Numerical Modeling of an Advanced Semi-SWATH Hull in Calm Water and Regular Head Wave

A small waterplane area twin hull (SWATH) has excellent seakeeping performance and low wave-making resistance, and it has been applied to small working craft, p...     

Reducing traffic congestion and increasing sustainability in special urban areas through one-way traffic reconfiguration

Transportation - In the contemporary sustainable urban set up, one of the critical issues adversely affecting the quality of life in urban areas and inflicting immense costs on cities is traffic...     

A Novel Optimal Routing for Navigation Systems/Services Based on Global Navigation Satellite System Quality of Service

The positioning quality of global navigation satellite system (GNSS), or GNSS quality of service (QoS), is a major factor impacting real-time navigation performance. Commonly requested routes (i.e., shortest or fastest) may include areas with poor GNSS QoS, which can subsequently degrade navigation performance. To provide alternative routes with high or acceptable GNSS QoS along a route, a novel optimal routing for navigation systems/services based on GNSS QoS by utilizing integrated GNSS (iGNSS) QoS prediction is presented in this article. New routing criteria based on GNSS QoS are maximum availability, maximum accuracy, maximum continuity, and maximum reliability. Two experiments were conducted to compare GNSS QoS-based routes against shortest routes. In one experiment, routes were simulated, and in another, generated routes based on GNSS QoS were evaluated against GPS-based trajectories as ground truths. The results show that GNSS QoS-based routes provide routes with higher QoS, more than 50%, and longer, about 50%, than shortest routes.     

Analysis and optimization of air suspension system with independent height and stiffness tuning

Suspensions play a crucial role in vehicle comfort and handling. Different types of suspensions have been proposed to address essential comfort and handling requirements of vehicles. The conventional air suspension systems use a single flexible rubber airbag to transfer the chassis load to the wheels. In this type of air suspensions, the chassis height can be controlled by further inflating the airbag; however, the suspension stiffness is not controllable, and it depends on the airbag volume and chassis load. A recent development in a new air suspension includes two air chambers (rubber airbags), allowing independent ride height and stiffness tuning. In this air suspension system, stiffness and ride height of the vehicle can be simultaneously altered for different driving conditions by controlling the air pressure in the two air chambers. This allows the vehicle’s natural frequency and height to be adjusted according to the load and road conditions. This article discusses optimization of an air suspension design with ride height and stiffness tuning. An analytical formulation is developed to yield the optimum design of the new air suspension system. Experimental results verify the mathematical modeling and show the advantages of the new air suspension system.     

A weight-based map-matching algorithm for vehicle navigation in complex urban networks

A map-matching algorithm is an integral part of every navigation system and reconciles raw and inaccurate positional data (usually from a global positioning system [GPS]) with digital road network data. Since both performance (speed) and accuracy are equally important in real-time map-matching, an accurate and efficient map-matching algorithm is presented in this article. The proposed algorithm has three steps: initialization, same-segment, and next-segment. Distance between the GPS point and road segments, difference between the heading of the GPS point and direction of road segments, and difference between the direction of consecutive GPS points and direction of road segments are used to identify the best segment among candidates near intersections. In contrast to constant weights applied in existing algorithms, the weight of each criterion in this algorithm is dynamic. The weights of criteria are calculated for each GPS point based on its: (a) positional accuracy, (b) speed, and (c) traveled distance from previous GPS point. The algorithm considers a confidence level on the assigned segment to each GPS point, which is calculated based on the density and complexity of roads around the GPS point. The evaluation results indicate 95.34% correct segment identification and 92.19% correct segment assignment. The most important feature of our algorithm is that the high correct segment identification percentage achieved in urban areas is through a simple and efficient weight-based method that does not depend on any additional data or positioning sensors other than digital road network and GPS.