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Network-wide on-line travel time estimation with inconsistent data from multiple sensor systems under network uncertainty
(2018-01-02)
Shao, Hu
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Lam, William H.K.
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Sumalee, Agachai
;
Chen, Anthony
This paper proposes a new modeling approach for network-wide on-line travel time estimation with inconsistent data from multiple sensor systems. It makes full use of both the available data from multiple sensor systems (on-line data) and historical data (off-line data). The first- and second-order statistical properties of the on-line data are investigated together with the data inconsistency issue to estimate network-wide travel times. The proposed model is formulated as a generalized least squares problem with non-linear constraints. A solution algorithm based on the penalty function method is adopted to solve the proposed model, whose application is illustrated by numerical examples using a local road network in Hong Kong.
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A real-time bus arrival time information system using crowdsourced smartphone data: a novel framework and simulation experiments
(2018-01-02)
Wepulanon, Piyanit
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Sumalee, Agachai
;
Lam, William H.K.
This paper proposes a novel framework for developing a real-time bus arrival time information system, using crowdsourced bus information contributed by bus passengers. On the one hand, passengers can derive the real-time information via their smartphones. On the other hand, they can provide some bus data in return. Particular characteristics of the participatory-based bus data are introduced. Also, a number of data processing steps are proposed in the framework to handle the data characteristics, which pose extra difficulties in real-time bus arrival time prediction. The proposed system is evaluated using simulated bus data sets. Practicality of the system is investigated in terms of prediction accuracy based on different participation percentages of bus passengers.
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Freight Traffic Analytics from National Truck GPS Data in Thailand
(2018-01-01)
Chankaew, Nattaphon
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Sumalee, Agachai
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Treerapot, Siripirote
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Threepak, Thanunchai
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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 of mean and covariance of stochastic multi-class OD demands from classified traffic counts
(2015-10-01)
Shao, Hu
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Lam, William H.K.
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Sumalee, Agachai
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Hazelton, Martin L.
This paper proposes a new model to estimate the mean and covariance of stochastic multi-class (multiple vehicle classes) origin-destination (OD) demands from hourly classified traffic counts throughout the whole year. It is usually assumed in the conventional OD demand estimation models that the OD demand by vehicle class is deterministic. Little attention is given on the estimation of the statistical properties of stochastic OD demands as well as their covariance between different vehicle classes. Also, the interactions between different vehicle classes in OD demand are ignored such as the change of modes between private car and taxi during a particular hourly period over the year. To fill these two gaps, the mean and covariance matrix of stochastic multi-class OD demands for the same hourly period over the year are simultaneously estimated by a modified lasso (least absolute shrinkage and selection operator) method. The estimated covariance matrix of stochastic multi-class OD demands can be used to capture the statistical dependency of traffic demands between different vehicle classes. In this paper, the proposed model is formulated as a non-linear constrained optimization problem. An exterior penalty algorithm is adapted to solve the proposed model. Numerical examples are presented to illustrate the applications of the proposed model together with some insightful findings on the importance of covariance of OD demand between difference vehicle classes.
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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.
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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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Estimation of Mean and Covariance of Stochastic Multi-class OD Demands from Classified Traffic Counts
(2015-01-01)
Shao, Hu
;
Lam, William H.K.
;
Sumalee, Agachai
;
Hazelton, Martin L.
This paper proposes a new model to estimate the mean and covariance of stochastic multi-class (multiple vehicle classes) origin-destination (OD) demands from hourly classified traffic counts throughout the whole year. It is usually assumed in the conventional OD demand estimation models that the OD demand by vehicle class is deterministic. Little attention is given on the estimation of the statistical properties of stochastic OD demands as well as their covariance between different vehicle classes. Also, the interactions between different vehicle classes in OD demand are ignored such as the change of modes between private car and taxi during a particular hourly period over the year. To fill these two gaps, the mean and covariance matrix of stochastic multi-class OD demands for the same hourly period over the year are simultaneously estimated by a modified lasso (least absolute shrinkage and selection operator) method. The estimated covariance matrix of stochastic multi-class OD demands can be used to capture the statistical dependency of traffic demands between different vehicle classes. In this paper, the proposed model is formulated as a non-linear constrained optimization problem. An exterior penalty algorithm is adapted to solve the proposed model. Numerical examples are presented to illustrate the applications of the proposed model together with some insightful findings on the importance of covariance of OD demand between difference vehicle classes.
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Estimation of mean and covariance of peak hour origin-destination demands from day-to-day traffic counts
(2014-01-01)
Shao, Hu
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Lam, William H.K.
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Sumalee, Agachai
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Chen, Anthony
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Hazelton, Martin L.
This paper proposes a generalized model to estimate the peak hour origin-destination (OD) traffic demand variation from day-to-day hourly traffic counts throughout the whole year. Different from the conventional OD estimation methods, the proposed modeling approach aims to estimate not only the mean but also the variation (in terms of covariance matrix) of the OD demands during the same peak hour periods due to day-to-day fluctuation over the whole year. For this purpose, this paper fully considers the first- and second-order statistical properties of the day-to-day hourly traffic count data so as to capture the stochastic characteristics of the OD demands. The proposed model is formulated as a bi-level optimization problem. In the upper-level problem, a weighted least squares method is used to estimate the mean and covariance matrix of the OD demands. In the lower-level problem, a reliability-based traffic assignment model is adopted to take account of travelers' risk-taking path choice behaviors under OD demand variation. A heuristic iterative estimation-assignment algorithm is proposed for solving the bi-level optimization problem. Numerical examples are presented to illustrate the applications of the proposed model for assessment of network performance over the whole year. © 2014 Elsevier Ltd.
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Optimal and robust strategies for freeway traffic management under demand and supply uncertainties: An overview and general theory
(2014-01-01)
Zhong, R. X.
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Sumalee, A.
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Pan, T. L.
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Lam, William H.K.
This paper investigates optimal decision-making for traffic management under demand and supply uncertainties by stochastic dynamic programming. Traffic flow dynamics under demand and supply uncertainties is described by a simplified version of the stochastic cell transmission model. Based on this model, the optimal traffic management problem is analysed wherein the existence of solution is guaranteed by verifying the well-posed condition. An analytical optimal control law is derived in terms of a set of coupled generalised recursive Riccati equations. As optimal control laws may be fragile with respect to model misspecification, a robust (optimal) decision-making law that aims to act robust with respect to the parameter misspecification in the traffic flow model (which can be originated from model calibration), and to attenuate the effect of disturbances in freeway networks (wherein demand uncertainty is usually regarded as a kind of disturbance) is proposed. Conventionally, network uncertainties have been considered to induce negative effects on traffic management in transportation literature. In contrast, the proposed methodology outlines an interesting issue that is to make benefit (or trade-off) from the inherent network uncertainties. Finally, some practical issues in traffic management that can be addressed by extending the current framework are briefly discussed. © 2014 Hong Kong Society for Transportation Studies Limited.

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