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ABSTRACTThe deterministic traffic assignment problem based on Wardrop's first criterion of traffic network utilization has been widely studied in the literature. However, the assumption of deterministic travel times in these models is restrictive, given the large degree of uncertainty prevalent in urban transportation networks. In this context, this paper proposes a robust traffic assignment model that generalizes Wardrop's principle of traffic network equilibrium to networks with stochastic and correlated link travel times and incorporates the aversion of commuters to unreliable routes.The user response to travel time uncertainty is modeled using the robust cost (RC) measure (defined as a weighted combination of the mean and standard deviation of path travel time) and the corresponding robust user equilibrium (UE) conditions are defined. The robust traffic assignment problem (RTAP) is subsequently formulated as a Variational Inequality problem. To solve the RTAP, a Gradient Projection algorithm is proposed, which involves solving a series of minimum RC path sub-problems that are theoretically and practically harder than deterministic shortest path problems. In addition, an origin-based heuristic is proposed to enhance computational performance on large networks. Numerical experiments examine the computational performance and convergence characteristics of the exact algorithm and establish the accuracy and efficiency of the origin-based heuristic on various real-world networks. Finally, the proposed RTA model is applied to the Chennai road network using empirical data, and its benefits as a normative benchmark are quantified through comparisons against the standard UE and System Optimum (SO) models. 相似文献
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The development and initial validation results of a micro-simulator for the generation of daily activity-travel patterns are
presented in this paper. The simulator assumes a sequential history and time-of-day dependent structure. Its components are
developed based on a decomposition of a daily activity-travel pattern into components to which certain aspects of observed
activity-travel behavior correspond, thus establishing a link between mathematical models and observational data. Each of
the model components is relatively simple and is estimated using commonly adopted estimation methods and existing data sets.
A computer code has been developed and daily travel patterns have been generated by Monte Carlo simulation. Study results
show that individuals' daily travel patterns can be synthesized in a practical manner by micro-simulation. Results of validation
analyses suggest that properly representing rigidities in daily schedules is important in simulating daily travel patterns.
This revised version was published online in June 2006 with corrections to the Cover Date. 相似文献
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Xie Yifei Danaf Mazen Lima Azevedo Carlos Akkinepally Arun Prakash Atasoy Bilge Jeong Kyungsoo Seshadri Ravi Ben-Akiva Moshe 《Transportation》2019,46(6):2017-2039
Transportation - This paper presents a systematic way of understanding and modeling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet... 相似文献
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