00804673

Component

https://doi.org/10.3141/1739-08

http://scholar.google.com...NAKA&publication_year=2000

A method is proposed that applies an artificial neural network model to estimate an origin-destination (O-D) matrix for a freeway network for which the data on inflow and outflow at the ramps are gathered regularly. This problem is the same as estimating the elements of an O-D table, given that many sets of data about the right-hand column total (trip production) and the bottom row total (trip attraction) are available. A neural network model is developed to emulate the stimulus-response process on the freeway traffic, in which the stimulus is the inflow at the entrance ramps and the response the outflow at the exit ramps. After the neural network of a particular structure is trained by many sets of data (e.g., sets of daily volumes), the weights of the neural network are found to represent the ramp-to-ramp volume expressed in the proportion of the inflow at the corresponding ramps. The model is applied to estimate a ramp-to-ramp O-D table for the Tokyo expressway network. The result is compared with the actual O-D table obtained from a survey. The model is found to be useful not only for estimating the O-D volume with much less data than for the traditional method, but also for verifying the existence of a pattern in the traffic flow.

This paper appears in Transportation Research Record No. 1739, Evaluating Intelligent Transportation Systems, Advanced Traveler Information Systems, and Other Artificial Intelligence Applications.

English

p. 59-66

2000

Transportation Research Record

0309067421

Figures (9) ; References (8) ; Tables (1)

Estimating; Expressways; Freeways; Interchange ramps; Neural networks; Origin and destination; Tables (Data); Traffic data

Traffic patterns

Tokyo (Japan)

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

TRIS, TRB, ATRI

Jan 16 2001 12:00AM

• ARTIMIS TELEPHONE TRAVEL INFORMATION SERVICE: CURRENT USE PATTERNS AND USER SATISFACTION
• ASSESSMENT OF RURAL INTELLIGENT TRANSPORTATION SYSTEM WIRELESS COMMUNICATIONS SOLUTIONS
• BENEFIT-COST EVALUATION OF TRAVELER INFORMATION: SEATTLE'S WASHINGTON STATE DEPARTMENT OF TRANSPORTATION WEBSITE
• COMPARISON OF LOW-FIDELITY TRANSIMS AND HIGH-FIDELITY CORSIM HIGHWAY SIMULATION MODELS WITH INTELLIGENT TRANSPORTATION SYSTEM DATA
• DETERMINING THE OVERALL VALUE OF IMPLEMENTED NEW TECHNOLOGY IN TRANSPORTATION: INTEGRATED MULTIPLE OBJECTIVE-ATTRIBUTE METHODOLOGY
• DYNAMIC ESTIMATION OF ORIGIN-DESTINATION TRAVEL TIME AND FLOW ON A LONG FREEWAY CORRIDOR: NEURAL KALMAN FILTER
• FAST EMERGENCY VEHICLE PREEMPTION SYSTEMS
• KNOWLEDGE ACQUISITION, VERIFICATION, AND VALIDATION IN AN EXPERT SYSTEM FOR IMPROVED TRAFFIC SAFETY
• LIKELY USERS OF ADVANCED TRAVELER INFORMATION SYSTEMS: EVIDENCE FROM THE SEATTLE REGION
• PERFORMANCE EVALUATION OF NEURAL NETWORKS IN CONCRETE CONDITION ASSESSMENT
• SEATTLE METROPOLITAN MODEL DEPLOYMENT INITIATIVE EVALUATION: RESULTS AND KEY FINDINGS FROM MODELING