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CHAPTER 5 Methods for Detailed Analysis This chapter describes methods that can be used for more detailed analysis of project cost, benefits, and feasibility, beyond the sketch planning approach for preliminary screening that was described in Chapter 3. The methods described here encompass six major steps that can be part of a more detailed analysis: 1. Assess Congestion Levels and Reduction Needs. Section 5.1 describes methods for deter- mining the severity of traffic congestion and the relative contribution of truck traffic to that problem. 2. Analyze Shipping Cost and Service Features. Section 5.2 describes methods for assessing differences in freight carrier cost and service levels associated with truck and rail freight options. 3. Analyze Overall Logistics Costs. Section 5.3 describes methods for assessing the overall logistics cost factors considered by freight shippers (users of freight transportation) when deciding between truck and rail freight options. 4. Calculate Truck to Rail Modal Diversion. Section 5.4 describes methods for estimating the impact of proposed project alternatives on diversion of freight from truck to rail along congested corridors. 5. Calculate Traffic and Economic Impacts. Section 5.5 describes methods for calculating impacts on transportation system efficiency (cost to carriers), benefit for freight system users (shippers), and broader impacts for other businesses (regional and national economy). 6. Present and Summarize Benefit-Cost Findings. Section 5.6 describes methods for portraying project benefits and costs from various perspectives that may be useful for public discussion and decision-making. For each element, this guide describes (1) an overview of the analysis step, (2) components of the analysis, (3) background considerations, (4) factors to be considered, (5) alternative methods for analysis and (6) resources required. 5.1 Assess Congestion Levels and Reduction Needs 5.1.1 Overview The first step is to estimate levels of current and projected future traffic congestion within the study area or along the highway corridor and the extent to which truck traffic contributes to that congestion. This is necessary to establish the potential benefit that could be achieved if some por- tion of the truck traffic could be shifted to rail freight alternatives. G-73

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G-74 Guidebook for Assessing Rail Freight Solutions to Roadway Congestion 5.1.2 Components Three factors should be considered in evaluating current congestion and forecasting future congestion levels. They are The measurement used for monitoring congestion levels and estimating future levels; The process of modeling of traffic growth in target areas and corridors; and The handling of reliability effects resulting from sporadic delays known to increase in incidence as traffic volumes approach the design capacity of highways. 5.1.3 Background Traffic congestion refers to the slowdown in travel speeds and increase in incidence of traffic backups that grow exponentially as the volume of traffic approaches the design capacity of a road, bridge, or intersection. Traffic congestion increases the travel time, operating expense, and safety costs of travel. With limited capability to further expand many highways in the future, it becomes particularly important to forecast the expected growth of congestion so that actions can be taken to mitigate negative effects. However, the costs of congestion are often under-estimated because state and regional travel demand and road network models typically focus on average daily traffic conditions and report them for large areas. Unless the analyst requests special reports for small areas, the extent of severe localized congestion will also be missed. Yet even if the analyst requests a report for a specific area or corridor, the measurement of daily average traffic volumes over a 24-hour period will tend to show moderate average volume/capacity ratios and travel speeds, while failing to identify the extent of peak period over-capacity conditions and delays in that area. This makes it particularly important to apply methods that can assess the extent of time-specific and location-specific congestion conditions. In addition, many state and regional travel network models count only total vehicles and do not track differences in car/bus/truck vehicle mix for specific areas and corridors. That can also leads to an under-estimation of the costs of congestion for two reasons: (1) trucks contribute more to congestion because they take up more road space and require broader separation than cars and (2) the business costs associated with truck delay can be substantially greater than the economic value of passenger car delay. In addition, options for shifting truck traffic to other modes (such as rail) can be quite different from the options available for shifting car traffic. This makes it particularly important to assess the vehicle mix in congested areas and identify the extent to which trucks contribute to that congestion. 5.1.4 Factors The analysis of congestion levels considers four dimensions: The spatial pattern of traffic congestion; delays can be area-wide or location-specific; The temporal pattern of traffic congestion; delays can occur during morning or afternoon peak periods or during off-peak periods; The stochastic element of congestion; delays can be predictable or occur sporadically as a result of traffic incidents (that rise exponentially as volume/capacity ratios increase); and The mix of vehicles and traffic classes affected; vehicles include cars, buses, and various categories of trucks, while traffic classes include local and through traffic.

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Methods for Detailed Analysis G-75 5.1.5 Methods Element 1 Measurement of Congestion Levels Congestion measurement can be grouped into four broad classes, which portray congestion levels on the basis of A congestion index related to the rate of travel delay (reflecting average speed); An excess delay measure for urban areas that is tied to total vehicles and minutes spent on facilities operating below a certain level-of-service; or The percentage of time at a given point on a highway system that average speed drops below some threshold value. Exhibit 5-1 provides a more detailed list of the various measures used to assess the severity of congestion in a given area. These various congestion measures can be derived from direct obser- vation (discussed here) or application of a travel demand and network models (discussed in the subsection that follows). A growing number of agencies are monitoring congestion levels via direct observation. Examples of alternative data collection approaches for direct observation are Houston's Real- Time Traffic Information System (which uses cellular telephone reporting and automatic vehicle identification techniques to record travel times); the TRANSCOMM Electric Toll and Traffic Management Project in New Jersey (which monitors the travel times of specially tagged vehicles); and the ADVANTAGE project in Chicago (which uses satellite global positioning systems and probe vehicles to record travel times). NCHRP Report 463: Economic Implications of Congestion provided a full discussion of the elements of traffic congestion and alternative ways of measuring it. It can be accessed in two volumes at: FHWA; Analytical Procedures to Support a Congestion Management System; Technical Memorandum 1; prepared by Cambridge Systematics; February 1994. Exhibit 5-1. Measures of Traffic Congestion.

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G-76 Guidebook for Assessing Rail Freight Solutions to Roadway Congestion http://gulliver.trb.org/publications/nchrp/nchrp_rpt_463-a.pdf http://gulliver.trb.org/publications/nchrp/nchrp_rpt_463-b.pdf To avoid redundancy, readers of this guide are referred to that document for a more complete discussion of congestion measurement. Element 2 Modeling of Traffic Growth The availability of data on actual congestion levels varies from one metropolitan area to another. Data may or may not be available at the level of detail desired, and data may or may not be updated regularly. Highway travel demand models are therefore frequently used to estimate traffic flows and congestion for specific facilities or for metropolitan road networks. Such models also provide a way to forecast future growth in traffic volumes and associated congestion levels. The traditional form of travel demand modeling is the four-step model process of trip generation, trip distribution, mode choice, and trip assignment via computer simulation models. There are also simpler sketch- planning spreadsheet-based models, sometimes using an approach known as "pivot-point analy- sis," to estimate future changes based on the application of growth rates to existing conditions. In general, these modeling methods yield estimates of highway system travel performance metrics in terms of highway volumes, speeds, travel time saved, operating cost changes, and safety effects. Travel demand models can be used to forecast the implications of alternative future conditions, by changing the assumptions about traffic growth. Thus, they can forecast how a reduction in truck traffic will lead to reduced congestion delays compared to what would otherwise be expected. They also provide a measure of the delay reduction benefit to remaining automobile travelers and truck carriers on the affected highways. However, the usefulness of travel demand models for truck reduction scenarios depends on two factors that are not always considered in statewide or regional travel demand modeling systems: 1. The ability of the modeling system to distinguish truck and car traffic changes. This is important since many regional and statewide highway network models assume a fixed truck/car ratio for all road segments and cannot distinguish the greater congestion reduction benefit that comes from reducing truck traffic. 2. The ability of the modeling system to distinguish concentrations of congestion at particu- lar times and places. This is important since the severity of congestion delays rises more than linearly as traffic volumes rise, so a system that can hone in on particular locations and peak periods will find greater overall regional congestion than one that only considers daily average regionwide levels. Since the truck percentage of vehicles on a highway can vary widely (from 2 to 10 percent or more), it can be particularly useful to observe the current truck share for specific congested areas and corri- dors and then be sure that the travel demand forecasts can be used to generate car/truck shares of forecast future traffic. In addition, since congestion can vary by time of day, it can also be useful to observe the current ratio of peak traffic to daily average and then be sure that the travel demand model can be used to generate peak-period forecasts for the specific areas and corridors of interest. The FHWA's Office of Operations has produced a web-based report called the Traffic Analysis Toolbox, which discusses and describes all the different types of travel demand, traffic forecasting and sketch planning models. It can be accessed at http://ops.fhwa.dot.gov/trafficanalysistools/ index.htm. To avoid redundancy, readers of this guide are referred to that document for a more complete discussion of the available options for traffic analysis tools. Regional travel demand models can also be supplemented with corridor-specific peak con- gestion measurements and truck congestion measurements in order to address the previously cited concerns about limitations of regionwide models. An example of that additional modeling is provided in the 2005 study, The Cost of Congestion to the Portland Region, available at http://www.portlandalliance.com/pdf/Congestion_Report.pdf.