Approaches to Forecasting the Third Performance Management Rulemaking (PM3) Measures for Target Setting
|Strategic Highway Research Program 2 Project||Analysis Scale (in order of increasing complexity)|
|L03 and C11||Sketch planning; system or project level.|
|L07||Detailed sketch planning; mainly project level.|
|L02||Performance monitoring and project evaluations using empirical data.|
|L10||Performance monitoring and project evaluations using empirical data.|
|L08||Project planning using Highway Capacity Manual scale of analysis.|
|C05||Project planning using mesoscopic simulation scale of analysis.|
|C10||Regional planning using linked travel demand and mesoscopic simulation analysis.|
|L04||Regional planning using linked travel demand and mesoscopic or microscopic simulation analysis.|
We assume that the TDM forecasts traffic by the following time periods (weekday):
Beyond the data requirements operating the TDM, data on hourly traffic distributions are required. These are required to split out the link volume forecasts into hours. Because most links in a TDM network are one way, the distributions need to be by direction, where direction is travel direction that peaks in either the morning or afternoon on weekdays. Links can be assigned to a “peaking direction” by examining speeds or v/c ratios; the AM or PM period with the highest value will indicate the peaking direction.
Table 5 shows an example of these hourly distributions. Because the System and Truck Reliability measures also consider weekend time periods, distributions for weekends also are required. Factors to compute annual average weekday traffic (AAWDT) and annual average weekend traffic (AAWET) also are needed.
TDMs predict single values for their performance measures meant to represent the average or typical condition. However, the System and Truck Reliability measures are based on the variability around this average condition. Therefore, some method of translating the average condition into the travel time percentiles for the performance metrics needs to be developed. The tactic used by SHRP 2 Project C11, where empirical data is used to develop relationships between the average condition and the percentiles can be used for this purpose. Figure 4 shows an example of this relationship developed from the NPMRDS data for Oregon. Note that the travel time index is used for this relationship in order to normalize the data for different section lengths.
|Hour||Weekday AM Peak Direction—Percent of Daily Traffic||Weekday PM Peak Direction—Percent of Daily Traffic||Weekend (both directions)—Percent of Daily Traffic|
In addition to the TDM, code will have to be developed to perform the processing steps below. Spreadsheets are probably not adequate.
The approach to developing the PM3 measures from TDM output involves the following processing steps:
TT = the predicted travel time on the link
TTff = the travel time at the free-flow speed
vcr = the v/c ratio
The above procedure is a simplistic approach to the problem because the volumes and capacities used are static; since reliability is defined by how travel times vary, then its determinants also should vary. Two methods exist to extend the procedure to include variability in demand (volumes) and capacity.
The simplest adjustment is to increase the v/c ratio to account for increased demand and/or decreased capacity due to disruptions such as incidents and inclement weather. However, the development of the v/c adjustments must account for the fact that incidents and weather occur with variable severities, e.g., incidents do not occur every day on a highway section and when they do, their blockage and duration characteristics vary.) From a prediction standpoint, in addition to being probabilistic, incidents also are a function of VMT, so care must be taken in reducing capacity in the v/c ratio.
A second more complex method is to compute travel times stochastically. In this approach, the volume and capacity on a link are allowed to vary using Monte Carlo simulation techniques, and travel times for each level of volume and capacity are computed until a complete travel time distribution is obtained. Distributions for the factors being varied are required for the Monte Carlo simulation. The resulting synthetic distribution of travel times can then be used in the same way that empirical data are used for computing the PM3 measures. The procedure works as follows for incidents, but weather and volumes also can be addressed: The idea is to cycle through individual “days” where each day has distinct incident characteristics:
Weather conditions can also be sampled using the above approach. Thus, each simulated “day” is a combination of incident and weather conditions.
An MPO wants to develop the PM3 measures for their region as part of their Long-Range Transportation Plan (LRTP). They have run their Travel Demand Forecasting (TDF) model for multiple scenarios and have developed code to process the TDF model output to develop the measures. The calculations follow the process below shown for a single freeway link one mile in length and three lanes wide for the first time period for the System Reliability measure:
|Time Period||Volume||Volume-to-Capacity||Travel Time (minutes)||Speed||Travel Time Index|
|6:00 a.m.–7:00 a.m.||4,800||0.808||1.064||56.4||1.064|
|7:00 a.m.–8:00 a.m.||5,700||0.960||1.127||53.2||1.127|
|8:00 a.m.–9:00 a.m.||5,200||0.875||1.088||55.1||1.088|
17 Transportation Research Board, Highway Capacity Manual 6th Edition: A Guide for Multimodal Mobility Analysis 2016. [Return to note 17]
United States Department of Transportation - Federal Highway Administration