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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_109.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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109 Urban Arterial Speed–Flow 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 speed–flow relationships for urban arterial streets that are contained in the Southern California Association of Governments’ (SCAG) metropolitan area travel demand model. Intersection traffic counts and floating car runs were made over 4-h-long periods on 1-mi-long sections of eight different arterial streets within the city of Los Angeles. The field data were then filtered to identify which speed measurements were taken during below- capacity conditions and which measurements were made during congested conditions when demand exceeded the capacity of one or more intersections on the arterial. Because the traditional manual intersection traffic count method that was used to gather volumes did not measure queue buildup, and therefore demand, the speed data points obtained during congested condi- tions were not used in the fitting of speed–flow equa- tions. Several different speed–flow relationships were evaluated against the field data for below-capacity con- ditions. The most promising speed–flow equations for 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 capacity. The theoretical delay due to vehicles waiting their turn to clear the bottleneck intersection on the arterial was computed by using classical deterministic queuing theory. Speed–flow equations that underpre- dicted the delay to clear a congested intersection were rejected. Of the speed–flow equations tested, the Akce- lik equation performed the best for above-capacity situ- ations and performed as well as other possible equa- tions for below-capacity conditions. The objective of the study was to develop improvedfield-calibrated speed–flow equations for use intravel demand models to predict the mean speed of traffic on signalized urban arterial streets. FIELD DATA COLLECTION Intersection movement counts and Global Positioning System–equipped floating cars were used to gather 216 hourly observations of speed and flow on 54 directional street segments (defined as a one-way link between two signalized intersections) at eight different sites in the Los Angeles basin (see Figure 1). A total of 12.8 directional miles of arterial streets were surveyed. Table 1 shows the salient characteristics of each survey site. DATA FILTERING The method used to collect intersection volumes mea- sured intersection discharge rates rather than demand. When the demand is less than the discharge capacity for the intersection approach, discharge rate and demand are identical. When the demand exceeds capacity, the demand diverges from the counted discharge rate. Con-

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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