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Estimation and analysis of Thai national freight demands from GPS data
(2018-01-01)
Siripirote, Treerapot
;
Sumalee, Agachai
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Ho, H. W.
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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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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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Statistical approach for activity-based model calibration based on plate scanning and traffic counts data
(2015-08-01)
Siripirote, Treerapot
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Sumalee, Agachai
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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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A stochastic multimodal reliable network design problem under adverse weather conditions
(2015-01-01)
Uchida, Kenetsu
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Sumalee, Agachai
;
Ho, H. W.
This paper formulates a network design problem (NDP) for finding the optimal public transport service frequencies and link capacity expansions in a multimodal network with consideration of impacts from adverse weather conditions. The proposed NDP aims to minimize the sum of expected total travel time, operational cost of transit services, and construction cost of link capacity expansions under an acceptable level of variance of total travel time. Auto, transit, bus, and walking modes are considered in the multimodal network model for finding the equilibrium flows and travel times. In the proposed network model, demands are assumed to follow Poisson distribution, and weather-dependent link travel time functions are adopted. A probit-based stochastic user equilibrium, which is based on the perceived expected travel disutility, is used to determine the multimodal route of the travelers. This model also considers the strategic behavior of the public transport travelers in choosing their routes, that is, common-line network. Based on the stochastic multimodal model, the mean and variance of total travel time are analytical estimated for setting up the NDP. A sensitivity-based solution algorithm is proposed for solving the NDP, and two numerical examples are adopted to demonstrate the characteristics of the proposed model.
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Optimal recovery plan after disaster: Continuum modeling approach
(2014-01-01)
Ho, H. W.
;
Sumalee, Agachai
This paper presents an application of continuum traffic equilibrium model for the design of optimal recovery plan after a disaster. The continuum traffic equilibrium model is adopted for its strength in defining: (1) alternative routes after disaster; (2) spatially varied impacts of the disaster; and (3) continuously distributed demand. In this study, demands for emergency services, reconstruction activities, and normaltravel activities are separately modeled throughout the recovery period. A bilevel model is set up for designing the optimal recovery plan in the modeled region. At the lower-level model, sets of differential equations are constructed to describe the traffic equilibrium problems at different times of the recovery period. In the upper-level model, a constrained minimization problem is set up to find the optimal recovery plan such that the total travel cost is minimized and the demand of normal/reconstruction traffic is maximized throughout the recovery period. A sensitivity-based solution algorithm that adopts the finite element method (FEM) is proposed to solve the bilevel model, and a numerical example is completed to demonstrate the characteristics of the proposed model.

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