Freight origin-destination estimation based on multiple data source

Abstract

² Freight origin-destination (OD) information is increasingly important for understanding the influence of transportation on network congestion. Traditional OD estimation methods based on a single data source, usually loop detectors, are not easily transferred to freight OD estimation. However, alternative data capture technologies are nowadays available to gather traffic information. Examples are automatic number plate recognition (ANPR), Bluetooth scanners, and Weigh-in-Motion systems. This paper aims to develop feasible approaches based on Entropy Maximization and Bayesian Networks to estimate freight OD matrix using multiple sources of captured data. In the case of the A15 motorway in the Netherlands, we illustrate how the captured data is informative about transport behavior in the area and how the proposed methods lead to an estimation of the freight OD matrix.

Estimation of the Origin-Destination Matrix for Trucks That Use Highways: A Case Study in Chile

“…Ma et al [19] uses data from multiple sources, including automated license plate recognition, Bluetooth, and GPS, to estimate an OD matrix in a specific section of a road linking a port in Rotterdam, the Netherlands, with different distribution centers in Germany. The authors propose the joint use of two models: a Bayesian Network model and an entropy-maximization model.…”

“…However, this type of information has a high acquisition cost and a long update period, which, depending on the type of survey, might be one year or more [17]. More recent contributions use passive data, mainly gathered from GPS devices, to estimate freight OD matrices [18][19][20][21][22]. The disadvantage of this information source is that it is usually biased.…”

Monitoring truck's operations at freight intermodal terminals: traffic observation by scanning on‐board devices

“…Recently, the number of traffic monitoring applications based on the use of BT data as a complementary traffic data source has been increasing, ranging from origin–destination (OD) matrixes estimation [2] to travel time monitoring along arterials [3]. However, the installation on the field of the scanner devices should be adequately planned and performed.…”

“…To estimate freight OD matrixes using multiple sources of captured data Ma et al [2] developed an approach based on entropy maximisation and Bayesian networks. BT scanners are among the sensors proposed and they are used to approximately estimate the OD, in combination with loop detectors, video cameras for plate number identification and other traffic sensors installed along the motorways observed.…”

Comparison of ASIM Traffic Profile Detectors and Floating Car Data During Traffic Incidents

“…The most common and useful information that FCD techniques and ITS provide is average travel times and speeds along road links or paths [7,17,11,16,6]. They deploy FCD in order to predict short-term travel conditions, to automatically detect incident or critical situations [3,13,15], or determine Origin-Destination traffic flow patterns [12,10].…”