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Freight Traffic Analytics from National Truck GPS Data in Thailand
(2018-01-01)
Chankaew, Nattaphon
;
Sumalee, Agachai
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Treerapot, Siripirote
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Threepak, Thanunchai
;
Ho, H. W.
Recently, demand of freight transport increases tremendously and cause serious impacts on traffic congestion and air quality. In 2015, The Department of Land Transport of Thailand has introduced a project named "Nationwide Confidence with GPS Onboard" to install GPS tracking system on all trucks in Thailand. This project provides new sources of data in analyzing the freight-related traffics and designing remedial measures for freight-related issues. This paper aims to demonstrate the use of GPS data in determining truck activities, estimating truck origin-destination matrix and estimating the flow of different commodities. Thailand is used as a case study to demonstrate the results.
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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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A Hybrid Method for Predicting a Potential Next Rest Stop of Commercial Vehicles
(2018-01-01)
Chawuthai, Rathachai
;
Chankaew, Nattaphon
;
Threepak, Thanunchai
Long-distance trips such as freight and passenger transports over cities can create driver fatigue, so drivers prefer to get a rest for a while during their long-time driving. In Thailand, there are rest stops along main roads between cities, such as petrol stations, travel plazas, wayside parks, and scenic areas. In order to provide a better service to customers, the rest stops must have a good management, so the prediction of the number of potential vehicles in a period of time is primarily needed. One important task is to predict the next rest stop of every car at a period of time. Due to this requirement, this paper aims to introduce a prediction model for predicting the next rest stop of a vehicle by analyzing the global positioning system (GPS) tracking data of all commercial vehicles in Thailand. The proposed prediction model is a hybrid model that comprises of three scoring functions depended on the frequent pattern of connected rest stops, the direction of connected rest stops in a route, and the popularity of the rest stops. The experimental result shows that the proposed prediction model gives high accurate result in terms of the area under the receiver-operating-characteristic curve (AUC). This predicted result is also useful for a government department and rest stops' owner to improve transportation, road safety, and other service.

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