- Accession
- Cube Analyst
- Cube Avenue
- Cube Base
- Cube Cargo
- Cube Land
- Cube Cluster
- Cube Dynasim
- Cube Reports
- Cube Voyager
Brochures
Cube Cargo
Model details
Generation
The generation model forecasts the number of tons, by commodity group, produced and consumed for each coarse-level zone. The productions are segmented into internal productions, which are to be transported to an internal zone, and exports, which are sent to external zones. Similarly, the consumptions are classified as internal or as imports.
Productions and consumptions are estimated using multivariate linear regression models estimated using local data.
Productions and consumptions are estimated using multivariate linear regression models estimated using local data.
Direct trips
Direct trips are routed in two ways. In the graphic, the default method is shown which has a truck going from A to B and back to C, running empty in one of the directions.
Alternatively, the Direct Trip model can be adjusted to accept certain distances to look
for return loads.
Direct trips are routed in two ways. In the graphic, the default method is shown which has a truck going from A to B and back to C, running empty in one of the directions.
Alternatively, the Direct Trip model can be adjusted to accept certain distances to look
for return loads.
Touring trips
The touring model is used to model short-haul vehicle trips whose
structure
is more complex than A-B-A. It is used separately for heavy and light
trucks.
structure
is more complex than A-B-A. It is used separately for heavy and light
trucks.
Distribution
The distribution models allocate the forecasted productions by commodity group from their zone of origin to their zone of consumption. The productions and consumptions are split into short and long-haul trips. Both trip types are distributed using gravity models using different generalized cost functions. Short trips are distributed using distance; long-haul trips are distributed using a composite cost of travel time, distance, and cost. These elements are weighted using parameters from the modal choice models.
Modal choice
The modal choice models are applied on the long-haul trip matrices only using multinomial logit choice functions. Short-haul trips are assumed to travel by road. Long-haul trips are split into truck, rail, inland waterway and modal combinations (combined transport).
Generalized cost functions are defined using local data for each combination of commodity group and mode and distance class. The modal choice functions incorporate time, distance and cost.
Transport Logistics Node model
Transport logistics nodes (TLN) are places such as major goods yards, multi-modal terminals, railway stations, and ports, where trip chaining occurs.
The Transport Logistics Node model examines the matrices created by the long-haul modal choice model and partitions them into direct transport and transport chain matrices.
The goods in the direct transport matrices will be transported directly from their initial origin to their final destination. The goods in the transport chain matrices are divided into two segments: from origin to the TLN and from the TLN to the destination. Of these two sections, one will be classified as long-haul and the other will be classified as short-haul. At this stage of the model, Cube Cargo has estimated the commodity flow matrices by product type and mode.
Fine distribution model
For each combination of mode and commodity group the matrices are converted using gravity formulations to the fine level zone system.
This transition is made in order to produce truck vehicle matrices at a zone level sufficiently fine to provide estimations of link-level truck flows.
Vehicle model
The vehicle model estimates the number of vehicle trips per day given the mode and commodity group matrices from the previous model steps.
The model iterates over all origins across all of the various matrices, by commodity class, and applies two models which separately model direct trips and touring vehicle trips.
The results are combined to provide matrices of vehicle truck volumes by truck type for assignment.
Service traffic model
In urban areas there is a significant amount of local delivery and non-goods related truck traffic.
This includes transport of relatively small amounts of goods and the transport of services.
The service traffic model generates local truck matrices for these purposes using linear regression generation models and gravity models for distribution
The distribution models allocate the forecasted productions by commodity group from their zone of origin to their zone of consumption. The productions and consumptions are split into short and long-haul trips. Both trip types are distributed using gravity models using different generalized cost functions. Short trips are distributed using distance; long-haul trips are distributed using a composite cost of travel time, distance, and cost. These elements are weighted using parameters from the modal choice models.
Modal choice
The modal choice models are applied on the long-haul trip matrices only using multinomial logit choice functions. Short-haul trips are assumed to travel by road. Long-haul trips are split into truck, rail, inland waterway and modal combinations (combined transport).
Generalized cost functions are defined using local data for each combination of commodity group and mode and distance class. The modal choice functions incorporate time, distance and cost.
Transport Logistics Node model
Transport logistics nodes (TLN) are places such as major goods yards, multi-modal terminals, railway stations, and ports, where trip chaining occurs.
The Transport Logistics Node model examines the matrices created by the long-haul modal choice model and partitions them into direct transport and transport chain matrices.
The goods in the direct transport matrices will be transported directly from their initial origin to their final destination. The goods in the transport chain matrices are divided into two segments: from origin to the TLN and from the TLN to the destination. Of these two sections, one will be classified as long-haul and the other will be classified as short-haul. At this stage of the model, Cube Cargo has estimated the commodity flow matrices by product type and mode.
Fine distribution model
For each combination of mode and commodity group the matrices are converted using gravity formulations to the fine level zone system.
This transition is made in order to produce truck vehicle matrices at a zone level sufficiently fine to provide estimations of link-level truck flows.
Vehicle model
The vehicle model estimates the number of vehicle trips per day given the mode and commodity group matrices from the previous model steps.
The model iterates over all origins across all of the various matrices, by commodity class, and applies two models which separately model direct trips and touring vehicle trips.
The results are combined to provide matrices of vehicle truck volumes by truck type for assignment.
Service traffic model
In urban areas there is a significant amount of local delivery and non-goods related truck traffic.
This includes transport of relatively small amounts of goods and the transport of services.
The service traffic model generates local truck matrices for these purposes using linear regression generation models and gravity models for distribution
Find a product
