The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Miguel A. Figliozzi - One of the best experts on this subject based on the ideXlab platform.
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Impact of last mile parking availability on Commercial Vehicle costs and operations
Supply Chain Forum: An International Journal, 2017Co-Authors: Miguel A. Figliozzi, Chawalit TipagornwongAbstract:ABSTRACTLogistics, queuing and optimisation models are combined to study the impact of last-mile parking availability on Commercial Vehicle costs and operations. Parking availability levels affect Commercial Vehicle parking costs and operations and has an impact on route characteristics and Commercial Vehicle fleet sizes. The magnitude of the parking availability impacts on costs is a function of customer and route characteristics. Elasticity values indicates that only a few variables have a significant impact on Commercial Vehicle parking behaviour. Productivity improvements like service time reductions may result in undesirable changes in Commercial Vehicle parking behaviour.
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collecting Commercial Vehicle tour data with passive global positioning system technology issues and potential applications
Transportation Research Record, 2008Co-Authors: Stephen Greaves, Miguel A. FigliozziAbstract:The assessment of strategies designed to manage the continued growth in road-based freight and associated externalities has been hampered by a paucity of disaggregate data on Commercial Vehicle movements. When disaggregated data are available, the analysis of Commercial Vehicle route and trip chain structure can provide insightful information about urban Commercial Vehicle tours, travel patterns, and congestion levels. Over the past 15 years, the ability to collect detailed travel information has been expanded by developments in global positioning system (GPS) technology. In mid-2006, a GPS survey of Commercial Vehicles was piloted in Melbourne, Australia, to support a major update of freight data and modeling capabilities in the metropolitan region. The survey used passive GPS methods in which the truck driver's involvement in the data collection effort was minimal. The contributions of this research to the field of urban freight data collection were fourfold: (a) describing implementation issues with th...
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analysis of the efficiency of urban Commercial Vehicle tours data collection methodology and policy implications
Transportation Research Part B-methodological, 2007Co-Authors: Miguel A. FigliozziAbstract:The emphasis of this research is on the analysis of Commercial Vehicle tours. Tours are disaggregated by their routing constraints. The generation of Vehicle kilometers traveled (VKT) by tour type is analytically modeled and analyzed. The relative influence of the number of stops per tour, tour duration, and time window constraints on VKT is discussed using an analytical framework. Multistop tours are shown to generate more VKT than direct deliveries even for equal payloads. Intuition about the impacts of network/logistics changes and policy implications on VKT is derived. Implications for the calibration of trip generation and distribution models are discussed. In the tour model, it is proven that the percentage of empty trips has no correlation with the efficiency of the tours regarding VKT generation. The shape of trip length distributions (TLD) is discussed. It is shown that the average trip length and the TLD shape are strongly dependent on the tour type, distance from the depot/distribution center to the service area, density of stops, and number of stops per tour. Implications for data collection needs are analyzed.
John Douglas Hunt - One of the best experts on this subject based on the ideXlab platform.
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establishment based survey of urban Commercial Vehicle movements in alberta canada survey design implementation and results
Transportation Research Record, 2006Co-Authors: John Douglas Hunt, Kevin Stefan, Alan T BrownleeAbstract:This paper describes a project to develop a more complete understanding of the nature of urban Commercial Vehicle movements in the Calgary and Edmonton regions, the two principal urban regions in the province of Alberta, Canada, each with a population near 1 million. It covers the design and implementation of the survey and an overview of the results. Slightly more than 3,000 business establishments in the Calgary region and 4,300 business establishments in the Edmonton region were interviewed concerning the Commercial movements that they generated on an assigned survey day. The surveys were performed in fall 2000 in the Calgary region and in fall 2001 through spring 2002 in the Edmonton region. The survey was done to obtain indications of the full range of commodities being transported, including goods and services, together with descriptions of the associated person and Vehicle movements arising with this transportation activity. The establishment-based process used was analogous to the household-based ...
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urban Commercial Vehicle movement model for calgary alberta canada
Transportation Research Record, 2005Co-Authors: Kevin Stefan, Jdp Mcmillan, John Douglas HuntAbstract:Commercial Vehicle movements compose perhaps 15% of all urban Vehicle trips and produce large impacts in key areas, such as congestion, emissions, road wear, and industrial area traffic. A system for modeling such movements was developed for Calgary, Alberta, Canada. It is a novel application of an agent-based microsimulation framework that uses a tour-based approach and emphasizes important elements of urban Commercial movement, including the role of service delivery, light Commercial Vehicles, and trip chaining. The microsimulation uses Monte Carlo techniques to assign tour purpose, Vehicle type, next-stop purpose, next-stop location, and next-stop duration. Tours are "grown" with a return-to-establishment alternative within the next-stop purpose allocation, which is consistent with the nature of tour making in urban Commercial movements. The Monte Carlo probabilities are established with the use of a series of logit models, with coefficients estimated on the basis of observed behavior of different comm...
Kevin Stefan - One of the best experts on this subject based on the ideXlab platform.
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establishment based survey of urban Commercial Vehicle movements in alberta canada survey design implementation and results
Transportation Research Record, 2006Co-Authors: John Douglas Hunt, Kevin Stefan, Alan T BrownleeAbstract:This paper describes a project to develop a more complete understanding of the nature of urban Commercial Vehicle movements in the Calgary and Edmonton regions, the two principal urban regions in the province of Alberta, Canada, each with a population near 1 million. It covers the design and implementation of the survey and an overview of the results. Slightly more than 3,000 business establishments in the Calgary region and 4,300 business establishments in the Edmonton region were interviewed concerning the Commercial movements that they generated on an assigned survey day. The surveys were performed in fall 2000 in the Calgary region and in fall 2001 through spring 2002 in the Edmonton region. The survey was done to obtain indications of the full range of commodities being transported, including goods and services, together with descriptions of the associated person and Vehicle movements arising with this transportation activity. The establishment-based process used was analogous to the household-based ...
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urban Commercial Vehicle movement model for calgary alberta canada
Transportation Research Record, 2005Co-Authors: Kevin Stefan, Jdp Mcmillan, John Douglas HuntAbstract:Commercial Vehicle movements compose perhaps 15% of all urban Vehicle trips and produce large impacts in key areas, such as congestion, emissions, road wear, and industrial area traffic. A system for modeling such movements was developed for Calgary, Alberta, Canada. It is a novel application of an agent-based microsimulation framework that uses a tour-based approach and emphasizes important elements of urban Commercial movement, including the role of service delivery, light Commercial Vehicles, and trip chaining. The microsimulation uses Monte Carlo techniques to assign tour purpose, Vehicle type, next-stop purpose, next-stop location, and next-stop duration. Tours are "grown" with a return-to-establishment alternative within the next-stop purpose allocation, which is consistent with the nature of tour making in urban Commercial movements. The Monte Carlo probabilities are established with the use of a series of logit models, with coefficients estimated on the basis of observed behavior of different comm...
Ellen Thorson - One of the best experts on this subject based on the ideXlab platform.
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Commercial Vehicle empty trip models with probabilities that depend on trip characteristics
2006Co-Authors: José Holguín-veras, Ellen Thorson, Juan C. ZorrillaAbstract:The multidimensional nature of freight demand has given rise to two major modeling platforms: Vehicle-trip based and commodity based (cargo value is only used in Input-Output models). Vehicle-based models focus on modeling the actual number of Vehicle trips, which has several advantages. Among them are the relative ease and high-quality with which traffic data can be obtained; and, since the model focuses on Vehicle trips, no distinction is made between empty and loaded trips. A key limitation of Vehicle-trip modes is that they cannot be applied to multimodal systems because the Vehicle-trip is already the result of a mode choice that already took place. Furthermore since the models assume that the Vehicle-trip is the unit of demand, as opposed to the commodity being transported, there is no way to consider the economic characteristics of the shipments. This is a rather serious limitation because the commodity type has been found to be a very important explanatory variable of a number of choice processes involving freight. Commodity based models, as the name points out, focus on modeling the flow of goods between zones (measured in a unit of weight). Since the cargo's weight is the unit of demand, the consideration of cargoes' attributes (e.g., value, weight, type) is straightforward. In this platform, the loaded trips are estimated by dividing the total flow from one region to the other by a suitable payload for all loaded trucks. The problem with commodity-based models is that they are unable to model empty trips, which can make up about 30 to 40 percent of the total trips in a region (Holguin-Veras and Thorson, 2003a). This occurs because the commodity flow in one direction determines the corresponding loaded trips, but does not bear a direct relationship with the empty trips. To resolve this, complementary empty trip models have been developed, such as Noortman and van Es' In this context, the empty trip models use the commodity flows estimated by a freight demand model as an input for the estimation of the corresponding empty trips. Having done that, the empty trips are added to the loaded trips to obtain the total Vehicle trips that would be used in the traffic assignment process. Far from being of purely academic interest, the correct estimation of Commercial Vehicle empty trips is very important for transportation planning purposes because not doing correctly will lead to severe directional errors in the estimation of Commercial Vehicle traffic, as shown in. This, in turn, may have important implications in terms of determining road capacity improvement needs. The main objective of this paper is to contribute to freight transportation modeling by enhancing the methodologies used to estimate empty trips from previously estimated commodity flow matrices. The paper builds on the developments of The consideration of a variable p significantly improved the relative performance of the models. For each data subset, the relative error for the models with constant p was higher than that for the models with p as a function of either commodity flow or distance. For small trucks, the relative error for HVT 2, HVT 3, and HVT 4 ranged from 9.7% to 19.3% greater than that for the best variable p model. For large trucks, the relative error for the models with constant p ranged from 5.8% to 11.4% greater than that for the best variable p model, Similarly, for semi-trailers, the relative error for the models with constant p ranged from 4.2% to 6.7% greater than that for the best variable p model. In spite of the acknowledged limitations of the work, it is clear that considering variable p functions holds the potential to significantly improve the performance of empty trips models, which would hopefully facilitate the development of new paradigms of freight transportation modeling. For the covering abstract see ITRD E135582.
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Modelling Commercial Vehicle empty trips with parameters that depend on trip characteristics
2005Co-Authors: José Holguín-veras, Juan C. Zorrilla, Ellen ThorsonAbstract:The 21st century has been characterized by the increasing role of information technology in everyday life. Modern computer systems allow for information to be transferred faster and safer through the Internet, thus making it convenient for consumers to shop online in the comfort of their homes. As this trend continues, the demand for lighter, higher-value goods increases, since they are likely to have a lower cost to the consumer on the Internet. This increase in demand, combined with population growth and other factors, is resulting in an increase in the amount of freight that has to be transported, particularly by truck. A hindering factor to efficient ground transportation is the larger Commercial Vehicle traffic that increases congestion on the roads. This results in a significant increase in air and noise pollution as well as the number of dangerous traffic accidents involving trucks.g increase in externalities, puts pressure on the trucking industry and on Metropolitan Planning Organizations . The trucking industry will need to handle larger delivery volumes, facing lower revenues due to the level of competition and increasingly stringent regulations for externalities. MPOs will have to improve their planning processes in order to accommodate the ever increasing demand for goods transportation. In order to do this, the organizations need more efficient demand models than the ones currently in use. In many cases, MPOs use adaptations of passenger car models to estimate freight demand in the area. Although these simplistic approaches can sometimes provide rough estimates, they are flawed since they do not capture the key dynamics of freight phenomena. Currently there are two major platforms for modelling freight transportation demand: Vehicle-trip and commodity based models. Vehicle-trip models focus on modelling the actual number of Vehicle trips, which has some practical advantages. Among them are the relative ease and high-quality with which data can be obtained due to an increasing number of Intelligent Transportation Systems. Also, since the model focuses on Vehicle trips it has no problem generating the number of empty trips between regions. However, these models have two fundamental limitations. The first one is that these models cannot be applied to multimodal transportation because the Vehicle-trip is in itself the result of a mode choice and the selection process is not represented in the data. Furthermore, since the models assume that the Vehicle is the unit of demand, as opposed to the commodity being transported, the model neglects the economic characteristics of the shipment that have been found to play a significant role in the majority of choice processes in the trucking industry. Commodity based models, as the name points out, focus on modelling the flow of goods from one region to the other . Since the commodities are the unit of demand, the modeler can capture the underlying factors that determine freight movement, such as value, weight, and volume. In this platform, the loaded trips are estimated by dividing the total flow from one region to the other by an average payload from all loaded trucks. The problem with commodity-based models is that they are unable to model empty trips, which can make up about 30 to 50 percent of the total trips in a region. This occurs since the commodity flow in one direction determines the loaded trips, but does not bear a relationship to the number of the empty trips in the same direction. To resolve this, some complementary models have been developed. The empty trip models mentioned before are some of the approaches for modelling empty Commercial Vehicle traffic that are discussed in the paper, which also proposes some new empty trip models. The models range from simple nave formulations to some more complex ones involving trip chains, probabilities and memory components. The performance of the alternative formulations to model empty trips is assessed. The paper starts with some background information on the subject, followed by a brief description of previous developments in the area, a description of the test cases for the model, the methodology, and finally the results and conclusions. A comprehensive discussion of the theoretical developments pertaining to Commercial Vehicle empty trip models, the corresponding estimation procedures is provided. For the covering abstract please see ITRD E135207.
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Modeling Commercial Vehicle Empty Trips: Theory and Application
2005Co-Authors: José Holguín-veras, Juan C. Zorrilla, Ellen ThorsonAbstract:This paper attempts to provide a comprehensive discussion of the theoretical developments pertaining to Commercial Vehicle empty trip models, the corresponding estimation procedures, empirical evidence, and practical implications of modeling empty trips. The paper also describes new mathematical formulations to model Commercial Vehicle empty trips. In doing so, it expands and synthesizes the previous research on the subject.
José Holguín-veras - One of the best experts on this subject based on the ideXlab platform.
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Commercial Vehicle empty trip models with probabilities that depend on trip characteristics
2006Co-Authors: José Holguín-veras, Ellen Thorson, Juan C. ZorrillaAbstract:The multidimensional nature of freight demand has given rise to two major modeling platforms: Vehicle-trip based and commodity based (cargo value is only used in Input-Output models). Vehicle-based models focus on modeling the actual number of Vehicle trips, which has several advantages. Among them are the relative ease and high-quality with which traffic data can be obtained; and, since the model focuses on Vehicle trips, no distinction is made between empty and loaded trips. A key limitation of Vehicle-trip modes is that they cannot be applied to multimodal systems because the Vehicle-trip is already the result of a mode choice that already took place. Furthermore since the models assume that the Vehicle-trip is the unit of demand, as opposed to the commodity being transported, there is no way to consider the economic characteristics of the shipments. This is a rather serious limitation because the commodity type has been found to be a very important explanatory variable of a number of choice processes involving freight. Commodity based models, as the name points out, focus on modeling the flow of goods between zones (measured in a unit of weight). Since the cargo's weight is the unit of demand, the consideration of cargoes' attributes (e.g., value, weight, type) is straightforward. In this platform, the loaded trips are estimated by dividing the total flow from one region to the other by a suitable payload for all loaded trucks. The problem with commodity-based models is that they are unable to model empty trips, which can make up about 30 to 40 percent of the total trips in a region (Holguin-Veras and Thorson, 2003a). This occurs because the commodity flow in one direction determines the corresponding loaded trips, but does not bear a direct relationship with the empty trips. To resolve this, complementary empty trip models have been developed, such as Noortman and van Es' In this context, the empty trip models use the commodity flows estimated by a freight demand model as an input for the estimation of the corresponding empty trips. Having done that, the empty trips are added to the loaded trips to obtain the total Vehicle trips that would be used in the traffic assignment process. Far from being of purely academic interest, the correct estimation of Commercial Vehicle empty trips is very important for transportation planning purposes because not doing correctly will lead to severe directional errors in the estimation of Commercial Vehicle traffic, as shown in. This, in turn, may have important implications in terms of determining road capacity improvement needs. The main objective of this paper is to contribute to freight transportation modeling by enhancing the methodologies used to estimate empty trips from previously estimated commodity flow matrices. The paper builds on the developments of The consideration of a variable p significantly improved the relative performance of the models. For each data subset, the relative error for the models with constant p was higher than that for the models with p as a function of either commodity flow or distance. For small trucks, the relative error for HVT 2, HVT 3, and HVT 4 ranged from 9.7% to 19.3% greater than that for the best variable p model. For large trucks, the relative error for the models with constant p ranged from 5.8% to 11.4% greater than that for the best variable p model, Similarly, for semi-trailers, the relative error for the models with constant p ranged from 4.2% to 6.7% greater than that for the best variable p model. In spite of the acknowledged limitations of the work, it is clear that considering variable p functions holds the potential to significantly improve the performance of empty trips models, which would hopefully facilitate the development of new paradigms of freight transportation modeling. For the covering abstract see ITRD E135582.
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Modelling Commercial Vehicle empty trips with parameters that depend on trip characteristics
2005Co-Authors: José Holguín-veras, Juan C. Zorrilla, Ellen ThorsonAbstract:The 21st century has been characterized by the increasing role of information technology in everyday life. Modern computer systems allow for information to be transferred faster and safer through the Internet, thus making it convenient for consumers to shop online in the comfort of their homes. As this trend continues, the demand for lighter, higher-value goods increases, since they are likely to have a lower cost to the consumer on the Internet. This increase in demand, combined with population growth and other factors, is resulting in an increase in the amount of freight that has to be transported, particularly by truck. A hindering factor to efficient ground transportation is the larger Commercial Vehicle traffic that increases congestion on the roads. This results in a significant increase in air and noise pollution as well as the number of dangerous traffic accidents involving trucks.g increase in externalities, puts pressure on the trucking industry and on Metropolitan Planning Organizations . The trucking industry will need to handle larger delivery volumes, facing lower revenues due to the level of competition and increasingly stringent regulations for externalities. MPOs will have to improve their planning processes in order to accommodate the ever increasing demand for goods transportation. In order to do this, the organizations need more efficient demand models than the ones currently in use. In many cases, MPOs use adaptations of passenger car models to estimate freight demand in the area. Although these simplistic approaches can sometimes provide rough estimates, they are flawed since they do not capture the key dynamics of freight phenomena. Currently there are two major platforms for modelling freight transportation demand: Vehicle-trip and commodity based models. Vehicle-trip models focus on modelling the actual number of Vehicle trips, which has some practical advantages. Among them are the relative ease and high-quality with which data can be obtained due to an increasing number of Intelligent Transportation Systems. Also, since the model focuses on Vehicle trips it has no problem generating the number of empty trips between regions. However, these models have two fundamental limitations. The first one is that these models cannot be applied to multimodal transportation because the Vehicle-trip is in itself the result of a mode choice and the selection process is not represented in the data. Furthermore, since the models assume that the Vehicle is the unit of demand, as opposed to the commodity being transported, the model neglects the economic characteristics of the shipment that have been found to play a significant role in the majority of choice processes in the trucking industry. Commodity based models, as the name points out, focus on modelling the flow of goods from one region to the other . Since the commodities are the unit of demand, the modeler can capture the underlying factors that determine freight movement, such as value, weight, and volume. In this platform, the loaded trips are estimated by dividing the total flow from one region to the other by an average payload from all loaded trucks. The problem with commodity-based models is that they are unable to model empty trips, which can make up about 30 to 50 percent of the total trips in a region. This occurs since the commodity flow in one direction determines the loaded trips, but does not bear a relationship to the number of the empty trips in the same direction. To resolve this, some complementary models have been developed. The empty trip models mentioned before are some of the approaches for modelling empty Commercial Vehicle traffic that are discussed in the paper, which also proposes some new empty trip models. The models range from simple nave formulations to some more complex ones involving trip chains, probabilities and memory components. The performance of the alternative formulations to model empty trips is assessed. The paper starts with some background information on the subject, followed by a brief description of previous developments in the area, a description of the test cases for the model, the methodology, and finally the results and conclusions. A comprehensive discussion of the theoretical developments pertaining to Commercial Vehicle empty trip models, the corresponding estimation procedures is provided. For the covering abstract please see ITRD E135207.
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Modeling Commercial Vehicle Empty Trips: Theory and Application
2005Co-Authors: José Holguín-veras, Juan C. Zorrilla, Ellen ThorsonAbstract:This paper attempts to provide a comprehensive discussion of the theoretical developments pertaining to Commercial Vehicle empty trip models, the corresponding estimation procedures, empirical evidence, and practical implications of modeling empty trips. The paper also describes new mathematical formulations to model Commercial Vehicle empty trips. In doing so, it expands and synthesizes the previous research on the subject.