The Experts below are selected from a list of 10245 Experts worldwide ranked by ideXlab platform
Moshe Benakiva - One of the best experts on this subject based on the ideXlab platform.
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assessing the reproducibility of Freight Vehicle flows using tour and trip based models for shipment to Vehicle flow conversion
Simulation Modelling Practice and Theory, 2021Co-Authors: Andre Romano Alho, Takanori Sakai, Ming Hong Chua, Max Raven, Yusuke Hara, Moshe BenakivaAbstract:Abstract Advances in urban Freight modeling and the availability of Freight Vehicle operations data have enabled the use of disaggregate models to evaluate urban delivery policies and solutions. This research assesses different model specifications for urban Freight simulation and their influence on flow reproducibility, with a focus on trip- and tour-based models for converting shipments to Vehicle flows. Using Vehicle operations data from Singapore, we study the performance of three models: Trip-based model, Tour-formation heuristic, and Delivery sequencing model. A comparison between the observed and estimated zone-to-zone Vehicle flows indicates the inadequacy of using a Trip-based model for urban Freight simulation and reveals potential estimation biases associated with select model specifications.
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exploring algorithms for revealing Freight Vehicle tours tour types and tour chain types from gps Vehicle traces and stop activity data
Journal of Big Data Analytics in Transportation, 2019Co-Authors: Andre Romano Alho, Takanori Sakai, Ming Hong Chua, Kyungsoo Jeong, Peiyu Jing, Moshe BenakivaAbstract:Freight Vehicle tours and tour-chains are essential elements of state-the-art agent-based urban Freight simulations as well as key units to analyse Freight Vehicle demand. GPS traces are typically used to extract Vehicle tours and tour-chains and became available in a large scale to, for example, fleet management firms. While methods to process this data with the objective of analysing and modelling tour-based Freight Vehicle operations have been proposed, they were not fully explored with regard to the implication of underlying assumptions. In this context, we test different algorithms of stop-to-tour assignment, tour-type and tour-chain identification, aiming to expose their implications. Specifically, we compare the traditional stop-to-tour assignment algorithm using the location of a “base” as the start/end point of tours, against other algorithms using stop activities or payload capacity usage. Furthermore, we explore high-resolution tour-type/chain identification algorithms, considering stop types and recurrence of visits. For tour-chain identification, we explore two algorithms: one defines the day-level tour-chain-type based on the predominant tour-type identified for the period of 1 day and another defines the tour-chain-type based on the average number of stops per tour by stop type. For a demonstration purpose, we apply the methods to data from a large-scale GPS-based survey conducted during 2017–2019 in Singapore. We compare the algorithms in an assessment of Freight Vehicle operations day-to-day pattern homogeneity. Our analysis demonstrates that the predictions of tours, tourtypes, and tour-chain-types are highly dependent on the assumptions used, underlining the importance of carefully selecting and disclosing the methods for data processing. Finally, the exploration of day-to-day pattern homogeneity reveals operational differences across Vehicle types and industries.
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next generation Freight Vehicle surveys supplementing truck gps tracking with a driver activity survey
International Conference on Intelligent Transportation Systems, 2018Co-Authors: Andre Romano Alho, Linlin You, Lynette Cheah, Fang Zhao, Moshe BenakivaAbstract:Freight road Vehicle operations vary widely depending on a multitude of factors such as industry type, commodities transported or geographical scope. Vehicle tracking is one of the most common approaches to understand operation patterns and it has been facilitated by the increasing availability of GPS-enabled devices. We describe a method that supplements Vehicle tracking data with day-to-day driver activity surveys to collect static and dynamic data related to Freight Vehicle operations. The survey was designed to enable innovative data analysis and modelling. We detail the data collection method demonstrated in Singapore and illustrate three data-driven insights which are of interest in the urban Freight domain: (1) Freight Vehicle overnight parking, (2) tour patterns and associated Vehicle usage characteristics, and (3) commodity flow patterns. The unique insights demonstrated by the analyses corroborate the potential of the described data collection method to further understand Freight Vehicle operations.
Andre Romano Alho - One of the best experts on this subject based on the ideXlab platform.
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assessing the reproducibility of Freight Vehicle flows using tour and trip based models for shipment to Vehicle flow conversion
Simulation Modelling Practice and Theory, 2021Co-Authors: Andre Romano Alho, Takanori Sakai, Ming Hong Chua, Max Raven, Yusuke Hara, Moshe BenakivaAbstract:Abstract Advances in urban Freight modeling and the availability of Freight Vehicle operations data have enabled the use of disaggregate models to evaluate urban delivery policies and solutions. This research assesses different model specifications for urban Freight simulation and their influence on flow reproducibility, with a focus on trip- and tour-based models for converting shipments to Vehicle flows. Using Vehicle operations data from Singapore, we study the performance of three models: Trip-based model, Tour-formation heuristic, and Delivery sequencing model. A comparison between the observed and estimated zone-to-zone Vehicle flows indicates the inadequacy of using a Trip-based model for urban Freight simulation and reveals potential estimation biases associated with select model specifications.
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exploring algorithms for revealing Freight Vehicle tours tour types and tour chain types from gps Vehicle traces and stop activity data
Journal of Big Data Analytics in Transportation, 2019Co-Authors: Andre Romano Alho, Takanori Sakai, Ming Hong Chua, Kyungsoo Jeong, Peiyu Jing, Moshe BenakivaAbstract:Freight Vehicle tours and tour-chains are essential elements of state-the-art agent-based urban Freight simulations as well as key units to analyse Freight Vehicle demand. GPS traces are typically used to extract Vehicle tours and tour-chains and became available in a large scale to, for example, fleet management firms. While methods to process this data with the objective of analysing and modelling tour-based Freight Vehicle operations have been proposed, they were not fully explored with regard to the implication of underlying assumptions. In this context, we test different algorithms of stop-to-tour assignment, tour-type and tour-chain identification, aiming to expose their implications. Specifically, we compare the traditional stop-to-tour assignment algorithm using the location of a “base” as the start/end point of tours, against other algorithms using stop activities or payload capacity usage. Furthermore, we explore high-resolution tour-type/chain identification algorithms, considering stop types and recurrence of visits. For tour-chain identification, we explore two algorithms: one defines the day-level tour-chain-type based on the predominant tour-type identified for the period of 1 day and another defines the tour-chain-type based on the average number of stops per tour by stop type. For a demonstration purpose, we apply the methods to data from a large-scale GPS-based survey conducted during 2017–2019 in Singapore. We compare the algorithms in an assessment of Freight Vehicle operations day-to-day pattern homogeneity. Our analysis demonstrates that the predictions of tours, tourtypes, and tour-chain-types are highly dependent on the assumptions used, underlining the importance of carefully selecting and disclosing the methods for data processing. Finally, the exploration of day-to-day pattern homogeneity reveals operational differences across Vehicle types and industries.
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next generation Freight Vehicle surveys supplementing truck gps tracking with a driver activity survey
International Conference on Intelligent Transportation Systems, 2018Co-Authors: Andre Romano Alho, Linlin You, Lynette Cheah, Fang Zhao, Moshe BenakivaAbstract:Freight road Vehicle operations vary widely depending on a multitude of factors such as industry type, commodities transported or geographical scope. Vehicle tracking is one of the most common approaches to understand operation patterns and it has been facilitated by the increasing availability of GPS-enabled devices. We describe a method that supplements Vehicle tracking data with day-to-day driver activity surveys to collect static and dynamic data related to Freight Vehicle operations. The survey was designed to enable innovative data analysis and modelling. We detail the data collection method demonstrated in Singapore and illustrate three data-driven insights which are of interest in the urban Freight domain: (1) Freight Vehicle overnight parking, (2) tour patterns and associated Vehicle usage characteristics, and (3) commodity flow patterns. The unique insights demonstrated by the analyses corroborate the potential of the described data collection method to further understand Freight Vehicle operations.
Sean M Puckett - One of the best experts on this subject based on the ideXlab platform.
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Refocusing the Modelling of Freight Distribution: Development of an Economic-Based Framework to Evaluate Supply Chain Behaviour in Response to Congestion Charging
Transportation, 2005Co-Authors: David A Hensher, Sean M PuckettAbstract:The distribution of Freight is a major contributor to the levels of traffic congestion in cities. However it is much neglected in the research and planning activities of government, where the focus is disproportionately on passenger Vehicle movements. Despite the recent recognition of the contribution of Freight transportation to the performance of urban areas under the rubric of city logistics, we see a void in the study of how the stakeholders in the supply chain might cooperate through participation in distribution networks, to reduce the costs associated with traffic congestion. Given that transport costs are typically over 45 of all distribution costs, with congestion a major contributor in the urban setting, the importance of establishing ways in which supply chain partnerships might cooperate to reduce levels of Freight Vehicle movements has much merit. This paper sets out a framework to investigate how agents in a retail supply chain might interact more effectively to reduce the costs of urban Freight distribution. We propose an interactive agency choice method as a way of formalising a framework for studying the preferences of participants in the supply chain to support specific policy initiatives. Such a framework is a powerful way of investigating the behavioural response of each agent to many policies, including congestion pricing, as a way of improving the efficient flow of traffic in cities.
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Freight distribution in urban areas the role of supply chain alliances in addressing the challenge of traffic congestion for city logistics
2004Co-Authors: David A Hensher, Sean M PuckettAbstract:Despite the recent recognition of the contribution of Freight transportation to the performance of urban areas under the rubric of city logistics, the authors see a void in the study of how the stakeholders in the supply chain associated with the distribution of goods (whose destination is an urban location) might cooperate through participation in distribution networks, to reduce the costs associated with traffic congestion. Given that transport costs are typically over 45 percent of all distribution costs, with congestion contributing a substantial amount of cost in the urban setting, the importance of establishing ways in which supply chain partnerships might aid in reducing the levels of Freight Vehicle movements in urban areas has much merit. This paper sets out a framework to investigate how agents in the supply chain might interact more effectively to reduce their costs of urban Freight distribution. The authors propose an interactive agency choice method as a way of formalising a framework for studying the preferences of participants in the supply chain to support specific policy initiatives. (a)
T Thoresen - One of the best experts on this subject based on the ideXlab platform.
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estimation of the marginal cost of road wear as a basis for charging Freight Vehicles
Research in Transportation Economics, 2015Co-Authors: T Martin, T ThoresenAbstract:Abstract In Australia one option for improved Freight Vehicle productivity, as part of major road reform, is increasing the allowable Freight Vehicle axle loads above current load limits and reduce the transport cost per tonne-km. This can also potentially reduce greenhouse gas (GHG) emissions by reducing the number of Freight Vehicle movements for a given Freight task. Decisions regarding increased axle loads on the existing road infrastructure can be founded on the marginal cost of road wear as the basis of a price for increasing axle loads. These prices can provide a clear signal for targeting maintenance and rehabilitation funding and works provided the revenue raised by the price is directly linked to the funding. The Freight Axle Limits Investigation Tool (FAMLIT) is a pavement life-cycle costing model that can be used to estimate load-wear-cost (LWC) relationships for a range of typical roads and pavement types for six heavy Vehicle axle groups. Loads were incrementally increased above current load limits to estimate the LWC relationships. Life-cycle road wear costs were based on the present value (PV) of the routine and periodic maintenance and rehabilitation costs associated with managing each road type within agreed functional and structural conditions. The PVs of these costs were subsequently converted into equivalent annual uniform costs (EAUC) which were used to form LWC relationships with axle load (tonne-km) and standard axle repetitions (SAR-km), providing alternative independent variables. The marginal cost of road wear was determined by the first derivative of the LWC relationships. The estimated marginal road wear costs, in both short-run marginal cost (SRMC) form and long-run marginal cost (LRMC) form were found to vary across a range of road types and were highly dependent on the pavement/subgrade strength and traffic load. The marginal costs based on the LWC relationship using SAR-km as the independent variable were a constant value until axle group loads were increased significantly above current limits.
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using the Freight axle mass limits investigation tool famlit to estimate the marginal cost of road wear
ARRB Conference 25th 2012 Perth Western Australia Australia, 2012Co-Authors: W Horelacy, T Thoresen, T MartinAbstract:Improved Freight Vehicle productivity can be obtained by increasing the allowable axle loads above current load limits. In turn improved productivity can potentially reduce greenhouse gas (GHG) emissions by reducing the number of Freight Vehicles for a given Freight task. Increased axle load pavement impacts can be managed using the marginal cost of road wear as the basis of a price for increasing axle loads. These prices can provide a basis for targeting maintenance and rehabilitation funding provided the revenue raised by the price is linked to funding. FAMLIT is a pavement life-cycle costing model that has been used to estimate load-wear-cost (LWC) relationships for a range of typical roads and pavement types subject to incremental loading over current load limits by six axle groups. These load wear costs were estimated as the present value (PV) of maintenance and rehabilitation costs incurred by managing road pavement within agreed surface and structural conditions. These costs have been converted to equivalent annual uniform costs (EAUC) and developed into LWC relationship against axle load (tonne-km) and standard axle repetitions (SAR-km). The marginal cost of road wear was determined by the first derivative of the LWC relationships. The estimated marginal road wear costs were found to vary across the various road types and were highly dependent on the pavement/subgrade strength.
Ming Hong Chua - One of the best experts on this subject based on the ideXlab platform.
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assessing the reproducibility of Freight Vehicle flows using tour and trip based models for shipment to Vehicle flow conversion
Simulation Modelling Practice and Theory, 2021Co-Authors: Andre Romano Alho, Takanori Sakai, Ming Hong Chua, Max Raven, Yusuke Hara, Moshe BenakivaAbstract:Abstract Advances in urban Freight modeling and the availability of Freight Vehicle operations data have enabled the use of disaggregate models to evaluate urban delivery policies and solutions. This research assesses different model specifications for urban Freight simulation and their influence on flow reproducibility, with a focus on trip- and tour-based models for converting shipments to Vehicle flows. Using Vehicle operations data from Singapore, we study the performance of three models: Trip-based model, Tour-formation heuristic, and Delivery sequencing model. A comparison between the observed and estimated zone-to-zone Vehicle flows indicates the inadequacy of using a Trip-based model for urban Freight simulation and reveals potential estimation biases associated with select model specifications.
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exploring algorithms for revealing Freight Vehicle tours tour types and tour chain types from gps Vehicle traces and stop activity data
Journal of Big Data Analytics in Transportation, 2019Co-Authors: Andre Romano Alho, Takanori Sakai, Ming Hong Chua, Kyungsoo Jeong, Peiyu Jing, Moshe BenakivaAbstract:Freight Vehicle tours and tour-chains are essential elements of state-the-art agent-based urban Freight simulations as well as key units to analyse Freight Vehicle demand. GPS traces are typically used to extract Vehicle tours and tour-chains and became available in a large scale to, for example, fleet management firms. While methods to process this data with the objective of analysing and modelling tour-based Freight Vehicle operations have been proposed, they were not fully explored with regard to the implication of underlying assumptions. In this context, we test different algorithms of stop-to-tour assignment, tour-type and tour-chain identification, aiming to expose their implications. Specifically, we compare the traditional stop-to-tour assignment algorithm using the location of a “base” as the start/end point of tours, against other algorithms using stop activities or payload capacity usage. Furthermore, we explore high-resolution tour-type/chain identification algorithms, considering stop types and recurrence of visits. For tour-chain identification, we explore two algorithms: one defines the day-level tour-chain-type based on the predominant tour-type identified for the period of 1 day and another defines the tour-chain-type based on the average number of stops per tour by stop type. For a demonstration purpose, we apply the methods to data from a large-scale GPS-based survey conducted during 2017–2019 in Singapore. We compare the algorithms in an assessment of Freight Vehicle operations day-to-day pattern homogeneity. Our analysis demonstrates that the predictions of tours, tourtypes, and tour-chain-types are highly dependent on the assumptions used, underlining the importance of carefully selecting and disclosing the methods for data processing. Finally, the exploration of day-to-day pattern homogeneity reveals operational differences across Vehicle types and industries.