01099218

Component

https://doi.org/10.3141/2085-15

http://scholar.google.com...kiva&publication_year=2008

Adaptive route choice models are studied that explicitly capture travelers’ route choice adjustments according to information on realized network conditions in stochastic time-dependent networks. Two types of adaptive route choice models are explored: an adaptive path model in which a sequence of path choice models are applied at intermediate decision nodes and a routing policy choice model in which the alternatives correspond to routing policies rather than paths at the origin. A routing policy in this study is a decision rule that maps from all possible pairs (e.g., node, time) to the next links out of the node. Existing route choice models that can be estimated on disaggregate revealed preferences assume a deterministic network setting from the traveler’s perspective and cannot capture the traveler’s proactive adaptive behavior under uncertain traffic conditions. The literature includes a number of algorithmic studies of optimal routing policy problems, but the estimation of a routing policy choice model is a new research area. The specifications of estimating the two adaptive route choice models are established and the feasibility of estimation from path observations is demonstrated on an illustrative network. Prediction results from three models—nonadaptive path model, adaptive path model, and routing policy model—are compared. The routing policy model is shown to better capture the option value of diversion than the adaptive path model. The difference between the two adaptive models and the nonadaptive model is larger in terms of expected travel time if the network is more stochastic, indicating that the benefit of adaptivity is more significant in a more unpredictable network.

01120896

English

pp 136-143

2008

Transportation Research Record: Journal of the Transportation Research Board

9780309125963

Print

Figures (4) ; References (32) ; Tables (1)

Choice models; Decision making; Policy; Route choice; Stochastic processes; Time dependence; Travel time

Adaptive routing

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB

Jan 29 2008 3:42PM

• Dynamic Network Loading Model with Explicit Traffic Wave Tracking
• Dynamic Origin–Destination Demand Estimation Using Turning Movement Counts
• Effectiveness of Origin–Destination Matrix Correction Procedure Using Traffic Counts
• Enhanced Lagged Cell-Transmission Model for Dynamic Traffic Assignment
• Generalized Wardrop Principle and Its Application in Regional Transportation
• Incorporating Within-Day Transitions in Simultaneous Offline Estimation of Dynamic Origin–Destination Flows Without Assignment Matrices
• Lower-Bound Solution Algorithm for Equilibrium Signal-Setting Problem
• Measurement of Uncertainty Costs with Dynamic Traffic Simulations
• Modeling User Responses to Pricing: Simultaneous Route and Departure Time Network Equilibrium with Heterogeneous Users
• Probit-Based Time-Dependent Stochastic User Equilibrium Traffic Assignment Model
• Robust Pricing of Transportation Networks Under Uncertain Demand
• Simultaneous Perturbation Stochastic Approximation Algorithm for Solving Stochastic Problems of Transportation Network Analysis: Performance Evaluation
• Stochastic Multiclass Traffic Assignment with Consideration of Risk-Taking Behaviors
• Stochastic User Equilibrium Model with Implicit Travel Time Budget Constraint