Test a wide variety of policies and infrastructure improvements, from pricing strategies to freight-specific facilities. Cube Cargo is the Cube functional library for freight forecasting, offering specific methodologies for studying freight demand using a commodity-based approach. Cube Cargo operates seamlessly with all Cube modules, including Cube Voyager and Cube Analyst for Matrix Estimation.

Image 1 - Cube Voyager – Cube Cargo complete integration

 

With Cube Cargo, you can add freight forecasting by leveraging your existing passenger data and models.

CUBE CARGO – COMMODITY BASED FREIGHT FORECASTING

Many areas are recognizing the need to have a better understanding of freight movements, how they will change over time, and how they can take actions to plan for and to improve the flow of goods while mitigating negative financial, environmental and travel impacts. Key elements in freight planning are matrices of commodity and truck flows.

Cube Cargo forecasts:

• Matrices of tons of goods by commodity type by mode for use in the analysis of goods flows
• Matrices of the number of trucks by truck type ready to be assigned to estimate truck vehicle flows

Long/Short-Haul and Urban Freight

Cube Cargo models three distinct freight segments:

• Long-haul bulk cargo typically moving from logistic node to logistic node (factories, warehouses, packaging centers)
• Short-haul freight trips for the distribution and collection of goods
• Urban freight and truck travel moving small amounts of goods, or workers, delivering services within a town or city

Unlimited Commodity Segmentation

Choose your commodity groups according to the level and quality of your data. Accurately model the movement of such diverse goods as milk, oil, grain, paper, and machine tools.

Regional Hierarchy

The diagram represents the typical hierarchy of regions for a Cube Cargo model. The study area is the smallest region in the diagram. The inner area is the largest enclosing region for which socioeconomic data is available. All of the places, not in the inner area, which can serve as the origin or destination of freight trips observed in the inner area, form the outer area.

Image 2 - Regional hierarchy

Cube Cargo uses a two-level zoning system:

• The Coarse Zone system where much of the modeling is conducted reflecting the lack of data at detailed zoning levels: Generation, Distribution, Mode Choice, and the incorporation of Transport Logistics Nodes
• The Fine Zone System typically based on the zoning system for the highway network onto which the vehicles will be assigned. Additional zones are usually added to represent logistic nodes such as ports, railway stations and goods yards, where freight is moved between vehicles. The trip chaining model within the vehicle model uses the logistics nodes when estimating truck pick-up and drop-off tours.

CUBE CARGO – APPLICATIONS

Cube Cargo is the Cube module that allows comprehensive freight forecasting for urban, regional, state and national planning, to answer policy questions like:

• Effects of alternative growth scenarios on freight movement
  • What if regional development patterns change?
  • What if major freight facilities are developed?
• Effects of alternative policies on freight movement
  • What if tolls were increased?
  • What if the price of fuel continues to increase?
• Impacts of major projects on freight movement
  • What if a two-lane highway was widened to four lanes?
  • What if major access improvements to a region were advanced?
• Costs we incur if we have blockage of the networks (e.g. bridge shut down) related to commodities values?

CUBE CARGO – METHODOLOGY

Cube Cargo is comprised of six programs, each of them with a specific model implemented:

• Generation - estimates the tons of goods produced and consumed by commodity class by zone
• Mode Distribution - two-step process:
  • Distribution - estimates matrices of the tons of goods by commodity class segmented by short-haul and long-haul
  • Modal Choice - estimates matrices of the tons of long-haul goods by mode and commodity class

• Transport Logistics Nodes – partitions the long-haul goods by mode and commodity class by direct transport and transport chain tours
• Fine Distribution Model - Distribution of coarse zone information to the finer level zone system
• Vehicle Model - estimates the number of vehicle tours per day by vehicle type
• Urban Goods Model – estimates matrices of local truck travel for local delivery and services

 

Image 3 - Cargo programs

The resulting matrices are assigned to the network to provide link-level truck flows by truck type. Assignments are done using Cube Voyager under Cube Base. The assignment models provide additional information such as vehicle miles or kilometers travelled, and average speed and volume by road type. This information can then be used to estimate environmental impacts.

 

Image 4 - Cube Cargo modelling steps in Application Manager

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.

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.

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, multimodal 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 (tonnage) 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

• 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.
• 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.

 

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.

CUBE CARGO – OUTPUTS

The outputs from the Cube Cargo model are the freight matrices at coarse and fine level, and the vehicles matrices at fine level. Several analysis can be achieved by combining these matrices with the travel time and distance matrices, disaggregated by commodity type

The demand is then directly assigned to the roadway network using Cube Voyager, but a multimodal approach can also be implemented, for current or forecasting scenarios.

 

Image 5 - Example of analysis of the outputs obtained with Cube Cargo