01023232

Component

https://doi.org/10.3141/1935-01

http://scholar.google.com...+Ran&publication_year=2005

In this paper a new robust optimization (RO) model is proposed for route guidance based on the advanced traveler information system. The arc travel time is treated as a random variable, and its distribution is estimated from historical data. Traditional stochastic routing models just minimize the expected travel time between the origin and the destination. Such approaches do not account for the fact that travelers often incorporate travel time variability in their decision making. Recently some RO models were proposed to incorporate more statistical information into the models, but these models still ignore much information available from historical travel time data. The probability measurement, time at risk (TaR), is introduced in this paper, and a multiobjective model is built up that allows a trade-off between the expected travel time and the TaR. Thus, the skewness and kurtosis of the arc travel time distribution are taken into consideration; that is important because the travel time distributions of typical arcs show high asymmetry and long tails on the right side as a result of the impact of random incidents and events. This approach is applied in two examples, one of which is a real traffic network.

01023220

English

pp 1-7

2005

Transportation Research Record: Journal of the Transportation Research Board

0309094097

Print

Figures (4) ; References (5) ; Tables (2)

Advanced traveler information systems; Optimization; Route guidance; Travel time

Multiobjective models; Time at risk

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

TRIS, TRB, ATRI

Apr 24 2006 10:02AM

• Calibration of Microsimulation Models Using Nonparametric Statistical Techniques
• Development of Methodology to Design Advanced Traffic Surveillance Systems for Traffic Information Based on Origin–Destination
• Efficient Map Matching of Large Global Positioning System Data Sets: Tests on Speed-Monitoring Experiment in Zürich
• Evaluation of AASHTO SiteManager Performance on High-Latency Satellite Communication Links
• Exploring the Relationship Between Data Aggregation and Predictability to Provide Better Predictive Traffic Information
• Geographically Stratified Importance Sampling for the Calibration of Aggregated Destination Choice Models for Trip Distribution
• ISO 19133 Tracking and Navigation Standard: 6.6 Linear Reference System Standard
• Lane-Based Network for Flow Analysis and Inventory Management of Transportation Networks
• Markov Chain Monte Carlo Multiple Imputation Using Bayesian Networks for Incomplete Intelligent Transportation Systems Data
• Modeling Location Choice of Secondary Activities with a Social Network of Cooperative Agents
• Modeling Urban Link Travel Time with Inductive Loop Detector Data by Using the k-NN Method
• Q-Learning for Flexible Learning of Daily Activity Plans
• Simple Map-Matching Algorithm Applied to Intelligent Winter Maintenance Vehicle Data
• Travel Time Prediction Algorithm Scalable to Freeway Networks with Many Nodes with Arbitrary Travel Routes
• Using a Fuzzy Quality Function Deployment Model to Identify Improvement Points in Airport Cargo Terminals
• Using Decision Support System and Graphical User Interface to Choose Appropriate Lane Configurations at Toll Facilities
• Using the Ant Algorithm to Derive Pareto Fronts for Multiobjective Siting of Emergency Service Facilities
• Vehicle Detection and Classification Using Model-Based and Fuzzy Logic Approaches