The vast majority of modeling approaches for commercial vehicles has focused on urban freight distribution and to a lesser degree on non-goods movement truck movements, including all other trucks not carrying goods. There are a few state of the practice models for school buses, airport shuttles and taxis, as described in Section 2.0, but these methods focus on the modal choice aspects of this demand rather than the impacts of these vehicles on congestion or air quality. Truck models have often been developed directly to support air quality studies so they are more likely to evaluate impacts on congestion and air quality.
Modeling approaches that are specific to one category of commercial vehicle are described in Section 5.0 as part of the literature review for individual categories. The remainder of this section discusses the various modeling approaches for urban goods movement models and non-goods movement truck models that have been under development in recent years. We also discuss the geospatial aspects of these studies and the integration of urban truck models and GIS.
Several years ago, Cambridge Systematics developed the FHWA Quick Response Freight Manual for use by state DOTs and MPOs in estimating truck trip tables and predicting truck flows. The manual was designed to provide simplified techniques and parameters to develop urban truck models. The manual provides background information on the freight transportation system and factors affecting freight demand; guidance to help planners locate and apply data and freight-related forecasts; sample techniques and transferable parameters that can be used to develop commercial vehicle trip tables; and techniques and transferable parameters for site planning. The manual also identifies alternative analytical methodologies and data collection techniques in order to improve the accuracy of the freight analysis and planning process.
In 1999, a recent Jack Faucett report establishes an approach for a suite of modeling tools to estimate freight demand. This approach included specifications for destination choice, mode choice and route choice models. The approach identified specific areas of innovation over the current state of the practice, as follows:
The treatment of light trucks (non-goods movement) in this approach is based on current state of the practice vehicle-based methods and does not offer any innovations, but the model design for heavy trucks (goods movement) offers new methods that may prove relevant for this study.
Boerkamps et al. (2000) discuss the need for behavioral urban freight models that can predict goods flow and vehicle flows and outline a conceptual framework consisting of the markets, actors, and supply chain elements of freight movements. The GoodTrip model (Boerkamps et al., 2000) develops estimates of goods flows, urban freight traffic, and their impact based on logistical chains. In GoodTrip, the logistical chain links activities of consumers, supermarkets, hypermarkets, distribution centers, and producers. The model was used in a case study for the city of Groningen, Holland.
There is also freight research that has used a variety of approaches, including mathematical modeling (Vanek, 2001); econometric methods such as a mixed discrete/continuous choice model of mode and shipment size (Abdelwahab, 1998); a multinomial probit model (Garrido and Mahmassani, 2000); and a variety of network-based approaches (Beuthe et al., 2001) and (Friesz et al., 1998). Faris and Ismart (1999) present a practical and low-cost modeling technique to include freight demand and truck movements in the development of long-range transportation plans for a small to medium urban area.
Some researchers developed gravity type models for urban freight vehicles (List and Turnquist, 1994, He and Crainic, 1998, Gorys and Hausmanis, 1999) and Oppenhiem (1993) developed a combined equilibrium model of urban passenger travel and good movement. Pendyala et al. (2000) provide a synthesis of approaches and the body of knowledge of freight transportation factors; freight travel demand modeling methods; freight transportation planning issues; and freight data needs, deficiencies, and collection methods. Prem & Yu (1995) applied traditional urban transportation planning techniques in a new way to perform detailed analysis of freight movement for the Quad County Regional Transportation Organization in Washington State.
Regan and Garrido (2001) discuss the state of the art and future directions in modeling freight demand and shipper behavior including a section on urban goods movement. Souleyrette et al. (1998) developed a freight planning typology and a layered architecture for freight demand modeling that separately simulates traffic for one commodity at a time. Shankar and Pendyala (2001) propose a comprehensive framework for the modeling of freight demand and discuss the econometric and statistical issues associated with estimating and applying such a framework. Taniguchi et al. (2002) review the development and application of mathematical computer-based models that have been used in the planning and evaluation of city logistic schemes.
In London (Visser et al., 1996) a disaggregated modeling approach to urban freight transportation was developed to evaluate policy measures on accessibility and environmental amenity (e.g., noise) and includes a decision support system developed in GIS. They characterize the model as a simulation model with GIS. The focus of the modeling activity is on the delivery of mostly consumer goods to retail stores, hotels, and restaurants. Other delivery services such as mail, courier, and waste removal are not included in this study.
There is significant research on the different aspects of logistics in transportation, including dynamic routing and scheduling, latest theory and techniques of logistics in supply chain management, and case studies of the impact of logistics in urban planning (Taniguchi et al., 2001). E. Taniguchi and R.G. Thompson provide an overview of recent advances in modeling city logistics. H. Ieda, A. Kimura, and Y. Yin, report the results of a study on the improvement of home delivery systems and the effect of introducing a new measure in a concentrated high-rise residential area. T. Yamada, E. Taniguchi and Y. Itoh, review the reasons why multi-carrier joint delivery services in urban areas are not popular by employing a gaming simulation of carriers' behavior. The impact of logistics on urban form and particularly the clustering of transportation logistics centers in urban areas is reviewed by K. Button, Konkani, and R. Stough. This raises many challenges for city planners as J. Boerkamps discusses in a conceptual study on urban freight transport policy planning from the viewpoint of logistics.
There is also a collection of papers that is probably the most comprehensive volume on GIS in transportation (Transportation Research, 2000). The volume covers many areas of GIS-T research and issues of urban freight are represented in two papers. Southworth and Peterson, describe the development and application of a single, integrated digital representation of a multimodal and transcontinental freight transportation network in GIS. While not focused on urban areas per se, the paper methods and issues discussed are applicable to urban modeling situations.
Arentze and Timmermans, focus on urban areas in their paper that describes a spatial decision support system for retail plan generation and impact assessment. The paper describes an operational system for integrated land-use and transportation planning called Location Planner. On the supply side, there are location-allocation models to estimate the number of facilities needed to serve the neighborhoods. The models are specified as spatial consumer choice models but by extension can indicate the concentration of retail outlets in future planning scenarios.
