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Abstract
An approach is presented to determine the most likely tour distributions and model behavior for investigating drayage truck movements in a coastal region. This was done by implementing a revised form of entropy maximization based on truck tours to model and better understand drayage truck tour behavior at the San Pedro Bay Ports (SPBPs) complex in Southern California. The drayage trucks at the SPBPs have features that are distinct from other commercial trucks. The tour-based entropy maximization model proposed in this paper provides an opportunity to incorporate periodically updated GPS data collected in Southern California into a large-scale tour-based model. With the dataset, four models were estimated by cargo movement: (1) year-based, (2) low period, (3) medium period, and (4) high period models. The findings were consistent with the tour patterns varying by season and by cargo movement. Furthermore, the medium period, which represented relatively steady cargo movement, indicated a better MAPE (mean absolute percent error) than did other models. This proposed approach provides a significant advantage in that the most recent touring information obtained from advanced technologies could be directly applied to the tour-based model and subsequently used to assess various strategies.
Highlights
Freight truck movements are complex and distinct owing to the fact that their pro ts and logistics decisions are greatly a ected by the increase in the number of trips. e extensive trip-chaining behavior of freight transportation cannot be represented without considering the dependencies among the trips. is is because these dependencies are heavily linked to the nature of the freight transportation utility and logistics decisions. is is the reason why there is no way to re ect a change in the origin of the following trip in the four-step model, which employs a trip-based approach
This study describes a tour-based entropy maximization formulation to estimate the flow of commercial vehicles on each tour using trip production/attraction by each node, sequentially visited nodes for each tour, and tour impedance from GPS data collected in the San Pedro Bay Ports (SPBPs) study area in Southern California
We review a vehicle tour-based model, entropy maximization, and primal dual method for a convex optimization (PDCO) algorithm, which is followed by a description of SPBP clean truck GPS tour data
Summary
Freight truck movements are complex and distinct owing to the fact that their pro ts and logistics decisions are greatly a ected by the increase in the number of trips. e extensive trip-chaining behavior of freight transportation cannot be represented without considering the dependencies among the trips. is is because these dependencies are heavily linked to the nature of the freight transportation utility and logistics decisions. is is the reason why there is no way to re ect a change in the origin of the following trip in the four-step model, which employs a trip-based approach. To properly capture the trip-chaining behavior of commercial vehicle movements, several tour-based model approaches have been proposed. An enormous amount of computation time is typically required For these reasons, elaborate analysis of tour-based models is di cult to duplicate for large-scale studies, such as freight models for metropolitan planning organizations (MPOs) and state agencies. Elaborate analysis of tour-based models is di cult to duplicate for large-scale studies, such as freight models for metropolitan planning organizations (MPOs) and state agencies From another point of view, tour-based approaches at the network level [13,14,15,16] have been introduced to forecast urban freight movements based on trip-chaining characteristics and are more tractable because these require a smaller amount of data and provide faster computational time. A potential application in freight-demand forecasting is discussed to conclude our paper
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