Bibliografías temáticas / Time travel

Literatura académica sobre el tema "Time travel"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 10 de marzo de 2023

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Artículos de revistas sobre el tema "Time travel"

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Barkasi, Michael y Melanie G. Rosen. "Is mental time travel real time travel?" Philosophy and the Mind Sciences 1, n.º 1 (26 de mayo de 2020): 1–27. http://dx.doi.org/10.33735/phimisci.2020.1.28.

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Episodic memory (memories of the personal past) and prospecting the future (anticipating events) are often described as mental time travel (MTT). While most use this description metaphorically, we argue that episodic memory may allow for MTT in at least some robust sense. While episodic memory experiences may not allow us to literally travel through time, they do afford genuine awareness of past-perceived events. This is in contrast to an alternative view on which episodic memory experiences present past-perceived events as mere intentional contents. Hence, episodic memory is a way of coming into experiential contact with, or being again aware of, what happened in the past. We argue that episodic memory experiences depend on a causal-informational link with the past events being remembered, and that, assuming direct realism about episodic memory experiences, this link suffices for genuine awareness. Since there is no such link in future prospection, a similar argument cannot be used to show that it also affords genuine awareness of future events. Constructivist views of memory might challenge the idea of memory as genuine awareness of remembered events. We explain how our view is consistent with both constructivist and anti-causalist conceptions of memory. There is still room for an interpretation of episodic memory as enabling genuine awareness of past events, even if it involves reconstruction.
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Somogyvári, Márk, Peter Bayer y Ralf Brauchler. "Travel-time-based thermal tracer tomography". Hydrology and Earth System Sciences 20, n.º 5 (12 de mayo de 2016): 1885–901. http://dx.doi.org/10.5194/hess-20-1885-2016.

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Abstract. Active thermal tracer testing is a technique to get information about the flow and transport properties of an aquifer. In this paper we propose an innovative methodology using active thermal tracers in a tomographic setup to reconstruct cross-well hydraulic conductivity profiles. This is facilitated by assuming that the propagation of the injected thermal tracer is mainly controlled by advection. To reduce the effects of density and viscosity changes and thermal diffusion, early-time diagnostics are used and specific travel times of the tracer breakthrough curves are extracted. These travel times are inverted with an eikonal solver using the staggered grid method to reduce constraints from the pre-defined grid geometry and to improve the resolution. Finally, non-reliable pixels are removed from the derived hydraulic conductivity tomograms. The method is applied to successfully reconstruct cross-well profiles as well as a 3-D block of a high-resolution fluvio-aeolian aquifer analog data set. Sensitivity analysis reveals a negligible role of the injection temperature, but more attention has to be drawn to other technical parameters such as the injection rate. This is investigated in more detail through model-based testing using diverse hydraulic and thermal conditions in order to delineate the feasible range of applications for the new tomographic approach.
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Seow, Victor. "Time/Travel". Historical Studies in the Natural Sciences 51, n.º 3 (1 de junio de 2021): 420–27. http://dx.doi.org/10.1525/hsns.2021.51.3.420.

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Lopez, Ruth Palan y Elaine J. Amella. "Time Travel". Research in Gerontological Nursing 4, n.º 2 (31 de agosto de 2010): 127–34. http://dx.doi.org/10.3928/19404921-20100729-02.

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Danos, Stephen. "Time Travel". Cream City Review 37, n.º 1 (2013): 69. http://dx.doi.org/10.1353/ccr.2013.0019.

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Santoro, Jonathan D. "Time Travel". Journal of Palliative Medicine 21, n.º 9 (septiembre de 2018): 1366. http://dx.doi.org/10.1089/jpm.2018.0121.

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Ahronheim, Sara R. "Time Travel". CJEM 22, n.º 3 (27 de febrero de 2020): 389–90. http://dx.doi.org/10.1017/cem.2019.461.

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Logan, Bruce. "Time Travel". Environmental Science & Technology Letters 1, n.º 1 (14 de enero de 2014): 1. http://dx.doi.org/10.1021/ez400200t.

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Chan, K. S., William H. K. Lam y Mei Lam Tam. "Real-Time Estimation of Arterial Travel Times with Spatial Travel Time Covariance Relationships". Transportation Research Record: Journal of the Transportation Research Board 2121, n.º 1 (enero de 2009): 102–9. http://dx.doi.org/10.3141/2121-11.

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Sorensen, Roy A. "Time Travel, Parahistory and Hume". Philosophy 62, n.º 240 (abril de 1987): 227–36. http://dx.doi.org/10.1017/s0031819100064068.

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Should I have the opportunity to travel back in time to converse with great philosophers, Hume would be high on my itinerary. My gratitude for his insights and hospitality would surely tempt me to reciprocate by telling him about my time travels. But I fear he would not believe me. For the reasoning underlying Hume's famous scepticism about miracles dooms my tales of time travel to an incredulous reception. The ensuing paragraphs will be dedicated to an elucidation of this fear. This elucidation is of more than historical interest, since Hume's reasoning about miracles still strikes many contemporary philosophers y as cogent. Thus the scepticism about time travel that I attribute to Hume should also be shared by his followers. It should be noted that the scepticism at issue is epistemological rather than metaphysical. The key question will not be ‘Is time travel possible?’ We shall instead ask whether it is possible to justify a belief in a report of time travel. The metaphysical issue will only be addressed in response to the question of whether one can be an epistemological sceptic about time travel without being a metaphysical sceptic.
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Tesis sobre el tema "Time travel"

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Wan, Ke. "Estimation of Travel Time Distribution and Travel Time Derivatives". Thesis, Princeton University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3642164.

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Given the complexity of transportation systems, generating optimal routing decisions is a critical issue. This thesis focuses on how routing decisions can be computed by considering the distribution of travel time and associated risks. More specifically, the routing decision process is modeled in a way that explicitly considers the dependence between the travel times of different links and the risks associated with the volatility of travel time. Furthermore, the computation of this volatility allows for the development of the travel time derivative, which is a financial derivative based on travel time. It serves as a value or congestion pricing scheme based not only on the level of congestion but also its uncertainties. In addition to the introduction (Chapter 1), the literature review (Chapter 2), and the conclusion (Chapter 6), the thesis consists of two major parts:

In part one (Chapters 3 and 4), the travel time distribution for transportation links and paths, conditioned on the latest observations, is estimated to enable routing decisions based on risk. Chapter 3 sets up the basic decision framework by modeling the dependent structure between the travel time distributions for nearby links using the copula method. In Chapter 4, the framework is generalized to estimate the travel time distribution for a given path using Gaussian copula mixture models (GCMM). To explore the data from fundamental traffic conditions, a scenario-based GCMM is studied. A distribution of the path scenario representing path traffic status is first defined; then, the dependent structure between constructing links in the path is modeled as a Gaussian copula for each path scenario and the scenario-wise path travel time distribution is obtained based on this copula. The final estimates are calculated by integrating the scenario-wise path travel time distributions over the distribution of the path scenario. In a discrete setting, it is a weighted sum of these conditional travel time distributions. Different estimation methods are employed based on whether or not the path scenarios are observable: An explicit two-step maximum likelihood method is used for the GCMM based on observable path scenarios; for GCMM based on unobservable path scenarios, extended Expectation Maximum algorithms are designed to estimate the model parameters, which introduces innovative copula-based machine learning methods.

In part two (Chapter 5), travel time derivatives are introduced as financial derivatives based on road travel times—a non-tradable underlying asset. This is proposed as a more fundamental approach to value pricing. The chapter addresses (a) the motivation for introducing such derivatives (that is, the demand for hedging), (b) the potential market, and (c) the product design and pricing schemes. Pricing schemes are designed based on the travel time data captured by real time sensors, which are modeled as Ornstein-Uhlenbeck processes and more generally, continuous time auto regression moving average (CARMA) models. The risk neutral pricing principle is used to generate the derivative price, with reasonably designed procedures to identify the market value of risk.

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Gilmore, G. Matthew. "The philosophy of time travel". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq24846.pdf.

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Radtke, William Joseph. "Three Attempts at Time Travel". OpenSIUC, 2016. https://opensiuc.lib.siu.edu/theses/1909.

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Three Attempts at Time Travel is a piece for wind ensemble that is approximately ten minutes in length. It consists of a brief introduction and three continuous movements based on the same starting material. It is semi-programmatic, but it is not an attempt to depict a specific narrative. Rather, it portrays a scenario involving time travel. The concept of the piece begins with a person who uses a time machine to travel back to a certain point in time to change the result of an event in the their own life. During the piece, the person goes back to the same starting point three times with each “restart” resulting in a different outcome. On the final restart, a positive resolution is reached, but it is ultimately a conflicted ending because it is not the ending that the time traveler was aiming for. At the end of the introduction and each of the first two movements, a “time machine” motive consisting of: a Mahler hammer, a concert bass drum, a drum set, a thunder sheet, a suspended cymbal and a triangle occurs to signal the return to the original point in time.
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Nykvist, Joar. "Data-Driven Travel Time Prediction". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255023.

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Being able to accurately forecast the time of arrival of a vehicle in traffic appeals both to private drivers aiming to keep up with their schedules, and to businesses that need to organize transport logistics. THis thesis is assigned by the Swedish truck manufacturer Scania CV AB, and sets out to use GPS data from Scania's vehicle fleet to train Machine LEarning models to predict the travel times of vehicles between stops. The predictive models implemented train on features engineered from quite simple information from the vehicles, yet reach high predictive accuracy in certain scenarios. Two approaches to predicting travel time are tested, one referred to as the Local Models approach, and the other as the Global Model approach. In the Local Models approach, many separate regressors are trained on geographical subsets of the datra and then comnbined to give global predictions. In the Global Model approach, a single regressor trains on the entire data set. The Global Model approch gives better performance than that of the Local Models in the experiment, but the Local Models approach shows some promising tendencies. It is found that a regressor predicts significantly more accurately when the geographical spread of the data is limited.
Att ge noggranna förutsager om restider för fordon i trafik är av intresse både för privata förare som försöker hinna med sina scheman, och företag som behöver organiska logistik för transporter. Denna rapport görs på uppdrag av svenska lastbilstillverkaren Scania CV AB, och har som mål att använda GPS-data från Scanias fordon till att träna maskininlärningsmodeller för att förutsäga hur lång tid det tar för fordon att resa mellan stopp. De prediktiva modellerna som implementeras använder variabler som konstrueras från relativt enkel information från fordonen, men lyckas ändå nå hög prediktiv prestanda i vissa scenarion. Två olika ansatser prövas för att förutsäga restider, en ansats med lokala modeller, och en ansats med global modell. I ansatsen med lokala modeller tränas flera separata regressorer på geografiska delmängder av datan, vilka sedan kombineras och ger globala förutsägor. I ansatsen med global modell tränas en enda regressor på hela datamängden. Ansatsen med global modell visar sig ha högre prestanda i experimenten, men ansatsen med lokala modeller förevisar ändå vissa lovande tendenser. Resultaten antyder att en regressor ger mycket noggrannare prediktioner när den geografiska spridningen i datan begränsas.
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Swärdh, Jan-Erik. "Commuting time choice and the value of travel time". Doctoral thesis, Örebro universitet, Handelshögskolan vid Örebro universitet, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-8524.

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In the modern industrialized society, a long commuting time is becoming more and more common. However, commuting results in a number of different costs, for example, external costs such as congestion and pollution as well as internal costs such as individual time consumption. On the other hand, increased commuting opportunities offer welfare gains, for example via larger local labor markets. The length of the commute that is acceptable to the workers is determined by the workers' preferences and the compensation opportunities in the labor market. In this thesis the value of travel time or commuting time changes, has been empirically analyzed in four self-contained essays. First, a large set of register data on the Swedish labor market is used to analyze the commuting time changes that follow residential relocations and job relocations. The average commuting time is longer after relocation than before, regardless of the type of relocation. The commuting time change after relocation is found to differ substantially with socio-economic characteristics and these effects also depend on where the distribution of commuting time changes is evaluated. The same data set is used in the second essay to estimate the value of commuting time (VOCT). Here, VOCT is estimated as the trade-off between wage and commuting time, based on the effects wage and commuting time have on the probability of changing jobs. The estimated VOCT is found to be relatively large, in fact about 1.8 times the net wage rate. In the third essay, the VOCT is estimated on a different type of data, namely data from a stated preference survey. Spouses of two-earner households are asked to individually make trade-offs between commuting time and wage. The subjects are making choices both with regard to their own commuting time and wage only, as well as when both their own commuting time and wage and their spouse's commuting time and wage are simultaneously changed. The results show relatively high VOCT compared to other studies. Also, there is a tendency for both spouses to value the commuting time of the wife highest. Finally, the presence of hypothetical bias in a value of time experiment without scheduling constraints is tested. The results show a positive but not significant hypothetical bias. By taking preference certainty into account, positive hypothetical bias is found for the non-certain subjects.
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Swärdh, Jan-Erik. "Commuting time choice and the value of travel time /". Örebro : Örebro Universitet, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-8524.

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Alvarez, Patricio A. "A Methodology to Estimate Time Varying User Responses to Travel Time and Travel Time Reliability in a Road Pricing Environment". FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/631.

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Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.
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Busby, Janie Amber. "The development of mental time travel /". [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18615.pdf.

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Sigakova, Ksenia. "Road Freight Transport Travel Time Prediction". Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3031.

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Road freight transport travel time estimation is an important task in fleet management and traffic planning. Goods often must be delivered in a predefined time window and any deviation may lead to serious consequences. It is possible to improve travel time estimation by considering more factors that may affect it. In this thesis work we identify factors that may affect travel time, find possible sources of information about them, propose a model for estimating travel time of heavy goods vehicles, and verify this model on real data. As results, the experiments showed that considering time related and weather related factors, it is possible to improve accuracy of travel time estimation. Also, it was shown that the influence of a particular factor on travel time depended on the considered road segment. Furthermore, it was shown that different data mining algorithms should be applied for different road segments in order to get the best estimation.
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Johansson, Martin. "Travel Time Estimations Using Bluetooth Data". Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112097.

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Restider är alltid intressanta, för de som använder vägen och de som är ansvariga för vägen. Det finns olika tekniker för att samla in information om restider, till exempel traditionella videobaserade system och nyare system som använder Bluetooth. Bluetooth är en kostnadseffektiv metod som använder väganvändarnas olika elektroniska enheter, till exempel smartphones eller trådlösa headsets, för att samla in information om restider. Den infrastruktur som behövs för att mäta restid är två stycken Bluetooth-mottagare varav en placeras i början och en i slutet av vägsträckan samt en server som tar emot och analyserar insamlad data. Bluetooth fungerar bra för att samla in information om restider, men är tekniken möjlig att använda för att identifiera restider för specifika transportslag? I det här projektet undersöks möjligheterna att identifiera restider som hör till cyklister i Bluetooth-data från stadstrafik. För att de restider som samlas in ska vara intressanta måste de rensas på så kallade outliers, det vill säga observationer som inte representerar den faktiska restiden på vägsträckan. Projektet undersöker därför ett antal olika filtreringsalgoritmer för att kunna rensa insamlad data från outliers. Filtreringsalgoritmerna jämförs först med varandra för att se vilken eller vilka som fungerar bäst i olika trafikmiljöer. Därefter jämförs filtreringsalgoritmerna med de interna filtreringsalgoritmerna som leverantören av Bluetooth-mottagarna, BLIP Systems, använder i sitt analysverktyg för att erbjuda en helhetslösning. Projektets slutsatser är att Bluetooth fungerar bra som insamlingsteknik för restidsinformation och att de restider som ges inte är signifikant skilda från restider som samlas in med andra, mer traditionella, insamlingstekniker. Det går dock inte att dra några konkreta slutsatser vad gäller möjligheterna att identifiera cyklister i blandad stadstrafik på den vägsträcka som testas i projektet. Detta på grund av att bilar och cyklister har för lika restid på vägsträckan. Metoden som används bör dock testas på ytterligare vägsträckor innan det är möjligt att fastslå om den fungerar eller inte. Av de olika filtreringsalgoritmerna som testas är det en Box och Whisker-algoritm, som använder IQR-teknik för filtreringen, som presterar bäst på indata från stadstrafik och indata från motorväg. Indata från mellanstora vägar tas inte med i resultatet. När Box och Whisker-algoritmen jämförs med Bluetooth-mottagarnas interna filtreringsalgoritmer är resultatet att Bluetooth-mottagarnas filtreringsalgoritmer ger ett något bättre resultat än Box och Whisker-algoritmen, men att den ändå ger ett fullgott resultat.
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Libros sobre el tema "Time travel"

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Time travel. Detroit: KidHaven Press, 2009.

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Nahin, Paul J. Time travel. Cincinnati, Ohio: Writer's Digest Books, 1997.

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Time travel. Edina, Minn: ABDO Pub. Co., 2007.

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Arias, Lisa. Time travel. Vero Beach, Florida]: Rourke Educational Media, 2015.

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Time travel. London: Faber, 2006.

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Kajioka, Miho. Time travel. Schiedam: De Ketelfactory, 2019.

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Macvey, John W. Time travel. Chelsea, Mich: Scarborough House, 1990.

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Jolley, Dan. Time travel. Minneapolis: Graphic Universe, 2008.

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Reverse time travel. London: Cassell, 1996.

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Parker, Barry. Cosmic Time Travel. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-6136-5.

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Capítulos de libros sobre el tema "Time travel"

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Anoshin, Dmitry, Dmitry Shirokov y Donna Strok. "Time Travel". En Jumpstart Snowflake, 251–59. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5328-1_14.

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Webb, Stephen. "Time Travel". En All the Wonder that Would Be, 119–49. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51759-9_5.

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Kirchmann, Kay. "Time Travel". En Critical Terms in Futures Studies, 313–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28987-4_48.

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Massey, Anne. "Time travel". En The Interior Architecture Theory Reader, 234–41. New York : Routledge, 2017.: Routledge, 2018. http://dx.doi.org/10.4324/9781315693002-28.

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Rea, Michael. "Time travel". En Metaphysics, 90–101. Second edition. | Abingdon, Oxon; New York, NY: Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9780429027444-6.

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Spieler, Christof. "Travel Time". En Trains, Buses, People, 28–29. Washington, DC: Island Press/Center for Resource Economics, 2018. http://dx.doi.org/10.5822/978-1-61091-904-3_12.

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Singha Deo, P. "Time Travel". En Mesoscopic Route to Time Travel, 101–16. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4465-8_6.

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McGee, Brenda y Debbie Keiser. "Time Travel". En Reader's Theater... and So Much More! Grades 5-6, 89–90. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003237556-17.

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Banister, David. "Time and Travel". En Methods and Models in Transport and Telecommunications, 35–333. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-28550-4_17.

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Soriguera Martí, Francesc. "Travel Time Definitions". En Highway Travel Time Estimation With Data Fusion, 43–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48858-4_2.

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Actas de conferencias sobre el tema "Time travel"

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Meyrowitz, Norman K. "Time Travel". En HT '20: 31st ACM Conference on Hypertext and Social Media. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3406853.3432661.

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Buzzo, Daniel. "Time Travel: Time Dilation". En Electronic Visualisation and the Arts (EVA 2014). BCS Learning & Development, 2014. http://dx.doi.org/10.14236/ewic/eva2014.40.

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Tran, Nam Khanh, Andrea Ceroni, Nattiya Kanhabua y Claudia Niederée. "Time-travel Translator". En WWW '15: 24th International World Wide Web Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2740908.2742841.

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Tang, John, Jennifer Marlow, Aaron Hoff, Asta Roseway, Kori Inkpen, Chen Zhao y Xiang Cao. "Time travel proxy". En the 2012 ACM annual conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2207676.2208725.

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Morton, S. "Automated Travel Time Estimation". En EAGE Winter Symposium - Reservoir Geophysics, the Road Ahead. European Association of Geoscientists & Engineers, 1996. http://dx.doi.org/10.3997/2214-4609.201406832.

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Aichelburg, P. C. "Wormholes and time travel". En HADRONS AND NUCLEI: First International Symposium. AIP, 2000. http://dx.doi.org/10.1063/1.1290914.

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Zhou, Min y Gerard T. Schuster. "Interferometric travel time tomography". En SEG Technical Program Expanded Abstracts 2000. Society of Exploration Geophysicists, 2000. http://dx.doi.org/10.1190/1.1815871.

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Phang, Khoo Yit, Jeffrey S. Foster y Michael Hicks. "Expositor: Scriptable time-travel debugging with first-class traces". En 2013 35th International Conference on Software Engineering (ICSE). IEEE, 2013. http://dx.doi.org/10.1109/icse.2013.6606581.

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Park, Sangjun, Hesham Rakha y Feng Guo. "Multi-state travel time reliability model: Impact of incidents on travel time reliability". En 2011 14th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/itsc.2011.6082874.

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Day, Christopher, Stephen Remias, Howell Li, Michelle Mekker, Margaret McNamara, Edward Cox y Darcy Bullock. "Performance Ranking of Arterial Corridors Using Travel Time and Travel Time Reliability Metrics". En Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316049.

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Informes sobre el tema "Time travel"

1

Martchouk, Maria, Fred Mannering y Lakhwinder Singh. Travel Time Reliability in Indiana. West Lafayette, Indiana: Purdue University, 2011. http://dx.doi.org/10.5703/1288284314263.

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Skarsoulis, Emmanuel, Bruce Cornuelle y Matthew Dzieciuch. Travel-Time and Amplitude Sensitivity Kernels. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2011. http://dx.doi.org/10.21236/ada571772.

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Chandrayadula, Tarun K. Travel Time Estimation Methods for Mode Tomography. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2010. http://dx.doi.org/10.21236/ada542281.

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Unnikrishnan, Avinash, Miguel Figliozzo y Subhash Kochar. Statistical Inference for Multimodal Travel Time Reliability. Transportation Research and Education Center (TREC), 2022. http://dx.doi.org/10.15760/trec.278.

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Skarsoulis, Emmanuel y Bruce Cornuelle. Travel-Time Sensitivity Kernels In Long-Range Propagation. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2007. http://dx.doi.org/10.21236/ada569085.

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Day, Christopher, Jason Wasson, Thomas Brennan y Darcy Bullock. Application of Travel Time Information for Traffic Management. Purdue University, agosto de 2012. http://dx.doi.org/10.5703/1288284314666.

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Mohammadian, Abolfazl, Homa Taghipour y Amir Bahador Parsa. Dynamic Travel Time Estimation for Northeast Illinois Expressways. Illinois Center for Transportation, junio de 2020. http://dx.doi.org/10.36501/0197-9191/20-012.

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Kim, David. Wireless Data Collection System for Real-Time Arterial Travel Time Estimates: Final Report. Portland State University Library, marzo de 2011. http://dx.doi.org/10.15760/trec.23.

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Tufte, Kristin. Assessment and Refinement of Real-Time Travel Time Algorithms for Use in Practice. Portland State University Library, octubre de 2008. http://dx.doi.org/10.15760/trec.91.

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Wu, Tong Qiang, Eil Kwon, Kevin Sommers, Michael Zhang y Ahsan Habib. Arterial Link Travel Time Estimation Using Loop Detector Data. Iowa City, Iowa: University of Iowa Public Policy Center, 1997. http://dx.doi.org/10.17077/zp8m-emq1.

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