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43 This chapter summarizes the current literature on FG and FTG modeling, and associated data collection. The review for FG and FTG provides a comprehensive review of the state-of- the-art research and practice in the area, with critical exami- nation of the technical merits, advantages, and disadvantages of different FTG methods and models. The review for data collection focused on techniques and sources. Freight Trip Generation (FTG) Modeling The review encompassed the state-of-the-art practices in the area, both domestic and international. The various factors that should be considered in developing and analyzing freight modeling techniques are given in Table 66 in Appendix D. They include: dependent and independent variables, levels of aggregation and geography, estimation techniques, and model structure. In terms of the dependent variable, from the models contained in the reviewed references, 47% use vehicle trips; 38% use commodity tonnage; and 15% use a combination of vehicle trips (usually for internal-internal trips) and com- modity tonnage (for the rest of the flows). About 38% of the models are aggregated, 48% are disaggregated, and others (14%) cannot be determined from the review. The inde- pendent variables used include: employment by industry sector (49%); building area (9%); commodity type (13%); land use (2%); and other variables (27%). As for modeling techniques, 25% use least square, 10% use trip rates, 6% use multi-classification analysis, and 33% use IO analysis. These three modeling methods constitute the majority of the FTG models used in practice (or about 74%). In addition, from the model information that is known, most of the models are linear (22 out of 33), while a small fraction of them are nonlinear. A summary of the various FTG models that were reviewed is given in Table 68 in Appendix D. A breakdown of the fea- tures of the models by level of geography showed that the majority of the models are for states (35%) and metropoli- tan areas (39%). The models are grouped into vehicle-trip- based models and commodity-based models. Appendix D contains a comprehensive review of the literature for both types of models. Review of TRB Synthesis Reports FTG has been a focus of several NCHRP studies, includ- ing NCHRP Synthesis 298, NCHRP Synthesis 384, NCHRP Synthesis 358, and NCHRP Synthesis 606. Brief summaries of these studies are provided in this section. NCHRP Synthesis 298: Truck Trip Generation Data This synthesis report mainly identifies available data sources and data collection techniques, and assesses the cur- rent state-of-the-practice in truck trip generation. The report discusses key considerations in the development of truck trip generation data needs, which include uses of truck trip gen- eration data, trip purposes, estimation techniques, and data collection. Two types of trip generating models are discussed: vehicle-based models and commodity-based models. Twelve vehicle-based travel demand models and 14 commodity- based travel demand models were presented. The report also reviews numerous projects related to FTG, especially on the topics of FTG data needs and survey methods. It lists three major methods to estimating truck trip generation data: estimation of simple rates, linear regression models, and commodity flow models. In chapter three of the report, data sources that were used to estimate truck trip generation in practice are compiled. This report also summarizes seven most commonly used approaches to collecting data for truck trip generation, including trip diaries, classification counts, published commodity flow data, collected commodity flow data, shipper/carrier/special generator surveys, intercept surveys, and published rates. C h a p t e r 5 Freight Trip Generation Models and Data Collection
44 NCHRP Synthesis 384: Forecasting Metropolitan Commercial and Freight Travel This synthesis reviews methods of freight and commer- cial vehicle forecasting in practice, together with promising methods emerging from ongoing research. The primary focus of the report is on metropolitan-level forecasting, although some consideration is also given to statewide freight forecast- ing models. The report reviews application of the four-step model process to freight demand modeling, including the process of FTG. Major sources of planning information to freight and commercial vehicle forecasting are presented in this report. Besides the four-step model process, the report also summarizes six emerging methods in freight demand models: time series modeling of freight traffic growth; behav- iorally focused demand models; commodity-based forecasts, including interregional IO models; methods that forecast flows over multimodal networks; micro-simulation and agent based simulation (ABS) techniques; and models that incorpo- rate supply chain/logistics chain considerations. The report also lists several methods acquiring FTG results, including developing truck trip generation rates, borrowing trip rates from one or more other regions, introducing special gen- erators, and using external stations. On urban freight data collection, the report presents two major methods: vehicle classification counts and origin-destination surveys, which include roadside intercept surveys, mail and telephone surveys, establishment surveys and carrier surveys. NCHRP Synthesis 358: Statewide Travel Forecasting Models This synthesis examines statewide travel forecasting mod- els, including passenger vehicles and freight components. It reviews the types and purposes of models being used. Data requirements, survey methods, funding, and staff resources are also reviewed to investigate the limitations and benefits of the models. In the survey of statewide freight forecast- ing practice, the report defines two fundamentally different styles of freight forecasting: direct forecast of vehicle flows without reference to commodities; and forecasting of com- modities, using the commodity flow forecast to estimate vehicle flows. The report includes five case studies, two of which are on freight components, including the Virginia freight component and the Wisconsin freight component, two are on passenger components, and one is a combined passenger and freight component. The report concludes that most statewide models are similar in structure to four- step urban transportation planning models, and that there exists no well-accepted definition of best practice in state- wide models. The report points out several distinct trends in recent statewide model development, such as the emerging of commodity-based models and more effective use of GIS to manage data, among others. NCHRP Report 606: Forecasting Statewide Freight Toolkit This report presents an analytical framework for forecast- ing freight movements at the statewide level to develop fore- casting models. The framework includes a tool kit of data collection techniques, analytical procedures, and computer models. It includes management approaches, decision-making procedures, and performance evaluation methods, which help improve statewide transportation under the increment of freight demands. The report also summarizes several classes of data sources, including: model development (local and national surveys, compilations); flow conversion (tons to vehicles and tons to value); network data (modal network and intermodal terminals); forecasting data (population and employment); and validation data and classification schemes (commodity classification and industry classifi- cation). Meanwhile it presents five forecasting models and performance measures. Ten case studies of statewide freight modeling projects are reviewed, including FTG models, and model application and validation. NCHRP Project 08-36/Task 79: Scoping Study for a Freight Data Exchange Network This report investigates the feasibility of building a freight data exchange network to provide access to higher quality freight data. It considers a centralized data repository from which data providers and users can access freight datasets, metadata, or reports of data quality. In this network, data providers can upload data while end-users can download them in the form of summary tables, reports, and customized tabular data. The report describes various types of freight- related datasets and suggests potential ways to utilize them, including the CFS, Rail Waybill Data, foreign trade data, Freight Analysis Framework 2 (FAF2), TranSearch Commod- ity Flows Database, freight databases from local and regional studies, socio-economic data from regional studies, and other data sources. It also conducted interviews with potential data users and providers. NCHRP Synthesis 410: Freight Transportation Surveys This synthesis examines the sample size, data accuracy, data comprehensiveness, and survey objectives for freight transpor- tation. It also includes a discussion of the feasibility and benefits of linking survey data with data from roadway and sensors.
45 NCHRP Report 404: Innovative Practices for Multimodal Transportation Planning for Freight and Passengers This report reviews innovative agency practices and meth- ods in multimodal planning. For a set of case studies, the report monitors the performances and public involvement in planning effects on rural areas. It also mentions fiscal con- straints in planning and programming. Summary The following summarizes the findings derived from the literature review of FG and FTG models: â¢ The bulk of the studies have focused on FTG, not FG. As illustrated in the literature review, the bulk of the models are based on vehicle trips, though a handful of studies con- sider FG in the context of IO models. This stands in contrast with European practices that emphasize commodity-based approaches that incorporate FG modeling as an endog- enously determined variable. â¢ It is not yet clear which modeling techniques are the best. Although extensive research has been conducted in the last several decades on developing FG/FTG models, there is no study to compare specifically the performances of these techniques; there is no consensus yet regarding which models can produce the most accurate results. This is reflected by the fact that different agencies are applying a variety of different freight (trip) generation models (see Appendix E) due to the lack of a commonly agreed âbest practiceâ model. However, based on previous research experiences, the research team does believe that certain modeling techniques, such as dis- aggregated models and regression analysis, do have advan- tages that stand out among all modeling techniques. â¢ There are no consistent definitions of trucks, truck trips, and land use classes. This point is made by the ITE Trip Generation Handbook and other publications. The incon- sistent definitions of these important variables contributes to shaky results regarding which factors are the most impor- tant in explaining FG/FTG, and which modeling techniques are the most effective. There is thus a need to standardize those definitions so that more consistent FG/FTG modeling approaches could be developed. FG and FTG Modeling Practice, Evaluation Criteria, and Evaluation Process Current practices, evaluation criteria and processes, both domestic and international, of FG and FTG modeling were reviewed. The different modeling applications of FG and FTG modeling are classified (Fischer and Han 2001) into two cat- egories: planning applications and engineering applications. The objective of planning applications is to provide estimates of FG/FTG for conglomerate users for the purpose of trans- portation planning at the state, regional, corridor, and urban level. Typically, these are medium- and long-term studies aimed at answering questions about medium- and long-term capacity needs and economic development. Engineering applications are intended to provide key input to a variety of engineering design questions concerning facility design issues, traffic operation studies, site impact analyses, provi- sion of on/off-street parking for trucks, etc. In some cases, the analysis could focus on a single establishment, a single loca- tion with multiple establishments, or an entire area such as a downtown area. These studies emphasize short-term analyses and improvements. A review of both types of applications is provided in Appendix E. Refer to Appendix D and Appendix E for a comprehensive description of the current literature and practice in FG and FTG modeling. Data The literature review focused on data collection techniques and sources. The major finding was that there is a lack of primary FG/FTG data. This is a major issue because of the need to effectively incorporate freight transportation into the planning process. The fact that many of the sources of FTG models are now dated, and that one of the most important primary freight data sources, the CFS, has not been widely used for freight modeling exacerbates this problem. Appen- dix F discusses in detail the literature review and findings for data collection, data needs, and sources. During the review, a number of the analyzed publications were found to contain FG and FTG models. However, these publications are mostly in the form of articles, research/synthesis reports, and books. This static format is not conducive to quick consultation and interactive queries. It was important, therefore, to use data- base tools to compile the information to make it more readily available. In this way, the data could be: â¢ Stored and made available on the Internet, which enables practitioners and researchers to have access to it when needed. â¢ Integrated with an expert system that, in return to a query about trip rates, would provide the closest match. As part of NCFRP Project 25, the research team compiled a comprehensive FG and FTG model database. The database can be accessed at: http://transp.rpi.edu/~NCFRP25/FTG- Database.rar. The information in the NCFRP Project 25 data- base encompasses thousands of lines of data assembled over
46 several years. The database is organized into three primary parts: publications, models, and case studies. The publications section contains an expansive literature database on FG/FTG references (e.g., books, journal papers, research reports, syn- thesis reports) including bibliographic citations. The reference table contains 46 records of which 15 contain case studies. The case study section of the database summarizes the case studies contained in the publications set. It details information such as project title (or chapter title when referenced in the particular project report) and location. Within the 15 publications that contained case studies, there are 233 individual case studies, most of which are reports from NCFRP or NCHRP. The model database summarizes the FG/FTG models in the literature. Models include, but are not limited to, trip rates, regression, and time series. Fields for this table include level of aggrega- tion, geography, estimation technique, model structure, time unit, and independent variables from the literature. Table 26 summarizes the type of models contained in the database and principal independent variables. A user manual for the database is found in Appendix G. This document presents detailed information about the vari- ables identified and briefly explains the basics of opening the database; how to navigate through the different sections (e.g., models, publications and case studies); and provides a usability walkthrough with examples such as searching for and viewing: the different models (trip rates and regression analysis) with employment and food; production models based on employment; and publications containing models dependent on employment. Surveys From the review, it was found that carefully designing an FTG survey was necessary to collect data to conduct FTG modeling. For this purpose, a sample of FTG surveys was reviewed. These included: the mail survey in Bartlett and Newton (1982); the receivers and carriers surveys in HolguÃn- Veras (2006); and the mail survey for truck trip generation at container terminals (HolguÃn-Veras et al. 2002). Bartlett and Newton (1982) developed a specific question- naire to request information from a select set of firms in three areas of England. The main intent was to derive âgoods vehi- cleâ trip generation and attraction at a wide range of indus- trial and commercial firms. The survey collected data on: (a) type of business activities; (b) total number of employees; (c) number of office employees; (d) site area; (e) gross floor area; (f) numbers of goods vehicles operated from the address (car-based vans upwards); (g) average numbers of journeys made per week by these vehicles, split into a number of vehicle weight categorizes; (h) average number of calls made per week by all visiting goods vehicles (including any calls by vehicles owned by the firm but based elsewhere); (i) details of any other mode of transport in use by the firm; and (j) location of the firm on a map attached to the questionnaire. A mail-back ques- tionnaire was developed based on these questions and mailed to selected firms. The firms were sampled and analyzed later on based on their SIC codes. The SICs were further grouped into five categories: (1) manufacture; (2) service; (3) construction; (4) wholesale/dealer; and (5) haulage/distribution. The survey resulted in a high response rate (more than 60%), as indicated in Bartlett and Newton (1982). To study the effect of off-hour delivery (OHD) in the New York City region, both receivers and carriers surveys were designed (HolguÃn-Veras 2006) to collect FTG related data. The surveys were conducted for Manhattan and Brooklyn. In these surveys four groups of questions were designed for both receivers and carriers: (1) whether the company is making deliveries in Manhattan; (2) the companyâs current opera- tions and flexibility in terms of making deliveries; (3) sce- nario testing regarding OHD; and (4) characteristics of the company, including business type, types of commodities, the number of truck drivers, among others. The survey was con- Production 689 Employment 565 Attraction 720 Area 786 Not Specified 481 Establishment 278 Total 1890 Household 47 Individuals 15 Fleet 36 Industry segment 2 Income 1 Land use 211 Parking 1 Traffic volumes 2 Sales 5 Cargo 13 Other 41 Type of Model Type of Independent Variable Table 26. Summary of models contained in the database.
47 ing, a survey prototype was developed as part of the NCFRP Project 25 research for use in FTG studies. The survey proto- type, which also inquires about service trips, can be found in Appendix H. This survey prototype: â¢ Was designed for consistency with the Freight Data Archi- tecture being developed as part of NCFRP Project 12, âSpecifications for Freight Transportation Data Architec- tureâ (Journal of Commerce 2011), so that the data col- lected is amenable for data pooling; a survey prototype is suggested for use in FTG studies. â¢ Enables practitioners and researchers to have access to a basic survey design that they could tailor to their specific needs. â¢ Facilitates data pooling (as a result of using the Freight Data Architecture), thus enabling practitioners and researchers to add data to a centralized database. â¢ Can be used to feed data and models to the relational FG/ FTG database. The survey instrument was pilot tested and the collected data was used to validate the models estimated with the case studies. ducted using computer-aided interviews, and the response rate was about 30%. A mail-back survey was designed by HolguÃn-Veras et al. (2002) to collect information regarding truck trip generation at container terminals. Two sets of questions were designed. The first set focused on general information about the con- tainer terminal including the terminal name, how many TEUs are handled per year by the terminal, operational hours of the gates, number of lanes at the gates, number of berths, number of gantry cranes, percentages of containers carried by railroads, trucks, and barges, the slowest and busi- est months, and the number of ships visiting the terminal each day for a typical week. The second set was designed for truck traffic information only for a typical day. The following specific questions were asked for both the inbound and out- bound cargos: the numbers of trucks with loaded and empty containers, respectively; daily truck traffic in the terminal; the morning and afternoon traffic peak hours; and the numbers of trucks going through gates during the morning and after- noon peak hours, respectively. Building on these documented experiences and taking into account the Freight Data Architecture (Journal of Commerce 2011), so that the data collected is amenable for data pool-