Search

• This work focuses on macroscopic traffic flow model calibration and validation of large freeway networks. • An intensive literature review is presented on the subject to determine the strengths and weaknesses of various technical paths, and figure out a viable roadmap for future studies. • The paper proposes a benchmarking framework concerning some of the key factors about macroscopic traffic flow model calibration and validation, which including congestion tracking, traffic flow inhomogeneity, adverse weather conditions and accidents, capacity drop, scattering, hysteresis, stop-and-go waves, and traffic heterogeneity. • The paper presents comprehensive results of model calibration and validation concerning key factors included in the benchmarking framework as stated above. • Works of the same focus were not reported before. Macroscopic traffic flow models are of paramount importance to traffic surveillance and control. Before their employments in applications, the models need to be calibrated and validated against real traffic data. The model calibration determines an optimal set of model parameters that minimizes the discrepancy between the modeling results and real traffic data. The model validation is furthermore performed to corroborate the accuracy of a calibrated model using data other than used for calibration. The model calibration aims to reflect traffic reality, while model validation focuses on the prediction of future traffic using calibrated models. This paper delivers a comprehensive review of state-of-the-art works on macroscopic model calibration and validation, proposes a benchmarking framework on traffic flow modeling, and has conducted a large number of case studies based on the framework using macroscopic traffic flow model METANET with respect to the urban expressway network in Shanghai. In comparison to previous works, quite more comprehensive results on model calibration have been presented in this paper, in consideration of congestion tracking, traffic flow inhomogeneity, capacity drop, stop-and-go waves, scattering, adverse weather conditions, and accidents. The paper has also reported many results of model validation with respect to the same field examples. The results demonstrate that METANET is able to model complex traffic flow dynamics in large-scale freeway networks with sufficient accuracy. The paper is closed with discussion on limitations and future works. [ABSTRACT FROM AUTHOR]

The well-known feedback ramp metering algorithm ALINEA can be applied for local ramp metering or included as a key component in a coordinated ramp metering system. ALINEA uses real-time occupancy measurements from the ramp flow merging area that may be at most a few hundred meters downstream of the metered on-ramp nose. In many practical cases, however, bottlenecks with smaller capacities than the merging area may exist further downstream, which suggests using measurements from those downstream bottlenecks. Recent theoretical and simulation studies indicate that ALINEA may lead to poorly damped closed-loop behavior in this case, but PI-ALINEA, a suitable Proportional-Integral (PI) extension of ALINEA, can lead to satisfactory control performance. This paper addresses the same local ramp-metering problem in the presence of far-downstream bottlenecks, with a particular focus on the employment of PI-ALINEA to tackle three distinct cases of bottleneck that may often be encountered in practice: (1) an uphill case; (2) a lane-drop case; and (3) an un-controlled downstream on-ramp case. Extensive simulation studies are conducted on the basis of a macroscopic traffic flow model to show that ALINEA is not capable of carrying out ramp metering in these bottleneck cases, while PI-ALINEA operates satisfactorily in all cases. A field application example of PI-ALINEA is also reported with regard to a real case of far downstream bottlenecks. With its control parameters appropriately tuned beforehand, PI-ALINEA is found to be universally applicable, with little fine-tuning required for field applications. [ABSTRACT FROM AUTHOR]

In diesem Aufsatz werden neueste Fortschritte bei der Echtzeitschätzung des Schnellstraßenverkehrs betrachtet. Besonderes Augenmerk wird dabei auf einen allgemeinen Ansatz gerichtet, der sowohl Zustands- als auch Parameterschätzung vereint. Ein weiterer Schwerpunkt ist der Einsatz des Schätzalgorithmus in großräumigen Anwendungen. Zunächst wird eine mathematische Beschreibung vorgestellt, die ein validiertes makroskopisches Verkehrsflussmodell samt Zustands- und Messrauschen enthält. Der Entwurf des Schätzalgorithmus erfolgt nach dem Prinzip des Erweiterten Kalman-Filters. Das Verhalten des Algorithmus bezüglich Folgefähigkeit und automatischer Störfallerkennung wird anschließend mit Hilfe realer Testdaten bzw. Anwendungen untersucht. Dies erfolgt unter diversen Bedingungen, wobei großräumige Netze, eine dünne Messstellenanordnung, heterogene Infrastruktur, Änderungen der Umfeldbedingungen, Verkehrsstörungen, Messwertausfälle und -ungenauigkeiten einbezogen werden. Der Aufsatz schließt mit Diskussion und Schlussfolgerung ab. Der in diesem Aufsatz gegebene Überblick behandelt den Stand der Forschung bei der Anwendung des Erweiterten Kalman-Filters auf die Zustandsschätzung des Schnellstraßenverkehrs. Laufende Forschungsaktivitäten in diesem Bereich beschäftigen sich mit einer Reihe von Erweiterungen der hier beschriebenen Arbeiten. Diese beinhalten: den Vergleich mit anderen Filtermethoden (Partikelfilter oder Unscented Filter); die Dezentralisierung der Filterstruktur; die Zustandsschätzung in städtischen Straßennetzen; neue Problemstellungen, die sich aus der zunehmenden Zahl von vernetzten oder auch automatisierten Fahrzeugen ergeben, und mehr. Recent advance in real-time freeway traffic state estimation is reviewed in this paper, with a particular focus on a general approach to traffic state estimation based on joint state and parameter estimation and another focus on the estimation performance in large-scale field applications. A mathematical model is first presented, including a validated macroscopic traffic flow model and a measurement model. The traffic state estimator is designed on the basis of extended Kalman filtering. The estimator's performance in tracking capability and automatic incident detection is then carefully examined via real-data tests or field applications, under various conditions involving large-scale networks, sparse measurements, infrastructure heterogeneity, changes of environmental conditions, traffic incidents, detector faults, and measurement inaccuracy. The paper is closed with discussions and conclusions. This paper overviews the state-of-the-art research on real-time freeway traffic state estimation using extended Kalman filtering. Ongoing research in this area addresses several extensions of the work reported, including: Comparison with other filtering methods (particle or unscented filters); decentralisation of the filtering structure; state estimation in urban road networks; new issues arising from the increasing presence of connected and even automated vehicles; and more. [ABSTRACT FROM AUTHOR]

• Background & Significance: connected and automated vehicles (CAVs) with wireless communication and vehicle automation will transform road transport. Impacts of CAVs on traffic flow are still uncertain, but it is vital to understand the impacts as early as possible. • Aim & purpose: This paper addresses lane-changing impacts of CAVs on traffic flow of human-driven vehicles and CAVs, and focuses on three important perspective questions. • Methods & solutions: Machine learning methods and large-scale microscopic traffic simulation​ were based on for the answers. • Results & Conclusions: Interesting and inspiring results were obtained, indicating that ​​CAVs may not simply be a magic cure for the current traffic problems, unless some upper-level coordination may be proposed for CAVs to benefit not only themselves but also the entire traffic flow. Connected and automated vehicles (CAVs) enabled by wireless communication and vehicle automation are believed to revolutionize the form and operation of road transport in the next decades. This paper addresses traffic flow effects of CAVs, and focuses on their lane-changing impacts on the mixed traffic flow of CAVs and human-driven vehicles (HVs). At present technical paths towards the development and deployment of CAVs are still uncertain. With CAV technologies getting matured, CAVs are supposed to provide rides of higher efficiency than HVs, beyond improved safety and comfort. In heavy traffic, this would only be achievable via agile and flexible lane changes of CAVs, because longitudinal acceleration would be unhelpful or even impossible. Such lane changes are expected to be ego-efficient in that they serve solely CAVs' interests without much considering surrounding vehicles, as long as safety constraints are not violated. As road resources are limited, the growth of the CAV population adopting such ego-efficient lane-changing strategies would probably lead to renowned "Tragedy of the Commons". In this context, this paper considers three important prospective questions: A: How to determine an ego-efficient lane-changing strategy for CAVs? B: With more CAVs introduced each adopting the ego-efficient lane-changing strategy, what is the impact on traffic flow? C: How to determine a system-efficient lane-changing strategy for CAVs? These forward-looking issues are addressed from the perspectives of microscopic traffic simulation and reinforcement learning. Without any constraint on the lane-changing incentive, the developed lane-changing strategy was found to be beneficial for CAVs and the entire traffic flow only if the market penetration rate (MPR) of CAVs is less than 50%. With an appropriate constraint placed, however, the lane-changing strategy was found to become consistently beneficial for the entire traffic flow at any MPR. These findings suggest that CAVs may not simply be a magic cure for traffic problems that the society is currently facing, unless some upper-level coordination may be proposed for CAVs to benefit not only themselves but also the entire traffic. This is also consistent with what "Tragedy of the Commons" suggests. [ABSTRACT FROM AUTHOR]

• Freeway traffic state estimation using fixed sensor and connected vehicle data. • A state-of-the-art overview. • Traffic state estimation based on online model parameter estimation. • Performance evaluation using NGSIM data. • Works of the same focus were not reported. This paper addresses real-time joint traffic state and model parameter estimation on freeways using data from fixed sensors and connected vehicles. It investigates how the combined usage of both types of sensing data improves the performance of traffic state estimation (TSE) and what role the online model parameter estimation (OMPE) plays therein. The paper first presents a state-of-the-art overview for freeway TSE with mixed sensing, focusing on a few critical issues such as filtering methods, Eulerian and Lagrangian formulation for traffic flow modeling/sensing/estimation, OMPE, and fusion of disparate sensing data, to determine the strengths and weaknesses of various technical paths, and figure out a viable roadmap for future studies. Three representative approaches to the design of freeway traffic state estimators using mixed sensing data are then investigated, which are based on a first-order, a second-order traffic flow model, and a speed-uniformity assumption, respectively. The paper intends to check if the gradual richness of mobile sensing data (in the era of connected vehicles) would compensate the deficiency of first-order models (as compared to second-order models) for TSE; if OMPE would still be essential for TSE in the mixed sensing case compared to the fixed sensing case; if the increasing usage of mobile sensing data would reduce the necessity of OMPE for TSE? The designed traffic state estimators have been evaluated thoroughly using NGSIM data, with the above questions answered. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a unified macroscopic model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic macroscopic freeway network traffic flow modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which the complete dynamic system model of RENAISSANCE is established with special attention to the handling of some important model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined. [Copyright &y& Elsevier]

Traffic congestion in urban road and freeway networks leads to a strong degradation of the network infrastructure and accordingly reduced throughput, which can be countered via suitable control measures and strategies. After illustrating the main reasons for infrastructure deterioration due to traffic congestion, a comprehensive overview of proposed and implemented control strategies is provided for three areas: urban road networks, freeway networks, and route guidance. Selected application results, obtained from either simulation studies or field implementations, are briefly outlined to illustrate the impact of various control actions and strategies. The paper concludes with a brief discussion of future needs in this important technical area. [ABSTRACT FROM AUTHOR]

This paper addresses model-based approach considering online model parameters estimation to estimate the real-time freeway traffic state for inhomogeneous traffic flow. Its effectiveness is demonstrated through macroscopic simulation and the influence of detector configuration on the estimation performance is investigated. The results indicate that when a freeway is inhomogeneity, additional detectors need to be placed at the boundary of traffic flow inhomogeneity to achieve better estimation performance. Finally, it is confirmed that considering traffic flow inhomogeneity can achieve better estimation performance using real expressway data from Shanghai. [ABSTRACT FROM AUTHOR]

Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a safety-enhanced eco-driving strategy for connected and autonomous vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The driving risk field, shockwave theory, and motion planning and control method are integrated into this framework to optimize the trajectories of CAVs on a signalized arterial under mixed traffic flow, with the aim of reducing the driving risk and fuel consumption of CAVs simultaneously, while ensuring traffic efficiency. The optimization procedure is mainly composed of two parts: long-term trajectory planning based on optimal control and short-term trajectory control based on model predictive control, which makes the strategy more adaptable to the various traffic conditions. The results show that the proposed framework can effectively reduce the safety risk that vehicles are exposed to and their fuel consumption by 18%–24% and 20%–27%, respectively. Furthermore, it reveals that conventional eco-driving strategies may result in negative safety issues when only considering the impact of preceding vehicles on the eco-CAV. However, these negative impacts can be eliminated when the impacts of following vehicles on the eco-CAV are taken into account. In addition, the sensitivity analysis on the Market Penetration Rate (MPR) of CAVs and traffic demand is performed. The results show that the framework is robust and can work under various traffic conditions (including under-saturated and over-saturated ones) and different MPRs. • Propose a safety-enhanced eco-driving strategy for CAVs under mixed traffic flow. • Develop two algorithms to alleviate intersection queuing effects for eco-driving. • While ensuring traffic efficiency, optimize CAVs' fuel consumption and driving risk. • Perform numerical experiments to assess the benefits of the proposed framework. • Reveal conventional eco-driving strategy may result in safety risks. [ABSTRACT FROM AUTHOR]