Travel Time Information Research Articles

Refraction waves full waveform inversion of deep reflection seismic profiles in the central part of Lhasa Terrane

The deep reflection seismic method is an effective technique for detecting the fine structures of the lithosphere and solving deep geological problems. At present, the methods for obtaining the underground velocity from land deep reflection seismic profile mainly rely on the travel-time information based velocity analysis and tomography, which theoretically limit the resolution of the imaging results. To achieve the goal of using deep reflection seismic profile for crustal-scale high-precision velocity imaging, we must finally overcome the problem of full waveform inversion (FWI) using full wavefield data. In this paper, we successfully conduct FWI using the early-arrival wavefields (mainly primary and multiple refraction wavefields) in the deep reflection seismic profile, which can be seen as the first important step towards the goal of crustal-scale high-precision velocity imaging. We propose a robust refraction waves FWI workflow with global normalized cross-correlation, multiple refraction matching, reverse time source estimation and wavenumber domain gradient filtering as its core algorithm. The application of the proposed FWI method to the refraction waves of the deep reflection seismic profiles in the central part of Lhasa Terrane shows that the refraction FWI images have a higher resolution to underground structures both in horizontal and vertical directions than the first-arrival tomography images. The lateral velocity change can be well-matched with the typical geological characteristics of the work area. The FWI result can better describe the lateral boundaries of the strata in different eras. At a depth of less than 3 km from the surface, the refraction FWI can correct the interface morphology of the tomography results and recover some small-scale velocity structures and high-velocity anomalies.

Estimation of lane-level travel time distributions under a connected environment

Travel time distribution estimation is fundamentally important for the evaluation of travel time variability and reliability. For urban roads, signal delays are key components of travel time. They are stochastic and differ for vehicular movements due to different signal timings for through, left-turning, and right-turning vehicles. To better assist travelers in making trip decisions under connected environments, this study seeks to investigate lane-level travel time distributions for signalized arterial roads by specifically considering the impacts of both the link travel time and the vehicle movement-based signal delays at an intersection. A simulation testbed based on the VISSIM microscopic traffic simulator and a Java plugin is developed to mimic the traffic flow dynamics of a signalized arterial, El Camino Real, in Palo Alto, California, under a connected environment. The detailed vehicle trajectory data obtained from the simulation can be leveraged to obtain lane-level travel time information. Typical normal, lognormal, gamma, and Weibull distributions, as well as kernel density estimation (KDE), are adopted to calibrate the lane-level travel time distributions. The estimation results demonstrate that conventional distribution models are suitable for estimating the travel time distributions of only a few road segments, while KDE fully captures travel time reliability metrics such as the buffer time index, skewness, and width of the travel time distributions for all road segments. This result will help traffic managers and engineers carry out effective traffic management and control to optimize the operation of arterial roads.

Sequential and coupled inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic properties at the field scale

Horizontal borehole ground penetrating radar (GPR) measurements can provide valuable information on soil water content (SWC) dynamics in the vadose zone, and hence show potential to estimate soil hydraulic properties. In this study, the performance of both sequential and coupled inversion workflows to obtain soil hydraulic properties from time-lapse horizontal borehole GPR data obtained during an infiltration experiment were compared using a synthetic modelling study and the analysis of actual field data. The sequential inversion using the vadose zone flow model HYDRUS-1D directly relied on SWC profiles determined from the travel time of GPR direct waves using the straight-wave approximation. The synthetic modelling study showed that sequential inversion did not provide accurate estimates of the soil hydraulic parameters due to interpretation errors in the estimated SWC near the infiltration front and the ground surface. In contrast, the coupled inversion approach, which combined HYDRUS-1D with a forward model of GPR wave propagation (gprMax3D) and GPR travel time information, provided accurate estimates of the hydraulic properties in the synthetic modelling study. The application of the coupled inversion approach to measured borehole GPR data also resulted in plausible estimates of the soil hydraulic parameters. It was concluded that coupled inversion should be preferred over sequential inversion of time-lapse horizontal borehole GPR data in the presence of strong SWC gradients that occur during infiltration events.

A comparison of catchment travel times and storage deduced from deuterium and tritium tracers using StorAge Selection functions

Abstract. Catchment travel time distributions (TTDs) are an efficient concept for summarizing the time-varying 3D transport of water and solutes towards an outlet in a single function of a water age and for estimating catchment storage by leveraging information contained in tracer data (e.g., deuterium 2H and tritium 3H). It is argued that the preferential use of the stable isotopes of O and H as tracers, compared to tritium, has truncated our vision of streamflow TTDs, meaning that the long tails of the distribution associated with old water tend to be neglected. However, the reasons for the truncation of the TTD tails are still obscured by methodological and data limitations. In this study, we went beyond these limitations and evaluated the differences between streamflow TTDs calculated using only deuterium (2H) or only tritium (3H). We also compared mobile catchment storage (derived from the TTDs) associated with each tracer. For this, we additionally constrained a model that successfully simulated high-frequency stream deuterium measurements with 24 stream tritium measurements over the same period (2015–2017). We used data from the forested headwater Weierbach catchment (42 ha) in Luxembourg. Time-varying streamflow TTDs were estimated by consistently using both tracers within a framework based on StorAge Selection (SAS) functions. We found similar TTDs and similar mobile storage between the 2H- and 3H-derived estimates, despite statistically significant differences for certain measures of TTDs and storage. The streamflow mean travel time was estimated at 2.90±0.54 years, using 2H, and 3.12±0.59 years, using 3H (mean ± 1 SD – standard deviation). Both tracers consistently suggested that less than 10 % of the stream water in the Weierbach catchment is older than 5 years. The travel time differences between the tracers were small compared to previous studies in other catchments, and contrary to prior expectations, we found that these differences were more pronounced for young water than for old water. The found differences could be explained by the calculation uncertainties and by a limited sampling frequency for tritium. We conclude that stable isotopes do not seem to systematically underestimate travel times or storage compared to tritium. Using both stable and radioactive isotopes of H as tracers reduced the travel time and storage calculation uncertainties. Tritium and stable isotopes both had the ability to reveal short travel times in streamflow. Using both tracers together better exploited the more specific information about longer travel times that 3H inherently contains due to its radioactive decay. The two tracers thus had different information contents overall. Tritium was slightly more informative than stable isotopes for travel time analysis, despite a lower number of tracer samples. In the future, it would be useful to similarly test the consistency of travel time estimates and the potential differences in travel time information contents between those tracers in catchments with other characteristics, or with a considerable fraction of stream water older than 5 years, since this could emphasize the role of the radioactive decay of tritium in discriminating younger water from older water.

The Time-Dependent Vehicle Routing Problem with Time Windows and Road-Network Information

Most time-dependent vehicle routing problems are based on a similar modeling paradigm: travel time information is represented by travel time functions between pairs of points of interest (e.g., depot or customers). Only a few papers investigate how these functions can be computed using the available travel time information. Furthermore, most of them neglect the possibility that different paths could be selected in the road network depending on the compromises they offer between cost (distance) and travel time. In this paper, we propose a new setting where travel time functions are defined on road-network arcs. We consider the Time-Dependent Vehicle Routing Problem with Time Windows and solve it with a branch-and-price algorithm. As far as we know, this is the first exact approach for a time-dependent vehicle routing problem when travel time functions are initially defined on the segments of a road-network.

Finite mixture survival model for examining the variability of urban arterial travel time for buses, passenger cars and taxis

Examining the travel time variability (TTV) of buses, passenger cars and taxis is essential to obtain reliable travel time in urban daily trips. TTV analyses of three travel modes are conducted using travel time data collected on two urban arterial roads in Xi'an City. Firstly, the TTV is evaluated using statistical indexes. The results reveal that the TTV differs from vehicle to vehicle, period to period and site to site. Secondly, the finite mixture survival model is proposed to address the heterogeneity of travel time data by decomposing the population into several sub-populations. Wasserstein distance and Kolmogorov–Smirnov test are used to further compare the sub-populations of different vehicle types during different periods on different roads. Finally, based on the model analysis, it can be found that the finite mixture survival model is an accurate tool to examine the variability by capturing the heterogeneity of travel time data. The difference among the sub-populations suggests different travel behaviours. It concludes that more diverse travel behaviours result in higher TTV. An accurate investigation on TTV is valuable for travellers’ mode choices and transportation management agencies to obtain reliable travel time information and improve traffic efficiency.

Wave Equation‐Based Local Traveltime Inversion

AbstractFull waveform inversion (FWI) is a strongly nonlinear optimization problem, which suffers from cycle skipping when the initial velocity model is not good enough or the seismic data lack low frequencies. Traveltime tomography is often used to invert the low‐wave number components of a velocity model, but traveltime inversion results show a low‐resolution model. To bridge the inversion wave number gap between the traveltime tomography and the FWI, a wave equation‐based local travetime inversion is proposed. In this case, the cross‐correlation algorithm is applied to calculate the traveltime differences between the localized synthetic and observed data. To mitigate traveltime crosstalk noise caused by multiple seismic events, a sliding Gaussian window is applied to transform the seismic data into local domain. In this way, the traveltime information of different seismic events can be simultaneously used to improve the inversion results. The numerical examples show that the combination of Local Traveltime Inversion (LTI) and FWI successfully mitigate the cycle skipping and produce satisfactory inversion results, even if the seismic data lack low frequencies and the initial velocity model is far from the true one.

Akıllı Trafik Sistemlerinde(ITS), Bluetooth Sensör Verileri Yardımıyla Seyahat Süresi Tahmini Gerçekleştirme

Travel time plays a major role in handling the traffic rate. Bluetooth technology is one of the approaches this time observable. Traffic tracking, vehicle determination on a certain route, and travel time information can be obtaine dusing the bluetooth data gathered using this tool. The Bluetooth technology will be used to analyze certain features affecting travel time results. Highway travel time can be used as a new and efficient data collection tool through the bluetooth sensors which are widely used today. The central control software system consists of a comprehensive system for storing and organizing data at a central location, processing data in vehicles and displaying it to drivers. The central system architecture can be used to display congested road data to the driver, including scenarios, text messages and visuals, identified by traffic information message signs (VMS), which are also linked to the system on the particular highway via a data fusion process in line with data from a variety of sources, for example sensors. Providing information about travel time distribution, both average and variance, will play a more effective role in drivers' high likelihood of arriving on time and in selecting efficient routes. In order to determine the travel time flow, an inhomogeneous data fusion tracking is performed by combining the scattered collected data with distance detectors. With this method preferred in the research, road travel time flows are determined with the help of sensors. The travel time of the roads without sensors is obtained from the data of GPS-based service providers. In addition to the travel time flow, the Dempster-Shafer theory is combined with the travel time results from the distance sensors. Based on the travel time results obtained, the method of improvement in travel time flow has been developed.

Predicting Travel Times of Bus Transit in Washington, D.C. Using Artificial Neural Networks

This study aimed to develop travel time prediction models for transit buses to assist decision-makers improve service quality and patronage. Six-months’ worth of Automatic Vehicle Location and Automatic Passenger Counting data for six Washington Metropolitan Area Transit Authority bus routes operating in Washington, DC was used for this study. Artificial Neural Network (ANN) models were developed for predicting travel times of buses for different peak periods. The analysis included variables such as length of route between stops, average dwell time and number of intersections between bus stops amongst others. Quasi-Newton algorithm was used to train the data to obtain the ideal number of perceptron layers that generated the least amount of error for all peak models. Comparison of the Normalized Squared Errors generated during the training process was done to evaluate the models. Travel time equations for buses were obtained for different peaks using ANN. The results indicate that the prediction models can effectively predict bus travel times on selected routes during different peaks of the day with minimal percentage errors. These prediction models can be adapted by transit agencies to provide patrons with more accurate travel time information at bus stops or online. Doi: 10.28991/cej-2020-03091615 Full Text: PDF

Delay‐aware privacy‐preserving location‐based services under spatiotemporal constraints

SummaryThe ubiquitous use of location‐based services (LBS) through smart devices produces massive amounts of location data. An attacker, with an access to such data, can reveal sensitive information about users. In this paper, we study location inference attacks based on the probability distribution of historical location data, travel time information between locations using knowledge of a map, and short and long‐term observation of privacy‐preserving queries. We show that existing privacy‐preserving approaches are vulnerable to such attacks. In this context, we propose a novel location privacy‐preserving approach, called KLAP, based on the three fundamental obfuscation requirements: minimum k‐locations, l‐diversity, and privacy area preservation. KLAP adopts a personalized privacy preference for sporadic, frequent, and continuous LBS use cases. Specifically, it generates a secure concealing region (CR) to obfuscate the user's location and directs that CR to the service provider. The main contribution of this work is twofold. First, a CR pruning technique is devised to establish a balance between privacy and delay in LBS usage. Second, a new attack model called a long‐term obfuscated location tracking attack, and its countermeasure is proposed and evaluated both theoretically and empirically. We assess KLAP with two real‐world datasets. Experimental results show that it can achieve better privacy, reduced delay, and lower communication costs than existing state‐of‐the‐art methods.

Multi-Stage Quantum Secure Direct Communication Using Secure Shared Authentication Key

The security of Quantum Secure Direct Communication (QSDC) and its authentication procedure based on multiple stages is analyzed. The security analysis shows that the process of authentication is required to be done three times based on the usage of unitary transformation that is only known between Alice and Bob. In the proposed protocol, a secure quantum handshake is utilized to share the secret polarization angle and an authentication key at the initial stage of authentication over the quantum channel. The symmetry key is used in this work to protect user data communication within the QSDC protocol, where the same secret key is used to encrypt and decrypt the message. This work adopts the information travel time (ITT) by allowing the sender to detect any interference from third parties. In addition, the operation of the Pauli-X quantum gate increases Eve’s difficulty in stealing the information. The information transmitted is then continued by sending photons once in the quantum channel, which improves the efficiency without losing the message’s security. In addition, to securely transfer the stream of messages, the proposed protocol is operated in single-stage, and the authentication is applied bit-by-bit, thus reducing the transmission time. Security checks are carried out along the data transmission process. Compared to previous protocols, this new initial authentication protocol has remarkable advantages since it does not require public communication to pre-share the authentication key and secret angles before the onset of the transmission, therefore, reducing the communication cost. Moreover, the secret authentication key and polarization angles are updated after a number of bits are sent to increase the security level. The verification process is also conducted to ensure the symmetry of the sender and receiver. The analyses presented herein demonstrate that the proposed authentication protocol is simple and secure in order to ensure the legitimacy of the users.

Performance Analyses of Information-Based Managed Lane Choice Decisions in a Connected Vehicle Environment

Connected vehicle (CV) technology can connect, communicate, and share information between vehicles, infrastructure, and other traffic management systems. Recent research has examined and promoted CV and connected automated vehicle (CAV) technology on managed lane systems to increase capacity and reduce congestion, as managed lane systems could be equipped with advanced infrastructure relatively quickly. However, the effect on travel considering, information-based managed lane choice decisions in a CV environment is not clear. Therefore, this research analyzed the potential effects on a managed lane system with connected vehicles considering several travel behavior elements, including drivers’ willingness to reroute and their choice of managed lanes based on individual travel time savings. This study analyzed the potential effects on a managed lane system by assigning different market penetration rates (0%, 10%, 50%, 100%) of CVs and informing CV drivers about travel time savings for a 10-mi stretch at 5-min intervals. How the traffic performance measurements (i.e., throughput, travel time saving, average speed and average travel time) vary under different market penetration rates of CVs is then investigated. Two major conclusions are reached: (i) although information exchange was assumed to be instantaneous between vehicles and the system, there existed a response time (or time delay) in the macroscopic traffic reflection; (ii) managed lane use may decrease, when travel time information becomes available, since drivers perceive they are saving more travel time than they actually do save.

Freeway Travel Time Prediction Using Deep Hybrid Model – Taking Sun Yat-Sen Freeway as an Example

As the population keeps growing, traffic congestion happens more and more often. Consequently, travel time has become an important indicator of driving experience. Accurate travel time information helps drivers plan their route more wisely and thus effectively alleviate traffic congestion. In this research, we propose a vehicle travel time prediction model for freeway traffic. The data used in this research are derived from the traffic dataset of the Taiwan Freeway Bureau, and the travel time prediction is made for the Sun Yat-sen Freeway between Taipei and Hsinchu. First, the missing value of the raw data is imputed by Autoencoder. The data are then segmented according to time series and are used to build the prediction model. To effectively capture the hidden features required to predict the travel time for the vehicle traveling on the freeway, a deep learning architecture is adopted in our system, which includes the GRU neural network model, the XGBoost model, and the Hybrid model that combines the GRU and XGBoost through linear regression. To increase computational efficiency, the travel time predictions for consecutive toll gates every 5 minutes apart are pre-computed offline, so that the online travel time prediction of the whole trip can be obtained by simply summing up a few numbers. Experimental results based on actual traffic data show that the proposed system can achieve good performance in terms of prediction accuracy and execution time.

Impact on Road Safety and Operation of Rerouting Traffic in Rural Travel Time Information System.

The Travel Time Information System (TTIS) is an Intelligent Traffic Control System installed in Poland. As is common, travel time is the only factor in the decision about rerouting traffic, while a route recommendation may consider multiple criteria, including road safety. The aim of the paper is to analyze the safety level of the entire road network when traffic is rerouted on paths with different road categories, intersection types, road environments, and densities of access points. Furthermore, a comparison between traffic operation and road safety performance was carried out, considering travel time and delay, and we predicted the number of crashes for each possible route. The results of the present study allow for maximizing safety or traffic operation characteristics, providing an effective tool in the management of the rural road system. The paper provides a methodology that can be transferred to other TTISs for real-time management of the road network.

Quality of location-based crowdsourced speed data on surface streets: A case study of Waze and Bluetooth speed data in Sevierville, TN

Obtaining accurate speed and travel time information is a challenge for researchers, geographers, and transportation agencies. In the past, traffic data were usually acquired and disseminated by government agencies through fixed-location sensors. High costs, infrastructure demands, and low coverage levels of these sensor devices require agencies and researchers to look beyond the traditional approaches. With the emergence of smartphones and navigation apps, location-based and crowdsourced Big Data are receiving increased attention. In this regard, location-based big data (LocBigData) collected from probe vehicles and road users can be used to provide speed and travel time information in different locations. Examining the quality of crowdsourced data is essential for researchers and agencies before using them. This study assessed the quality of Waze speed data from surface streets and conducted a case study in Sevierville, Tennessee. Typically, examining the quality of these data in surface streets and arterials is more challenging than freeways data. This research used Bluetooth speed data as the ground truth, which is independent of Waze data. In this study, three steps of methodology were used. In the first step, Waze speed data was compared to Bluetooth data in terms of accuracy, mean difference, and distribution similarity. In the second step, a k-means algorithm was used to categorize Waze data quality, and a multinomial logistics regression model was performed to explore the significant factors that impact data quality. Finally, in the third step, machine learning techniques were conducted to predict the data quality in different conditions. The result of the comparison showed a similar pattern and a slight difference between datasets, which verified the quality of Waze speed data. The statistical model indicates that that Waze speed data are more accurate in peak hours than in night hours. Also, the traffic speed, traffic volume, and segment length have a significant association on the accuracy of Waze data on surface streets. Finally, the result of machine learning prediction showed that a KNN method performed the highest prediction accuracy of 84.5% and 82.9% of the time for training and test datasets, respectively. Overall, the study results suggest that Waze speed data is a promising data source for surface streets.

Dealing with latency effects in travel time prediction on motorways

Real-time traffic information is now a crucial part of operating a road network. The quality, accuracy and reliability of such information is critical to the road operators and users. Real-time travel time prediction methods using Automatic Number Plate Recognition cameras or Bluetooth/Wi-Fi readers that use matching algorithms to generate travel times in real-time can be vulnerable to an inherent latency issue. Measured travel times are based on vehicles that have already completed the journey and may not be representative for users about to commence that same journey. The aim of this research was to identify the latency in travel time prediction, quantify its effect, and develop a model to remove it. Datasets for the M50 motorway in Dublin, Ireland, were used to conduct the analysis. The results show that real-time travel times can be more accurately predicted when combined with historical travel time information. The approach was found to be valid and achievable and the developed tool can predict and inform both road operators and users during regular periods of congestion. The project also identified other data sources, such as real-time Automated Incident Detection (AID) loop data, incident and weather data, that can further enhance the predicted travel time calculation.

Data Processing Techniques for Real-Time Traveler Information: Use of Dedicated Short-Range Communications Probes on Suburban Arterial

Background:As wireless communication technologies evolve, probe-based travel-time collection systems are becoming popular around the globe. However, two problems generally arise in probe-based systems: one is the outlier and the other is time lag. To resolve the problems, methods for outlier removal and travel-time prediction need to be applied.Methods:In this study, data processing methods for addressing the two issues are proposed. After investigating the characteristic of the travel times on the test section, the modified z-score was suggested for censoring outliers contained in probe travel times. To mitigate the time-lag phenomenon, a recurrent neural network, a class of deep learning where temporal sequence data are normally treated, was applied to predict travel times.Results:As a result of evaluation with ground-truth data obtained through test-car runs, the proposed methods showed enhanced performances with prediction errors lower than 13% on average compared to current practices.Conclusion:The suggested methods can make drivers to better arrange their trip schedules with real-time travel-time information with improved accuracy.

A generative adversarial network for travel times imputation using trajectory data

AbstractKnowledge of travel times serves an important role in traffic control and management. As an increasingly popular data source, vehicle trajectories can provide large‐scale travel time information. However, real‐world travel time information extracted from sparse or low‐resolution trajectory data often contains missing data that need to be imputed for further traffic analysis. Thus, this study proposes a travel times imputation generative adversarial network (TTI‐GAN) for travel times imputation. Considering the network‐wide spatiotemporal correlations, the TTI‐GAN can generate travel times for links without sufficient observations by modeling travel time distributions (TTDs) for links with rich data. Then, numerical experiments are carried out with trajectory data from Didi Chuxing. The results show that the TTI‐GAN can well estimate link TTDs and performs better than other counterparts in imputing mean travel times under various data missing rates.

Three-dimensional data-domain full traveltime inversion using a practical workflow of early arrival selection

A new method of data-domain full traveltime inversion (FTI) is proposed to estimate the near-surface velocity model using early arrivals in seismic shot gathers. Data-domain FTI is capable of generating a background velocity model from which the predicted early arrivals can kinematically match the observed ones. Such a match is measured and quantified in terms of the crosscorrelation function between the computed and observed traces. Our method aims to find an optimal estimated velocity model that minimizes the crosscorrelation computed from the selected early arrivals. The early arrivals are isolated via a sequence of operations, including the [Formula: see text]-[Formula: see text] scan, autopicking, multidomain quality control, and guide interpolation. Because windows, rather than exact arrival times, are constructed, the difficulties encountered while picking precise arrivals are reduced. In addition, the gradient of data-domain FTI is derived based on an amplitude-constrained optimization problem, which makes the gradient essentially different from that derived with the Born approximation in which no constraint is used. The constraint requires the inversion to honor traveltime information only, and it thus ignores any amplitude changes caused by velocity variations. This method is validated using 3D synthetic as well as field data sets. The results show that data-domain FTI, combined with the early arrival selection workflow, is able to generate reasonable background velocities that kinematically match the predicted early arrivals with the observed ones, and the associated depth-domain images are clearly improved.

Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior.

The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for five origin-destination pairs over multiple days after providing them with dynamically updated travel time information (average travel time and travel time variability). The results demonstrate that historical travel time information enhances behavioral rationality by 10% on average and reduces inertial tendencies to increase risk seeking in the gain domain. Furthermore, expected travel time information is demonstrated to be more effective than travel time variability information in enhancing rational behavior when drivers have limited experiences. After drivers gain sufficient knowledge of routes, however, the difference in behavior associated with the two information types becomes insignificant. The results also demonstrate that, when drivers lack experience, the faster less reliable route is more attractive than the slower more reliable route. However, with cumulative experiences, drivers become more willing to take the more reliable route given that they are reluctant to become risk seekers once experience is gained. Furthermore, the effect of information on driver behavior differs significantly by participant and trip, which is, to a large extent, dependent on personal traits and trip characteristics.