Day-to-day dynamic traffic assignment with imperfect information, bounded rationality and information sharing

This paper presents a doubly dynamic day-to-day (DTD) traffic assignment model with simultaneous route-and-departure-time (SRDT) choices while incorporating incomplete and imperfect information as well as bounded rationality. Two SRDT choice models are proposed to incorporate imperfect travel information: One based on multinomial Logit (MNL) model and the other on sequential, mixed multinomial/nested Logit model. These two variants, serving as base models, are further extended with two features: bounded rationality (BR) and information sharing. BR is considered by incorporating the indifference band into the random utility component of the MNL model, forming a BR-based DTD stochastic model. A macroscopic model of travel information sharing is integrated into the DTD dynamics to account for the impact of incomplete information on travelers’ SRDT choices. These DTD choice models are combined with within-day dynamics following the Lighthill-Whitham-Richards (LWR) fluid dynamic network loading model. Simulations on large-scale networks (Anaheim) illustrate the interactions between users’ adaptive decision making and network conditions (including local disruption) with different levels of information availability and user behavior. Our findings highlight the need for modeling network transient and disequilibriated states, which are often overlooked in equilibrium-constrained network design and optimization. The MATLAB package and computational examples are available at https://github.com/DrKeHan/DTD

Travel Decision Making Under Uncertainty and Road Traffic Behavior: The Multifold Role of Ambiguity Attitude

To aggregate commuters’ mode choices to traffic behavior in the presence of travel time uncertainty, we develop a dynamic traffic simulation in terms of an agent-based model, which consists of two sub-models, the mode choice model and the traffic flow simulation model. The modeling framework accommodates the interplay between the two models and their co-evolution over time. We embed an extended list of empirical parameters including ambiguity/risk attitudes and heterogeneity , and time-money trade-offs within a rank-dependent and source-dependent utility framework to imitate commuters’ daily mode choice behaviors. The improved behavioral realism at the micro-level results in an improved understanding of traffic flow in terms of modal split and average speed at equilibrium, compared to a conventional model which assumes risk neutrality and ambiguity neutrality. A novel finding is that ambiguity seeking, a typical behavior in the loss domain but largely ignored in the transport literature, acts as an important driver that shifts commuters from cars to public transport

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

Design and Analysis of Mobility Permit-based Traffic Management Schemes

High demand for mobility has undeniably been causing numerous negative impacts on the economy, the society and the environment. As a potential solution to address this challenge, a rapid transition is taking place in the transportation sector with emerging concepts of mobility marketplace. The basic premise is to treat the transportation system and its use as a collection of commodities or services that can be bought from the transportation market. This concept is increasingly becoming a reality with the technological developments in automotive industry such as connected and autonomous vehicles (CAVs). However, there are many policy, design and operation related issues that must be addressed before these traffic management schemes become reality. This thesis research aims at addressing some of these challenges and issues with a specific focus on the two most promising market-driven instruments, namely, mobility permits (MP)- and mobility credits (MC)-based traffic management schemes, which have been proposed to manage travel demand and mitigate traffic congestion by controlling roadway-use right. This research has made several distinctive contributions into the literature. We first conduct a critical review of the state-of-the-art methodological advances on MP- and MC-based travel demand management schemes. We synthesize the relevant body of literature with an in-depth discussion on related studies to provide an improved understanding of the fundamental constructs of these problems, including problem variants, methodologies, and modeling attributes. We also discuss the research gaps and challenges and suggest some possible perspectives and directions for future research. Based on the gaps identified in the literature review, an integrated framework is proposed for implementing various roadway-use right-based traffic management programs such as MP and MC-based schemes. This framework entails a unique construct for integrating the needs of multiple stakeholders (e.g., road users and authorities), diverse network conditions, and traffic control methods. It allows easy incorporation of different components required for implementing a coordinative mobility scheme, taking into account the influence of the participating players and the underlying issues. The framework can be served as a road-map to future studies on different roadway-use right-based solutions for traffic congestion management. With our proposed framework, we then focus on addressing various specific challenges arising in designing and implementing MP-based and MC-based schemes, such as, representation of realistic user characteristics (e.g., utility function, user priorities and cooperation), availability of information on users and traffic conditions, uncertainty in system conditions and user behaviors, and circulation of mobility rights in market place. For the MP-based scheme, we focus specifically on designing a mobility scheme for single-bottleneck roadways. Roads with bridges, tunnels and business districts with limited parking spaces are the most obvious examples of a simple roadway with a single-bottleneck in a transportation network. We deal with observing operational objectives, specifically, balancing efficiency, equity (users priorities), and revenue outcome of distributing mobility permits under the “fairness” constraint. We explore the theoretical properties of the proposed scheme and show that the proposed scheme can achieve an optimal traffic pattern. Particularly, we show that the proposed scheme is a Pareto-improving and strategy-proof scheme capable of achieving efficient and effective market prices suitable for travelers. Our computational results indicate the effectiveness of the proposed scheme as an alternative solution for MP-based traffic management on single-bottleneck roadways. We then investigate the case of traffic congestion management in a general road network through a MC-based scheme. Specifically, we propose a MC-based traffic management scheme in a road network consisting of a mixed-fleet traffic with connected and autonomous vehicles (CAVs) and conventional vehicles (non-CAVs). The basic premise of the proposed scheme is to regulate or influence travel demand and congestion with regards to the supply (capacity) of road networks, implementing a market-driven traffic management paradigm. A set of revenue-neutral, Pareto-improving MC-based charge and reward policies applicable to stochastic traffic environments are developed, considering different characteristics of users such as cooperative versus selfish routing behaviors, human-associated factors (e.g., level of uncertainty) and interactions due to a shared infrastructure setting. Path-free mathematical programming models are formulated, obviating computationally intractable path enumeration process pertinent to the existing studies. This makes the proposed scheme suitable for examining the theoretical characteristics of large-scale realistic transport networks. We examine several theoretical properties related to the proposed MC-based scheme, including the existence and uniqueness of the equilibrium price, and existence of Pareto-improving credit charges and rewards rates that can promote travel decision behaviors of individual travelers towards a network-wide optimal state. Our comprehensive computational results indicate that the proposed MC-based scheme can be an effective tool for managing travel demand and routing decisions in mixed-vehicle traffic settings

On agent-based modeling: Multidimensional travel behavioral theory, procedural models and simulation-based applications

This dissertation proposes a theoretical framework to modeling multidimensional travel behavior based on artificially intelligent agents, search theory, procedural (dynamic) models, and bounded rationality. For decades, despite the number of heuristic explanations for different results, the fact that "almost no mathematical theory exists which explains the results of the simulations" remains as one of the large drawbacks of agent-based computational process approach. This is partly the side effect of its special feature that "no analytical functions are required". Among the rapidly growing literature devoted to the departure from rational behavior assumptions, this dissertation makes effort to embed a sound theoretical foundation for computational process approach and agent-based microsimulations for transportation system modeling and analyses. The theoretical contribution is three-fold: (1) It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modeled and integrated in a unified and coherent approach. (2) Procedural and dynamic agent-based decision-making is modeled. Within the model, agents make decisions. They also make decisions on how and when to make those decisions. (3) Replace conventional user equilibrium with a dynamic behavioral user equilibrium (BUE). Search start/stop criteria is defined in the way that the modeling process should eventually lead to a steady state that is structurally different to user equilibrium (UE) or dynamic user equilibrium (DUE). The theory is supported by empirical observations and the derived quantitative models are tested by agent-based simulation on a demonstration network. The model in its current form incorporates short-term behavioral dimensions: travel mode, departure time, pre-trip routing, and en-route diversion. Based on research needs and data availability, other dimensions can be added to the framework. The proposed model is successfully integrated with a dynamic traffic simulator (i.e. DTALite, a light-weight dynamic traffic assignment and simulation engine) and then applied to a mid-size study area in White Flint, Maryland. Results obtained from the integration corroborate the behavioral richness, computational efficiency, and convergence property of the proposed theoretical framework. The model is then applied to a number of applications in transportation planning, operations, and optimization, which highlights the capabilities of the proposed theory in estimating rich behavioral dynamics and the potential of large-scale implementation. Future research should experiment the integration with activity-based models, land-use development, energy consumption estimators, etc. to fully develop the potential of the agent-based model

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

Development of a dynamic traffic assignment system for short-term planning applications

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (p. 141-147).Evaluation of Intelligent Transportation Systems (ITS) at the planning level, as well as various short-term planning projects, require the use of appropriate tools that can capture the dynamic and stochastic interactions between demand and supply. The objective of this thesis is to develop a methodological framework for such applications and implement it in the context of an existing dynamic traffic assignment system, DynaMIT. The methodological framework captures the day-to-day evolution of traffic. Furthermore, it models traveler behavior and network performance, in response to special events and situations such as incidents, weather emergencies, sport events etc. The new planning tool DynaMIT-P, consists of a supply (network performance) simulator, a demand simulator and algorithms that capture their interactions. The supply simulator captures traffic dynamics in terms of evolution and dissipation of queues, spill-backs etc. The demand simulator estimates OD flows that best match current measurements of them in the network, and models travel behavior in terms of route choice, departure time choice and response to information. DynaMIT-P is particularly suited to evaluate Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS) at various levels of sophistication. The results of a case study, focusing on the evaluation of alternative designs of Variable Message Signs (VMS) using a network in Irvine, California, illustrate the functionality and potential of the system.by Srinivasan Sundaram.S.M