Project Manager(s):
Tomer Toledo
Team:
Tomer Toledo, Moshe Ben-Akiva, Jorge Santos, Eunice Kim, Katie Rosa
Research Highlights:
- The analysis of the data that was collected in the first phase surveys allowed us to infer about the fractions in the population of various segments in terms of types of drivers and shipments, the common methods of calculation of the driver pay and other employment terms. It was also used to identify the routing decision-makers and the association of the identity of the decision-maker with various characteristics of the shipment and the employment terms. Counter to the assumption in some previous studies, we find that the drivers are most frequently the sole decision-makers with respect to routing. This is especially the case for owner-operators (OOs), that operate in a very different environment, compared to hired drivers, in terms of not only taking the responsibility for routing, but also incurring the costs associated with fuel and tolls and making decisions regarding toll tag equipment.
- An SP experiment was conducted in which drivers were asked to choose a route between two route alternatives in scenarios that mimic common settings for toll roads. In most cases, this choice involved selecting between toll and free alternatives. The responses to the SP experiment were used to develop a route choice model. The model shows large variation in preferences among various groups. For example, drivers’ willingness to pay toll changes drastically when they are responsible for the toll costs compared to when they are not. Similarly the value drivers place on the risk of unexpected delays varies based on the method of calculating their pay (by hours or otherwise) and for shipments with temperature control (refrigeration) services. In addition, the estimated model shows substantial taste heterogeneity among drivers with respect to the use of toll roads, suggesting against the use of deterministic VOTs in freight models and in forecasting flows on toll roads.
- An experiment to collect data on actual routing decision truckers make in their trips has been developed. This experiment introduces new methods, based on information and communication technologies, to collect much richer and more accurate and reliable dataset on truck route choices in actual real-world situations. This data will be used to further study and refine models of truck routing behaviors.
Description:
This research intends to identify the key factors that affect heavy truck route choices. In particular, it aims to evaluate the choice among free and toll road alternatives. For that purpose, discrete choice models to predict the routing decisions are developed. These models will help gain insight on the route choice scenarios faced for truck trips, and may suggest new tolling strategies and products. The model will be used to evaluate the potential of these products to increase the toll road market share for various truck trip segments.
The data collection and analysis are organized in two phases. In the first phase, an exploratory stated preferences (SP) survey took place in three corridors served by toll roads. This exploratory data collection served to understand the common truck trips that use the toll road corridors, the toll and non-toll alternatives, identify the decision-makers and factors that potentially affect route choices. The information gathered in the exploratory stage was used to develop a revealed preferences (RP) experiment to collect route choices by the decision-makers in the various decision scenarios and conditions they face in real life.
CHALLENGES:
The RP experiment involves instrumentation of trucks with GPS loggers that record the movement of trucks in the road network. This detailed spatial-temporal information needs to be processed in order to obtain meaningful observations on stops and trip segments and to relate this information to a road network represented by a GIS database. The routes identified in the GPS data are shown to the drivers in a dedicated personal webpage, where they are requested to provide additional information about the trips they have made, such as on the purposes of stops, loading and unloading locations and schedules.
This is a first-of-its-kind experiment in the context of trucking. The experiment setup poses significant challenges in the development of the database to store and access the gathered information, the algorithms to extract useful information from the row observations (e.g. detect stops on the travel route, match the GPS traces to a GIS map database) and develop a easy to use and intuitive user interface for the truckers to provide the additional information requested of them.
References:
- Sun Y., Toledo T., Rosa K., Ben-Akiva M.E., Flanagan K., Sanchez R. and Spissu E., Route choice characteristics for truckers, Transportation Research Record 2354, pp. 115-121, 2013.
- Toledo T., Sun Y., Rosa K., Ben-Akiva M.E., Flanagan K., Sanchez R. and Spissu E., Decision making processes and factors affecting truck routing, in Freight Transport Modeling, Ben-Akiva M, Meersman H. and van de Voorde E., eds., Emerald, pp. 239-255, 2013.