Travel Time Measurements Research Articles

Deep water acoustic range estimation based on an ocean general circulation model: Application to PhilSea10 data.

This study identifies general characteristics of methods to estimate the absolute range between an acoustic transmitter and a receiver in the deep ocean. The data are from three days of the PhilSea10 experiment with a single fixed transmitter depth (∼998 m) and 150 receiver depths (∼210-5388 m) of known location, and a great-circle transmitter-receiver distance of ∼510 km. The proposed ranging methods compare observed acoustic records with synthetic records computed through the HYCOM (hybrid coordinate ocean model) model. More than 8900 transmissions over 3 days characterize the statistical variation of range errors. Reliable ranging methods de-emphasize the parts of the data records least likely to be reproduced by the synthetics, which include arrival amplitudes, the later parts of the acoustic records composed of nearly horizontally launched rays (i.e., the finale), and waves that sample a narrow span of ocean depths. The ranging methods proposed normalize amplitudes, measure travel times, or reject parts of the waveforms beyond a critical time. All deliver reliable range estimates based on the time and path-averaged HYCOM model, although the final method performs best. The principles behind these methods are transportable and expected to provide reliable range estimates in different deep water settings.

Tutorial — time conversion of depth migrated data: part II, TTI preSDM

Contemporary depth imaging projects often require final pre-Stack Depth-Migrations (preSDM) to be converted to time for comparison to vintage pre-Stack Time-Migrations (preSTM), or to facilitate conversion to ‘geological’ depth through calibration to well and check-shot data. Here, I consider the situation of having performed an anisotropic Tilted Transverse Isotropy (TTI) preSDM and wanting to convert it to time via vertical stretch in order to compare it, say, to an anisotropic preSTM. Such a comparison is inherently invalid, as time-migration will explicitly treat any anisotropy as if it were Vertical Transverse Isotropy (VTI), and, in addition, the lateral positioning error inherent in preSTM will render such comparisons questionable on steeply dipping structures. I show here that the most appropriate type of ‘velocity’ to use for conversion to time of TTI preSDM reflection events should be the vertical component of the phase velocity. Conversely, if we are considering point-to-point measurements, such as the direct arrival travel time, a down-hole or check-shot measurement, then the group velocity should be used, as it is with this speed that energy travels. In addition, subsequent depth conversion of any time product for interpretational purposes would best be accomplished using a velocity calibrated to well check-shots.

Synthetic baseline navigation using phase-coherent acoustic communication signals.

The development of a synthetic baseline navigation technique that self-localizes an autonomous underwater vehicle (AUV) using intermittent acoustic communications signals received by a single transducer is described, along with field results from in-ocean tests. The method uses the phase measurement at the output of a second-order phase-locked loop to create fine-scale pseudorange estimates in addition to, or in the absence of, a one-way travel time measurement based on the arrival time of the acoustic data packet. These range measurements are incorporated by an adaptive particle filter. This technique allows the vehicle navigation system to take advantage of multiple phase-derived range measurements made over the duration of a communication packet. These measurements, when incorporated with an appropriate filter and vehicle kinematic model, improve vehicle navigation at no additional cost in navigation-specific acoustic transmissions. This approach was demonstrated and evaluated with data collected at-sea using a REMUS 100 AUV (Hydroid, Inc., Pocasset, MA).

Link-level travel time measures-based level of service thresholds by the posted speed limit

Macroscopic parameters, such as density, volume-to-capacity ratio, and speed, are traditionally used to assess the level of service (LOS) of freeway/expressway or arterial road links. Capturing real-world data pertaining to these macroscopic parameters for all the links in the transportation network is expensive, tedious, and challenging. Additionally, density and volume-to-capacity ratio are not easily perceived by the commuters. Recent trends indicate that large-scale travel time data can be collected anonymously and without the use of field technicians, through global positioning systems (GPS), sensors, smartphones with apps, and other technological devices. The focus of this research is to develop link-level travel time measures-based LOS thresholds for urban areas, using large-scale travel time data from a private data source. The posted speed limit for each selected link in the city of Charlotte, North Carolina was integrated with the raw travel time data for the corresponding link. Travel time measures such as the average travel time, the 95th percentile travel time (planning time, PT), the planning time index (PTI), and the buffer time index (BTI) were computed and categorized by the posted speed limit of each selected link. The relationships between estimated speeds from the regional network model and computed travel time measures were then examined to develop LOS thresholds at link-level by the posted speed limit.

Modelling travel time uncertainty in urban networks based on floating taxi data

The prediction of the uncertainty of route travel time predictions for all possible routes in an urban road network is of importance for example for logistics. Such predictions need to take the essential features of the data set as well as the underlying traffic dynamics into account.In this paper a large floating taxi data set is used in order to derive predictions of route travel time uncertainty based on link travel time uncertainty predictions. Prediction errors, that is actual travel times minus predicted travel times, are differentiated from model errors, that is measured travel times minus predicted travel times. These two errors are related, but not identical, as model errors contain measurement noise while the prediction errors do not. Detailed models for the variance of the link travel time prediction errors as well as the correlation between the model errors for different links are derived. The models are validated in depth using two different validation data sets.Estimates for the variance of prediction errors are obtained. The standardized model error distributions show a remarkable stability, such that modelling the variance appears to be sufficient for quantifying the uncertainty of the model errors.Furthermore we show that the model errors for adjacent links are highly correlated but correlations fade with increasing distance. Additionally usage of the road network plays a role with high correlation for links along common routes and low correlations for links along seldom used routes. We assume identical features for the prediction errors which is partly validated based on additional data.The paper provides a way to estimate the complete distribution of route travel time prediction errors for any given route in the street network.

Measuring travel time reliability and risk: A nonparametric approach

The reliability of travel time significantly affects individual travelers’ decision-making behaviour and hence in turn influences the overall travel demand at the macroscopic level. The travel time reliability ratio (TTRR), defined to be the ratio of the value of travel time variability to the value of travel time, is an important metric for measuring such reliability. In this paper, we first show that the TTRR is closely related to a widely used risk measure in financial economics, i.e. conditional value at risk (CVaR). Then based on the connection between the TTRR and CVaR, we develop a nonparametric approach to estimate the TTRR. In the literature, to compute the TTRR, it usually needs to assume a specific statistical distribution for the travel time. This can produce a misleading result when this assumption goes awry due to the potential complexity of travel time distributions. Based on the relationship between the TTRR and CVaR, this paper proposes a new nonparametric method, i.e. the kernel density estimation method, to overcome this problem. We show that this new nonparametric method is robust in terms that it does not depend on any assumptions about the shape of the travel time distribution. The simulation studies demonstrate that the proposed method outperforms the existing methods and substantially improves the numerical accuracy. Finally, a practical example is used to illustrate the proposed method.

Multi-class AR model: comparison with microsimulation model for traffic flow variables at network level of interest and the two-dimensional formulation

ABSTRACT This paper discusses on the choice of a recently proposed multi-class, speed-area occupancy based, one-dimensional continuum model towards the measurement of network level traffic flow variables, namely, travel time and outflow. The parameters of the model are calibrated for a hypothetical mid-block with four vehicle classes, using the micro simulation data from VISSIM software. Analysis result indicates that the model produces travel time and outflow result closer to that by the VISSIM micro simulation. To explore this macroscopic model towards accurate modelling of lacking lane disciplined traffic, i.e. the traffic with frequent lateral movements, a two-dimensional formulation of the model is proposed. It is shown that the new formulation retains the properties of its original version as well as the desirable properties of any traffic flow model.

Recursive least squares calibration and tomographic imaging with an ultrasonic transducer array

Travel time measurements from an acoustic array interrogating a fluid within the cross-section of a pipe include systematic errors: from mispositioning of array elements, group delays from the transceiver circuits at each element, and acoustic propagation effects between transmitter/receiver pairs. An enhanced calibration model is described which includes the influence of these error sources, and a recursive least squares solution is applied to find calibration travel-time corrections that minimize systematic errors. This enhanced calibration model is applied in-situ to an acoustic transducer array, resulting in increased accuracy and precision of acoustic travel-time measurements. Tomographic images of fluid velocities are derived from these travel-time measurements. The fluid velocities are mapped to known velocities of individual fluid phases, resulting in tomographic images of multiphase flows.Travel time measurements from an acoustic array interrogating a fluid within the cross-section of a pipe include systematic errors: from mispositioning of array elements, group delays from the transceiver circuits at each element, and acoustic propagation effects between transmitter/receiver pairs. An enhanced calibration model is described which includes the influence of these error sources, and a recursive least squares solution is applied to find calibration travel-time corrections that minimize systematic errors. This enhanced calibration model is applied in-situ to an acoustic transducer array, resulting in increased accuracy and precision of acoustic travel-time measurements. Tomographic images of fluid velocities are derived from these travel-time measurements. The fluid velocities are mapped to known velocities of individual fluid phases, resulting in tomographic images of multiphase flows.

Unsupervised machine learning for acoustic tomography and event detection

We present acoustic tomography and event detection approaches based on unsupervised machine learning (ML). ML describes a set of techniques for automatically detecting and utilizing patterns in data. These techniques can roughly be categorized as either supervised or unsupervised learning. In supervised learning, an ML system (e.g., neural network (NN)) is trained to produce a desired output based on labeled training examples. In unsupervised learning, no labels are given and the task is to discover interesting or useful structure within the data. In acoustic and geophysical signal processing, often large training datasets are available, but have few labeled examples. We discuss how unsupervised ML can leverage training data without explicit labels to improve acoustic models. We first give an overview of the relevant ML theory. Next, we describe an approach to array tomography, called locally-sparse travel time tomography (LST). LST regularizes the inversion of travel time measurements for geophysical structure using dictionary learning (unsupervised). Dictionary learning is here used to obtain a dictionary of small-scale geophysical features that best represent the measurements. Finally, we discuss an approach to automated event detection in acoustic time series based on autoencoder NNs, which learn salient features from NN inputs with unsupervised learning.

Obtaining free USArray data by multi-dimensional seismic reconstruction

USArray, a pioneering project for the dense acquisition of earthquake data, provides a semi-uniform sampling of the seismic wavefield beneath its footprint and greatly advances the understanding of the structure and dynamics of Earth. Despite continuing efforts in improving the acquisition design, network irregularity still causes spatial sampling alias and incomplete, noisy data, which imposes major challenges in array-based data analysis and seismic imaging. Here we employ an iterative rank-reduction method to simultaneously reconstruct the missing traces and suppress noise, i.e., obtaining free USArray recordings as well as enhancing the existing data. This method exploits the spatial coherency of three-dimensional data and recovers the missing elements via the principal components of the incomplete data. We examine its merits using simulated and real teleseismic earthquake recordings. The reconstructed P wavefield enhances the spatial coherency and accuracy of tomographic travel time measurements, which demonstrates great potential to benefit seismic investigations based on array techniques.

Measurement of travel time reliability of road transportation using GPS data: A freight fluidity approach

Travel time reliability is one of the most important assets when it comes to designing and assessing freight fluidity of transportation systems. Guaranteeing reliable delivery times is, according to different international studies, a strategic competitive advantage. Nonetheless, emerging countries such as Mexico, do not currently count with specific indicators to measure travel time reliability from a freight fluidity approach. As such, there is no clear measurement of the impact and propagation of the variability of transit times throughout the supply chains. Since road transportation is the main mean of transportation in emerging countries, the aim of this paper is to present the analysis carried out to measure the travel time reliability thanks to different methodologies based on freight data collection using GPS (Global Positioning System), as a key element to assess freight fluidity in freight transportation corridors. The analysis measured items such as: (i) percentiles of travel time; (ii) planning time index; (iii) buffer time index; (iv) skew and range of travel. Different probability distributions were analysed and the distribution of travel time data was determined through statistical hypothesis test. The conclusion was that distributions with upper tails longer than usual fit better to the collected data. Evidences of mixture distributions in the travel time data were also found. Finally, the paper also presents useful conclusions to develop future research as well as to improve practices for decision makers.

Movement and Apparent Survival of Acoustically Tagged Juveline Late-Fall Run Chinook Salmon Released Upstream of Shasta Reservoir, California

Stakeholder interests have spurred the reintroduction of the critically endangered populations of Chinook Salmon to tributaries upstream of Shasta Dam, in northern California. We released two groups of acoustically tagged, juvenile hatchery, late-fall Chinook Salmon to determine how juvenile salmon would distribute and survive. We measured travel times to Shasta Dam, and the number of fish that moved between locations within Shasta Reservoir. We used mark-recapture methods to determine detection and apparent survival probabilities of the tagged fish as they traveled through five reaches of the Sacramento River from the McCloud River to San Francisco Bay (~590 km) over the two 3-month observation periods. After our first (February) release of 262 tagged fish, 182 fish (70%) were detected at least once at the dam, 41 (16%) were detected at least once downstream of Shasta Dam, and 3 (1%) traveled as far as San Francisco Bay. After the second (November) release of 355 tagged fish, only 4 (1%) were detected at Shasta Dam. No fish were detected below Shasta Dam, so we could not estimate survival for this second release group. The first release of fish was fortuitously exposed to exceptionally high river flows and dam discharges, which may have contributed to the more distant downstream migration and detection of these fish — though other factors such as season, diploid versus triploid, and fish maturation and size may have also contributed to release differences. The reported fish travel times as well as detection and survival rates are the first estimates of juvenile salmon emigration from locations above Shasta Dam in more than 70 years. This information should help inform resource managers about how best to assess juvenile winter-run Chinook Salmon and assist in their reintroduction to watersheds upstream of Shasta Dam.

A Review of Service Reliability Measures for Public Transportation Systems

The aim of this paper is to review various public transport reliability measures including the factors responsible for causing variability in the travel time. A four quadrant approach is used to summarize various reliability measures. These indicators use both Intelligent Transportation System (ITS) data and the stated preference data to measure reliability. Further various supply and demand side factors causing uncertainties in travel time are listed and discussed. In the end a brief case study on two routes of Bus Rapid Transit System(BRTS) of Ahmedabad is reported to apply and test the reviewed travel time measures on the ITS data.

Comparison of time–distance inversion methods applied to SDO/HMI Dopplergrams

Context. The Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) satellite has been observing the Sun since 2010. The uninterrupted series of Dopplergrams are ideal for studying the dynamics of the upper solar convection zone. Within the Joint Science Operations Center (JSOC) the time–distance inversions for flows and sound-speed perturbations were introduced. The automatic pipeline has produced flow and sound-speed maps every 8 h. We verify the results of JSOC inversions by comparing the data products to equivalent results from inverse modelling obtained by an independent inversion pipeline. Aims. We compared the results from the JSOC pipeline for horizontal flow components and the perturbations of the speed of sound at set of depths with equivalent results from an independently implemented pipeline using a different time–distance inversion scheme. Our inversion pipeline allows inversion for all quantities at once while allowing minimisation of the crosstalk between them. This gives us an opportunity to discuss the possible biases present in the JSOC data products. Methods. For the tests we used the subtractive optimally localised averaging (SOLA) method with a minimisation of the cross-talk. We compared three test inversions for each quantity at each target depth. At first, we used the JSOC setup to reproduce the JSOC results. Subsequently, we used the extended pipeline to improve these results by incorporating more independent travel-time measurements but keeping the JSOC-indicated localisation in the Sun. Finally, we inverted for flow components and sound-speed perturbations using a localisation kernel with properties advertised in the JSOC metadata. Results. We successfully reproduced the horizontal flow components. The sound-speed perturbations are strongly affected by the high level of the cross-talk in JSOC products. This leads to larger amplitudes in the inversions for the sound-speed perturbations. Different results were obtained when a target function localised around the target depth was used. This is a consequence of non-localised JSOC averaging kernels. We add that our methodology also allows inversion for the vertical flow.

City-scale car traffic and parking density maps from Uber Movement travel time data

Car parking is of central importance to congestion on roads and the urban planning process of optimizing road networks, pricing parking lots and planning land use. The efficient placement, sizing and grid connection of charging stations for electric cars makes it even more important to know the spatio-temporal distribution of car parking densities on the scale of entire cities. Here, we generate car parking density maps using travel time measurements only. We formulate a Hidden Markov Model that contains non-linear functional relationships between the changing average travel times among the zones of a city and both the traffic activity and flow direction probabilities of cars. We then sample the traffic flow for 1,000 cars per city zone for each city from these probability distributions and normalize the resulting spatial parking distribution of cars in each time step. Our results cover the years 2015–2018 for 34 cities worldwide. We validate the model for Melbourne and reach about 90% accuracy for parking densities and over 93% for circadian rhythms of traffic activity.

Measurement of Friction Coefficient between Glass and Rubik's Beam on the Sloping Field using Rubik Lubricant

This research is based on a rubik game involving the coefficient of friction. Rubik when difficult to rotate then the coefficient of friction is large. Rubik has several lubricants including Maru Lube, Ellips, and Kit Shampoo. This study aims to determine friction coefficient between the inclined plane of glass and rubik beams with lubricating. The beam is launched on an inclined plane and measured travel time. The results of the measurement of the coefficient of friction of the three lubricants namely Maru Lube lubricant has a coefficient of friction value of 0.4586, Ellips lubricant of 0.4375, and shampoo Kit lubricant of 0.4630. Based on these data it can be concluded that Ellips lubricant is the most slippery and more effective lubricant to use.

Time-distance helioseismology of solar Rossby waves

Context. Solar Rossby waves (r modes) have recently been discovered in the near-surface horizontal flow field using the techniques of granulation-tracking and ring-diagram analysis applied to six years of SDO/HMI data. Aims. Here we apply time-distance helioseismology to the combined SOHO/MDI and SDO/HMI data sets, which cover 21 years of observations from May 1996 to April 2017. The goal of this study is to provide an independent confirmation over two solar cycles and in deeper layers of the Sun. Methods. We have measured south-north helioseismic travel times along the equator, which are sensitive to subsurface north-south flows. To reduce noise, the travel times were averaged over travel distances from 6° to 30°; the mean distance corresponds to a p-mode lower turning point of 0.91 R⊙. The 21-year time series of travel-time measurements was split into three seven-year subsets and transformed to obtain power spectra in a corotating frame. Results. The power spectra all show peaks near the frequencies of the classical sectoral Rossby waves for azimuthal wavenumbers in the range 3 ≤ m ≤ 15. The mode frequencies and linewidths of the modes with m ≤ 9 are consistent with a previous study whereas modes with m ≥ 10 are shifted toward less negative frequencies by 10–20 nHz. While most of these modes have e-folding lifetimes on the order of a few months, the longest lived mode, m = 3, has an e-folding lifetime of more than one year. For each mode, the rms velocity at the equator is in the range of 1–3 m s−1, with the largest values for m ∼ 10. No evidence for the m = 2 sectoral mode is found in the power spectrum, implying that the rms velocity of this mode is below ∼0.5 m s−1. Conclusions. This work confirms the existence of equatorial global Rossby waves in the solar interior over the past two solar cycles and shows that time-distance helioseismology is a promising technique to study them deep in the convection zone.

Using the Average Wage Rate to Assess the Merit of Value of Travel Time Savings: A Concern and Clarification

There is a renewed reference to relating the value of travel time to the average wage rate with 40 percent mentioned as the benchmark. This rigid link may have had value when interest focussed on a single homogeneous measure of a single trip travel time and working hours were somewhat rigid for the bulk of workers; however with flexible work practices and heterogeneous interpretations of travel time, the imposition of such a strict assumption is both problematic and misleading as suggested in this paper.

Estimating price gradient in Bratislava with different distance measurements

PurposeThis paper aims to analyse the price gradient of apartments in the city of Bratislava with different measurements of travel time and distance to the city centre.Design/methodology/approachThe price gradient is analysed by means of a hedonic price model. To overcome the problem with spatial autocorrelation in the data, the authors apply a spatial error model.FindingsThe paper provides empirical insights into the size of the price gradient in the city of Bratislava. In addition, it suggests that even in the case of a city with complicated urban structure, Euclidean distance is the best proxy for distance to the city centre and it is not necessary to use a more demanding distance calculation in hedonic price models.Originality/valuePrice gradients are usually analysed in western European or American cities whose urban structure differ from the cities in central and eastern Europe. This paper is the first in which the price gradient is estimated with different measurements of time and distance to the city centre using a spatial econometric model.

Spatial Accessibility to Mammography Services in the Lower Mississippi Delta Region States.

To characterize spatial access to mammography services across 8 Lower Mississippi Delta Region (LMDR) states. These states include the Delta Region, a federally designated, largely rural, and impoverished region with a high proportion of black residents and low mammography utilization rates. Using the enhanced 2-step floating catchment area method, we calculated spatial accessibility scores for mammography services across LMDR census tracts. We compared accessibility scores between the Delta and non-Delta Regions of the LMDR. We also performed hotspot analysis and constructed spatial lag models to detect clusters of low spatial access and to identify sociodemographic factors associated with access, respectively. We obtained mammography facility locations data from the Food and Drug Administration and sociodemographic variables from the American Community Survey and the US Department of Agriculture. Overall, there were no differences in spatial accessibility scores between the Delta and non-Delta Regions, though there was some state-to-state variation. Clusters of low spatial access were found in parts of the Arkansas, Mississippi, and Tennessee Delta. Spatial lag models found that poverty was associated with greater spatial access to mammography. The lack of identified differences in spatial access to mammography in the Delta and non-Delta Regions suggests that psychosocial or financial barriers play a larger role in lower mammography utilization rates. Identifying clusters of low spatial access to mammography services can help inform resource allocation. Further, our study underscores the value of using coverage-based methods rather than travel time or container measures to evaluate spatial access to care.