00978545

Component

https://doi.org/10.3141/1867-22

http://scholar.google.com...erma&publication_year=2004

A new approach is presented to the estimation of travel times (in real time) on arterials on the basis of second-by-second data from detectors that count vehicles. Unlike most approaches, which attempt to track individual vehicles as they move throughout the network, the proposed approach tracks clusters, or platoons, of vehicles. This approach offers several advantages over existing methods and provides good travel time estimates for arterial networks. Although other methods of estimating travel times have varying degrees of success, they each suffer from limitations ranging from processing, equipment, and infrastructure requirements to personnel cost for probe vehicles, sociopolitical issues of surveillance, and the perceived loss of privacy. The proposed method attempts to overcome some of these limitations. Because existing detection and controller infrastructure is used, additional equipment is not required. Second-by-second detector data are used to identify platoons of vehicles but not individual vehicles. The platoons, and not individual vehicles, are then tracked as they travel from upstream detectors to downstream detectors on a route segment, producing travel time estimates for this segment. In addition, since the proposed method captures the aggregate flow of vehicles, it should better measure the central tendencies of the driver population and current traffic conditions as reflected by travel times at the prevailing congestion levels.

This paper appears in Transportation Research Record No. 1867, Freeway Operations and Traffic Signal Systems 2004.

00978523

English

p. 193-201

2004

Transportation Research Record

0309094615

Figures (7) ; References (17) ; Tables (4)

Arterial highways; Estimating; Real time data processing; Traffic platooning; Travel time; Vehicle detectors

Highways; Operations and Traffic Management; I71: Traffic Theory

TRIS, TRB, ATRI

Sep 23 2004 12:00AM

• ANOTHER VIEW OF TRUCK LANE RESTRICTIONS
• ASSESSMENT OF THE IMPACT OF INCIDENTS NEAR BOTTLENECKS: STRATEGIES TO REDUCE DELAY
• BENEFIT ASSESSMENT OF IMPLEMENTING WEATHER-SPECIFIC SIGNAL TIMING PLANS BY USING CORSIM
• COMPREHENSIVE EVALUATION OF FEEDBACK-BASED FREEWAY RAMP-METERING STRATEGY BY USING MICROSCOPIC SIMULATION: TAKING RAMP QUEUES INTO ACCOUNT
• CONFIGURATION OF TRAFFIC-RESPONSIVE PLAN SELECTION SYSTEM PARAMETERS AND THRESHOLDS: ROBUST BAYESIAN APPROACH
• CROSS-CORRELATION TRACKING TECHNIQUE FOR EXTRACTING SPEED FROM CAMERAS UNDER ADVERSE CONDITIONS
• ESTIMATION OF TRAVEL TIMES ON URBAN FREEWAYS UNDER INCIDENT CONDITIONS
• EVALUATION OF TOLL PLAZA PERFORMANCE AFTER ADDITION OF EXPRESS TOLL LANES AT MAINLINE TOLL PLAZA
• FIELD TESTING FOR AUTOMATED IDENTIFICATION OF TURNING MOVEMENTS AT SIGNALIZED INTERSECTIONS
• INCLEMENT WEATHER AND TRAFFIC FLOW AT SIGNALIZED INTERSECTIONS: CASE STUDY FROM NORTHERN NEW ENGLAND
• METHOD FOR PRIORITY-RANKING AND EXPANDING FREEWAY SERVICE PATROLS
• METHODOLOGY FOR MEASURING RECURRENT AND NONRECURRENT TRAFFIC CONGESTION
• MINNESOTA'S NEW RAMP CONTROL STRATEGY: DESIGN OVERVIEW AND PRELIMINARY ASSESSMENT
• MODELING IMPACT OF RAMP METERING QUEUES ON DIAMOND INTERCHANGE OPERATIONS
• NEW ALGORITHMS FOR FILTERING AND IMPUTATION OF REAL-TIME AND ARCHIVED DUAL-LOOP DETECTOR DATA IN I-4 DATA WAREHOUSE
• OBSERVED DYNAMIC TRAFFIC FEATURES ON FREEWAY SECTION WITH MERGES AND DIVERGES
• OPTIMAL DETECTOR LOCATION FOR BUS SIGNAL PRIORITY
• OPTIMIZATION OF TIME-OF-DAY BREAKPOINTS FOR BETTER TRAFFIC SIGNAL CONTROL
• RED CLEARANCE INTERVALS: THEORY AND PRACTICE
• RESEARCH NEEDS FOR WEATHER-RESPONSIVE TRAFFIC MANAGEMENT
• SAMPLING STRATEGIES FOR PROBE-VEHICLE-BASED FREEWAY INCIDENT DETECTION ALGORITHMS
• SCOOT AND INCIDENTS: PERFORMANCE EVALUATION IN SIMULATED ENVIRONMENT
• SIMPLE WORKSHEET METHOD TO EVALUATE EMERGENCY VEHICLE PREEMPTION AND ITS IMPACTS ON SAFETY
• SINGLE-INTERSECTION EVALUATION OF REAL-TIME ADAPTIVE TRAFFIC SIGNAL CONTROL ALGORITHMS
• SYSTEMATIC IDENTIFICATION OF FREEWAY BOTTLENECKS
• TRAINING NEURAL NETWORKS TO DETECT FREEWAY INCIDENTS BY USING PARTICLE SWARM OPTIMIZATION