19 ATTACHMENT - Laserfiche WebLink

The travel demand forecasting model predicts future travel demand given forecasts of local and regional future land uses and highway improvements, including the addition of freeway HOV and auxiliary lanes and ramp metering. It accomplishes this in four steps: trip generation, trip distribution, mode choice, and traffic assignment. The trip generation step converts land uses into estimates of daily trip generation. The trip distribution step determines where the trips will go. The mode choice step estimates whether the trips will be made by auto. The assignment step converts the daily trips into peak hour trips, with and without peak hour spreading, and assigns the trips to specific streets and freeways. The travel demand forecasting model was recently converted to Citilabs CubeNoyager software, the new software adopted by the Alameda County Congestion Management Agency. . The City's travel demand model includes the Tri-Valley "Triangle" land development and roadway network data. . The model also includes county-to-county travel data for forecasting San Joaquin trips through the Tri-valley and to other Bay Area destinations. . The model estimates traffic generated within Pleasanton on a house-by- house, business-by-business basis, aggregated to traffic analysis zones (TAZs). . The model automatically assigns vehicles to fastest available route taking into account regional freeway congestion and local street congestion. . The model estimates regional cut-through traffic as well as local neighborhood cut-through. . Travel mode choice data from local and regional surveys reflects regional transit improvements and shortcomings. . Level of service is estimated on an integrated "system" basis, rather than as isolated intersections. Approach specific level of service computed based on delay and volume/capacity ratio. . Micro-simulation analysis enables consideration of upstream and downstream effects of congestion on intersection operation. Model Study Area and Traffic Analysis Zone (TAZs) System Traffic in and through the City of Pleasanton is affected by local trip generation and travel destinations as well as traffic that can enter or exit the City. The study area for the model should include all of the areas where changes in travel demand and transportation networks would affect travel patterns in the City of Pleasanton. For example, traffic patterns in Pleasanton are affected by relative congestion levels on 1-580, 1-680 and SR 84, so the model should include the areas served by each of these highways (see graphic of model study area in the appendix). City of Pleasanton Travel Demand Forecasting and Micro-Simulation Models Page 2 January 23, 2007