Application of experimental economics in transport and logistics

There is scope for applying experimental economics in transport and logistics analysis. Experimental economics is a set of techniques for gathering (and analysing) data by inducing people (through specific rewards) to act as economic agents and observing the choices they then make in experimental situations. These experiments often involve interactions between the respondents, possibly in a market setting, and this can be applied in transport to study for instance shipper – carrier interaction. Various subfields of experimental economics that might be relevant for transport and logistics research are described. We also review past applications of experimental economics in transport and logistics and work out some ideas for future applications

A bi-level programming approach for the shipper-carrier network problem

The Stackelberg game betweenshippers and carriers in an intermodal network is formulated as a bi-levelprogram. In this network, shippers make production, consumption, androuting decisions while carriers make pricing and routing decisions.The oligopolistic carrier pricing and routing problem, which comprisesthe upper level of the bi-level program, is formulated either as a nonlinearconstrained optimization problem or as a variational inequality problem,depending on whether the oligopolistic carriers choose to collude orcompete with each other in their pricing decision. The shippers\u27 decisionbehavior is defined by the spatial price equilibrium principle. Forthe spatial price equilibrium problem, which is the lower level of thebi-level program, a variational inequality formulation is used to accountfor the asymmetric interactions between flows of different commoditytypes. A sensitivity analysis-based heuristic algorithm is proposedto solve the program. An example application of the bi-level programmingapproach analyzes the behavior of two marine terminal operators. Theterminal operators are considered to be under the same Port Authority.The bi-level programming approach is then used to evaluate the PortAuthority\u27s alternative investment strategies

A GIS-based multi-commodity freight model: typology, model refinement and field validation

Historically, public sector transportation planning activities, especially in urban areas, have focused primarily on the movement of passengers. Recent emphasis, however, has been placed on statewide planning, and the movement of freight has received increased attention. This research developed a layered, statewide freight model of Iowa. In the layered approach, each model layer represents a commodity grouping or economic sector.;The primary commodity data used in the model was Reebie Associates\u27 TRANSEARCH database. Fifteen commodity groups were selected to be included in the layered model. For each commodity model, a production/attraction table was synthesized from the TRANSEARCH data. The production and attractions data were distributed with a gravity model and assigned to the network using an all-or-nothing assignment algorithm. The assigned flows were then converted to truck or carloads, as appropriate, using commodity specific factors.;The innovation of this research was to create validation data based on observations of trucks and railcars in the field. An extensive data collection effort was undertaken where some 11,400 trucks and 4,400 railcars were observed at 20 locations around the state. For highway flows, commodity estimates were made for each observed truck, based on the type of trailer observed and information in the Motor Carrier Management Information System (MCMIS) database. Commodities were estimated for approximately 50 percent of the trucks observed. The estimates compared with an independent source, the 1991 Iowa Truck Survey, which stopped trucks to determine their commodities. For rail, commodity flows were estimated based upon the observed car type and commodity.;The commodity flow data were then used to validate the model. Results of the validation varied, depending on the commodity group. For highways, the technique was most effective for validating flows where specialized equipment was required (automobiles, chemicals, farm machinery). Average model errors for these commodity groups ranged from 8% to 70%. Other commodities transported in more general equipment had a larger variation in model error. For rail, model errors ranged from 20% to 90% for commodities that could be validated

National and international freight transport models: overview and ideas for further development

This paper contains a review of the literature on freight transport models, focussing on the types of models that have been developed since the nineties for forecasting, policy simulation and project evaluation at the national and international level. Models for production, attraction, distribution, modal split and assignment are discussed in the paper. Furthermore, the paper also includes a number of ideas for future development, especially for the regional and urban components within national freight transport models

Assignment of Freight Traffic in a Large-Scale Intermodal Network under Uncertainty

This paper presents a methodology for freight traffic assignment in a large-scale road-rail intermodal network under uncertainty. Network uncertainties caused by natural disasters have dramatically increased in recent years. Several of these disasters (e.g., Hurricane Sandy, Mississippi River Flooding, Hurricane Harvey) severely disrupted the U.S. freight transport network, and consequently, the supply chain. To account for these network uncertainties, a stochastic freight traffic assignment model is formulated. An algorithmic framework, involving the sample average approximation and gradient projection algorithm, is proposed to solve this challenging problem. The developed methodology is tested on the U.S. intermodal network with freight flow data from the Freight Analysis Framework. The experiments consider four types of natural disasters that have different risks and impacts on the transportation network: earthquake, hurricane, tornado, and flood. The results demonstrate the feasibility of the model and algorithmic framework to obtain freight flows for a realistic-sized network in reasonable time (between 417 and 716 minutes). It is found that for all disaster scenarios the freight ton-miles are higher compared to the base case without uncertainty. The increase in freight ton-miles is the highest under the flooding scenario; this is due to the fact that there are more states in the flood-risk areas and they are scattered throughout the U.S