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Chan, Ping-ching Winnie. "The value of travel time savings in Hong Kong". Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk:8888/cgi-bin/hkuto%5Ftoc%5Fpdf?B23425003.
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Lu, Chenxi. "Improving Analytical Travel Time Estimation for Transportation Planning Models". FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/237.
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This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.
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Chan, Ping-ching Winnie, e 陳冰淸. "The value of travel time savings in Hong Kong". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31954789.
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Respati, Sara Wibawaning. "Network-scale arterial traffic state prediction: Fusing multisensor traffic data". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/202990/1/Sara%20Wibawaning_Respati_Thesis.pdf.
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Road traffic congestion is an increasing societal problem. Road agencies and users seeks accurate and reliable travel speed information. This thesis developed a network-scale traffic state prediction based on Convolutional Neural Network (CNN). The method can predict the speed over the network accurately by preserving road connectivity and incorporating historical datasets. When dealing with an extensive network, the thesis also developed a clustering method to reduce the complexity of the prediction. By accurately predict the traffic state over a network, traffic operators can manage the network more effectively and travellers can make informed decision on their journeys.
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Soriguera, Martí Francesc. "Highway travel time estimation with data fusion". Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/108910.
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La informació sobre el temps de viatge és l'indicador clau en el funcionament operatiu d’una autopista i un dels inputs més apreciats pels seus usuaris. Tot i això, no ha estat fins molt darrerament que els operadors d'autopistes han començat a analitzar el trànsit de la carretera amb l’objectiu de proporcionar informació acurada sobre el temps de viatge. També recentment, les administracions titulars de les autopistes han començat a demanar que es proporcioni tal informació com a mesura del servei d'accessibilitat proporcionat per la carretera, en termes de qualitat i fiabilitat.
Durant el segle passat, els detectors d’espira magnètica jugaven un paper primordial en la monitorització del trànsit al proporcionar informació sobre el volum de trànsit i també sobre la velocitat i longitud mitjanes dels vehicles, encara que generalment amb menys precisió en aquests dos darrers casos. En les darreres dècades han aparegut noves tecnologies de control de trànsit (càmeres intel•ligents, seguiment mitjançant GPS o telèfon mòbil, identificació de dispositius bluetooth, nous detectors MeMS, etc.) que permeten millorar considerablement la recollida de dades sobre el temps de viatge. Algunes d’aquestes tecnologies són barates (bluetooth), d’altres no ho són (càmeres); però en qualsevol cas la major part de la xarxa d’autopistes encara està controlada per detectors d’espira magnètica. Té sentit doncs, emprar la seva informació bàsica i enriquir-la, quan calgui, amb noves fonts de dades.
Aquesta tesi presenta una metodologia nova i simple per a la previsió a curt termini del temps de viatge en autopistes de peatge basada en la fusió de dades provinents de detectors d’espira magnètica i de tiquets de peatge. La metodologia és genèrica i no és tecnològicament captiva: es podria generalitzar fàcilment a uns altres tipus de dades.
L'anàlisi Bayesià permet obtenir dades fusionades que són més fiables que les dades d’entrada originals, superant alguns problemes habituals en l’estimació del temps de viatge a partir de fonts úniques d’informació. La metodologia desenvolupada aporta valor afegit a les dades actuals (detectors d’espira i tiquets de peatge) en autopistes de peatge tancat, i aprofita al màxim (en termes d'estimació del temps de viatge) les dades disponibles, sense caure en la demanda recurrent i costosa d’una major necessitat de dades.
L'aplicació dels algoritmes a l’autopista de peatge AP-7 als voltants de Barcelona demostra empíricament la tesi: és possible desenvolupar un sistema acurat d'informació de temps de viatge, en temps real, en autopistes de peatge tancat amb la monitorització existent. Per això, d'ara en endavant els operadors d'autopistes podran oferir aquest valor afegit als seus clients sense gairebé cap inversió extra.Travel time information is the key indicator of highway management performance and one of the most appreciated inputs for highway users. Despite this relevance, the interest of highway operators in providing approximate travel time information is quite recent. Besides, highway administrations have also recently begun to request such information as a means to measure the accessibility service provided by the road, in terms of quality and reliability. In the last century, magnetic loop detectors played a role in providing traffic volume information and also, with less accuracy, information on average speed and vehicle length. New traffic monitoring technologies (intelligent cameras, GPS or cell phone tracking, Bluetooth identification, new MeMS detectors, etc.) have appeared in recent decades which permit considerable improvement in travel time data gathering. Some of the new technologies are cheap (Bluetooth), others are not (cameras); but in any case most of the main highways are still monitored by magnetic loop detectors. It makes sense to use their basic information and enrich it, when needed, with new data sources. This thesis presents a new and simple approach for the short term prediction of toll highway travel times based on the fusion of inductive loop detector and toll ticket data. The methodology is generic and it is not technologically captive: it could be easily generalized to other equivalent types of data. Bayesian analysis makes it possible to obtain fused estimates that are more reliable than the original inputs, overcoming some drawbacks of travel time estimations based on unique data sources. The developed methodology adds value and obtains the maximum (in terms of travel time estimation) of the available data, without falling in the recurrent and costly request of additional data needs. The application of the algorithms to empirical testing in AP-7 toll highway in Barcelona proves our thesis that it is possible to develop an accurate real-time travel time information system on closed toll highways with the existing surveillance equipment. Therefore, from now on highway operators can give this added value to their customers at almost no extra investment. Finally, research extensions are suggested, and some of the proposed lines are currently under development.
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Shen, Luou. "Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks". FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/17.
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Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.
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Yang, Shu, e Shu Yang. "Estimating Freeway Travel Time Reliability for Traffic Operations and Planning". Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/623003.
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Travel time reliability (TTR) has attracted increasing attention in recent years, and is often listed as one of the major roadway performance and service quality measures for both traffic engineers and travelers. Measuring travel time reliability is the first step towards improving travel time reliability, ensuring on-time arrivals, and reducing travel costs. Four components may be primarily considered, including travel time estimation/collection, quantity of travel time selection, probability distribution selection, and TTR measure selection. Travel time is a key transportation performance measure because of its diverse applications and it also serves the foundation of estimating travel time reliability. Various modelling approaches to estimating freeway travel time have been well developed due to widespread installation of intelligent transportation system sensors. However, estimating accurate travel time using existing freeway travel time models is still challenging under congested conditions. Therefore, this study aimed to develop an innovative freeway travel time estimation model based on the General Motors (GM) car-following model. Since the GM model is usually used in a micro-simulation environment, the concepts of virtual leading and virtual following vehicles are proposed to allow the GM model to be used in macro-scale environments using aggregated traffic sensor data. Travel time data collected from three study corridors on I-270 in St. Louis, Missouri was used to verify the estimated travel times produced by the proposed General Motors Travel Time Estimation (GMTTE) model and two existing models, the instantaneous model and the time-slice model. The results showed that the GMTTE model outperformed the two existing models due to lower mean average percentage errors of 1.62% in free-flow conditions and 6.66% in two congested conditions. Overall, the GMTTE model demonstrated its robustness and accuracy for estimating freeway travel times. Most travel time reliability measures are derived directly from continuous probability distributions and applied to the traffic data directly. However, little previous research shows a consensus of probability distribution family selection for travel time reliability. Different probability distribution families could yield different values for the same travel time reliability measure (e.g. standard deviation). It is believe that the specific selection of probability distribution families has few effects on measuring travel time reliability. Therefore, two hypotheses are proposed in hope of accurately measuring travel time reliability. An experiment is designed to prove the two hypotheses. The first hypothesis is proven by conducting the Kolmogorov–Smirnov test and checking log-likelihoods, and Akaike information criterion with a correction for finite sample sizes (AICc) and Bayesian information criterion (BIC) convergences; and the second hypothesis is proven by examining both moment-based and percentile-based travel time reliability measures. The results from the two hypotheses testing suggest that 1) underfitting may cause disagreement in distribution selection, 2) travel time can be precisely fitted using mixture models with higher value of the number of mixture distributions (K), regardless of the distribution family, and 3) the travel time reliability measures are insensitive to the selection of distribution family. Findings of this research allows researchers and practitioners to avoid the work of testing various distributions, and travel time reliability can be more accurately measured using mixture models due to higher value of log-likelihoods. As with travel time collection, the accuracy of the observed travel time and the optimal travel time data quantity should be determined before using the TTR data. The statistical accuracy of TTR measures should be evaluated so that the statistical behavior and belief can be fully understood. More specifically, this issue can be formulated as a question: using a certain amount of travel time data, how accurate is the travel time reliability for a specific freeway corridor, time of day (TOD), and day of week (DOW)? A framework for answering this question has not been proposed in the past. Our study proposes a framework based on bootstrapping to evaluate the accuracy of TTR measures and answer the question. Bootstrapping is a computer-based method for assigning measures of accuracy to multiple types of statistical estimators without requiring a specific probability distribution. Three scenarios representing three traffic flow conditions (free-flow, congestion, and transition) were used to fully understand the accuracy of TTR measures under different traffic conditions. The results of the accuracy measurements primarily showed that: 1) the proposed framework can facilitate assessment of the accuracy of TTR, and 2) stabilization of the TTR measures did not necessarily correspond to statistical accuracy. The findings in our study also suggested that moment-based TTR measures may not be statistically sufficient for measuring freeway TTR. Additionally, our study suggested that 4 or 5 weeks of travel time data is enough for measuring freeway TTR under free-flow conditions, 40 weeks for congested conditions, and 35 weeks for transition conditions. A considerable number of studies have contributed to measuring travel time reliability. Travel time distribution estimation is considered as an important starting input of measuring travel time reliability. Kernel density estimation (KDE) is used to estimate travel time distribution, instead of parametric probability distributions, e.g. Lognormal distribution, the two state models. The Hasofer Lind - Rackwitz Fiessler (HL-RF) algorithm, widely used in the field of reliability engineering, is applied to this work. It is used to compute the reliability index of a system based on its previous performance. The computing procedure for travel time reliability of corridors on a freeway is first introduced. Network travel time reliability is developed afterwards. Given probability distributions estimated by the KDE technique, and an anticipated travel time from travelers, the two equations of the corridor and network travel time reliability can be used to address the question, "How reliable is my perceived travel time?" The definition of travel time reliability is in the sense of "on time performance", and it is conducted inherently from the perspective of travelers. Further, the major advantages of the proposed method are: 1) The proposed method demonstrates an alternative way to estimate travel time distributions when the choice of probability distribution family is still uncertain; 2) the proposed method shows its flexibility for being applied onto different levels of roadways (e.g. individual roadway segment or network). A user-defined anticipated travel time can be input, and travelers can utilize the computed travel time reliability information to plan their trips in advance, in order to better manage trip time, reduce cost, and avoid frustration.
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Xiao, Yan. "Hybrid Approaches to Estimating Freeway Travel Times Using Point Traffic Detector Data". FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/356.
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The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery.
Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents.
The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.
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Al, Adaileh Mohammad Ali. "A Travel Time Estimation Model for Facility Location on Real Road Networks". Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1557421387196019.
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Danielsson, Anna, e Gabriella Gustafsson. "Link flow destination distribution estimation based on observed travel times for traffic prediction during incidents". Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170080.
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In a lot of big cities, the traffic network is overloaded, with congestion and unnecessary emissions as consequence. Therefore, different traffic control methods are useful, especially in case of an incident. One key problem for traffic control is traffic prediction and the aim of this thesis is to develop, calibrate and evaluate a route flow model using only observed travel times and travel demand as input. The route flow model was used to calculate the metric link flow destination distribution, that presents to which destinations the travelers on a link are going in percentage.
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Henclewood, Dwayne Anthony. "Real-time estimation of arterial performance measures using a data-driven microscopic traffic simulation technique". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44792.
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Traffic congestion is a one hundred billion dollar problem in the US. The cost of congestion has been trending upward over the last few decades, but has experienced slight decreases in recent years partly due to the impact of congestion reduction strategies. The impact of these strategies is however largely experienced on freeways and not arterials. This discrepancy in impact is partially linked to the lack of real-time, arterial traffic information. Toward this end, this research effort seeks to address the lack of arterial traffic information.
To address this dearth of information, this effort developed a methodology to provide accurate estimates of arterial performance measures to transportation facility managers and travelers in real-time. This methodology employs transmitted point sensor data to drive an online, microscopic traffic simulation model. The feasibility of this methodology was examined through a series of experiments that were built upon the successes of the previous, while addressing the necessary limitations. The results from each experiment were encouraging. They successfully demonstrated the method's likely feasibility, and the accuracy with which field estimates of performance measures may be obtained. In addition, the method's results support the viability of a "real-world" implementation of the method. An advanced calibration process was also developed as a means of improving the method's accuracy. This process will in turn serve to inform future calibration efforts as the need for more robust and accurate traffic simulation models are needed.
The success of this method provides a template for real-time traffic simulation modeling which is capable of adequately addressing the lack of available arterial traffic information. In providing such information, it is hoped that transportation facility managers and travelers will make more informed decisions regarding more efficient management and usage of the nation's transportation network.
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Nam, Do H. "Methodologies for integrating traffic flow theory, ITS and evolving surveillance technologies". Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06062008-165829/.
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Torrisi, Vincenza. "Monitoraggio, stima e previsione real-time del traffico veicolare con tecnologie ITS. Implementazione di un sistema sperimentato nell'area urbana di Catania". Doctoral thesis, Università di Catania, 2017. http://hdl.handle.net/10761/4072.
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First of all, the main focus of this study is the design and development (installation, implementation and calibration) of a traffic monitoring, estimating and short-term forecasting system, through the integration of real-time traffic data in a dynamic simulation model. The proposed methodology allows the identification of the conceptual model that represents the basis of the system's architecture, in order to define a set of required services, functional relationships and specific characteristics identified from the stakeholders needs, which will be involved by this system.
The described methodology is applied to the urban area of Catania (Italy) through the implementation of a traffic monitoring, estimation and forecasting system, equipped with radar sensor and a central control station for traffic data elaborations.
By using obtained data from the implemented simulation model, two further methodologies are presented in this thesis. The first methodology is based on the development of a detailed statistical analysis procedure for reliability assessment of data coming from traffic monitoring systems and simulation models. The second methodology develops a statistical approach for capturing the variability of travel time reliability (TTR) in an extended traffic network, incorporating travel time analysis in order to evaluate their variation and to define the workability of the transport network.
To conclude, the final aim of this work is to increase the knowledge of the actual dynamic of road traffic in urban areas, to monitor the reliability of the transport system and to obtain useful data and information that can contribute to the implementation of an optimal control of mobility system, through a more efficient and integrated use of the road infrastructure and the available devices on it.
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Petrlík, Jiří. "Multikriteriální genetické algoritmy v predikci dopravy". Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-412573.
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Porozumění chování silniční dopravy je klíčem pro její efektivní řízení a organizaci. Tato úloha se stává čím dál více důležitou s rostoucími požadavky na dopravu a počtem registrovaných vozidel. Informace o dopravní situaci je důležitá pro řidiče a osoby zodpovědné za její řízení. Naštěstí v posledních několika dekádách došlo k značnému rozvoji technologií pro monitorování dopravní situace. Stacionární senzory, jako jsou indukční smyčky, radary, kamery a infračervené senzory, mohou být nainstalovány na důležitých místech. Zde jsou schopny měřit různé mikroskopické a makroskopické dopravní veličiny. Bohužel mnohá měření obsahují nekorektní data, která není možné použít při dalším zpracování, například pro predikci dopravy a její inteligentní řízení. Tato nekorektní data mohou být způsobena poruchou zařízení nebo problémy při přenosu dat. Z tohoto důvodu je důležité navrhnout obecný framework, který je schopný doplnit chybějící data. Navíc by tento framework měl být také schopen poskytovat krátkodobou predikci budoucího stavu dopravy. Tato práce se především zabývá vybranými problémy v oblasti doplnění chybějících dopravních dat, predikcí dopravy v krátkém časovém horizontu a predikcí dojezdových dob. Navrhovaná řešení jsou založena na kombinaci současných metod strojového učení, například Support vector regression (SVR) a multikriteriálních evolučních algoritmů. SVR má mnoho meta-parametrů, které je nutné dobře nastavit tak, aby byla dosažena co nejkvalitnější predikce. Kvalita predikce SVR dále silně závisí na výběru vhodné množiny vstupních proměnných. V této práci používáme multiktriteriální optimalizaci pro optimalizaci SVR meta-parametrů a množiny vstupních proměnných. Multikriteriální optimalizace nám umožňuje získat mnoho Pareto nedominovaných řešení. Mezi těmito řešeními je možné dynamicky přepínat dle toho, jaká data jsou aktuálně k dispozici tak, aby bylo dosaženo maximální kvality predikce. Metody navržené v této práci jsou především vhodné pro prostředí s velkým množstvím chybějících hodnot v dopravních datech. Tyto metody jsme ověřili na reálných datech a porovnali jejich výsledky s metodami, které jsou v současné době používány. Navržené metody poskytují lepší výsledky než stávající metody, a to především ve scénářích, kde se vyskytuje mnoho chybějících hodnot v dopravních datech.
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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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Chen, Daizhuo. "Modeling travel time uncertainty in traffic networks". Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61889.
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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 147-154).
Uncertainty in travel time is one of the key factors that could allow us to understand and manage congestion in transportation networks. Models that incorporate uncertainty in travel time need to specify two mechanisms: the mechanism through which travel time uncertainty is generated and the mechanism through which travel time uncertainty influences users' behavior. Existing traffic equilibrium models are not sufficient in capturing these two mechanisms in an integrated way. This thesis proposes a new stochastic traffic equilibrium model that incorporates travel time uncertainty in an integrated manner. We focus on how uncertainty in travel time induces uncertainty in the traffic flow and vice versa. Travelers independently make probabilistic path choice decisions, inducing stochastic traffic flows in the network, which in turn result in uncertain travel times. Our model, based on the distribution of the travel time, uses the mean-variance approach in order to evaluate travelers' travel times and subsequently induce a stochastic traffic equilibrium flow pattern. In this thesis, we also examine when the new model we present has a solution as well as when the solution is unique. We discuss algorithms for solving this new model, and compare the model with existing traffic equilibrium models in the literature. We find that existing models tend to overestimate traffic flows on links with high travel time variance-to-mean ratios. To benchmark the various traffic network equilibrium models in the literature relative to the model we introduce, we investigate the total system cost, namely the total travel time in the network, for all these models. We prove three bounds that allow us to compare the system cost for the new model relative to existing models. We discuss the tightness of these bounds but also test them through numerical experimentation on test networks.
by Daizhuo Chen.
S.M.
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Hodges, Fiona. "Travel time budgets in an urban area /". Connect to thesis, 1994. http://eprints.unimelb.edu.au/archive/00000227.
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Wang, Heng. "Travel Time Estimation on Arterial Streets". Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/36235.
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Estimation of real-time travel times on arterial streets has been a challenging task due to the intersection control delay as well as bottleneck delay from the downstream link. Therefore, few transportation professionals have conducted research at utilizing the dynamic flow methods to estimate travel times on arterial street networks.
This thesis is to develop dynamic flow algorithms that estimates the real-time travel time on an arterial street network by utilizing the traffic information obtained from detectors. A modified method to the one adopted in HCM2000 in computing the intersection control delay is developed and utilized to estimate the real-time travel time for a short-time interval update under non-incident and incident situations. Simulation model is developed in CORSIM to validate developed algorithms under different traffic situations.Master of Science
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Pereira, Iman, e Guangan Ren. "Travel time estimation for emergency services". Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158178.
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Emergency services has a vital function in society, and except saving lifes a functioning emergency service system provides the inhabitants of any give society with a sence of feeling secure. Because of the delicate nature of the services provided there is always an interest in improvement with regards to the performance of the system. In order to have a good system there are a variety of models that can be used as decision making support. An important component in many of these models are the travel time of an emergency vehicle. In In this study the focus lies in travel time estimation for the emergency services and how it could be estimated by using a neural network, called a deep learning process in this report. The data used in the report is map matched GPS points that have been collected by the emergency services in two counties in Sweden, Östergötland and Västergötland. The map matched data has then been matched with NVDB, which is the the national road database, adding an extra layer of information, such as roadlink geometry, number of roundabouts etc. To find the most important features to use as input in the developed model a Pearson and Spearman correlation test was performed. Even if these two tests do not capture all possible relations between features they still give an indication of what features that can be included. The deep learning process developed within this study uses route length, average weighted speed limit, resource category, and road width. It is trained with 75% of the data leaving the remaining 25% for testing of the model. The DLP gives a mean absolute error of 51.39 when trained and 59.21 seconds when presented with new data. This in comparison a simpler model which calculates the travel time by dividing the route length with the weighted averag speed limt, which gives a mean absolute error of 227.48 seconds. According to the error metrics used in order to evaluate the models the DLP performs better than the current model. However there is a dimension of complexity with the DLP which makes it sort of a black box where something goes in and out comes an estimated travel time. If the aim is to have a more comprehensive model, then the current model has its benefits over a DLP. However the potential that lies in using a DLP is entruiging, and with a more in depth analysis of features and how to classify these in combination with more data there may be room for developing more complex DLPs.
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Wu, Seung Kook. "Adaptive traffic control effect on arterial travel time charateristics". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31839.
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Thesis (Ph.D)--Civil and Environmental Engineering, Georgia Institute of Technology, 2010.Committee Chair: Hunter, Michael; Committee Member: Guensler, Randall; Committee Member: Leonard, John; Committee Member: Rodgers, Michael; Committee Member: Roshan J. Vengazhiyil. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Wu, Jingcheng. "Travel time estimation on urban arterials ? a real time aspect". Thesis, The University of Wisconsin - Milwaukee, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10250523.
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This dissertation attempts to develop simple and direct approaches to estimate the vehicle queue length and travel time along signalized arterial links for real-time traffic operations. This dissertation is the first to demonstrate a process using vehicle trajectory data to generate detector volume, speed and time occupancy data, along with the generalized flow rate, density and space mean speed data. This approach minimizes detector over-counting and miss-counting issues. The detection zone can be of any shape or size and at any location along the trajectory. The relationships among detector volume, speed and time occupancy along signalized arterials are analyzed theoretically and experientially. If the generalized definitions of flow rate, density and space mean speed are used, the fundamental relationship, v = ds, holds valid in a signalized arterial environment. The fundamental relationship diagram plotted using field signalized arterial data has not been seen in any of the literatures reviewed.
Within the defined time-space region, the scatter diagram of the generalized density and the detector time occupancy presents a strong linear correlation. Simply converting detector volume counts within one data collection time period to use as the generalized flow rate introduces estimation errors. There are two major reasons. The first is that vehicles don’t completely cross the detector during the data collection time period. The second is that it assumes vehicles would evenly spread across the data collection time period when crossing the detection zone. Traffic flow intensity is introduced and defined within the time-space regions to provide much more accurate description of the traffic flow arrival and departure conditions.
This dissertation attempts to make improvements to the input-output technique for queue estimation along signalized links. Based on analyses of the theoretical and experiential cumulative input-output diagrams, also known as the Newell Curves, two major improvements are proposed to improve the performance of the input-output technique. The improvements take into account vehicles stop on top of detectors in the estimation, make necessary adjustments to detector vehicle counts, and introduce a reset mechanism to remove the accumulated estimation errors during a long time period. The improvements are tested using two sets of field data. One set of data are 10-second queue and virtual detector data generated using the Federal Highway Administration Next Generation Simulation Peachtree Street dataset. The other set of data are field manually collected 20-second queue, and loop detector vehicle count and time occupancy data at metered on-ramps. It is concluded that both improvements help to produce estimation results far better than the original input-output technique. With adjusted detector vehicle counts, the performance of the Kalman Filter queue estimation model is also improved.
A simple conservation law approach is developed to estimate travel time along signalized arterial links. Inputs used include the traffic flow intensity at input and out detectors, plus the initial vehicle queue. The estimated travel time is tested with the field travel time data to evaluate the performance of the estimation. The developed model is also compared with the NCHRP Project 3-79 model and the Little’s Law queueing theory model. The developed model performs much better for per short interval travel time estimation.
The proposed travel time estimation approach only uses the detector volume and time occupancy data. It does not rely on signal timing data to estimate the control delay or a delay model to estimate the queueing delay. In addition, neither roadway geometry nor vehicle length data are used.
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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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Chin, Kian Keong. "Departure time choice in equilibrium traffic assignment". Thesis, University of Leeds, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364638.
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Ding, Silin. "Freeway Travel Time Estimation Using Limited Loop Data". University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1205288596.
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Krishnamoorthy, Rajesh Krishnan. "Travel time estimation and forecasting on urban roads". Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/7320.
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Han, Jiang. "Multi-sensor data fusion for travel time estimation". Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9603.
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The importance of travel time estimation has increased due to the central role it plays in a number of emerging intelligent transport systems and services including Advanced Traveller Information Systems (ATIS), Urban Traffic Control (UTC), Dynamic Route Guidance (DRG), Active Traffic Management (ATM), and network performance monitoring. Along with the emerging of new sensor technologies, the much greater volumes of near real time data provided by these new sensor systems create opportunities for significant improvement in travel time estimation. Data fusion as a recent technique leads to a promising solution to this problem. This thesis presents the development and testing of new methods of multi-sensor data fusion for the accurate, reliable and robust estimation of travel time. This thesis reviews the state-of-art data fusion approaches and its application in transport domain, and discusses both of opportunities and challenging of applying data fusion into travel time estimation in a heterogeneous real time data environment. For a particular England highway scenario where ILDs and ANPR data are largely available, a simple but practical fusion method is proposed to estimate the travel time based on a novel relationship between space-mean-speed and time-mean-speed. In developing a general fusion framework which is able to fuse ILDs, GPS and ANPR data, the Kalman filter is identified as the most appropriate fundamental fusion technique upon which to construct the required framework. This is based both on the ability of the Kalman filter to flexibly accommodate well-established traffic flow models which describe the internal physical relation between the observed variables and objective estimates and on its ability to integrate and propagate in a consistent fashion the uncertainty associated with different data sources. Although the standard linear Kalman filter has been used for multi-sensor travel time estimation in the previous research, the novelty of this research is to develop a nonlinear Kalman filter (EKF and UKF) fusion framework which improves the estimation performance over those methods based on the linear Kalman filter. This proposed framework is validated by both of simulation and real-world scenarios, and is demonstrated the effectiveness of estimating travel time by fusing multi-sensor sources.
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Singh, Darshan R. "Estimation of Travel Time on Signalized Arterial Highway Corridor". Cincinnati, Ohio University of Cincinnati, 2005. http://www.ohiolink.edu/etd/view.cgi?acc%5Fnum=ucin1116258396.
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Wedin, Daniel. "Travel Time Estimation in Stockholm Using Historical GPS Data". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260692.
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The current traffic situation in Stockholm with heavy traffic and congested roads makes accurate travel time estimation both difficult and important for several different types of businesses. In this thesis a method of estimating travel time based on historical GPS data from taxi vehicles is presented. One of the major problems faced is to match the reported GPS location to a position in the actual road network. The proposed probabilistic method for finding the most likely position includes two features, the travel time of the vehicle and distance of the GPS error. The historical GPS data is analyzed in order to create a database with historical traffic patterns; average velocities for different roads at different times are logged. To create and estimation the route is estimated using the path finding algorithm A* and the expected traffic patterns are found from the historical data. When comparing the travel time estimation to known travel times, the method display promising results with a mean average percentage error of 16.8%.
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Dhulipala, Sudheer. "A System for Travel Time Estimation on Urban Freeways". Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/33426.
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Travel time information is important for Advanced Traveler Information Systems (ATIS) applications. People traveling on urban freeways are interested in knowing how long it will take them to reach their destinations, particularly under congested conditions. Though many advances have been made in the field of traffic engineering and ITS applications, there is a lack of practical travel time estimation procedures for ATIS applications.
Automatic Vehicle Identification (AVI) and Geographic Information System (GPS) technologies can be used to directly estimate travel times, but they are not yet economically viable and not widely deployed in urban areas. Hence, data from loop detectors or other point estimators of traffic flow variables are predominantly used for travel time estimation. Most point detectors can provide this data efficiently. Some attempts have been made in the past to estimate travel times from point estimates of traffic variables, but they are not comprehensive and are valid for only particular cases of freeway conditions. Moreover, most of these methods are statistical and thus limited to the type of situations for which they were developed and are not of much general use.
The purpose of current research is to develop a comprehensive system for travel time estimation on urban freeways for ATIS applications. The system is based on point estimates of traffic variables obtained from detectors. The output required from the detectors is flow and occupancy aggregated for a short time interval of 5 minutes. The system for travel time estimation is based on the traffic flow theory rather than statistical methods. The travel times calculated using this system are compared with the results of FHWA simulation package TSIS 5.0 and the estimation system is found to give reasonable and comparable results when compared with TSIS results.Master of Science
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Zhang, Wang. "Freeway Travel Time Estimation Based on Spot Speed Measurements". Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/28156.
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As one of the kernel components of ITS technology, Travel Time Estimation (TTE) has been a high-interest topic in highway operation and management for years. Out of numerous vehicle detection technologies being applied in this project, intrusive loop detector, as the representative of spot measurement devices, is the most common. The ultimate goal of this dissertation is to seek a TTE approach based primarily on spot speed measurement and capable of successfully performing in a certain accuracy range under various traffic conditions.
The provision of real-time traffic information could offer significant benefits for commuters looking to make optimum travel decisions. The proposed research effort attempts to characterize typical variability in traffic conditions using traffic volume data obtained from loop detectors on I-66 Virginia during a 3-month period. The detectors logged time-mean speed, volume, and occupancy measurements for each station and lane combination. Using these data, the study examines the spatiotemporal link and path flow variability of weekdays and weekends. The generation of path flows is made through the use of a synthetic maximum likelihood approach. Statistical Analysis of Variance (ANOVA) tests are performed on the data. The results demonstrate that in terms of link flows and total traffic demand, Mondays and Fridays are similar to core weekdays (Tuesdays, Wednesdays, and Thursdays). In terms of path flows, Fridays appear to be different from core weekdays.
A common procedure for estimating roadway travel times is to use either queuing theory or shockwave analysis procedures. However, a number of studies have claimed that deterministic queuing theory and shock-wave analysis are fundamentally different, producing different delay estimates for solving bottleneck problems. Chapter 5 demonstrates the consistency in the delay estimates that are derived from both queuing theory and shock-wave analysis and highlights the common errors that are made in the literature with regards to shock-wave analysis delay estimation. Furthermore, Chapter 5 demonstrates that the area between the demand and capacity curves can represent the total delay or the total vehicle-hours of travel if the two curves are spatially offset and queuing theory has its advantages on this because of its simplicity.
As the established relationship between time-mean and space-mean speed is suitable for estimating time-mean speeds from space-mean speeds in most cases, it is also desired to estimate the space-mean speeds from time-mean speeds. Consequently, Chapter 6 develops a new formulation that utilizes the variance of the time-mean speed as opposed to the variance of the space-mean speed for the estimation of space-mean speeds. This demonstrates that the space-mean speeds are estimated within a margin of error of 0 to 1 percent. Furthermore, it develops a relationship between the space- and time-mean speed variance and between the space-mean speed and the spatial travel-time variance. In addition, the paper demonstrates that both the Hall and Persaud and the Dailey formulations for estimating traffic stream speed from single loop detectors are valid. However, the differences in the derivations are attributed to the fact that the Hall and Persaud formulation computes the space-mean speed (harmonic mean) while the Dailey formulation computes the time-mean speed (arithmetic mean).
Chapter 7 focuses on freeway Travel Time Estimation (TTE) algorithms that are based on spot speed measurements. Several TTE approaches are introduced including a traffic dynamics TTE algorithm that is documented in literature. This traffic dynamics algorithm is analyzed, highlighting some of its drawbacks, followed by some proposed corrections to the traffic dynamics formulation. The proposed approach estimates traffic stream density from occupancy measurements, as opposed to flow measurements, at the onset of congestion. Next, the study validates the proposed model using field data from I-880 and simulated data. Comparison of five different TTE algorithms is conducted. The comparison demonstrates that the proposed approach is superior to the TTE traffic dynamics approach. Particularly, a multi-link simulation network is built to test spot-speed-measurement TTE performance on multi links, as well as the data smoothing techniqueâ s effect on TTE accuracy. Findings further prove advantages of utilizing space-mean speed in TTE rather than time-mean speed. In summary, a feasible TTE procedure that is adaptive to various traffic conditions has been established. Since each approach would under-/over-estimate travel time depending on the concrete traffic condition, different models will be selected to ensure TTEâ s accuracy window. This approach has broad applications because it is based on popular loop detectors.Ph. D.
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Li, Lok-man Jennifer. "Schedule delay of work trips in Hong Kong an empirical analysis /". Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B40988041.
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Adams, David Lewis. "Integrating travel time reliability into management of highways". Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 52 p, 2008. http://proquest.umi.com/pqdweb?did=1459913561&sid=3&Fmt=2&clientId=8331&RQT=309&VName=PQD.
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Choy, Wing-pong. "A review of the value of travel time in Hong Kong". Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31937068.
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Misra, Rajul. "Toward a comprehensive representation and analysis framework for non-worker activity-travel pattern modeling /". Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.
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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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Glick, Travis Bradley. "Utilizing High-Resolution Archived Transit Data to Study Before-and-After Travel-Speed and Travel-Time Conditions". PDXScholar, 2017. https://pdxscholar.library.pdx.edu/open_access_etds/4065.
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Travel times, operating speeds, and service reliability influence costs and service attractiveness. This paper outlines an approach to quantify how these metrics change after a modification of roadway design or transit routes using archived transit data. The Tri-County Metropolitan Transportation District of Oregon (TriMet), Portland's public transportation provider, archives automatic vehicle location (AVL) data for all buses as part of their bus dispatch system (BDS). This research combines three types of AVL data (stop event, stop disturbance, and high-resolution) to create a detailed account of transit behavior; this probe data gives insights into the behavior of transit as well as general traffic. The methodology also includes an updated approach for confidence intervals estimates that more accurately represent of range of speed and travel time percentile estimates. This methodology is applied to three test cases using a month of AVL data collected before and after the implementation of each roadway change. The results of the test cases highlight the broad applicability for this approach to before-and-after studies.
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Elesawey, Mohamed. "Travel time estimation in urban areas using neighbour links data". Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/29151.
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Travel time is a simple and robust network performance measure that is perceived and well understood by the public and politicians. However, travel time data collection can be costly especially if the analysis area is extensive. This thesis proposes a solution to the problem of limited network sensor coverage caused by insufficient sample size of probe vehicles or inadequate numbers of fixed sensors. The approach makes use of travel time correlation between nearby (neighbour) links to estimate travel times on links with no data using neighbour links travel time data. A framework is proposed that estimates link travel times using available data from neighbouring links. The proposed framework was validated using real-life data from the City of Vancouver, British Columbia. The travel time estimation accuracy was found comparable to the existing literature. The concept of neighbour links travel time estimation was extended and applied at a corridor level. Regression and Non-Parametric (NP) models were developed to estimate travel times of one corridor using data from another corridor. To analyze the impact of the probes’ sample size on the accuracy of the proposed methodology, a case study was undertaken using a VISSIM microsimulation model of downtown Vancouver. The simulation model was calibrated and validated using field traffic volumes and travel time data. The methodology provided reasonable estimation accuracy even using small probe samples. The use of bus travel time data to estimate automobile travel times of neighbour links was explored. The results showed that bus probes data on neighbour links can be useful for estimating link travel times in the absence of vehicle probes. The fusion of vehicle and bus probes data was analyzed. Using transit data for neighbour links travel time estimation was shown to improve the accuracy of estimation at low market penetration levels of passenger probes. However, the significance of transit probe data diminishes with the increase of market penetration level of probe vehicles. Overall, the results of this thesis demonstrate the feasibility of using neighbour links data as an additional source of information that might not have been extensively explored.
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Wang, Zhuojin. "Incident-Related Travel Time Estimation Using a Cellular Automata Model". Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/33644.
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The purpose of this study was to estimate the driversâ travel time with the occurrence of an incident on freeway. Three approaches, which were shock wave analysis, queuing theory and cellular automata models, were initially considered, however, the first two macroscopic models were indicated to underestimate travel time by previous literature. A microscopic simulation model based on cellular automata was developed to attain the goal. The model incorporated driving behaviors on the freeway with the presence of on-ramps, off-ramps, shoulder lanes, bottlenecks and incidents. The study area was a 16 mile eastbound section of I-66 between US-29 and I-495 in northern Virginia. The data for this study included loop detector data and incident data for the road segment for the year 2007. Flow and speed data from the detectors were used for calibration using quantitative and qualitative techniques. The cellular automata model properly reproduced the traffic flow under normal conditions and incidents. The travel time information was easily obtained from the model. The system is promising for travel time estimation in near real time.Master of Science
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Bowman, John L. (John Lawrence). "The day activity schedule approach to travel demand analysis". Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/16731.
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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1998.Includes bibliographical references (p. 181-184) and index.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
This study develops a model of a person's day activity schedule that can be used to forecast urban travel demand. It is motivated by the notion that travel outcomes are part of an activity scheduling decision, and uses discrete choice models to address the basic modeling problem-capturing decision interactions among the many choice dimensions of the immense activity schedule choice set. An integrated system of choice models represents a person's day activity schedule as an activity pattern and a set of tours. A pattern model identifies purposes, priorities and structure of the day's activities and travel. Conditional tour models describe timing, location and access mode of on-tour activities. The system captures trade-offs people consider, when faced with space and time constraints, among patterns that can include at-home and on-tour activities, multiple tours and trip chaining. It captures sensitivity of pattern choice to activity and travel conditions through a measure of expected tour utility arising from the tour models. When travel and activity conditions change, the relative attractiveness of patterns changes because expected tour utility changes differently for different patterns. An empirical implementation of the model system for Portland, Oregon, establishes the feasibility of specifying, estimating and using it for forecasting. Estimation results match a priori expectations of lifestyle effects on activity selection, including those of (a) household structure and role, such as for females with children, (b) capabilities, such as income, and (c) activity commitments, such as usual work levels.
(cont.) They also confirm the significance of activity and travel accessibility in pattern choice. Application of the model with road pricing and other policies demonstrates its lifestyle effects and how it captures pattern shifting-with accompanying travel changes-that goes undetected by more narrowly focused trip-based and tour-based systems. Although the model has not yet been validated in before-and-after prediction studies, this study gives strong evidence of its behavioral soundness, current practicality, potential to generate cost-effective predictions superior to those of the best existing systems, and potential for enhanced implementations as computing technology advances.
by John L. Bowman.
Ph.D.
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Chen, Hao. "Real-time Traffic State Prediction: Modeling and Applications". Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64292.
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Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system.
The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future.
The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future.
The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods.
Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future.Ph. D.
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Agafonov, Evgeny. "Fuzzy and multi-resolution data processing for advanced traffic and travel information". Thesis, Nottingham Trent University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271790.
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Sanaullah, Irum. "Real-time estimation of travel time using low frequency GPS data from moving sensors". Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/11938.
Texto completo da fonteResumo:
Travel time is one of the most important inputs in many Intelligent Transport Systems (ITS). As a result, this information needs to be accurate and dynamic in both spatial and temporal dimensions. For the estimation of travel time, data from fixed sensors such as Inductive Loop Detectors (ILD) and cameras have been widely used since the 1960 s. However, data from fixed sensors may not be sufficiently reliable to estimate travel time due to a combination of limited coverage and low quality data resulting from the high cost of implementing and operating these systems. Such issues are particularly critical in the context of Less Developed Countries, where traffic levels and associated problems are increasing even more rapidly than in Europe and North America, and where there are no pre-existing traffic monitoring systems in place. As a consequence, recent developments have focused on utilising moving sensors (i.e. probe vehicles and/or people equipped with GPS: for instance, navigation and route guidance devices, mobile phones and smartphones) to provide accurate speed, positioning and timing data to estimate travel time. However, data from GPS also have errors, especially for positioning fixes in urban areas. Therefore, map-matching techniques are generally applied to match raw positioning data onto the correct road segments so as to reliably estimate link travel time. This is challenging because most current map-matching methods are suitable for high frequency GPS positioning data (e.g. data with 1 second interval) and may not be appropriate for low frequency data (e.g. data with 30 or 60 second intervals). Yet, many moving sensors only retain low frequency data so as to reduce the cost of data storage and transmission. The accuracy of travel time estimation using data from moving sensors also depends on a range of other factors, for instance vehicle fleet sample size (i.e. proportion of vehicles equipped with GPS); coverage of links (i.e. proportion of links on which GPS-equipped vehicles travel); GPS data sampling frequency (e.g. 3, 6, 30, 60 seconds) and time window length (e.g. 5, 10 and 15 minutes). Existing methods of estimating travel time from GPS data are not capable of simultaneously taking into account the issues related to uncertainties associated with GPS and spatial road network data; low sampling frequency; low density vehicle coverage on some roads on the network; time window length; and vehicle fleet sample size. Accordingly this research is based on the development and application of a methodology which uses GPS data to reliably estimate travel time in real-time while considering the factors including vehicle fleet sample size, data sampling frequency and time window length in the estimation process. Specifically, the purpose of this thesis was to first determine the accurate location of a vehicle travelling on a road link by applying a map-matching algorithm at a range of sampling frequencies to reduce the potential errors associated with GPS and digital road maps, for example where vehicles are sometimes assigned to the wrong road links. Secondly, four different methods have been developed to estimate link travel time based on map-matched GPS positions and speed data from low frequency data sets in three time windows lengths (i.e. 5, 10 and 15 minutes). These are based on vehicle speeds, speed limits, link distances and average speeds; initially only within the given link but subsequently in the adjacent links too. More specifically, the final method draws on weighted link travel times associated with the given and adjacent links in both spatial and temporal dimensions to estimate link travel time for the given link. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-Berkeley s Mobile Century Project. The original GPS dataset which was broadcast on a 3 second sampling frequency has been extracted at different sampling frequencies such as 6, 30, 60 and 120 seconds so as to evaluate the performance of each travel time estimation method at low sampling frequencies. The results were then validated against reference travel time data collected from 4,126 vehicles by high resolution video cameras, and these indicate that factors such as vehicle sample size, data sampling frequency, vehicle coverage on the links and time window length all influence the accuracy of link travel time estimation.
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Choy, Wing-pong, e 蔡榮邦. "A review of the value of travel time in Hong Kong". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31937068.
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Qin, Xiao. "Traffic flow modeling with real-time data for on-line network traffic estimation and prediction". College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3628.
Texto completo da fonteResumo:
Thesis (Ph. D.) -- University of Maryland, College Park, 2006.Thesis research directed by: Civil Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Yeon, Ji Youn. "Travel time estimation as a function of the probability of breakdown". [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0015666.
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Mahmoud, Anas M. "TRAVEL TIME ESTIMATION IN CONGESTED URBAN NETWORKS USING POINT DETECTORS DATA". MSSTATE, 2009. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04022009-163043/.
Texto completo da fonteResumo:
A model for estimating travel time on short arterial links of congested urban networks, using currently available technology, is introduced in this thesis. The objective is to estimate travel time, with an acceptable level of accuracy for real-life traffic problems, such as congestion management and emergency evacuation. To achieve this research objective, various travel time estimation methods, including highway trajectories, multiple linear regression (MLR), artificial neural networks (ANN) and K nearest neighbor (K-NN) were applied and tested on the same dataset. The results demonstrate that ANN and K-NN methods outperform linear methods by a significant margin, also, show particularly good performance in detecting congested intervals. To ensure the quality of the analysis results, set of procedures and algorithms based on traffic flow theory and test field information, were introduced to validate and clean the data used to build, train and test the different models.
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Astahovs, Ilja. "Travel time estimation based on previous experience - Pre-study and prototyping". Thesis, Umeå universitet, Institutionen för datavetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-109596.
Texto completo da fonteResumo:
Travel time depends on various factors, which can be described by data coming from sensors. The author makes an assumption that for the same trip conditions travel time will be the same, and if we can collect enough information on the current trip conditions and find a matching trip which took place in the past, we can estimate the travel time for future trips. The project aim is to design and prototype a system capable of collecting this data, organizing, storing and using it to find matching trips, with the real-time performance being the main consideration. The scope of the system is limited by the needs of a logistic company which wants to be able to track its vehicles and estimate their travel times. The resulting system is tested in various settings to find out how well it performs. The author identifies the settings which are suitable for the particular implementation and suggests further improvements which are meant to extend the settings.
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Roberts, Craig Arnold. "Modeling the relationships between microscopic and macroscopic travel activity on freeways : bridging the gap between current travel demand models and emerging mobile emission models". Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/32873.
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Zhang, Xu. "INCORPORATING TRAVEL TIME RELIABILITY INTO TRANSPORTATION NETWORK MODELING". UKnowledge, 2017. http://uknowledge.uky.edu/ce_etds/54.
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Travel time reliability is deemed as one of the most important factors affecting travelers’ route choice decisions. However, existing practices mostly consider average travel time only. This dissertation establishes a methodology framework to overcome such limitation.
Semi-standard deviation is first proposed as the measure of reliability to quantify the risk under uncertain conditions on the network. This measure only accounts for travel times that exceed certain pre-specified benchmark, which offers a better behavioral interpretation and theoretical foundation than some currently used measures such as standard deviation and the probability of on-time arrival.
Two path finding models are then developed by integrating both average travel time and semi-standard deviation. The single objective model tries to minimize the weighted sum of average travel time and semi-standard deviation, while the multi-objective model treats them as separate objectives and seeks to minimize them simultaneously. The multi-objective formulation is preferred to the single objective model, because it eliminates the need for prior knowledge of reliability ratios. It offers an additional benefit of providing multiple attractive paths for traveler’s further decision making.
The sampling based approach using archived travel time data is applied to derive the path semi-standard deviation. The approach provides a nice workaround to the problem that there is no exact solution to analytically derive the measure. Through this process, the correlation structure can be implicitly accounted for while simultaneously avoiding the complicated link travel time distribution fitting and convolution process.
Furthermore, the metaheuristic algorithm and stochastic dominance based approach are adapted to solve the proposed models. Both approaches address the issue where classical shortest path algorithms are not applicable due to non-additive semi-standard deviation. However, the stochastic dominance based approach is preferred because it is more computationally efficient and can always find the true optimal paths.
In addition to semi-standard deviation, on-time arrival probability and scheduling delay measures are also investigated. Although these three measures share similar mathematical structures, they exhibit different behaviors in response to large deviations from the pre-specified travel time benchmark. Theoretical connections between these measures and the first three stochastic dominance rules are also established. This enables us to incorporate on-time arrival probability and scheduling delay measures into the methodology framework as well.
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Aljamal, Mohammad Abdulraheem. "Real-Time Estimation of Traffic Stream Density using Connected Vehicle Data". Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/100149.
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The macroscopic measure of traffic stream density is crucial in advanced traffic management
systems. However, measuring the traffic stream density in the field is difficult since it is a
spatial measurement. In this dissertation, several estimation approaches are developed to
estimate the traffic stream density on signalized approaches using connected vehicle (CV)
data. First, the dissertation introduces a novel variable estimation interval that allows for
higher estimation precision, as the updating time interval always contains a fixed number of
CVs. After that, the dissertation develops model-driven approaches, such as a linear Kalman
filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate
the traffic stream density using CV data only. The proposed model-driven approaches are
evaluated using empirical and simulated data, the former of which were collected along a
signalized approach in downtown Blacksburg, VA. Results indicate that density estimates
produced by the linear KF approach are the most accurate. A sensitivity of the estimation
approaches to various factors including the level of market penetration (LMP) of CVs, the
initial conditions, the number of particles in the PF approach, traffic demand levels, traffic
signal control methods, and vehicle length is presented. Results show that the accuracy
of the density estimate increases as the LMP increases. The KF is the least sensitive to
the initial traffic density estimate, while the PF is the most sensitive to the initial traffic
density estimate. The results also demonstrate that the proposed estimation approaches work
better at higher demand levels given that more CVs exist for the same LMP scenario. For
traffic signal control methods, the results demonstrate a higher estimation accuracy for fixed
traffic signal timings at low traffic demand levels, while the estimation accuracy is better
when the adaptive phase split optimizer is activated for high traffic demand levels. The
dissertation also investigates the sensitivity of the KF estimation approach to vehicle length,
demonstrating that the presence of longer vehicles (e.g. trucks) in the traffic link reduces
the estimation accuracy. Data-driven approaches are also developed to estimate the traffic
stream density, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN),
and a random forest (RF). The data-driven approaches also utilize solely CV data. Results
demonstrate that the ANN approach outperforms the k-NN and RF approaches. Lastly, the
dissertation compares the performance of the model-driven and the data-driven approaches,
showing that the ANN approach produces the most accurate estimates. However, taking
into consideration the computational time needed to train the ANN approach, the large
amount of data needed, and the uncertainty in the performance when new traffic behaviors
are observed (e.g., incidents), the use of the linear KF approach is highly recommended in
the application of traffic density estimation due to its simplicity and applicability in the field.Doctor of Philosophy
Estimating the number of vehicles (vehicle counts) on a road segment is crucial in advanced traffic management systems. However, measuring the number of vehicles on a road segment in the field is difficult because of the need for installing multiple detection sensors in that road segment. In this dissertation, several estimation approaches are developed to estimate the number of vehicles on signalized roadways using connected vehicle (CV) data. The CV is defined as the vehicle that can share its instantaneous location every time t. The dissertation develops model-driven approaches, such as a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate the number of vehicles using CV data only. The proposed model-driven approaches are evaluated using real and simulated data, the former of which were collected along a signalized roadway in downtown Blacksburg, VA. Results indicate that the number of vehicles produced by the linear KF approach is the most accurate. The results also show that the KF approach is the least sensitive approach to the initial conditions. Machine learning approaches are also developed to estimate the number of vehicles, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The machine learning approaches also use CV data only. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Finally, the dissertation compares the performance of the model-driven and the machine learning approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the huge amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the KF approach is highly recommended in the application of vehicle count estimation due to its simplicity and applicability in the field.