A TRAFFIC ASSIGNMENT PLANNING MODEL: THE LOAD-NODE CONCEPT
Abstract The use of the traffic assignment model in urban transportation planning is universal; however, traffic assignment in its present form is expensive, cumbersome, and has serious limitations. In this research the authors have developed and tested the load-node concept of traffic assignment which reduces many of these problems. In the load-node concept the corners of zone boundaries are used for points of trip origins and destinations. In this manner the zone centroids and associated connector linkages of the network are removed. This results in a substantial reduction in network coding. In this study the total number of nodes required to define the network decreased by almost 3000 to 995 and the number of links in the network decreased by two-thirds. The forecasted 1990 Phoenix metropolitan area was used to test this approach and the average link volumes of all-or-nothing capacity restraint assignments using both the load-node and zone centroid concepts formed the basis of the comparison. The analyses indicated the average link volumes of the two concepts are comparable. A probable reduction in computer time of 40 + per cent was obtained when the load-node concept was used. The authors suggest that this approach is primarily adaptable for planning purposes and not design. With respect to the design use of traffic assignment results, the travel between adjacent zones is seldom included in the major arterial street and freeway traffic volumes obtained from zone centroid assignment. This may result in a substantial underestimation of future traffic volume and turning movements particularly if stepped down 24-hr assignments are used for design purposes. The load-node approach does assign travel between adjacent zones.
TRAFFIC ASSIGNMENT ANALYSIS AND EVALUATION
TRAFFIC ASSIGNMENT BY COMPUTER IS ONE OF THE MOST USEFUL TOOLS PRESENTLY AVAILABLE TO THE TRANSPORTATION PLANNER. TO USE IT MOST ADVANTAGEOUSLY REQUIRES UNDERSTANDING OF THE CAPABILITIES AND LIMITATIONS NOT ONLY OF THE TOOL BUT ALSO OF THE INPUTS AND RELATED ASSUMPTIONS THAT GO HAND IN HAND. THE POSSIBLE USES AND MISUSES OF TRAFFIC ASSIGNMENTS ARE DISCUSSED, AND ASSIGNMENT ANALYSIS IS REVIEWED. TRAFFIC ASSIGNMENT DOES NOT TAKE THE PLACE OF PLANNING, IT MERELY ENABLES THE PLANNER OR DESIGNER TO DETERMINE THE SALIENT GOOD AND BAD CHARACTERISTICS OF VARIOUS PRECONCEIVED PLANS QUICKLY AND CHEAPLY. ITS VIRTUES LIE IN PERMITTING MORE TESTING OF ALTERNATES, MORE FREEDOM OF CHOICE, AND THE BRINGING TO BEAR OF MORE COMPLEX RELATIONSHIPS, ALL OF WHICH REQUIRE JUDGEMENT IN EVALUATION AND MAKING SUBSEQUENT DECISIONS. DEVELOPERS OF TRIP DISTRIBUTION MODELS AND ASSIGNMENT PROGRAMS ARE FAR FROM AGREEMENT ON THE BEST APPROACH TO TRAFFIC FORECASTING. IT IS NECESSARY THAT THE PLANNER AND THE HIGHWAY DESIGNER REALIZE THE ASSUMPTIONS MADE IN THE ASSIGNMENT, AND THEIR EFFECTS. THE SENSITIVITY OF THE COMPUTER TECHNIQUE EMPLOYED IN MANY TRANSPORTATION STUDIES IS MORE THAN SUFFICIENT FOR AREA PLANNING BUT, PERHAPS, IS INSUFFICIENT FOR THE DEGREE OF DETAIL DESIRED BY A DESIGNER DESPITE THE LIMITATIONS THAT EXIST WITHIN ANY ASSIGNMENT TECHNIQUE, TRAFFIC ASSIGNMENT DOES PROVIDE THE MOST SOUND BASIS ON WHICH TO ESTABLISH DESIGN-HOUR VOLUMES. ASSIGNMENTS CAN PROVIDE PEAK HOUR RAMP VOLUMES AND TURNING MOVEMENTS AT ARTERIAL INTERSECTIONS, BUT COMPUTER PRODUCED NUMBERS IN SUCH DETAIL ARE ALMOST CERTAINLY SPURIOUS IF TAKEN LITERALLY. THE DANGER LIES IN THE ASSUMPTION OF ACCURACY MERELY BECAUSE RESULTS ARE DETAILED. THE AGENCY MAKING THE ASSIGNMENTS MUST PROVIDE ASSIGNMENT USERS WITH INFORMATION ON THE SIGNIFICANCE OF OUTPUT DATA, AND ON ITS PROPER USE.
Automatic classification of TV news articles based on telop character recognition
The purpose of this study is to develop a multimedia database system for TV news video data. TV news video data consist of speech, characters and images. In this study, telop characters are recognized and given to the news articles as indices for classification. At first, telop frames which include telop characters are detected and then the telop characters are extracted and recognized. Through morphological analysis of the recognized telop characters, keywords are extracted which consist of more than two characters. Their keywords are used as indices to classify the TV news articles. We carried out experiments for 30 days of NHK 5 minutes news and obtained 95.4% telop character extraction rate, 81.4% character recognition rate and 83.8% article classification rate. We improved the article classification rate by 43.4% through character recognition improvement from 44.1% to 81.4%.