Study on modeling truck trip generation: Disaggregate approach based on the survey of industries.

Study on modeling truck trip generation: Disaggregate approach based on the survey of industries

Framework for a Disaggregate Truck Trip Generation Model Based on a Survey of Retail Businesses

While considerable strides have been made in forecasting truck travel demand in the past several years, there remain several critical gaps that need to be addressed. The new trends in goods movements like the growth of e-commerce and distribution systems will likely affect the patterns of truck trip generation. Through an extensive literature review, it was found that past truck trip generation analyses used only aggregate variables or proxies of economic activities such as land use types, number of employees, and the gross floor space. Such analyses only indicate the relative importance of trip generators at a general level and ignore the influence of business management and operations decisions such as sales, types of goods, various physical constraints of stores, and socioeconomic characteristics surrounding communities. Preliminary interviews with the experts from a manufacturing plant, a trucking company, and two logistics and supply chain solution providers were conducted. Based on the interviews and literature review, a conceptual framework of truck trip generation analysis has been developed. This paper argues that the truck trip generation should be estimated at the individual facility level because the number and type of freight truck trips are the outcome of a series of decisions about products, sales, locations, delivery times, and frequencies, where the strategic and tactical decisions are made in order to maximize the facility’s efficiency and profit by minimizing costs. As an issue paper, this paper reports the experience from an ongoing effort of modeling truck trip generation. First, the paper describes the current trends of truck dominance in freight shipments and its relevance to the current research. Second, a brief discussion of the definition of truck trip generation is followed by the summary of the literature regarding TTG models used in past studies. Then the paper provides the new framework of truck trip generation analysis that is based on the findings from the literature review, studies on business behavior and preliminary interviews. Before concluding, the most difficult task for this study, data requirement and collection strategies are discussed. The paper ends with the discussions on expected outcomes, implications, and contributions of the study

Business and site specific trip generation methodology for truck trips

109 p. (Final report); 23 p. (Presentation paper); 18 slides (Presentation slides)The motivation for this research comes from the recognition that recent developments in supply chain management (SCM) have altered the mechanism of truck trip generation at the individual facility level. This research develops models of truck trip generation (TTG) at the disaggregate level that incorporate strategic supply chain decisions made by individual businesses. The main assumption is that the TTG is an outcome of a series of strategic and operational business decisions. The research team conducted a survey of national retail chains. The data sets obtained from two furniture chains were used to develop binary logit models. Empirical data, although limited, validated the potential of building a disaggregate TTG model at the individual store level. Inclusion of location and store type dummy variables almost always improved model's predictive power, often dramatically. The findings presented in this report also underscore various shortcomings of existing methods. We found that commonly used independent variables such as the store floor space or the number of employees are poor predictor of truck trip generation at retail stores.U.S. Department of Transportation--Research and Special Programs Administration; Wisconsin Department of Transportation; University of Wisconsin--Madison; University of Illinois--Chicago; Prime Focus LLC; DTRS 99-G-000

Peer-to-Peer Information Exchange on Bus Rapid Transit (BRT) and Bus Priority Best Practices

The purpose of this effort was to foster a dialogue among peers at transportation and planning agencies about their experiences with promoting public transit and, in particular, the challenges they face related to Bus Rapid Transit (BRT) projects, as well as the solutions they have developed in response. Agencies from dozens of large cities around the United States participated at three peer-to-peer exchanges in New York City, Los Angeles, and Cleveland. The facilitated discussions were structured to address the unique barriers to BRT implementation on the streets of dense and/or highly-congested large urban centers. Three major themes were the focus of the workshops: Network, Route and Street Design; Traffic Operations; and Building Political, Interagency and Stakeholder Support—BRT as a Driver of Economic Development. The results of the workshops make clear that better public transportation in general and BRT in particular can be cost-effective and useful tools for improving transportation and the environment and for restoring the livability of America’s large cities

Network-Based Highway Crash Prediction Using Geographic Information Systems

The objectives of this project were to estimate network-based crash prediction models that will predict the expected crash experience in any given geographic area as a function of the highway link, intersection and land use features observed in the area. The result is a system of GIS programs that permit a polygon to be drawn on a map, or a set of links and intersections to be selected, and then predict the number of crashes expected to occur on the selected traffic facilities. These expected values can then be compared with observed values to identify locations with higher than usual crash incidence and may require attention to improve the safety of the location. Alternatively, this tool could be used to estimate the safety impacts of proposed changes in highway facilities or in different land development scenarios. A network approach was chosen to solve this problem, in which separate models were estimated for crashes at major intersections, and intersection-related and segment-related crashes on road segments. All three sets of models can then be used to predict the number of crashes for an entire highway facility delineated as the user desires – including all intersections. These models also consider all relevant road features, in particular the intensity of traffic at intersections and driveways resulting from the surrounding land use. Gathering traffic volumes at every intersection and driveway on the road network would preclude the feasibility of such an approach, both for estimation and in practice. Instead, the link between land development and trip generation was exploited to estimate the driveway and minor road volumes. Land development intensity variables were generated from land use inventories organized using Geographic Information Systems (GIS), permitting virtually automatic preparation of the required data sets for model estimation and application and prediction of crash counts on roads. Specifically, population and retail and non-retail employment counts were associated with each analysis segment to represent vehicle exposure to intersection-related crashes. GIS was used for two purposes in this project: 1) distributing population and employment counts in a traffic analysis zone (TAZ) among all the links in that zone. 2) Visually comparing the predicted and observed accident counts in order to identify higher than usual crash locations

User Preference Analysis for Mobility-as-a-Service (MaaS) and Its Impact

69A43551747123Mobility as a Service (MaaS) integrates diverse transportation modes into a unified platform to offer users personalized, convenient, flexible, and cost-effective trip options. It can address accessibility challenges, particularly for underserved population groups without cars or no driving capability, by providing them various (combinations of) alternative modes such as carpools, shared mobility\u2014such as ridesharing (e.g., Uber and Lyft), bikes and scooters\u2014and public transit. This research consists of two parts; 1) A Comparison of Mobility as a Service (MaaS) Alternatives for Access to Public Transportation Terminals and 2) User Preference for Micro Mobility: An Adaptive Choice-Based Conjoint Analysis Approach