Link travel time plays a significant role in traffic planning, traffic management and Advanced Traveler Information Systems (ATIS). A public probe vehicle dataset is a probe vehicle dataset that is collected from public people or public transport. The appearance of public probe vehicle datasets can support travel time collection at a large temporal and spatial scale but at a relatively low cost. Traditionally, link travel time is the aggregation of travel time by different movements. A recent study proved that link travel time of different movements is significantly different from their aggregation. However, there is still not a complete framework for estimating movement-based link travel time. In addition, probe vehicle datasets usually have a low penetration rate but no previous study has solved this problem. To solve the problems above, this study proposed a detailed framework to estimate movement-based link travel time using a high sampling rate public probe vehicle dataset. Our study proposed a k-Nearest Neighbors (k-NN) regression method to increase travel time samples using incomplete trajectory. An incomplete trajectory was compared with historical complete trajectories and the link travel time of the incomplete trajectory was represented by its similar complete trajectories. The result of our study showed that the method can significantly increase link travel time samples but there are still limitations. In addition, our study investigated the performance of k-NN regression under different parameters and input data. The sensitivity analysis of k-NN algorithm showed that the algorithm performed differently under different parameters and input data. Our study suggests optimal parameters should be selected using a historical dataset before real-world application.

The travel-time flow relationship is not always increasing in nature, it is very difficult to predict precisely. Traditional method fails to replicate this unique conditions. Until millennium, although various researchers and practitioners have given much attention to develop travel-time flow relationships, the advancement to improve travel-time flow relationships was not substantial. The knowledge about the travel-time flow relationship is not commensurate with or parallel to the advancement of new knowledge in other fields. After millennium, most investigators did not devote enough attention to create new knowledge, except for application and performance evaluation of the existing knowledge. Therefore, it is necessary to provide a new theoretical and methodological advancement in travel-time flow relationship. Consequentially, this research proposes a new methodology, which considers stochastic behavior of travel-time flow relationship with probabilistic Bayesian statistics and logistic growth mapping techniques. This research moderately improves the travel-time flow relationship. The unique contribution of this research is that the proposed methods outperforms the existing traditional travel-time flow theory, assumptions, and modeling techniques. The results shows that the proposed model is considerably a good candidate for travel-time predictions. The proposed model performs 36 percent better and accurate travel-time predictions in compared to the existing models. Furthermore, travel-time flow relationship need capacity and free-flow speed estimations. Traditionally, practice of capacity estimation is mostly practical, subjective, and not steady-state capacity. Therefore, a robust and stable capacity-estimation method was developed to eliminate the subjectivity of capacity estimation. The proposed model shows robust and capable of replicating steady-state capacity estimation. The free-flow speed estimation should relate to the traffic-flow speed model while the density is zero. Therefore, this research investigates the existing deterministic speed-density models and recommends a better methodology in free-flow speed estimation. This research presents how the undefined practice of free-flow speed selection can be sensitive. Additionally, finding suitable concurrent travel-time data and traffic volume is crucial and very challenging. To collect concurrent data, this research investigates and develops several technologies such as crowdsource, web app, virtual sensor method, test vehicle, smartphone, global positioning system, and utilized several state and local agencies data collection efforts. Keywords: Travel-Time Flow, Travel-Time Delay, Volume-Delay Function, Travel Time, Origin-Destination Survey, Travel Demand Model, Travel Data Collection, Transportation Survey, Internet Sensor, Crowdsourcing, Virtual Sensor Method, VSM, Transportation Planning, GPS, Smartphone, Loop Detector, Travel -Time Prediction, Travel-Speed Prediction, TDM, Bayesian Inference, Logistic Growth Function.

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.

Multiple distribution travel time data has been observed in signalized corridors as well as freeway corridors. This behavior is typically caused by congestion, uncoordinated signals, or routes through a coordinated corridor that are not a priority. On the SR140 corridor near the Jimmy Carter Boulevard / I-85 Interchange, it was found that the travel times recorded on the corridor contained multiple distributions and thus a methodology was sought to properly separate the distributions in order to perform more robust statistical analysis. Next, an R statistical language library was found, called “mixtools”, which contained a multiple gamma distribution fitting function called “gammamixEM”. Gamma distributions were chosen for this application as typical travel time distributions tend contain a one sided tail. This function was used in conjunction with a monte-carlo approach to find fits for one to six distributions. The accuracy of the fit was confirmed through visual inspection of the plotted distributions. Then, the Akaike Information Criteria were used to compare the fits to determine the best fit number of distributions. This thesis contains a detailed outline of the algorithm as well as results from the algorithm for the combined Tuesday dataset from this project. It was found that the approach worked well for 60 out of 70 cases. In the 10 cases that were not ideal, the distributional fits make sense on a statistical level, however, for the purposes of the before and after project the next best Akaike Information Criteria value fit may need to used. These 10 cases tended to split obvious single distributions into two distributions, which is not desirable in a before and after analysis where one is not only testing individual distributions before and after construction but also determining if distributions were created or removed as a result of the change in operation of the interchange.

This thesis compares actual and perceived travel times and presents a model for predicting traffic flows when there is a network disruption. The goal of this research is to demonstrate the necessity of accounting for possible differences in travel time perception and actual travel times, and also to show trends in how the route choices change based on the transformation of the perceived travel times. A pilot test was done to determine actual travel time perceptions, and the results provided the foundation for the tests presented in this thesis and the model framework. The model is separated into three phases: equilibrium assignment, link travel time transform, and logit assignment. The transform of the link travel times is best represented by an inverse cumulative Normal distribution, and the corresponding values provide quantifiable measure of the severity of a traffic network disruption. The methodology is presented and applied to two test networks to demonstrate the resulting route choice patterns. Both networks are tested for three severity levels and three levels of demand. / text

In this research, a new approach for arterial road travel time estimation and prediction has been proposed and developed for providing reliable dynamic travel time information for arterial road networks. The results of the research should benefit arterial road traffic management and some travel time related applications. / Thesis (PhD)--University of South Australia, 2008

In this research, a new approach for arterial road travel time estimation and prediction has been proposed and developed for providing reliable dynamic travel time information for arterial road networks. The results of the research should benefit arterial road traffic management and some travel time related applications. / Thesis (PhD)--University of South Australia, 2008

Thesis (M. Phil.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 61-63) Also available in print.

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.

This licentiate thesis consists of an introduction (‘kappa’) and three papers discussing various aspects of time as a commodity and the practice of valuing travel time. The first paper is an analysis of the properties of time as an economic resource taking into account literature on behavior with regard to time. The intent is to provide better understanding of the underlying assumption of transferability between time and money in the context of transportation. The second paper builds on the analysis in the first paper combined with the findings of a study of commuters travel experiences during disruptions in the train traffic on the Øresund strait between Sweden and Denmark. It contrasts the theoretical account of value of travel time with the experiences reported by commuters and argues that the view of travel time as strictly a disutility can be limiting from a planning perspective. Instead, it is argued that an alternative approach can be to make travel time ‘plannable’, meaning viewing travel time as time travellers can plan to spend in a certain way at a certain time. The third paper argues that the diversity of possible mobility solutions based on self-driving vehicles has been somewhat overlooked in the current literature on value of travel time. Thus, the complexity of valuing travel time for self-driving vehicles has not been fully addressed. The paper consists of a morphological analysis of the parameters that might impact value of travel time for self-driving vehicles and a deeper analysis of five plausible self-driving vehicle mobility concepts. It is claimed that not all such concepts can be easily mapped into transport modes and that it might be more appropriate to differentiate value of travel based on travel characteristics. / Denna licentiatavhandling består av en introduktion och treartiklar som på olika sätt berör värdering av restid. Vare sigvi vill det eller inte är vår vardag driven av och bunden av tid.Vi planerar vår tid, spenderar vår tid och stressar när tideninte räcker till. Det vi önskar, vill och måste göra tar tid; tidenvillkorar helt enkelt mycket i våra liv. Om det är så att vi villförflytta oss från en fysisk plats till en annan, kanske mellanhem och jobb eller skola, tar det tid. Den här specifika tiden,restiden, antas behövas på grund av behovet av att jobba, fikaeller handla, inte genom en önskan om att resa i sig (även omdet självklart finns resor vi gör för nöjes skull, där nöjet är självaresan). I och med att resan (och restiden) är nödvändig men intei sig önskvärd är det ett grundläggande antagande inom fältetför transportekonomi att människor vill minimera sin restid i såstor utsträckning som möjligt. Det är det här antagandet sommycket av planeringen och investeringarna i transporter grundarsig på. Genom att undersöka betalningsvilja hos resenärer kanman sätta ett monetärt värde på potentiellt insparad restid: tidblir pengar. Men är det verkligen så enkelt? Till att börja medär tid och pengar de facto inte samma sak. Vi kan inte sparatid på samma sätt som pengar utan sparad tid måste användasomedelbart. Det blir därmed rimligt att anta att vad man gör medden insparade tiden spelar roll eftersom tiden känns mer värd omden kan spenderas på något meningsfullt. Vad man har möjlighetatt göra beror ofta på sammanhanget och upplevs därför mindreflexibelt än när det gäller insparade pengar.Denna avhandling resonerar vidare kring frågor om förhållandetmellan tid och pengar, i synnerhet den vanligt förekommandeoch generellt accepterade monetära värderingen av restid. Tillviss del problematiserar avhandlingen antagandet att tid är pengar och frågar sig om inte för mycket fokus läggs på tidskvantitetframför tidskvalitet och att kan det vara värdefullt att reflekterakring vilka transportinvesteringar som detta gynnar. Syftet äratt undersöka om de vedertagna transportekonomiska modellersom tillämpas idag speglar sådant vi som samhälle värdesätteroch lyfta aspekter som möjligen förbises.Introduktionen till avhandlingen består av en metodologiskdiskussion kring filosofins roll i tvärvetenskapliga projekt, enöversiktlig teoretisk bakgrund till begrepp såsom rationalitetoch välfärdsekonomi och en genomgång av teman som på etteller annat sätt berör värdering av tid. Därefter sammanfattasartiklarna och introduktionen avslutas med slutsatser och ettavsnitt om möjliga framtida forskningsämnen.Den första artikeln i den här avhandlingen handlar om hurförhållandet mellan tid och pengar kan bättre förstås genom attutgå från tiden som det primära att värdesätta. Denna analysoch de insikter som analysen leder till kan därefter förklara ochbättre underbygga antaganden som görs vid modellering av beslutrörande tid. I artikeln analyseras egenskaper av tid i relation tillpengar som framkommit i beteendevetenskaplig och psykologiskforskning. I transportekonomi, likt traditionell mikroekonomi,utgår man från ett antagande om stabila rationella preferenser hosindivider. Givet skillnader mellan hur individer verkar resonerakring tid jämfört med pengar kan man dock ställa sig frågan omdet skulle kunna vara annorlunda att vara rationell med avseendepå tid jämfört med att vara rationell med avseende på pengar. Isynnerhet då det finns egenskaper hos tid som är så pass specifikaatt motsvarande egenskaper inte finns hos andra typer av objekteller varor. Sammantaget hävdar vi att det enkla förhållandetmellan tid och pengar inte är tillräckligt rättfärdigat i ljuset av defaktiska skillnaderna mellan tid och pengar som verkar föreligga.Den andra artikeln i avhandlingen rör upplevelser av restid ochförhållandet mellan upplevelsen och de teoretiska antagandenasom görs i transportekonomi. I artikeln analyserar vi upplevelser av restid hos resenärer som påverkades av det plötsliga införandetav identitetskontroller vid resor mellan Sverige och Danmark 2015.Mot bakgrund av en studie där upplevelserna dokumenteradesvisar vi på aspekter av restid som upplevs men inte speglas i vedertagnatransportekonomiska modeller. Artikeln delar upp dessaaspekter i tre kategorier: (i) aspekter rörande den faktiska restatiden och upplevelser av själva resan, (ii) kortsiktiga anpassningartill rådande omständigheter och (iii) långsiktiga anpassningar tillrådande omständigheter. Vi menar att restiden behöver sättasi ett vidare perspektiv genom att se resan och restiden i ettsammanhang där planering av vardagen är en förutsättning föratt få livet att gå ihop. Ett möjligt sådant perspektiv är att urplaneringssynpunkt sträva efter att göra tiden så ‘planerbar’ sommöjligt, alltså att underlätta individers långsiktiga och kortsiktigaplanering av både restid och resor, istället för att enkom serestid som onytta.I tredje artikeln tillämpas till viss del insikter om vad som skiljertid från pengar och dessa appliceras på värdering av restid försjälvkörande fordon. Värdet av restid beror traditionellt (blandannat) på transportmedel, det vill säga om resan görs med bil,buss eller tåg. Självkörande bilar har i litteraturen setts som ytterligareresslag, ofta en ny sorts bil. Vi menar dock att självkörandefordon kan mynna ut i många olika typer av transportmedel därvissa kommer att likna de vi har idag medan andra kommer attvara nya sett till resegenskaper. Givet att dessa egenskaper är relateradetill aspekter som påverkar resenärers värdering av restidkommer tiden alltså vara olika mycket värd. Värdering av restidför självkörande fordon bli därför mer komplext än att lägga tillett eller ens några ytterligare transportmedel. För att belysa dettagör vi i artikeln en så kallad morfologisk analys där vi spännerupp ett lösningsfält vi menar täcker in aspekter som påverkarvärderingen av restid för självkörande fordon. Sedan analyserarvi möjliga (och troliga) lösningar, där varje lösning motsvararett möjligt transportmedel, och menar att restidsvärdet för dessa lösningar rimligen bör skilja sig åt. Det leder oss till att föreslåatt ett alternativt sätt att segmentera restidsvärde skulle kunnavara att utgå från resegenskaper, snarare än transportmedel somsådana. Sådana resegenskaper skulle kunna vara privat/deladresa eller om resan sker efter tidtabell eller är “on-demand“.Sammanfattningsvis menar jag att monetär värdering av tidkan ses från tre perspektiv: (i) det linjära förhållandet mellan tidoch pengar som sådant, (ii) aggregeringen av individers insparaderestid till faktisk samhällsnytta och (iii) restidsförkortningarsplats i kostnadsnyttoanalys och transportplanering i allmänhet.Transportinvesteringar görs på lång sikt och de samhällsekonomiskakalkyler som ligger till grund för dessa investeringar behöverdärmed spegla både vårt förhållande till tid idag men även hur vikommer att förhålla oss till tiden i framtiden. Rimligen kommervi då ha lika mycket tid som idag, men kommer vårt förhållandetill tid vara detsamma?Slutligen föreslår jag i avhandlingen möjliga framtida temanatt undersöka vidare, såsom transporträttvisa, aggregering avväldigt små restidsvinster och förhållandet mellan risker ochtidsvinster. / <p>QC 20200819</p>