-driven approach to study the possible effects of this type of infrastructure inconsistency on the variation of the number of bicycle crashes from one region to another in the city is the primary motivation of this paper. A highly portable and inexpensive sensing device for measuring the distance between a bicycle and lateral objects is designed and developed from scratch. The developed mobile sensing device can also map bicycle trajectories to highlight critical segments where the safe distance from passing vehicles is not respected. This mobile device is powered by a portable power source and it is comprised of two ultrasonic sensors, a Global Positioning System (GPS) receiver, and a real-time clock (RTC). The sensor is secured inside a custom design 3D printed case. The case can be easily attached to any bicycle including shared bikes for testing. The final prototype is entirely functional and used to collect sample data to demonstrate its effectiveness to address safety-related problems mentioned above. Finally, a dashboard is created to display collected key information. This key information can be used by researches and agencies for a better understanding of the factors contributing to the safety of bicycle routes.
... This portable and multi-functional sensing device which can collect bicycle trajectory data and lateral distances between the bicycle and objects around it is built using ultrasonic sensors connected to a Raspberry Pi (RPi) to measure the distance between the bicycle and lateral obstacles, especially moving vehicles. RPi is a single board computer using a Linux based operating system, and it is widely used in research projects in the literature (Miha [12], Dozza et al. [13], Ambrož [12], Kurkcu and Ozbay [14], and Kurkcu, Ozbay [15]). Fig. 1 shows how the bicycle is usually positioned on the road and a representation of ultrasonic sensor measurements. ...
The number of bicycle riders in New York City has been increasing steadily in the past few years. These numbers include private and shared bicycles. NYC bicycle network has been expanded to accommodate this new volume. Although this new infrastructure
has reduced the number of cyclists killed or seriously injured (KSI) in some areas, in other areas similar improvements were not
observed. This inconsistency of how the number of bicycle crashes varies from one region to another in the city is the primary
motivation of this paper. A highly portable and inexpensive sensing device for measuring the distance between a bicycle and lateral
objects is designed from scratch and developed. The developed mobile sensing device can also map bicycle trajectories to highlight
critical segments where the safe distance from passing vehicles is not respected. This device which is powered by a portable power
source is comprised of two ultrasonic sensors namely, a Global Positioning System (GPS) receiver, and a real-time clock (RTC).
The sensor is secured inside a custom design 3D printed case. The case can be easily attached to any bicycle including shared Citi
Bike bicycles for testing. The final prototype is entirely functional and used to collect sample data to demonstrate its effectiveness
to address safety-related problems mentioned above. Finally, a dashboard is created to display collected key information. This key
information can be used by researches and agencies for a better understanding of the factors contributing to the safety of bicycle
routes.
The paper develops a data-driven approach for the inference of passenger itineraries in urban heavy rail systems, the Passenger Itinerary Inference Model (PIIM). In light of increasing demand and crowding, the model can be used to assess the impact of near capacity operations on customers, evaluate system performance, and understand passenger behavior when choosing alternative routes. PIIM uses data from Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) systems and is applicable to general urban rail networks with entry and exit fare transactions. PIIM consists of three main modules: the left behind model, the route fractions inference model, and the inference model. The left behind model estimates the probability of any passenger being left behind by station and time interval. The route fractions inference model estimates the route choice fractions given the left behind probabilities. The inference model maps each pair of fare transaction records (entry and exit) to a set of feasible itineraries and infers the train(s) boarded by passengers based on the left behind probabilities and the route choice fractions. Synthetic data using AFC and AVL data from a major, congested urban rail system were generated to validate the model. The results show that the model accurately predicts the itineraries used by passengers, the left behind probabilities, route choice fractions, train loads, and other performance metrics of interest. PIIM is also used with actual data to illustrate its applicability.
