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Author: search.filters.author.Jedwanna, Krit

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    Item type:Publication,
    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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    Probabilistic fusion of vehicle features for reidentification and travel time estimation using video image data
    (2012-12-01)
    Sumalee, Agachai
    ;
    Wang, Jiankai
    ;
    Jedwanna, Krit
    ;
    Suwansawat, Suchatvee
    This paper proposes a probabilistic vehicle reidentification algorithm for estimating travel time using the image data provided by traffic surveillance cameras. Each vehicle is characterized by its color, type, and length, which are extracted from the video record using image processing techniques. A data fusion rule is introduced to combine these three features to generate a probabilistic measure for a reidentification (matching) decision. The vehicle-matching problem is then reformulated as a combinatorial problem and solved by a minimum-weight bipartite matching method. To reduce the computational time, the algorithm uses the potential availability of historic travel time data to define a potential time window for vehicle reidentification. This probabilistic approach does not require vehicle sequential information and hence allows vehicle reidentification across multiple lanes. The algorithm is tested on a 5-km section of the expressway system in Bangkok, Thailand. The travel time estimation result is also compared with the directly observed data.