Truck Trip Generation Research Articles

Regression Analysis to Create New Truck Trip Generation Equations for Medium Sized Communities

This paper uses data from a trucking origin/destination study conducted with global positioning system (GPS) technology to develop a truck trip generation model for medium sized urban communities—in this study taken to be communities between 200,000 and 1,000,000 people. The difficulty with developing truck trip generation equations centers on the limitation of data. For passenger transportation, data are collected from household surveys. For truck transportation, if available, data are typically collected from a small collection of shippers/businesses within the urban area and extrapolated to cover the entire study area. Because of the data limitations, truck transportation is typically indirectly modeled or as an after-thought. Increasing truck volumes, coupled with cost saving strategies such as just-in-time delivery systems, require that transportation policymakers analyze infrastructure needs and make investment decisions that explicitly include truck volumes as a component. This paper contains a case study using a medium sized urban area and a GPS collected set of truck origins and destinations to develop a truck specific trip generation equation using standard employment data. The paper presents the models developed and validates the models to the case study community. The paper concludes that the trip generation equations developed can be incorporated into medium sized community travel models to provide a framework for truck planning that can be used to improve resource allocation decisions.

Truck trip generation in small- and medium-sized urban areas

ABSTRACTThis paper describes procedures to develop truck trip generation (TTG) rates for small- and medium-sized urban areas and its implications. Ordinary least squares models are used to develop separate truck production and attraction equations with the number of employees as the independent variable for three industrial groups – retail, transportation and warehousing, and manufacturing. Results from this research indicate that number of employees is a statistically significant predictor, and has significant explanatory power in predicting the number of truck trips produced and attracted. The rates developed in this study are also found to be significantly different from rates developed in other studies with the implication that caution needs to be taken when transferring TTG rates. The rates are applied in a travel demand model as the initial step of incorporating truck traffic into the modeling process.

Estimation of Truck Trips on Large-Scale Irrigation Project: A Combinatory Input-Output Commodity-Based Approach

For the agricultural-based countries, the requirement on transportation infrastructure should not only be limited to accommodate general traffic but also the transportation of crop and agricultural products during the harvest seasons. Most of the past researches focus on the development of truck trip estimation techniques for urban, statewide, or nationwide freight movement but neglect the importance of rural freight movement which contributes to pavement deterioration on rural roads especially during harvest seasons. Recently, the Thai Government initiated a plan to construct a network of reservoirs within the northeastern region, aiming at improving existing irrigation system particularly in the areas where a more effective irrigation system is needed. It is expected to bring in new opportunities on expanding the cultivation areas, increasing the economy of scale and enlarging the extent market of area. As a consequence, its effects on truck trip generation needed to be investigated to assure the service quality of related transportation infrastructure. This paper proposes a combinatory input-output commodity-based approach to estimate truck trips on rural highway infrastructure network. The large-scale irrigation project for the northeastern of Thailand is demonstrated as a case study.

Freight Truck Trip Generation Modelling at Regional Level

Freight truck trip generation is a crucial part of a 4-stage model, especially in regional freight model development. Data needed to construct trip generation equations are ususally gathered at company level using the trip diary. Although this approach seems to be most suitable it may not cover all trips made by freight vehicles in analysed area. On the other hand, response rate may be unsatisfactory. Thus other methods of trip generation estimation should be explored. Based on results of roadside surveys O-D matrices for freight vehicles were estimated. In the next step, using large set of traffic measurements on national and regional roads, O-D matrices were calibrated. In order to calculate trip generations a step backwards was made. Additionally, the results of comprehensive travel studies and secondary data were used. Developed data sets were used to estimate trip generation equations, applying linear and nonlinear regression as well as artificial neural networks (ANN). The aim of this paper is to develop freight truck trip generation equations at regional level using different data sources, secondary data and indirect approaches.

Using OD Estimation Techniques to Determine Freight Factors in a Medium Sized Community

Developing an understanding of the socio-economic factors that can be used to generate truck trip productions and attractions in small and medium sized communities can be used to improve travel models and provide better information upon which infrastructure decisions are made. Unfortunately, it is difficult to collect this data in a timely, cost-effective manner. This paper presents a methodology that uses matrix estimation techniques from existing traffic counts to develop origin/destination pairs that can be used to statistically develop truck trip generation models. A case study is presented and a model is presented for one smaller urban community.

Estimating truck trips with product specific data: a disruption case study in Washington potatoes

Currently, knowledge of actual freight flows in the US is insufficient at a level of geographic resolution that permits corridor-level freight transportation analysis and planning. Commodity specific origins, destinations, and routes are typically estimated from four-step models or commodity flow models. At a sub-regional level, both of these families of models are built on important assumptions driven by the limited availability of data. This study was motivated by a desire to determine whether efforts to gather corridor-level freight movement data will bring significant new insights over current approaches to freight transportation modeling. Through a case study of Washington State's potato and value added potato products industry, we show that significant insight can be gained by collecting commodity-specific truck trip generation and destination data: the approach allows product specific truck trips to be estimated for each roadway link. When considering a network change, the number of affected trips can be identified, and their re-route distance quantified.

Empirical Method for Predicting Internal-External Truck Trips at a Major Port

This paper presents a case study to explore the truck-trip generation model for hauling containers at a major international seaport. An internal-external truck-trip forecast model is examined. It incorporates influential factors of regional freight activity attributes, economic growth attributes, and natural disaster attributes based on monthly data from 2000–2008. A best-fit truck-trip forecasting model is determined by comparing the prediction accuracy of a multiple regression model, time-series models, and a neural network model. The findings indicate that the back propagation neural network model generates better forecasting performance than the regression and time-series approaches. Additionally, this paper identifies the difference between truck trips and commodity-flow tonnages converted by truck payload factors, which would be significantly affected by truck-trip chains and truck drivers’ route choice behaviors. The analysis also reveals that a port truck-trip forecast model based on commodity flows would be very sensitive to the events of oil price fluctuations and new operation or infrastructure upgrade of competitive ports nearby, once the conversion difference goes up to 30%.

Evaluation of Two Modeling Methods for Generating Heavy-Truck Trips at an Intermodal Facility by Using Vessel Freight Data

A methodology for building a truck trip generation model by use of artificial neural networks from vessel freight data has been developed and successfully applied to five Florida seaports. The backpropagation neural network (BPNN) algorithm was used in the design. Although the methodology was sound, a new model had to be developed for each of these intermodal facilities. Lead and lag variables were necessary input variables for most models to account for commodities stored on port property before export or pickup after import. Other modeling techniques were researched, and a fully recurrent neural network (FRNN) trained by the real-time recurrent learning algorithm was selected to develop a model for Port Canaveral and compare with a BPNN model. FRNN is dynamic in nature and was found to relate to the storage time of the commodities to truck trip generation. A developed Port Canaveral BPNN model was successfully validated at the 95% confidence level with collected field data. It was applied to conduct a short-term forecast of the port's truck traffic for 5 years. The average annual growth of trucks based on the estimated freight activity under the BPNN model was 5.07%. The Port Canaveral FRNN model adequately estimated the current conditions but failed to forecast truck growth. The FRNN model required more data for forecasting than backpropagation. However, when more consecutive data are available for training, FRNN may produce more accurate results.

Evaluation of Two Modeling Methods for Generating Heavy-Truck Trips at an Intermodal Facility by Using Vessel Freight Data

A methodology for building a truck trip generation model by use of artificial neural networks from vessel freight data has been developed and successfully applied to five Florida seaports. The backpropagation neural network (BPNN) algorithm was used in the design. Although the methodology was sound, a new model had to be developed for each of these intermodal facilities. Lead and lag variables were necessary input variables for most models to account for commodities stored on port property before export or pickup after import. Other modeling techniques were researched, and a fully recurrent neural network (FRNN) trained by the real-time recurrent learning algorithm was selected to develop a model for Port Canaveral and compare with a BPNN model. FRNN is dynamic in nature and was found to relate to the storage time of the commodities to truck trip generation. A developed Port Canaveral BPNN model was successfully validated at the 95% confidence level with collected field data. It was applied to conduct a s...

Development of a Java Applet for Generating Truck Trips from Freight Data

As freight transportation becomes a more significant concern, the ability to estimate accurately truck trips generated by freight activity at an intermodal facility is important for transportation engineering and planning. Truck trip generation models that used vessel freight data were developed by the University of Central Florida Transportation Systems Institute and have been statistically proved to determine accurately the number of trucks generated at a seaport. To apply these previously developed models more efficiently, a Java applet was developed to execute a selected artificial neural network (ANN) port model and a trainable ANN port model. This applet provides the user with an easy-to-understand interface for entering data and executing models. The two models developed were the Port Everglades ANN model and a hybrid of the Port Everglades model that allowed the user to retrain the ANN model before execution for desired data. Although the trainable model requires additional retraining with new data, the added complexity comes with the benefit of producing a network with a higher degree of accuracy. This applet equips the user with two models and therefore has expanded the capabilities of the previously limited Port Everglades ANN truck trip generation model. The developed Java applet can be expanded to include more ANN models and thus more flexibility, depending on the type of freight data available. This successful adaptation of the ANN model into a Java applet sets the foundation for further applications.

Methodology for Modeling a Road Network with High Truck Volumes Generated by Vessel Freight Activity from an Intermodal Facility

An innovative methodology has been developed for analyzing freight movement on local road networks by merging previously developed truck trip generation models using artificial neural networks (ANNs) and a microscopic network simulation model. Through computer simulation, this methodology comprehensively analyzes a seaport considered a special generator of heavy truck traffic and an adjacent road network that includes identified intermodal routes that connect to a seaport. Truck traffic from the seaport is initially modeled with ANNs using vessel freight activity at the seaport. These ANN models have been incorporated into the methodology to provide accurate truck and total traffic volumes for modeling the networks. This methodology was successfully tested with two network microscopic simulation models. Transferability was successfully tested with two seaports with different characteristics. Three months of field data from each port and selected locations on the networks were used in calibration and validation. Both models were successfully validated and showed no statistically significant difference between the field and model output data. This methodology can be used to evaluate local port networks to manage traffic efficiently during heavy congestion or investigate forecasted port growth. These networks can also be used for information technology applications such as incident management or alternative route choice. The ability to develop a network that includes significant truck volumes generated by freight activity is a useful tool especially for engineers and planners involved in intermodal transportation analysis.

Transferability of an Intermodal Freight Transportation Forecasting Model to Major Florida Seaports

Seaports are important economic generators, and identifying necessary infrastructure improvements is essential to accommodate potential growth at these intermodal facilities. The ability of heavy trucks to access a port’s freight terminals is one such operational improvement that needs to be addressed. Freight activity from Florida’s major seaports generates more than 10,000 trucks per day. Efficient accessibility to freight terminals and storage facilities at ports can be provided by identifying needed improvements in transportation operations and by developing truck trip generation models to forecast truck trips in and out of the ports. In July 2001 an artificial neural network (ANN) model was developed successfully by using backpropagation techniques to simulate the transportation of freight by heavy trucks generated from an intermodal activity center such as a seaport. The general methodology for developing the model was applied to the Port of Tampa and Port Canaveral in Florida to test the transferability of the ANN modeling technique. From daily vessel freight data, models for both ports were developed successfully and validated at the 95% confidence level with data collected from the field. The validated models were executed for short-term forecasts of truck trips at both ports. Port of Tampa was forecast to have a 0.31% average annual decrease in heavy trucks, attributed to the decreasing trend in bulk commodity shipments. A 5.07% average annual increase was forecast for Port Canaveral, which correlated with historical trends and future estimates for freight activity. Output from these models can be directly used as truck variable inputs to route assignment models used by local and regional government agencies.

Truck-Trip Generation at Container Terminals: Results from a Nationwide Survey

Research conducted on truck-trip generation at marine container terminals based on a nationwide survey gathered data from 21 container terminals in the United States. The main features of the data are analyzed to provide a characterization of the problem. The data were used to generate trip-generation rates as a function of the area of the container terminal, and through a set of regression models of one-way truck traffic, as a function of a set of independent variables—most notably, the number of 20-ft-equivalent units and the number of container boxes handled by the terminal in a given year. The analyses of both trip-generation rates and the regression models indicate that there are statistically significant differences in the regional patterns of trip generation.

Use of Vessel Freight Data to Forecast Heavy Truck Movements at Seaports

Ports are primary generators of truck traffic in the United States. Seaport operations will require operational and infrastructure changes to maintain the growth of international cargo operations. Truck trip generation models will provide transportation planners and public agencies with valuable information necessary for prioritizing funds for roadway upgrade projects and port infrastructure modifications. A new methodology is presented that combines backpropagation neural networks (BPN) and time series to forecast inbound and outbound heavy truck movements at seaports. The new method uses vessel freight data to identify which parameters are relevant for use as model input in predicting truck traffic at seaports. The method is successfully applied to five ports in Florida—Miami, Tampa, Palm Beach, Jacksonville, and Everglades—thus demonstrating its transferability. Details are provided for the Port of Everglades. The commodities at this port are classified into tons, barrels, and containers. It was found that the primary factors affecting truck traffic are imported containers, imported tonnage, imported barrels, exported containers, exported tonnage, and the particular weekday of operation. Separate BPN models were developed for inbound and outbound truck traffic at the ports. The new method forecasts that the Port of Everglades will have a 33% increase in average daily inbound heavy trucks and a 30% increase in average daily outbound heavy trucks by 2005 (2000 is the base year). The accuracy of the inbound and the outbound truck models is 93% and 92%, respectively.

Comparison of Two Approaches for Modeling Freight Movement at Seaports

Seaports host international cargo operations and are primary generators of freight traffic in the United States. Truck/rail trip generation and modal split models provide public agencies with valuable information necessary to prioritize funds for roadway upgrade projects and port infrastructure modifications. This paper presents two approaches for developing freight trip generation models: regression analysis and backpropagation neural networks (BPN). These models are used for predicting the levels of cargo truck traffic moving inbound and outbound at seaports. Based on the Port of Miami case, it was found that the BPN model is more accurate than the regression model. However, the BPN model requires a sizable database. Using the BPN approach, the paper presents a new combined truck trip generation and truck-rail modal split model for the Port of Jacksonville. It was found that the primary factors affecting truck-rail volume are the amount and direction of cargo vessel freight, commodity type, and the particular weekday of operation. In summary, the neural network model results were found significantly accurate for both Florida ports.

Which Method Is Better for Developing Freight Planning Models at Seaports—Neural Networks or Multiple Regression?

Ports are the primary generators of freight traffic in the United States. Seaport operations will require operational and infrastructure changes to maintain the growth of international cargo operations. Truck and rail trip generation and modal split models will provide transportation planners and public agencies with valuable information for prioritizing funds for roadway upgrade projects and port infrastructure modifications. Two methods are presented for developing freight trip generation models—regression analysis and back-propagation neural networks. These models are applied in predicting the levels of cargo truck traffic moving inbound and outbound at seaports. For the Port of Miami, the back-propagation neural network model was more accurate than the regression analysis model. However, the neural network model requires a sizable database. A second application of the back-propagation neural networks approach developed a truck trip generation model and a truck-rail modal split model for the Port of Jacksonville. The primary factors affecting truck and rail volume were found to be the amount and direction of cargo vessel freight, commodity type (bulk, break bulk, or liquid bulk), and weekday of operation. In summary, the neural network model results were found significantly accurate for both Florida ports.

Truck Trip Generation Models for Seaports with Container and Trailer Operation

Freight movement throughout the United States continues to evolve as a significant challenge to the transportation industry. Seaport operations dominated by container and trailer movements will require operational and infrastructure changes to maintain the growth of international cargo operations. Transportation planning models can be used to determine the needs of port and street network modifications. Described is the research and initial development process of models for predicting the levels of cargo truck traffic moving inbound and outbound at the Port of Miami. The models were restricted to container and trailer truck configurations that transport virtually all of the Port of Miami’s freight. Consequently, this associated truck traffic moves through the nearby street network within downtown Miami. The purpose of the trip generation models is to predict volumes of large inbound and outbound trucks for specified time frames. The concern is to know how many large cargo vehicles are traveling on the only road leading to the port. Primary factors affecting truck volume were found to be the amount and direction of cargo vessel freight and the particular weekday of operation. Time series models for predicting seasonal variations in freight movements were developed as part of the study. These models are useful for long-term forecasts of the input variables used in the trip generation models. Truck trip generation models will provide transportation planners and public agencies with valuable information when making transportation management decisions and infrastructure modifications. This information also is necessary for prioritizing funds for roadway upgrade projects.

Truck Movement and Access in Urban Areas

This paper discusses ways in which trucks may be specifically incorporated in the planning, design, and operation of urban traffic facilities. It is argued that if traffic engineers were to take greater and more explicit recognition of the needs of trucks in traffic management, this would not only aid truck movements, and thus contribute to economic efficiency, but would also assist the movement of passenger vehicles, as well as contribute to broader goals related to such aspects as road safety and regional economic development. The paper provides practical guidelines, based upon the results of recent research, to assist the practicing traffic engineer. It includes a discussion of the objectives of truck‐oriented traffic management, a review of problems in urban trucking, on‐street and off‐street traffic management solution strategies for solving those problems, and information concerning truck trip generation.