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Urban Arterial SpeedFlow Equations for Travel Demand Models Richard Dowling, Dowling Associates, Inc. Alexander Skabardonis, Institute of Transportation Studies, University of California, Berkeley This paper describes the effort to improve the ations and performed as well as other possible equa- speedflow relationships for urban arterial streets that tions for below-capacity conditions. are contained in the Southern California Association of Governments' (SCAG) metropolitan area travel demand T model. Intersection traffic counts and floating car runs he objective of the study was to develop improved were made over 4-h-long periods on 1-mi-long sections field-calibrated speedflow equations for use in of eight different arterial streets within the city of Los travel demand models to predict the mean speed Angeles. The field data were then filtered to identify of traffic on signalized urban arterial streets. which speed measurements were taken during below- capacity conditions and which measurements were made during congested conditions when demand FIELD DATA COLLECTION exceeded the capacity of one or more intersections on the arterial. Because the traditional manual intersection Intersection movement counts and Global Positioning traffic count method that was used to gather volumes Systemequipped floating cars were used to gather 216 did not measure queue buildup, and therefore demand, hourly observations of speed and flow on 54 directional the speed data points obtained during congested condi- street segments (defined as a one-way link between two tions were not used in the fitting of speedflow equa- signalized intersections) at eight different sites in the Los tions. Several different speedflow relationships were Angeles basin (see Figure 1). A total of 12.8 directional evaluated against the field data for below-capacity con- miles of arterial streets were surveyed. Table 1 shows the ditions. The most promising speedflow equations for salient characteristics of each survey site. below-capacity conditions were then evaluated for their ability to predict delays for congested conditions where one or more intersections on the arterial are above DATA FILTERING capacity. The theoretical delay due to vehicles waiting their turn to clear the bottleneck intersection on the The method used to collect intersection volumes mea- arterial was computed by using classical deterministic sured intersection discharge rates rather than demand. queuing theory. Speedflow equations that underpre- When the demand is less than the discharge capacity for dicted the delay to clear a congested intersection were the intersection approach, discharge rate and demand rejected. Of the speedflow equations tested, the Akce- are identical. When the demand exceeds capacity, the lik equation performed the best for above-capacity situ- demand diverges from the counted discharge rate. Con- 109