Travel Time Data Research Articles

3-D isotropic and anisotropic shallow crustal structure on Pingtan Island, Fujian, southeastern coast of China

Pingtan Island is located on the southeastern coast of China between the Changle-Zhao'an and Binhai fault zones. We use P- and S-wave traveltime data from marine large-volume airgun sources for 3-D velocity structure inversion, and the spatial resolution is high at depths of 1–3 km. The isotropic inversion results show that the velocity in the island is higher in the midwestern area, which consists of sedimentary plains. The velocity is lower in the southern and eastern areas, which consist of eroded hills. Then we perform azimuthally anisotropic inversion using the obtained isotropic model as the initial model. The fast directions in the island are predominantly NE-SW and NNE-SSW, consistent with the main fault structures along the southeastern coast of China. The magnitude of anisotropy gradually decreases from south to north, consistent with the decreasing intensity of fault activity from south to north. On the southeastern coast of China, the fast direction in the upper crust is nearly orthogonal to that in the upper mantle from previous studies, which is nearly parallel to the absolute plate motion in the lower crust and upper mantle. The faults along the Fujian coastal area and Taiwan formed at different time period. But the fast directions in these two areas are similar. Therefore, the fast direction is mainly dominated by the trends of faults and fractures in the upper crust. And it is dominated by aligned olivine crystals induced by plate motion in the lower crust and upper mantle.

Lithospheric mantle underneath the Tibetan Plateau does not escape southeastward

A mass of lithospheric shortening occurred beneath the Tibetan Plateau due to the collision between the Indian and Eurasian plates. The extrusion of the upper crust beneath the Tibetan Plateau has been widely accepted, but whether the lithospheric mantle escapes laterally by creep deformation or rigid-block extrusion remains unclear. Based on the Pn travel time data, the velocity structure of the uppermost mantle in eastern and southeastern Tibetan Plateau was obtained by using the interstation travel-time difference method. The results show an obviously high Pn velocity beneath the Sichuan Basin and Qaidam Basin, indicating that the areas are stable blocks. The high Pn velocity beneath the eastern Lhasa terrane may be related to the underplating of the Indian lithospheric plate to the north. The Qilian block located in the north of the Kunlun fault appears as a normal Pn velocity, which indicates a mean lithospheric mantle thickness. A significantly low Pn velocity with an average of ~7.8 km/s in eastern and southeastern Tibetan Plateau, including the eastern Songpan–Ganzi terrane, the Chuan–Dian Diamond Block, and the northern part of Yunnan Province, indicates that the lithospheric mantle is relatively thin in these regions. Combined with other geologic and geophysical results, we propose that the lithospheric mantle of the plateau did not escape to eastern and southeastern Tibetan Plateau where the crust has been thickened by adding crustal material from the interior of the plateau.

Mapping of bus travel time to traffic stream travel time using econometric modeling

Travel time is one of the most important traffic parameters for travelers, traffic managers, planners, and operators alike. Travel time estimation is a significant component of any intelligent transportation systems (ITS) operations. In countries like India, which are at a nascent stage of ITS deployment, one of the main hurdles is the lack of accurate and automated systems for travel time data collection. However, in most of the cities in India, public transit buses are equipped with global positioning system (GPS) devices providing rich bus travel time data. Privacy issues deter the use of such GPS devices in private vehicles present in the stream. Hence, estimating the stream travel time using buses as probes is a relevant problem. In countries like India, where a heterogeneous and less lane disciplined traffic condition exists and the buses have to share the road space with other vehicles (no separate bus lanes); this mapping of bus travel time to stream travel time becomes challenging. The present study attempts to estimate stream travel time using buses as probes in a heterogeneous and less lane disciplined urban traffic scenario. Preliminary analysis of the collected travel time data indicated significant variability across time and links. Panel modeling can capture both this cross-sectional and time-dependent behavior and hence is used in this study. The estimation model is corroborated with field data and the performance is found to be satisfactory.

Bus Dynamic Travel Time Prediction: Using a Deep Feature Extraction Framework Based on RNN and DNN

Travel time data is an important factor for evaluating the performance of a public transport system. In terms of time and space within the nature of uncertainty, bus travel time is dynamic and flexible. Since the change of traffic status is periodic, contagious or even sudden, the changing mechanism of that is a hidden mode. Therefore, bus travel time prediction is a challenging problem in intelligent transportation system (ITS). Allowing for a large amount of traffic data can be collected at present but lack of precisely-conducting, it is still worth exploring how to extract feature sets that can accurately predict bus travel time from these data. Hence, a feature extraction framework based on the deep learning models were developed to reflect the state of bus travel time. First, the study introduced different historical stages of bus signaling time, taxi speed, the stop identity (ID) of spatial characteristics, and real-time possible arrival time, signified by fourteen spatiotemporal characteristic values. Then, an embedding network is proposed to leverage a wide and deep structure to mate the spatial and temporal data. In order to meet the temporal dependence requirements, an attention mechanism for a Recurrent Neural Network (RNN) was designed in this research in order to capture the temporal information. Finally, a Deep Neural Networks (DNN) was implemented in this research in order to achieve the dynamic bus travel time prediction. Two case studies of Guangzhou and Shenzhen were tested. The results showed that the performance of the algorithm was more efficient than that of the traditional machine-learning model and promoted by 4.82% compared to the deep neural network applied to the initial feature space. Moreover, the study visualized the weighted cost of attention on the bus’s travel time features during a certain running state. Therefore, the study demonstrated the proposed model enabled to understand the characteristic data of transit travel time with visualization.

Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint

Deep learning is applied to assist the joint inversion for audio-magnetotelluric and seismic travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) are designed to learn both structural similarity and resistivity-velocity relationships according to prior knowledge. During the inversion, the unknown resistivity and velocity are updated alternatingly with the Gauss-Newton method, based on the reference model generated by the trained DRCNNs. The workflow of this joint inversion scheme and the design of the DRCNNs are explained in detail. Compared with describing the resistivity-velocity relationship using empirical equations, this method can avoid the necessity in modeling the correlations in rigorous mathematical forms and extract more hidden prior information embedded in the training set, meanwhile preserving the structural similarity between different inverted models. Numerical tests show that the inverted resistivity and velocity have similar profiles, and their relationship can be kept consistent with the prior joint distribution. Furthermore, the convergence is faster, and final data misfits can be lower than separate inversion.

RETRACTED ARTICLE: Motorized Level of Service Classification for Urban Uncontrolled Intersections

Service and total delay are considered for classifying MLOS and intersections, respectively. GPS is used to collect travel time and speed data for turning movements that are transformed into average delay values. Thirteen junctions from eight different cities in India form the dataset. Divisive followed by agglomerative clustering (DAC-HAC) algorithm is applied as a two-step process for obtaining the service and total delay ranges. Validation of clusters is performed based on the Davies-Bouldin score, Calinski-Harabasz index, and Silhouette gaps. Based on DAC-HAC, uncontrolled intersections are classified into six categories (Cat-I, II, III, IV, V, and VI). Results indicate MLOS classes “D”, “E” and “F” have significantly higher service delay ranges as compared to Highway capacity manual “control” delay ranges indicating mixed traffic conditions. Most of the uncontrolled intersections under mixed traffic fall under Cat-IV, V, and VI, having higher total delay ranges (> 60 s/ vehicle/ approach). Finally, validation of the clustering results is done for geometric and roadside environmental features.

Origin-Destination-Based Travel Time Reliability under Different Rainfall Intensities: An Investigation Using Open-Source Data

Origin-destination- (O-D-) based travel time reliability (TTR) is fundamental to next-generation navigation tools aiming to provide both travel time and reliability information. While previous works are mostly focused on route-based TTR and use either ad hoc data or simulation in the analyses, this study uses open-source Uber Movement and Weather Underground data to systematically analyze the impact of rainfall intensity on O-D-based travel time reliability. The authors classified three years of travel time data in downtown Boston into one hundred origin-destination pairs and integrated them with the weather data (rain). A lognormal mixture model was applied to fit travel time distributions and calculate the buffer index. The median, trimmed mean, interquartile range, and one-way analysis of variance were used for quantification of the characteristics. The study found some results that tended to agree with the previous findings in the literature, such that, in general, rain reduces the O-D-based travel time reliability, and some seemed to be unique and worthy of discussion: firstly, although in general the reduction in travel time reliability gets larger as the intensity of rainfall increases, it appears that the change is more significant when rainfall intensity changes from light to moderate but becomes fairly marginal when it changes from normal to light or from moderate to extremely intensive; secondly, regardless of normal or rainy weather, the O-D-based travel time reliability and its consistency in different O-D pairs with similar average travel time always tend to improve along with the increase of average travel time. In addition to the technical findings, this study also contributes to the state of the art by promoting the application of real-world and publicly available data in TTR analyses.

Dynamic estimation system for fire station service areas based on travel time data

Fire stations play an important role in improving firefighting capabilities, and their service areas should be determined based on the travel times from each fire station to the surrounding fire risk points. Based on real-time travel time data extracted from online maps, we developed a dynamic estimation system for fire station service areas called FSSA v1.0 and integrated it with the boundary search algorithm for fire service areas. To validate the developed method, 39,616 fire risk points (points of interest (POIs)) around an urban fire station in Changsha city (CS-FS) were selected as the sample dataset. The service areas of the CS-FS during different time periods (00:00–01:00, 07:40–08:40, 12:00–13:00, and 17:40–18:40) were estimated by calculating and visualizing the travel times from the CS-FS to the POIs. The results indicate that the actual service areas in all scenarios are much smaller than the area of 7 km2 specified in China's GB 51080-2015 standard. Additionally, the service area of the CS-FS under traffic congestion is significantly smaller than that under normal road traffic conditions. Therefore, fire engines should be temporarily deployed before traffic congestion occurs to reduce travel times. The service areas of other fire stations can also be estimated using FSSA v1.0.

Fire stations siting with multiple objectives and geospatial big data

In recent years, with the rapid development of urbanization, the urban public emergency management faces increasing challenges, and the number of fire incidents has increased largely. For mitigating injury risk and reducing property loss, fire station locations need to be optimized to provide efficient fire emergency services. However, the locations of fire facilities in China are mainly determined according to administrative divisions, lacking effective data-driven applications. In addition, existing location models are designed for general purpose, and few have taken into account the unique characteristics of fire services with multiple objectives, i.e., maximum coverage, minimum overlapping coverage and balanced workloads. With the recent development of geospatial big data, lots of fire risk related data (e.g. fire incidents data and travel time data) can be obtained, which provides an unprecedented opportunity for urban fire emergency facilities planning. In this paper, we propose to establish the multi-objective maximal covering location model by accurately estimating fire rescuing demands and travel cost from geospatial big data. A case study was carried out in Nanjing, China, and the result shows that our method is effective in optimizing the locations of fire stations.

Delay and driver turning time evaluation for uncontrolled intersections under diverse traffic operational situations

This study estimates mean service delay for turning movements with significant emphasis on a proposal of total delay models using microscopic analysis of travel time data under indiscipline driver behavior. Field service delay estimates are validated using microsimulation for all the turning movements. The authors utilized a manual video graphic technique for data collection of thirteen uncontrolled intersections from all over India. Conflicting flow rate is determined for a certain number of conflicting vehicles as obtained for subject right and left-turning vehicles. Driver turning time for motions concerning vehicular classifications is determined. Moreover, the effects of service delay and oncoming traffic flow on turning driver waiting time are investigated. Validation of service delay is also done against delay estimated using effective capacity (Ci) for the “Four-wheeler SUV” stream. It is validated that the conflict technique can predict 52% of field estimated service delays with a sensitivity of as low as 3.0 seconds/ vehicle/ approach for various traffic streams.

Numerical Stability of Conservation Equation for Bus Travel Time Prediction Using Automatic Vehicle Location Data

Travel time is a variable that varies over both time and space. Hence, an ideal formulation should be able to capture its evolution over time and space. A mathematical representation capturing such variations was formulated from first principles, using the concept of conservation of vehicles. The availability of position and speed data obtained from GPS enabled buses provide motivation to rewrite the conservation equation in terms of speed alone. As the number of vehicles is discrete, the speed-based equation was discretized using Godunov scheme and used in the prediction scheme that was based on the Kalman filter. With a limited fleet size having an average headway of 30 min, availability of travel time data at small interval that satisfy the requirement of stability of numerical solution possess a big challenge. To address this issue, a continuous speed fill matrix spatially and temporally was developed with the help of historic data and used in this study. The performance of the proposed Advanced Time-Space Discterization (AdTSD) method was evaluated with real field data and compared with existing approaches. Results show that AdTSD approach was able to perform better than historical average approach with an advantage up to 11% and 5% compared to Base Time Space Discretization (BTSD) approach. Also, from the results it was observed that the maximum deviation in prediction was in the range of 2–3 min when it is predicted 10 km ahead and the error is close to zero when it is predicted a section ahead i.e. when the bus is close to a bus stop, indicating that the prediction accuracy achieved is suitable for real field implementation.

Q-compensated reverse time migration in tilted transversely isotropic media

Anisotropy and absorption are critical to the modeling and analysis of seismic amplitude,phase, and traveltime data. To neglect any of these phenomena, which are often bothoperating simultaneously, degrades the resolution and interpretability of migrated images.However, a full accounting of anisotropy and anelasticity is computationally complex andexpensive. One strategy for accommodating these aspects of wave propagation, while keepingcost and complexity under control, is to do so within an acoustic approximation. Weset up a procedure for solving the time-domain viscoacoustic wave equation for tilted transverselyisotropic (TTI) media, based on a standard linear solid model and, from this, developa viscoacoustic reverse time migration (Q-RTM) algorithm. In this approach, amplitudecompensation occurs within the migration process through a manipulation of attenuationand phase dispersion terms in the time domain differential equations. Specifically, theback-propagation operator is constructed by reversing the sign only of the amplitude lossoperators, but not the dispersion-related operators, a step made possible by reformulatingthe absorptive TTI equations such that the loss and dispersion operators appear separately.The scheme is tested on synthetic examples to examine the capacity of viscoacoustic RTM to correct for attenuation, and the overall stability of the procedure.

Characterizing Bus Travel Time using Advanced Data Visualization Techniques

With the introduction of various automated sensors, traffic data collection has become easier and huge amount of data are getting accumulated over time. One of the interesting challenges in the field of intelligent transportation systems is to effectively utilize such large-scale database. Making meaningful inferences out of this data by conducting in-depth analyses to identify different patterns/trends followed by the traffic variables can lead to the development of more efficient end-applications. The current study analyzes travel time data obtained from buses fitted with global positioning system devices to understand the temporal and spatial variations in travel time in the city of Chennai. For this, data visualization tools such as tree maps and heat maps were used. From temporal analysis, it was observed that travel times are increasing over the years and it was also observed that there is a discernible pattern in travel between weekdays and weekend. From spatial analysis, it was found that there exists a segment specific characteristic of travel time and certain segments experiencing higher travel times in urban areas particularly at intersections. The findings from the study were further used in demonstrating a possible user application, bus travel time prediction system, based on the identified patterns. Performance analysis showed a combination of inputs from same month last year, day of the week, and traffic conditions performing better for the considered dataset.

Compound generalized extreme value distribution for modeling the effects of monthly and seasonal variation on the extreme travel delays for vulnerability analysis of road network

This paper proposes a new class of extreme value distribution called compound generalized extreme value (CGEV) distribution for investigating the effects of monthly and seasonal variation on extreme travel delays in road networks. Since the frequency and severity of extreme events are highly correlated to the variation in weather conditions as an extrinsic cause of incidents and long delays, monthly and seasonal changes in weather contributes to extreme travel time variability. The change in driving behavior, which itself varies according to road/weather conditions, also contributes to the monthly and seasonal variation in observed extreme travel times. Therefore, it is critical to model the effect of monthly and seasonal changes on observed extreme travel delays on road networks. Based on the empirically revealed linear relationship between mean and standard deviation (SD) of extreme travel delays for both monthly and seasonal levels, two multiplicative error models are formulated. The CGEV distribution is then obtained by linking the two multiplicative error models and forming a compound distribution that characterizes the overall variation in extreme travel delay. The CGEV distribution parameters are calibrated and the underlying assumptions that are used to derive the CGEV distribution are validated using multi-year observed travel time data from the City of Calgary road network. The results indicate that accounting for the seasonality by identifying seasonal specific parameters provides a flexible and not too complex CGEV distribution that is shown to outperform the traditional GEV distribution. Finally, the application of the proposed CGEV distribution is evaluated in the context of road network vulnerability taking into account the observed probability of extreme event occurrences and the link importance. This derived data-driven vulnerability index incorporates a wealth of information related to both network topography in terms of connectivity and the dynamic interaction between travel demand and supply. This new data-driven vulnerability measure can thus be used as a decision support tool to inform decision-makers in prioritizing improvements to critical links to enhance overall network vulnerability, reliability, and resilience.

Finding the Reasons for the Delay Time in a Highway by Analyzing the Travel Time, Delay Time and Traffic Flow Data

The objectives of this study are to show a comparison among travel time, running time, delay in peak and off-peak hours on different days of a week, and reasons behind the delay time. Moving car observer method has been carried out to count the traffic flow, journey time, running time, and delay time. Total vehicle flow, and comparative vehicle flow during the peak hour and off-peak hour for workdays and weekend days were surveyed to show a relationship between delay time and traffic flow. As the traffic flow increases the delay time also will increase. To measure the reasons behind the delay time of Fulbarigate-Daulatpur road, spot speed study was done in two intersections of the road. The 15th percentile speed for Religate intersection road is 10 K.P.H. That means 85% of vehicles tends to go faster than 10 K.P.H. in this section. And hence vehicles with less than or equal 10 K.P.H. are responsible for increasing the delay time in this area. This speed limit should be prohibited in this section to reduce delay time and congestion. For the Mohosin More road intersection, the prohibited speed is also 10 K.P.H. Easy bike and Mahindra account for the congestion of these intersections and tend to stop in these intersections to collect passengers which creates unwanted queue in this study area. Controlling traffic flow at intersections can be a possible way to reduce the congestion rate of Fulbarigate-Daulatpur road.

Innovative Nonparametric Method for Data Outlier Filtering

Outlier filtering of empirical travel time data is essential for traffic analyses. Most of the widely applied outlier filtering algorithms are parametric in nature and based on assumed data distributions. The assumption, however, might not hold under unstable traffic conditions. This paper proposes a nonparametric outlier filtering method based on a robust locally weighted regression scatterplot smoothing model. The proposed method identifies outliers based on a data point’s standard residual in the robust local regression model. This approach fits a regression surface with no constraint on parametric distributions and limited influence from outliers. The proposed outlier filtering algorithm can be applied to various data collection technologies and for real-time applications. The performance of the new outlier filtering algorithm is compared with the moving standard deviation method and other traditional filtering algorithms. The test sites include GPS data of an Interstate highway in Indiana and Bluetooth data of an urban arterial roadway in Texas. It is shown that the proposed filtering algorithm has several advantages over the traditional filtering algorithms.

Travel Time Variability and Congestion Assessment for Origin–Destination Clusters through the Experience of Mobility Companies

Reliability is a measure of network performance that reflects the ability of the network to provide predictable travel times. Deviations from planned travel times can increase travel costs for users. To improve the system's performance, it is crucial to identify sources of unreliability, particularly the location on the network of unreliable performance. The large amount of travel time data recorded by Transportation Network Providers (TNPs) in recent years has enabled researchers to study the performance of entire networks. In this study, a real-world dataset provided by TNPs in Chicago is used to determine time of day, and day of week distribution of travel time per unit distance for origin–destination (OD) pairs. Eight measures of reliability are calculated for OD pairs in the network. Standard deviation (SD), planning time index (PTI), and on-time measure (PR) are used for a network-wide comparison of reliability performance. K-means clustering is performed on more than 21.3 million trips to divide 3,450 eligible OD pairs in the Chicago network into three groups with low, medium, and high intensity of each reliability metric. Lastly, metrics in each cluster of SD, PTI, and PR are compared. The results show that ranking PTI and PR is not sufficient for identifying unreliable/congested OD pairs in the network. Approaches for comparing reliability performance over different periods of the day for the same segment and over different segments in the network are discussed, along with network-wide measures of reliability.

DirectP-wave anisotropy measurements at Homestake Mine: implications for wave propagation in continental crust

SUMMARYWe measured anisotropic seismic properties of schists of the Homestake Formation located at a depth of 1478 m in the Sanford Underground Research Facility (SURF) in the Black Hills of South Dakota, USA. We deployed a 24-element linear array of three-component geophones in an area in the Homestake Mine called 19-ledge. An airless jackhammer source was used to shoot two profiles: (1) a walkaway survey to appraise any distance dependence and (2) a fan shot profile to measure variations with azimuth. Slowness estimates from the fan shot profile show a statistically significant deviation with azimuth with the expected 180° variation with azimuth. We measured P-wave particle motion deviations from data rotated to ray coordinates using three methods: (1) a conventional principal component method, (2) a novel grid search method that maximized longitudinal motion over a range of search angles and (3) the multiwavelet method. The multiwavelet results were computed in two frequency bands of 200–600 and 100–300 Hz. Results were binned by azimuth and averaged with a robust estimation method with error bars estimated by a bootstrap method. The particle motion results show large, statistically significant variations with azimuth with a 180° cyclicity. We modelled the azimuthal variations in compressional wave speed and angular deviation from purely longitudinal particle motion of P-waves using an elastic tensor method to appraise the relative importance of crystalline fabric relative to fracturing parallel to foliation. The model used bulk averages of crystal fabric measured for an analogous schist sample from southeast Vermont rotated to the Homestake Formation foliation directions supplied by SURF from old mine records. We found with average crustal crack densities crack induced anisotropy had only a small effect on the observables. We found strong agreement in the traveltime data. The observed amplitudes of deviations of P particle motion showed significantly larger variation than the model predictions and a 20° phase shift in azimuth. We attribute the inadequacies of the model fit to the particle motion data to inadequacies in the analogue rock and/or near receiver distortions from smaller scale heterogeneity. We discuss the surprising variability of signals recorded in this experimental data. We show clear examples of unexplained resonances and unexpected variations on a scale much smaller than a wavelength that has broad implications for wave propagation in real rocks.

Quantifying Inequitable Access to Rapid Burn and Reconstructive Care through Geospatial Mapping.

Summary:Time-critical pathologies, such as the care of burn-injured patients, rely on accurate travel time data to plan high-quality service provision. Geospatial modeling, using data from the Malaria Atlas Project, together with census data, permits quantification of the huge global discrepancies in temporal access to burn care between high-income and low-resource settings. In this study, focusing on the United Kingdom and Ghana, we found that a 3-fold population difference exists with, respectively, 95.6% and 29.9% of the population that could access specialist burn care within 1-hour travel time. Solutions to such inequalities include upscaling of infrastructure and specialist personnel, but this is aspirational rather than feasible in most low- to middle-income countries. Mixed models of decentralization of care that leverage eHealth strategies, such as telemedicine, may enhance quality of local burns and reconstructive surgical care through skills transfer, capacity building, and expediting of urgent transfers, while empowering local healthcare communities. By extending specialist burn care coverage through eHealth to 8 district hospitals in rural Ghana, we demonstrate the potential to increase specialist population coverage within 1-hour travel time from 29.9% to 45.3%—equivalent to an additional 5.1 million people.

Evaluation of Alternative Methods for Dynamic I-95 Express Lane Pricing

Developing dynamic toll algorithms is a major task for express lanes (EL) policy makers. The majority of pricing methods applied in the field utilize traffic data collected at several points along an EL segment to estimate traffic conditions (mainly traffic density) and calculate tolls. While travel time (TT) data can directly capture field conditions and users’ delay on a segment, such data are rarely utilized to calculate tolls for EL users. The literature suggests minor utilization of TTs in EL toll calculations, but not as the main target performance measure. More importantly, there is a need to look at performance of EL tolls from a multi-criteria perspective where the goal is not only to maintain certain level of service on the ELs. To address these topics, this study tests alternative toll calculation methods that rely mainly on TT data and compare their performance with the currently utilized toll calculation algorithm on I-95 EL in South Florida, U.S. In addition, this study proposes an evaluation framework which includes five different criteria to select the best-performing algorithms: 1. policy, 2. efficiency, 3. capacity utilization, 4. cost recovery, and 5. reliability. Results of the study show that a TT-based algorithm, which is less sensitive than conventional methods, performs similarly to a density-based algorithm, and that a travel time savings (TTS)-based algorithm maintains speeds on the ELs more efficiently than the alternative methods. Future research should apply and validate similar methods on other EL cases around the country.