Models and Algorithms for Addressing Travel Time Variability: Applications from Optimal Path Finding and Traffic Equilibrium Problems

An optimal path finding problem and a traffic equilibrium problem are two important, fundamental, and interrelated topics in the transportation research field. Under travel time variability, the road networks are considered as stochastic, where the link travel times are treated as random variables with known probability density functions. By considering the effect of travel time variability and corresponding risk-taking behavior of the travelers, this dissertation proposes models and algorithms for addressing travel time variability with applications from optimal path finding and traffic equilibrium problems. Specifically, two new optimal path finding models and two novel traffic equilibrium models are proposed in stochastic networks. To adaptively determine a reliable path with the minimum travel time budget required to meet the user-specified reliability threshold α, an adaptive α-reliable path finding model is proposed. It is formulated as a chance constrained model under a dynamic programming framework. Then, a discrete-time algorithm is developed based on the properties of the proposed model. In addition to accounting for the reliability aspect of travel time variability, the α-reliable mean-excess path finding model further concerns the unreliability aspect of the late trips beyond the travel time budget. It is formulated as a stochastic mixed-integer nonlinear program. To solve this difficult problem, a practical double relaxation procedure is developed. By recognizing travelers are not only interested in saving their travel time but also in reducing their risk of being late, a α-reliable mean-excess traffic equilibrium (METE) model is proposed. Furthermore, a stochastic α-reliable mean-excess traffic equilibrium (SMETE) model is developed by incorporating the travelers’ perception error, where the travelers’ route choice decisions are determined by the perceived distribution of the stochastic travel time. Both models explicitly examine the effects of both reliability and unreliability aspects of travel time variability in a network equilibrium framework. They are both formulated as a variational inequality (VI) problem and solved by a route-based algorithm based on the modified alternating direction method. In conclusion, this study explores the effects of the various aspects (reliability and unreliability) of travel time variability on travelers’ route choice decision process by considering their risk preferences. The proposed models provide novel views of the optimal path finding problem and the traffic equilibrium problem under an uncertain environment, and the proposed solution algorithms enable potential applicability for solving practical problems

Optimization-based decision-making models for disaster recovery and reconstruction planning of transportation networks

The purpose of this study is to analyze optimization-based decision-making models for the problem of Disaster Recovery Planning of Transportation Networks (DRPTN). In the past three decades, seminal optimization problems have been structured and solved for the critical and sensitive problem of DRPTN. The extent of our knowledge on the practicality of the methods and performance of results is however limited. To evaluate the applicability of those context-sensitive models in real-world situations, there is a need to examine the conceptual and technical structure behind the existing body of work. To this end, this paper performs a systematic search targeting DRPTN publications. Thereafter, we review the identified literature based on the four phases of the optimization-based decision-making modeling process as problem definition, problem formulation, problem-solving, and model validation. Then, through content analysis and descriptive statistics, we investigate the methodology of studies within each of these phases. Eventually, we detect and discuss four research improvement areas as [1] developing conceptual or systematic decision support in the selection of decision attributes and problem structuring, [2] integrating recovery problems with traffic management models, [3] avoiding uncertainty due to the type of solving algorithms, and [4] reducing subjectivity in the validation process of disaster recovery models. Finally, we provide suggestions as well as possible directions for future research.TU Berlin, Open-Access-Mittel - 202

INCORPORATING TRAVEL TIME RELIABILITY INTO TRANSPORTATION NETWORK MODELING

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

Agent-Based Models of Highway Investment Processes: Forecasting Future Networks under Public and Private Ownership Regimes

The present highway funding system, especially fuel taxes, may become a less reliable revenue source in the future, while the transportation public agencies do not have sufficient financial resources needed to meet the increasing traffic demand. In the last two decades there has been increasing interest in utilizing private sector to develop, finance and operate new and existing roadways in the United States. While transportation privatization projects have shown signs of success, it is not always clear how to measure the true benefits associated with these projects for all stakeholders, including the public sector, the private sector and the public. "Win-win" privatization agreements are tricky to make due to conflicting nature of the various stakeholders involved. Therefore, there is a huge need to study the welfare impacts of various road privatization arrangements for the society as a whole, and the financial implications for private investors and public road authorities. In order to address these needs, first, an empirical analysis is performed to study the investment decision processes of public transportation agencies. Second, the agent-based decision-making model is developed to consider transportation investment processes at different levels of government which forecasts future transportation networks and their performance under both existing and alternative transportation planning processes. Third, various highway privatization schemes currently practiced in the U.S. are identified and an agent-based model for analyzing regulatory policies on private-sector transportation investments is developed. Fourth, the above mentioned models are demonstrated on the networks with grid and beltway topologies to study the impacts of topology configuration on the privatization arrangements. Based on the simulation results of developed models, a number of insights are provided about impacts of ownership structures on the socio-economic performance in transportation systems and transportation network changes over time. The proposed models and the approach can be used in long-run prediction of economic performance intended for describing a general methodology for transportation planning on large networks. Therefore, this research is expected to contribute significantly to the understanding and selecting proper road privatization programs on public networks

통행시간 신뢰도의 개별 리스크 선호도를 고려한 경로선택행태 모형

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 건설환경공학부, 2018. 2. 고승영.The route choice problem is an important factor in traffic operation and transportation planning. There have been many studies to analyze the route choice behavior using travel data. There was a limit in constructing a route choice model by generating an appropriate choice set due to the limitations of the data. In this study, we construct a choice set generation model and a stochastic route choice model using the observed data. This study estimates the parameters incorporating travelers heterogeneity according to the choice set from the choice set generation model and the route choice model. We define the individual confidence level according to perceived travel time distribution to reflect travelers heterogeneity on choice set generation model. In addition, the parameters were estimated using the mixed path-size correction logit model (MPSCL) considering the path overlapping and individual risk preference in the route choice model. We compare the experienced paths and the derived choice set to construct choice set generation model. In addition, it is possible to estimate better parameters incorporating travelers heterogeneity for choice set generation model and route choice model. We compare the choice set from the developed model with that of the conventional choice set generation model. This study shows the superior prediction result in route choice model reflecting the individual behaviors of the route choice in the urban area on the transportation demand forecasting and traffic operation.1. Introduction 1 1.1 Backgrounds 1 1.2 Research Purpose 4 1.3 Main contents 5 1.4 Research Scope 9 2. Literature Review 11 2.1 Choice Set Generation 13 2.2 Route Choice Model 30 2.3 Review result and limitation 58 2.4 Research Contributions 62 3. Modelling Framework 66 3.1 Overview 66 3.2 Terminology 68 3.3 Choice set generation model 76 3.4 Route choice model 92 4. Revealed Preference Routing Data 100 4.1 Data Characteristics 100 4.2 Data Collection & Description 106 4.3 Data Processing 109 4.4 Missing Data Correction 113 5. Model Estimation & Validation 120 5.1 Overview 120 5.2 Choice set generation 121 5.3 Model estimation & Validation 124 5.4 Model verification 135 5.5 Discussion 138 6. Conclusion 140 6.1 Conclusion 140 6.2 Further research 142 REFERENCES 145 APPENDIX 158Docto

A new behavioral principle for urban transportation networks

Bibliography: p. 58-59."March 1982."U. S. Department of Transportation Contract DOT-TSC-RSPA-81-9by Stanley B. Gershwin, David M. Orlicki

Modeling Overlapping and Heterogeneous Perception Variance in Stochastic User Equilibrium Problem with Weibit Route Choice Model

In this study, a new SUE model using the Weibull random error terms is proposed as an alternative to overcome the drawbacks of the multinomial logit (MNL) SUE model. A path-size weibit (PSW) model is developed to relax both independently and identically distributed assumptions, while retaining an analytical closed-form solution. Specifically, this route choice model handles route overlapping through the path-size factor and captures the route-specific perception variance through the Weibull distributed random error terms. Both constrained entropy-type and unconstrained equivalent MP formulations for the PSW-SUE are provided. In addition, model extensions to consider the demand elasticity and combined travel choice of the PSW-SUE model are also provided. Unlike the logit-based model, these model extensions incorporate the logarithmic expected perceived travel cost as the network level of service to determine the demand elasticity and travel choice. Qualitative properties of these minimization programs are given to establish equivalency and uniqueness conditions. Both path-based and link-based algorithms are developed for solving the proposed MP formulations. Numerical examples show that the proposed models can produce a compatible traffic flow pattern compared to the multinomial probit (MNP) SUE model, and these models can be implemented in a real-world transportation network

Value of Travel-Time Reliability: Commuters’ Route-Choice Behavior in the Twin Cities

Travel-time variability is a noteworthy factor in network performance. It measures the temporal uncertainty experienced by users in their movement between any two nodes in a network. The importance of the time variance depends on the penalties incurred by the users. In road networks, travelers consider the existence of this journey uncertainty in their selection of routes. This choice process takes into account travel-time variability and other characteristics of the travelers and the road network. In this complex behavioral response, a feasible decision is spawned based on not only the amalgamation of attributes, but also on the experience travelers incurred from previous situations. Over the past several years, the analysis of these behavioral responses (travelers’ route choices) to fluctuations in travel-time variability has become a central topic in transportation research. These have generally been based on theoretical approaches built upon Wardropian equilibrium, or empirical formulations using Random Utility Theory. This report focuses on the travel behavior of commuters using Interstate 394 (I-394) and the swapping (bridge) choice behavior of commuters crossing the Mississippi River in Minneapolis. The inferences of this report are based on collected Global Positioning System (GPS) tracking data and accompanying surveys. Furthermore, it also employs two distinct approaches (estimation of Value of Reliability [VOR] and econometric modeling with travelers’ intrapersonal data) in order to analyze the behavioral responses of two distinct sets of subjects in the Minneapolis-Saint Paul (Twin Cities) area

The Siting Of Multi-User Inland Intermodal Container Terminals In Transport Networks

Almost without exception, cargo movements by sea have their origins and destinations in the hinterlands and efficient land transport systems are required to support the transport of these cargo to and from the port. Furthermore, not all goods produced are exported or all goods consumed are imported. Those produced and consumed domestically also require efficient transport to move them from their production areas to areas of consumption. The use of trucks for these transport tasks and their disproportionate contribution to urban congestion and harmful emissions has led governments, transport and port authorities and other policy-makers to seek for more efficient and sustainable means of transport. A promising solution to these problems lies in the implementation of intermodal container terminals (IMTs) that interface with both road and rail or possibly inland waterway networks to promote the use of intermodal transport. This raises two important linked questions; where should IMTs be located and what will be their likely usage by individual shippers, each having a choice of whether or not to use the intermodal option. The multi-shipper feature of the problem and the existence of competing alternative modes means the demand for IMTs are outcome of many individual mode choice decisions and the prevailing cargo production and distribution patterns in the study area. This thesis introduces a novel framework underpinned by the principle of entropy maximisation to link mode choice decisions and variable cargo production and distribution problems with facility location problems. The overall model allows both decisions on facility location and usage to be driven by shipper preferences, following from the random utility interpretation of the discrete choice model. Several important properties of the proposed model are presented as propositions including the demonstration of the link between entropy maximisation and welfare maximisation. Exact and heuristic algorithms have been also developed to solve the overall problem. The computational efficiency, solution quality and properties of the heuristic algorithm are presented along with extensive numerical examples. Finally, the implementation of the model, illustration of key model features and use in practice are demonstrated through a case study