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    Estimation and analysis of Thai national freight demands from GPS data
    (2018-01-01)
    Siripirote, Treerapot
    ;
    Sumalee, Agachai
    ;
    Ho, H. W.
    ;
    Chankaew, Nattaphon
    ;
    Jedwanna, Krit
    Freight transport has been increased in both demand and distance travelled in recent years due to the changes in consumer purchasing patterns and the improvements in the transportation systems. Such increases cause serious adverse impacts on traffic conditions and air qualities. This paper proposes a framework that make use of passively collected truck Global Positioning System (GPS) data and link counts in the estimation of national freight demands, which are the crucial quantities in the design of management and remedial measures. Effective truck stops and the corresponding activities will be determined from the GPS data and used in estimating the observed freight demands. Such observed demands will be used with the link counts to determine the national freight demands. An empirical example from Thailand is adopted in this paper to illustrate the proposed framework in estimating the national freight demands from GPS data and link counts.
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    Statistical approach for activity-based model calibration based on plate scanning and traffic counts data
    (2015-08-01)
    Siripirote, Treerapot
    ;
    Sumalee, Agachai
    ;
    Ho, H. W.
    ;
    Lam, William H.K.
    Traditionally, activity-based models (ABM) are estimated from travel diary survey data. The estimated results can be biased due to low-sampling size and inaccurate travel diary data. For an accurate calibration of ABM parameters, a maximum-likelihood method that uses multiple sources of roadside observations (link counts and/or plate scanning data) is proposed. Plate scanning information (sensor path information) consists of sequences of times and partial paths that the scanned vehicles are observed over the preinstalled plate scanning locations. Statistical performances of the proposed method are evaluated on a test network using Monte Carlo technique for simulating the link flows and sensor path information. Multiday observations are simulated and derived from the true ABM parameters adopted in the choice models of activity pattern, time of the day, destination and mode. By assuming different number of plate scanning locations and identification rates, impacts of data quantity and data quality on ABM calibration are studied. The results illustrate the efficiency of the proposed model in using plate scanning information for ABM calibration and its potential for large and complex network applications.
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    Updating of travel behavior model parameters and estimation of vehicle trip chain based on plate scanning
    (2014-10-01)
    Siripirote, Treerapot
    ;
    Sumalee, Agachai
    ;
    Watling, David P.
    ;
    Shao, Hu
    This article proposes a maximum-likelihood method to update travel behavior model parameters and estimate vehicle trip chain based on plate scanning. The information from plate scanning consists of the vehicle passing time and sequence of scanned vehicles along a series of plate scanning locations (sensor locations installed on road network). The article adopts the hierarchical travel behavior decision model, in which the upper tier is an activity pattern generation model, and the lower tier is a destination and route choice model. The activity pattern is an individual profile of daily performed activities. To obtain reliable estimation results, the sensor location schemes for predicting trip chaining are proposed. The maximum-likelihood estimation problem based on plate scanning is formulated to update model parameters. This problem is solved by the expectation-maximization (EM) algorithm. The model and algorithm are then tested with simulated plate scanning data in a modified Sioux Falls network. The results illustrate the efficiency of the model and its potential for an application to large and complex network cases.