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At least 19 records

A new traffic control design method for large networks with signalized intersections

The paper presents a traffic control design technique for application to large traffic networks with signalized intersections. It is shown that the design method adopts a macroscopic viewpoint to establish a new traffic modelling procedure in which vehicle platoons are subdivided into main stream queues and turning queues. Optimization of the signal splits minimizes queue lengths in the steady state condition and improves traffic flow conditions, from the viewpoint of the traveling public. Finally, an application of the design method to a traffic network with thirty-three signalized intersections is used to demonstrate the effectiveness of the proposed technique.

Leininger, G. G.↗

Behavior of queues at signalized intersections in heavy traffic

This paper derives the asymptotic behavior of traffic queues at signalized intersections when the excess of departure capacity over arrivals approaches zero from above. The most interesting result is that the distribution of queue length approaches a negative exponential, with fewer restrictive assumptions than hitherto known. The main improvement results from more precise use of a combinatorial lemma of Spitzer, giving the maximum of the partial sums of a sequence of independent identically distributed random variables, plus some specific constructive probability calculations, many of them involving the Fourier transform. New results are presented on the probability that the queue be below a fixed bound and/or on the probability that the queue be empty. Applications to on-line estimators for real-time traffic control are suggested.

Posner, E. C.↗

A decision model applied to alcohol effects on driver signal light behavior

A decision model including perceptual noise or inconsistency is developed from expected value theory to explain driver stop and go decisions at signaled intersections. The model is applied to behavior in a car simulation and instrumented vehicle. Objective and subjective changes in driver decision making were measured with changes in blood alcohol concentration (BAC). Treatment levels averaged 0.00, 0.10 and 0.14 BAC for a total of 26 male subjects. Data were taken for drivers approaching signal lights at three timing configurations. The correlation between model predictions and behavior was highly significant. In contrast to previous research, analysis indicates that increased BAC results in increased perceptual inconsistency, which is the primary cause of increased risk taking at low probability of success signal lights.

Schwartz, S. H.↗

Crossover Concept for Optical Printed Circuits

It is possible to reduce crosstalk between optical signals in intersecting optical conductors by taking two steps: Minimize amount of light diffracted into wrong conductor by selecting optimum conductor width, and place light traps in each conductor. How well techniques work depends in part on quality of optical conductors (partically surface smoothness) and on how closely optical-signal mode of propagation approaches ideal. Nature of signals also important: With digital signals, less crosstalk attenuation required than for analog signals.

Jamieson, R. S.↗

Detection and enforcement of failure-to-yield in an emergency vehicle preemption system

An intersection controlled by an intersection controller receives trigger signals from on-coming emergency vehicles responding to an emergency call. The intersection controller initiates surveillance of the intersection via cameras installed at the intersection in response to a received trigger signal. The surveillance may begin immediately upon receipt of the trigger signal from an emergency vehicle, or may wait until the intersection controller determines that the signaling emergency vehicle is in the field of view of the cameras at the intersection. Portions of the captured images are tagged by the intersection controller based on tag signals transmitted by the vehicle or based on detected traffic patterns that indicate a potential traffic violation. The captured images are downloaded to a processing facility that analyzes the images and automatically issues citations for captured traffic violations.

Bachelder, Aaron↗

Distributed traffic signal control using fuzzy logic

We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.

Chiu, Stephen↗

Cellular-based preemption system

A cellular-based preemption system that uses existing cellular infrastructure to transmit preemption related data to allow safe passage of emergency vehicles through one or more intersections. A cellular unit in an emergency vehicle is used to generate position reports that are transmitted to the one or more intersections during an emergency response. Based on this position data, the one or more intersections calculate an estimated time of arrival (ETA) of the emergency vehicle, and transmit preemption commands to traffic signals at the intersections based on the calculated ETA. Additional techniques may be used for refining the position reports, ETA calculations, and the like. Such techniques include, without limitation, statistical preemption, map-matching, dead-reckoning, augmented navigation, and/or preemption optimization techniques, all of which are described in further detail in the above-referenced patent applications.

Bachelder, Aaron D.↗

Intersection Monitor for Traffic-Light-Preemption System

The figure shows an intersection monitor that is a key subsystem of an emergency traffic-light-preemption system that could be any of the systems described in the three immediately preceding articles and in Systems Would Preempt Traffic Lights for Emergency Vehicles (NPO-30573), NASA Tech Briefs, Vol. 28, No. 10 (October 2004), page 36. This unit is so named because it is installed at an intersection, where it monitors the phases (in the sense of timing) of the traffic lights. The mode of operation of this monitor is independent of the type of traffic-light-controller hardware or software in use at the intersection. Moreover, the design of the monitor is such that (1) the monitor does not, by itself, affect the operation of the traffic- light controller and (2) in the event of a failure of the monitor, the trafficlight controller continues to function normally (albeit without preemption). The monitor is installed in series with the traffic-light controller at an intersection. The control signals of interest are monitored by use of high-impedance taps on affected control lines. These taps are fully isolated and further protected by high-voltage diodes that prevent any voltages or short circuits that arise within the monitor from affecting the controller. The signals from the taps are processed digitally and cleaned up by use of high-speed logic gates, and the resulting data are passed on to other parts of the traffic-light-preemption intersection subsystem. The data are compared continuously with data from vehicles and used to calculate timing for reliable preemption of the traffic lights. The pedestrian crossing at the intersection is also monitored, and pedestrians are warned not to cross during preemption.

Bachelder, Aaron↗

Conical-Domain Model for Estimating GPS Ionospheric Delays

The conical-domain model is a computational model, now undergoing development, for estimating ionospheric delays of Global Positioning System (GPS) signals. Relative to the standard ionospheric delay model described below, the conical-domain model offers improved accuracy. In the absence of selective availability, the ionosphere is the largest source of error for single-frequency users of GPS. Because ionospheric signal delays contribute to errors in GPS position and time measurements, satellite-based augmentation systems (SBASs) have been designed to estimate these delays and broadcast corrections. Several national and international SBASs are currently in various stages of development to enhance the integrity and accuracy of GPS measurements for airline navigation. In the Wide Area Augmentation System (WAAS) of the United States, slant ionospheric delay errors and confidence bounds are derived from estimates of vertical ionospheric delay modeled on a grid at regularly spaced intervals of latitude and longitude. The estimate of vertical delay at each ionospheric grid point (IGP) is calculated from a planar fit of neighboring slant delay measurements, projected to vertical using a standard, thin-shell model of the ionosphere. Interpolation on the WAAS grid enables estimation of the vertical delay at the ionospheric pierce point (IPP) corresponding to any arbitrary measurement of a user. (The IPP of a given user s measurement is the point where the GPS signal ray path intersects a reference ionospheric height.) The product of the interpolated value and the user s thin-shell obliquity factor provides an estimate of the user s ionospheric slant delay. Two types of error that restrict the accuracy of the thin-shell model are absent in the conical domain model: (1) error due to the implicit assumption that the electron density is independent of the azimuthal angle at the IPP and (2) error arising from the slant-to-vertical conversion. At low latitudes or at mid-latitudes under disturbed conditions, the accuracy of SBAS systems based upon the thin-shell model suffers due to the presence of complex ionospheric structure, high delay values, and large electron density gradients. Interpolation on the vertical delay grid serves as an additional source of delay error. The conical-domain model permits direct computation of the user s slant delay estimate without the intervening use of a vertical delay grid. The key is to restrict each fit of GPS measurements to a spatial domain encompassing signals from only one satellite. The conical domain model is so named because each fit involves a group of GPS receivers that all receive signals from the same GPS satellite (see figure); the receiver and satellite positions define a cone, the satellite position being the vertex. A user within a given cone evaluates the delay to the satellite directly, using (1) the IPP coordinates of the line of sight to the satellite and (2) broadcast fit parameters associated with the cone. The conical-domain model partly resembles the thin-shell model in that both models reduce an inherently four-dimensional problem to two dimensions. However, unlike the thin-shell model, the conical domain model does not involve any potentially erroneous simplifying assumptions about the structure of the ionosphere. In the conical domain model, the initially four-dimensional problem becomes truly two-dimensional in the sense that once a satellite location has been specified, any signal path emanating from a satellite can be identified by only two coordinates; for example, the IPP coordinates. As a consequence, a user s slant-delay estimate converges to the correct value in the limit that the receivers converge to the user s location (or, equivalently, in the limit that the measurement IPPs converge to the user s IPP).

Sparks, Lawrence↗

A computerized traffic control algorithm to determine optimal traffic signal settings

An algorithm was developed to optimally control the traffic signals at each intersection using a discrete time traffic model applicable to heavy or peak traffic. Off line optimization procedures were applied to compute the cycle splits required to minimize the lengths of the vehicle queues and delay at each intersection. The method was applied to an extensive traffic network in Toledo, Ohio. Results obtained with the derived optimal settings are compared with the control settings presently in use.

Seldner, K.↗

Digital signal processing and control and estimation theory -- Points of tangency, area of intersection, and parallel directions

A number of current research directions in the fields of digital signal processing and modern control and estimation theory were studied. Topics such as stability theory, linear prediction and parameter identification, system analysis and implementation, two-dimensional filtering, decentralized control and estimation, image processing, and nonlinear system theory were examined in order to uncover some of the basic similarities and differences in the goals, techniques, and philosophy of the two disciplines. An extensive bibliography is included.

Willsky, A. S.↗

Vehicle Transponder for Preemption of Traffic Lights

The purpose of this article is to describe, in more detail, the transponder installed in each vehicle that participates in the emergency traffic-light-preemption system described in the immediately preceding article. The transponder (see figure) is a fully autonomous data--collection, data-processing, information-display, and communication subsystem that performs robustly in preemption of traffic lights and monitoring of the statuses of street intersections. This transponder monitors the condition of the emergency vehicle in which it is installed and determines when the vehicle has been placed in an emergency-response condition with its siren and/or warning lights activated. Upon detection of such a condition, the transponder collects real-time velocity and acceleration data from the onboard diagnostic (OBD) computer of the vehicle. For this purpose, the transponder contains an OBD interface circuit, including a microprocessor that determines the manufacturer and model of the vehicle and then sends the appropriate commands to the OBD computer requesting the speed and acceleration data. At the same time, data from an onboard navigation system are collected to determine the location and the heading of the vehicle. Then acceleration, speed, position, and heading data are processed and combined with a vehicle-identification number and the resulting set of data is transmitted to monitoring and control units located at all intersections within communication range. When the unit at an intersection determines that this vehicle is approaching and has priority to preempt the intersection, it transmits a signal declaring the priority and the preemption to all participating vehicles (including this one) in the vicinity. If the unit at the intersection has determined that other participating vehicles are also approaching the intersection, then this unit also transmits, to the vehicle that has priority, a message that the other vehicles are approaching the same intersection. The texts of these messages, plus graphical symbols that show the directions and numbers of the approaching vehicles, are presented on the display panel of a computer that is part of the transponder.

Foster, Conrad↗

Geometric rectification and geocoding of JPL's AIRSAR data over hilly terrain

A post-processing system was developed to georeference and geocode airborne synthetic aperture radar (SAR) data collected by the JPL-Airborne SAR (AIRSAR) System over hilly terrain during the 1990 MAESTRO1 campaign. Georectification relates image coordinates and object coordinates while the geocoding involves resampling. The georectification method uses a hybrid method based upon a number of navigational parameters and minimal two ground-control points which are referenced in both image and map space. The calculation of the intersection of the SAR signal wavefront and a digital elevation model allowed, within certain error bounds, acquisition of the object-to-image and image-to-object relationships.

Degroof, H.↗

One-third selection scheme for addressing a ferroelectric matrix arrangement

An improved scheme for selectively addressing a matrix arrangement comprised of ferroelectrics having x and y orthogonally disposed intersecting lines. A one-third selection scheme is utilized that includes normalized selection signals having amplitudes: V.sub.x =0; V.sub.x =2/3; V.sub.y =1/3; and V.sub.y =1, which signals can be applied to the intersection of an x and y-line. The instant selection scheme minimizes both hysteresis creep and the cross-coupling voltage between x and y-lines to prevent undesirable hysteresis switching of the ferroelectric matrix arrangement.

Tannas, Jr., Lawrence E.↗

Nanotunneling Junction-based Hyperspectal Polarimetric Photodetector and Detection Method

A photodetector, detector array, and method of operation thereof in which nanojunctions are formed by crossing layers of nanowires. The crossing nanowires are separated by a few nm thick electrical barrier layer which allows tunneling. Each nanojunction is coupled to a slot antenna for efficient and frequency-selective coupling to photo signals. The nanojunctions formed at the intersection of the crossing wires defines a vertical tunneling diode that rectifies the AC signal from a coupled antenna and generates a DC signal suitable for reforming a video image. The nanojunction sensor allows multi/hyper spectral imaging of radiation within a spectral band ranging from terahertz to visible light, and including infrared (IR) radiation. This new detection approach also offers unprecedented speed, sensitivity and fidelity at room temperature.

Son, Kyung-ah↗

Strobe Traffic Lights Warn of Approaching Emergency Vehicles

Strobe-enhanced traffic signals have been developed to aid in the preemption of road intersections for emergency vehicles. The strobe-enhanced traffic signals can be incorporated into both new and pre-existing traffic-control systems in which the traffic-signal heads are of a relatively new type based on arrays of light-emitting diodes (LEDs). The strobe-enhanced traffic signals offer a less expensive, less complex alternative to a recently developed system of LED-based warning signs placed next to traffic signals. Because of its visual complexity, the combination of traffic signals and warning signs is potentially confusing to motorists. The strobe-enhanced traffic signals present less visual clutter. In a given traffic-signal head, the strobe-enhanced traffic signal is embedded in the red LED array of the stop signal. Two strobe LED strips one horizontal and one vertical are made capable of operating separately from the rest of the red LED matrix. When no emergency vehicle is approaching, the red LED array functions as a normal stop signal: all the red LEDs are turned on and off together. When the intersection is to be preempted for an approaching emergency vehicle, only the LEDs in one of the strobe strips are lit, and are turned on in a sequence that indicates the direction of approach. For example (see figure), if an emergency vehicle approaches from the right, the strobe LEDs are lit in a sequence moving from right to left. Important to the success of strobe-enhanced traffic signals is conformance to city ordinances and close relation to pre-existing traffic standards. For instance, one key restriction is that new icons must not include arrows, so that motorists will not confuse new icons with conventional arrows that indicate allowed directions of movement. It is also critical that new displays like strobe-enhanced traffic signals be similar to displays used in traffic-control systems in large cities. For example, Charleston, South Carolina uses horizontal strobes on red traffic lights to alert motorists and thereby help motorists not to miss red lights. The one significant potential disadvantage of strobe-enhanced traffic lights is initial unfamiliarity on the part of motorists.

Bachelder, Aaron↗

Volume Raycasting of GNSS Signals through Ground Structure Lidar for UAV Navigational Guidance and Safety Estimation

Autonomous UAS navigation at low altitudes is often hindered by degradation of GNSS position estimates. The line of sight from the UAS to orbital satellites may be intersected by foliage (which attenuates the received signal) and by buildings (which block the signal). Since the geometric ray from the presumed UAS position to each GNSS satellite orbital location is predictable, if a 3D survey of ground structures is available, the degree of blockage of each GNSS signal can be estimated. In this study we show raycasting from a UAS location to GNSS satellites at two flight locations: one with overlying structures and bordered by tall trees, and another in an arboreal canyon bordered by tall trees. We confirm the intermittent blockage of satellites in the first location sufficient to lose GNSS position fix. We demonstrate low-altitude GNSS fidelity forecasting via the raycasting method at the second location that can be used to plan navigable flight locations and altitudes. Finally, we match the GNSS signal strength with raycast-derived foliage obstruction depth at hundreds of observation times from 55 recordings collected over 14 days from November 2018 to February 2021 at the second location. This matching confirms that signal attenuation varies with the depth of foliage blockage along a saturating exponential curve, as found in prior continuous-wave RF studies. The exponent and saturation value are species dependent and therefore vary from site to site; once determined empirically, they can be used to characterize foliage along a particular flight path, and refine GNSS fidelity forecasts of flights along that path. The techniques described in this study show the feasibility of a survey method to construct low-altitude navigation safety maps and forecasts.

Navigation↗

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗