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A Dense Reference Network for Mass-Market Centimeter-Accurate Positioning
The quality of atmospheric corrections provided
by a dense reference network for centimeter-accurate carrierphase
differential GNSS (CDGNSS) positioning is investigated.
A dense reference network (less than 20 km inter-station distance)
offers significant benefits for mass-market users, enabling lowcost
(including single-frequency) CDGNSS positioning with rapid
integer ambiguity resolution. Precise positioning on a massmarket
platform would significantly influence the world economy,
ushering in a host of consumer-focused applications such as
globally-registered augmented and virtual reality and improved
all-weather safety and efficiency for intelligent transportation
systems, applications which have so far been hampered by the
several-meter-level errors in standard GNSS positioning. This
contribution examines CDGNSS integer ambiguity resolution
performance in terms of network correction uncertainty, and
network correction uncertainty, in turn, in terms of network
density. It considers the total error in network corrections: a
sum of ionospheric, tropospheric, and reference station multipath
components. The paper’s primary goal is to identify the network
density beyond which mass-market users would see no further
significant improvement in ambiguity resolution performance. It
finishes by describing development and deployment of a low-cost
dense reference network in Austin, Texas.Aerospace Engineering and Engineering Mechanic
17-11 Evaluation of Transit Priority Treatments in Tennessee
Many big cities are progressively implementing transit friendly corridors especially in urban areas where traffic may be increasing at an alarming rate. Over the years, Transit Signal Priority (TSP) has proven to be very effective in creating transit friendly corridors with its ability to improve transit vehicle travel time, serviceability and reliability. TSP as part of Transit Oriented Development (TOD) is associated with great benefits to community liveability including less environmental impacts, reduced traffic congestions, fewer vehicular accidents and shorter travel times among others.This research have therefore analysed the impact of TSP on bus travel times, late bus recovery at bus stop level, delay (on mainline and side street) and Level of Service (LOS) at intersection level on selected corridors and intersections in Nashville Tennessee; to solve the problem of transit vehicle delay as a result of high traffic congestion in Nashville metropolitan areas. This study also developed a flow-delay model to predict delay per vehicle for a lane group under interrupted flow conditions and compared some measure of effectiveness (MOE) before and after TSP. Unconditional green extension and red truncation active priority strategies were developed via Vehicle Actuated Programming (VAP) language which was tied to VISSIM signal controller to execute priority for transit vehicles approaching the traffic signal at 75m away from the stop line. The findings from this study indicated that TSP will recover bus lateness at bus stops 25.21% to 43.1% on the average, improve bus travel time by 5.1% to 10%, increase side street delay by 15.9%, and favour other vehicles using the priority approach by 5.8% and 11.6% in travel time and delay reduction respectively. Findings also indicated that TSP may not affect LOS under low to medium traffic condition but LOS may increase under high traffic condition
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