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Boundary conditions and behavior of the macroscopic fundamental diagram based network traffic dynamics: A control systems perspective
(2018-05-01)
Zhong, R. X.
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Huang, Y. P.
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Chen, C.
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Lam, W. H.K.
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Xu, D. B.
Macroscopic fundamental diagram (MFD), establishing a mapping from the network flow accumulation to the trip completion rate, has been widely used for aggregate modeling of urban traffic network dynamics. Based on the MFD framework, extensive research has been dedicated to devising perimeter control strategies to protect the network from gridlock. Recent research has revealed that the stochasticity and time-varying nature of travel demand can introduce significant scattering in the MFD, thus reducing the definition of the MFD dynamics. However, this type of demand effect on the behavior of the MFD dynamics has not been well studied. In this article, we investigate such effect and propose some appropriate boundary conditions to ensure that the MFD dynamics are well-defined. These boundary conditions can be regarded as travel demand adjustment in traffic rationing. For perimeter control design, a set of sufficient conditions that guarantee the controllability, an important but yet untouched issue, are derived for general multi-region MFD systems. The stability of the network equilibrium and convergence of the network dynamics are then analyzed in the sense of Lyapunov. Both theoretical and numerical results indicate that the network traffic converges to the desired uncongested equilibrium under proper boundary conditions in conjunction with proper control measures. The results are consistent with some existing studies and offer a control systems perspective regarding the demand-oriented behavior analysis of MFD-based network traffic dynamics. A surprising finding is that if the control purpose is to regulate the traffic to a desired level of service, the perimeter control gain can be simply chosen as its desired steady state, that is, the control gain is a constant and can be implemented as proportional control. This property sheds light on the road pricing design based on the MFD framework by minimizing the gap between the actual traffic state and the desired traffic state.
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Forecasting journey time distribution with consideration to abnormal traffic conditions
(2017-12-01)
Zhong, R. X.
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Luo, J. C.
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Cai, H. X.
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Sumalee, A.
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Yuan, F. F.
Travel time is an important index for managers to evaluate the performance of transportation systems and an intuitive measure for travelers to choose routes and departure times. An important part of the literature focuses on predicting instantaneous travel time under recurrent traffic conditions to disseminate traffic information. However, accurate travel time prediction is important for assessing the effects of abnormal traffic conditions and helping travelers make reliable travel decisions under such conditions. This study proposes an online travel time prediction model with emphasis on capturing the effects of anomalies. The model divides a path into short links. A Functional Principal Component Analysis (FPCA) framework is adopted to forecast link travel times based on historical data and real-time measurements. Furthermore, a probabilistic nested delay operator is used to calculate path travel time distributions. To ensure that the algorithm is fast enough for online applications, parallel computation architecture is introduced to overcome the computational burden of the FPCA. Finally, a rolling horizon structure is applied to online travel time prediction. Empirical results for Guangzhou Airport Expressway indicate that the proposed method can capture an abrupt change in traffic state and provide a promising and reliable travel time prediction at both the link and path levels. In the case where the original FPCA is modified for parallelization, accuracy and computational effort are evaluated and compared with those of the sequential algorithm. The proposed algorithm is found to require only a piece rather than a large set of traffic incident records.
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National freight behaviours analysis from global position system data
(2017-01-01)
Chankaew, N.
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Sumalee, A.
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Threepak, T.
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Sutakham, N.
Thailand has been identifying points of departure and terminal by the analysis of transportation origin-destination records. The record is the collection of report performed manually by departure's or terminal's authority. However, the nature of transportation is always dynamic which causes record became inaccuracy overtime. Moreover, the resource needed to collect such data is enormous. To resolve such need for more accurate and modernize data, the researchers purposed the methodology of using real-time data processing through automatic sensors. The research subjects in this research are 100,000 of public transport vehicles. The data is collected from various sensor attached to each vehicle. The amount of three months of data collections are used for study and develop an Analytic Algorithm to correctly and assisting in identifying points of departure and terminal. The result will be present in geographic map in the last stage for aiding executives or authority to efficiently developing traffic management plan.
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Scheduled bus service operators in Bangkok: Challenge for quality improvement
(2017-01-01)
Thongphat, N.
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Ongkittkul, S.
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Sumalee, A.
Scheduled public bus service in Bangkok is literally in crisis with a lack of a mechanism to make the system responsive to demand and an inappropriate regulatory framework. The reform with the competitive tendering scheme is urgently needed to improve the quality service. Nonetheless, the first step mostly relies on the decision made by the incumbent operators who undergo the hardship from the last deteriorated system. Concerning of their impediments would be beneficial for policy deciding. This paper reviews the regulatory frameworks of scheduled bus service in Bangkok and studies the market structure for understanding the operators' burden and behavior, measure the impact of the previous regulatory framework on firms' decision to improve quality by conducting interviews with private operators and bus drivers. The study discovers that fleet and drivers' management is the primary challenge for incumbents that clarified awarding criteria and well-established database of operators and drivers could solve.
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A cross-entropy method and probabilistic sensitivity analysis framework for calibrating microscopic traffic models
(2016-02-01)
Zhong, R. X.
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Fu, K. Y.
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Sumalee, A.
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Ngoduy, D.
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Lam, W. H.K.
Car following modeling framework seeks for a more realistic representation of car following behavior in complex driving situations to improve traffic safety and to better understand several puzzling traffic flow phenomena, such as stop-and-go oscillations. Calibration and validation techniques pave the way towards the descriptive power of car-following models and their applicability for analyzing traffic flow. However, calibrating these models is never a trivial task. This is caused by the fact that some parameters, such as reaction time, are generally not directly observable from traffic data. On the other hand, traffic data might be subject to various errors and noises. This contribution puts forward a Cross-Entropy Method (CEM) based approach to identify parameters of deterministic car-following models under noisy data by formulating it as a stochastic optimization problem. This approach allows for statistical analysis of the parameter estimations. Another challenge arising in the calibration of car following models concerns the selection of the most important parameters. This paper introduces a relative entropy based Probabilistic Sensitivity Analysis (PSA) algorithm to identify the important parameters so as to reduce the complexity, data requirement and computational effort of the calibration process. Since the CEM and the PSA are based on the Kullback-Leibler (K-L) distance, they can be simultaneously integrated into a unified framework to further reduce the computational burden. The proposed framework is applied to calibrate the intelligent driving model using vehicle trajectories data from the NGSIM project. Results confirm the great potential of this approach.
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Simultaneous optimization of fuel surcharges and transit service runs in a multimodal transport network: A time-dependent activity-based approach
(2016-01-01)
Li, Z. C.
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Yin, Y.
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Lam, W. H.K.
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Sumalee, A.
This paper addresses the simultaneous optimization problem of fuel surcharges and transit service runs for energy sustainability of multimodal transport network using a time-dependent activity-based approach. To model commuters' choices of trip chain, travel mode, departure time, route, and activity timing and duration over the times of a day, a time-dependent activity and multimodal travel choice equilibrium problem is first addressed and formulated as an equivalent variational inequality (VI) problem. A new model for optimizing the fuel surcharges and transit vehicle runs is proposed to maximize the total social net benefit of the multimodal transport system. The proposed model explicitly considers the interaction between the fuel surcharges and transit service runs and the commuters' activity-travel scheduling behavior. A heuristic solution algorithm is then developed to solve the proposed model. Finally, an illustrative example is given to show the application of the proposed model with various sensitivity tests. Insightful findings are presented with particularly the effects of the fuel surcharges and transit service improvement on the performance of the multimodal transport system in terms of the modal split, fuel consumption, and total social net benefit.
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Sensitivity analysis ofwelfare, equity, and acceptability level of transport policies
(2015-01-01)
Connors, R.
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Patriksson, M.
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Rydergren, C.
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Sumalee, A.
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Watling, D.
Transport planners face a major challenge to devise policies to meet multiple expectations and objectives. While we know that transport networks are complex, multi-modal, and spatially distributed systems, there is now a long history of mathematical tools which assist planners in understanding travel movements. However, the objectives that they are asked to achieve do not always admit such a quantification, and so there is a potential mismatch between seemingly qualitatively driven objectives and quantitatively expressed models of the transport system. In the present chapter we address this mismatch, by focusing on three objectives that we believe represent the typical interests of a planner. These are namely: is the policy economically justifiable (efficient), is it “fair” (equitable), and is it justifiable to a democratic society (acceptable)? We provide mathematical representations of these three objectives and link them to mathematical theory of transport networks, in which we may explore the sensitivity of travel behaviour (and hence the objectives) to various multi-modal transport policies. The detailed steps for representing the policy objectives and sensitivities in the network are set out, and the results of a case study reported in which road tolls, road capacities, and bus fares are the policy variables. Overall, the chapter sets out a systematic method for planners to choose between multi-modal policies based on these three objectives.
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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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