This thesis presents an investigation of arterial travel time and reliability. Specifically an examination of the proposed arterial travel time reliability performance measures detailed in Federal Highway Administration’s Notice of Proposed Rulemaking on national performance management measures are performed. These measures, including level of travel time reliability and peak hour travel time ratio, are computed and compared to those currently used to quantify congestion and travel time reliability. Within this process several commonly used data sources are evaluated to determine the effects of data quality and data source on performance measure evaluation. The newly created Urban Streets Reliability tool is also evaluated for its ability to estimate the effect of several proposed projects on the travel time reliability of a transportation network. In conclusion, this thesis found that the proposed travel time reliability performance measures show definite differences in estimates of facility reliability as compared with currently used performance measures such as travel time index and planning time index. A variation in the magnitude of this difference was also observed based on a rural vs. urban roadway setting. Finally, further areas of research involving the use of the Urban Streets Reliability tool to estimate the impact of reliability improvements on side streets and the transportation network as a whole are discussed.

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.

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.

Adaptive ramp metering (ARM) is a critical component of smart freeway corridors under an active traffic management portfolio. While improving capacity through smart corridors and application of proactive traffic management solutions is less costly and easier to deploy than freeway widening, conversion to smart corridors still represents a sizable investment for a state department of transportation. Early evidence of improvements following these projects can be valuable to agencies. However, in the U.S. there have been limited evaluations, of smart corridors in general and ARM in particular, based on real operational data. This thesis explores travel time reliability measures for the eastbound (EB) Interstate 80 (I-80) corridor in the San Francisco Bay Area before and after implementation of ARM using INRIX data. These measures include buffer index, planning time, and measures from the literature that account for both skew and width of the travel time distribution. The measures are estimated for the entire corridor as well as corridor segments upstream of a bottleneck that historically have the worst measures of reliability. A new metric for measuring unreliability that may be derived from readily available INRIX data is also proposed in the thesis using data from the study corridor. While the ARM system is relatively new, the results indicate positive trends in measures of reliability even as the number of incidents on the corridor has increased in line with the national crash trends. The spatio-temporal trend evaluation framework used here may be used in the future to obtain more robust conclusions. However, since multiple smart corridor components were installed simultaneously, it may not be possible to fully isolate the effects of the ARM, or any of the other systems, individually.

Bluetooth technology applications have improved travel time data collection efforts and allowed for collection of large data sets at a low cost per data unit. Mean travel times between pairs of points are available, but the primary value of this technique is the availability of the entire distribution of travel times throughout multiple days and time periods, allowing for a greater understanding of travel time variations and reliability. The use of these data for transportation planning, engineering and operations continues to expand. Previous applications of similar data sources have included travel demand and simulation model validation, work zone traffic patterns, transit ridership and reliability, pedestrian movement patterns, and before-after studies of transportation improvements. This thesis investigates the collection and analysis of Bluetooth-enabled travel time data along a multimodal arterial corridor in San Luis Obispo, California. Five BlueMAC devices collected multimodal travel time data in January and February 2016 along Los Osos Valley Road. These datasets were used to identify and process known sources of error such as occasions where vehicles using the roadway turn off and make an intermediate stop and multiple reads from the same vehicle; quantify travel time performance and reliability along arterial streets; and compare transit, bicycle, and pedestrian facility performance. Additionally, a travel time model was estimated based on segment characteristics and Bluetooth data to estimate average speeds and travel time distributions.

This research examined if travelers are paying for travel on managed lanes (MLs) as they indicated that they would in a 2008 survey. The other objectives of this research included estimating travelers’ value of travel time savings (VTTS) and their value of travel time reliability (VOR), and examining the multiple survey designs used in a 2008 survey to identify which survey design better predicted ML traveler behavior. To achieve the objectives, an Internet-based follow-up stated preference (SP) survey of Houston’s Katy Freeway travelers was conducted in 2010. Three survey design methodologies—Db-efficient, random level generation, and adaptive random—were tested in this survey. A total of 3,325 responses were gathered from the survey, and of those, 869 responses were from those who likely also responded to the previous 2008 survey. Mixed logit models were developed for those 869 previous survey respondents to estimate and compare the VTTS to the 2008 survey estimates. It was found that the 2008 survey estimates of the VTTS were very close to the 2010 survey estimates. In addition, separate mixed logit models were developed from the responses obtained from the three different design strategies in the 2010 survey. The implied mean VTTS varied across the design-specific models. Only the Db-efficient design was able to estimate a VOR. Based on this and several other metrics, the Db-efficient design outperformed the other designs. A mixed logit model including all the responses from all three designs was also developed; the implied mean VTTS was estimated as 65 percent ($22/hr) of the mean hourly wage rate, and the implied mean VOR was estimated as 108 percent ($37/hr) of the mean hourly wage rate. Data on actual usage of the MLs were also collected. Based on actual usage, the average VTTS was calculated as $51/hr. However, the $51/hr travelers are paying likely also includes the value travelers place on travel time reliability of the MLs. The total (VTTS+VOR) amount estimated from the all-inclusive model from the survey was $59/hr, which is close to the value estimated from the actual usage. The Db-efficient design estimated this total as $50/hr. This research also shows that travelers have a difficulty in estimating the time they save while using a ML. They greatly overestimate the amount of time saved. It may well be that even though travelers are saving a small amount of time they value that time savings (and avoiding congestion) much higher – possibly similar to their amount of perceived travel time savings. The initial findings from this study, reported here, are consistent with the hypothesis that travelers are paying for their travel on MLs, much as they said that they would in our previous survey. This supports the use of data on intended behavior in policy analysis.

A confiabilidade do tempo de viagem é atualmente considerada como um fator de elevada importância nos estudos de demanda por transportes, com base no reconhecimento que sistemas congestionados são uma realidade inevitável nos grandes centros urbanos, gerando incertezas nas estimativas do tempo de viagem e tornando sua representação através de uma variável de tempo médio excessivamente simplista. O acesso terrestre aos aeroportos em São Paulo constitui um contexto interessante para o estudo da confiabilidade, considerando os altos custos atribuídos à eventual perda do voo e o ambiente de alta variabilidade de tempos de viagem na região. O estudo da confiabilidade do tempo de viagem tem sido em geral desenvolvido com um enfoque exclusivamente quantitativo, usando modelos matemáticos que se baseiam em teorias de maximização da utilidade estimados a partir de dados de preferência declarada. Em geral, são ignorados: (i) os efeitos de fatores latentes no comportamento, (ii) o fato de que o comportamento nem sempre reflete as intenções, (iii) a complexidade dos fatores envolvidos nas escolhas e (iv) os fatores que descrevem o contexto em que ocorre a decisão. O trabalho utiliza métodos mistos para a coleta e análise dos dados, procurando obter um conjunto abrangente de informações sobre o comportamento. Tanto a coleta de dados como os modelos estimados baseiam-se nos fundamentos da Teoria do Comportamento Planejado, que afirma que o comportamento revelado pode ser estimado a partir de uma intenção que, por sua vez, pode ser estimada a partir de atitudes, normas subjetivas e controle percebido. O controle percebido representa a percepção individual quanto à facilidade em desempenhar um comportamento. Neste estudo, a confiabilidade do tempo de viagem é incluída como um fator de controle percebido, assim como outros indicadores de controle não comumente considerados. A análise é conduzida usando-se uma técnica de Modelos de Equações Estruturais denominada Mínimos Quadrados Parciais. O uso desta técnica permitiu uma descrição abrangente dos mecanismos envolvidos no processo de escolha de acesso terrestre ao aeroporto e confirmou a importância dos fatores latentes na escolha, particularmente os relacionados ao controle percebido e real. Foi possível também verificar que conjuntos distintos de fatores influenciam a formação da intenção (e portanto a preferência declarada) e o comportamento propriamente dito (e portanto o comportamento revelado). / Travel time reliability is now considered a major factor in explaining travel demand since its underlying cause congestion seems to be an unavoidable reality in large urban centers. This brings uncertainty to travel time estimates, rendering its representation through travel time averages excessively simplistic. Ground access to airports serving the city of São Paulo makes an interesting context to study reliability, given the considerable annoyance and cost associated with the possibility of missing a flight and the high variability of travel times prevailing in the area. Studies of the reliability of travel time have generally been based on a purely quantitative approach, using utility-based mathematical models, mostly estimated with stated preference data. They usually ignore: (i) the effects of latent factors on behavior, (ii) the fact that behavior does not always reflect intentions, (iii) the complexity of factors involved in choice processes and (iv) the factors describing the choice context. This study uses mixed methods for data collection and analysis, aiming to gather a comprehensive set of information about behavior. Both data collection and modeling are based on the Theory of Planned Behavior, which states that behavior can be predicted from intention; intention, by its turn, can be predicted from attitudes, subjective norms and perceived behavioral control. The latter refers to peoples perception of the ease or difficulty of performing the behavior of interest. In this study, we include travel time reliability as a perceived behavioral control factor, in addition to other indicators of control that are not commonly considered. Analysis is conducted using Partial Least Squares, a technique from the family of Structural Equations Models. The use of this technique allowed for a more complete description of the mechanisms involved in the choice process of ground access to airports and confirmed the importance of latent factors on choice, particularly those related to perceived and actual control. The results also indicate that different sets of factors affect the formation of intention (and thus the stated choice) and the behavior itself (and thus actual behavior).

A confiabilidade do tempo de viagem é atualmente considerada como um fator de elevada importância nos estudos de demanda por transportes, com base no reconhecimento que sistemas congestionados são uma realidade inevitável nos grandes centros urbanos, gerando incertezas nas estimativas do tempo de viagem e tornando sua representação através de uma variável de tempo médio excessivamente simplista. O acesso terrestre aos aeroportos em São Paulo constitui um contexto interessante para o estudo da confiabilidade, considerando os altos custos atribuídos à eventual perda do voo e o ambiente de alta variabilidade de tempos de viagem na região. O estudo da confiabilidade do tempo de viagem tem sido em geral desenvolvido com um enfoque exclusivamente quantitativo, usando modelos matemáticos que se baseiam em teorias de maximização da utilidade estimados a partir de dados de preferência declarada. Em geral, são ignorados: (i) os efeitos de fatores latentes no comportamento, (ii) o fato de que o comportamento nem sempre reflete as intenções, (iii) a complexidade dos fatores envolvidos nas escolhas e (iv) os fatores que descrevem o contexto em que ocorre a decisão. O trabalho utiliza métodos mistos para a coleta e análise dos dados, procurando obter um conjunto abrangente de informações sobre o comportamento. Tanto a coleta de dados como os modelos estimados baseiam-se nos fundamentos da Teoria do Comportamento Planejado, que afirma que o comportamento revelado pode ser estimado a partir de uma intenção que, por sua vez, pode ser estimada a partir de atitudes, normas subjetivas e controle percebido. O controle percebido representa a percepção individual quanto à facilidade em desempenhar um comportamento. Neste estudo, a confiabilidade do tempo de viagem é incluída como um fator de controle percebido, assim como outros indicadores de controle não comumente considerados. A análise é conduzida usando-se uma técnica de Modelos de Equações Estruturais denominada Mínimos Quadrados Parciais. O uso desta técnica permitiu uma descrição abrangente dos mecanismos envolvidos no processo de escolha de acesso terrestre ao aeroporto e confirmou a importância dos fatores latentes na escolha, particularmente os relacionados ao controle percebido e real. Foi possível também verificar que conjuntos distintos de fatores influenciam a formação da intenção (e portanto a preferência declarada) e o comportamento propriamente dito (e portanto o comportamento revelado). / Travel time reliability is now considered a major factor in explaining travel demand since its underlying cause congestion seems to be an unavoidable reality in large urban centers. This brings uncertainty to travel time estimates, rendering its representation through travel time averages excessively simplistic. Ground access to airports serving the city of São Paulo makes an interesting context to study reliability, given the considerable annoyance and cost associated with the possibility of missing a flight and the high variability of travel times prevailing in the area. Studies of the reliability of travel time have generally been based on a purely quantitative approach, using utility-based mathematical models, mostly estimated with stated preference data. They usually ignore: (i) the effects of latent factors on behavior, (ii) the fact that behavior does not always reflect intentions, (iii) the complexity of factors involved in choice processes and (iv) the factors describing the choice context. This study uses mixed methods for data collection and analysis, aiming to gather a comprehensive set of information about behavior. Both data collection and modeling are based on the Theory of Planned Behavior, which states that behavior can be predicted from intention; intention, by its turn, can be predicted from attitudes, subjective norms and perceived behavioral control. The latter refers to peoples perception of the ease or difficulty of performing the behavior of interest. In this study, we include travel time reliability as a perceived behavioral control factor, in addition to other indicators of control that are not commonly considered. Analysis is conducted using Partial Least Squares, a technique from the family of Structural Equations Models. The use of this technique allowed for a more complete description of the mechanisms involved in the choice process of ground access to airports and confirmed the importance of latent factors on choice, particularly those related to perceived and actual control. The results also indicate that different sets of factors affect the formation of intention (and thus the stated choice) and the behavior itself (and thus actual behavior).