Vehicle Class–Specific Route Guidance of Freeway Traffic by Model-Predictive Control

Schreiter, T.; Landman, R. L.; Lint, Hans van; Hegyi, Andreas

doi:10.3141/2324-07

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…With the increase of traffic demand, expressways have gradually become the focus of traffic management. ATM was introduced in expressways to alleviate traffic congestion and reduce accidents [95]. The commonly used ATM strategies at this stage included variable speed limit [96][97][98], route guidance [99], ramp metering [100], lane control [101], and dynamic hard shoulder running [102].…”

Section: Research Domain Evolution Analysismentioning

confidence: 99%

Trend analysis of traffic management based on literature data mining and graph analysis tools

Ding

Liu

Wang

et al. 2023

IET Intelligent Trans Sys

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Studites on traffic management is crucial for the development of intelligent transportation systems and smart cities. However, identifying the development stages of traffic management field based on bibliometric analysis is still lacking. In this study, CiteSpace and VOSviewer software are used to explore “traffic management” field by summarizing development process and predicting future research trend. A total of 3,028 relevant documents over the past 40 years were collected from Web of Science. Results show that (1) studies on traffic management were mainly published by researchers from USA (30.55%), China (20.90%), and some European countries; (2) the key traffic management research contents can be classified into four categories, that is, background requirements, traffic problems, method models, and control strategies; (3) the evolution process can be divided into four stages, that is, budding stage (1990–1994), development stage (1995–2003), calm stage (2004–2010), and maturation stage (2011–); (4) machine learning, deep learning and other intelligent algorithms have played more important roles in recent years, and connected vehicle management is also a potential development trend. Results suggest that cooperative vehicle‐infrastructure systems or machine learning‐based studies might be the hotspots on traffic management studies.

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Section: Research Domain Evolution Analysismentioning

confidence: 99%

Trend analysis of traffic management based on literature data mining and graph analysis tools

Ding

Liu

Wang

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Regarding the second reason, it is generally accepted (i.e. there is abundant evidence [21], [25]- [27]) that the dominant congestion wave speed is in the vicinity of −20km/h (the negative sign indicates opposite direction of traffic); hence, by introducing a filter along this direction, one can expect to eliminate the interference of wide moving jam in the detection of activations of secondary bottlenecks.…”

Section: Bottleneck Identificationmentioning

confidence: 99%

An Automated Detection Framework for Multiple Highway Bottleneck Activations

Nguyen

Calvert

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Highway bottlenecks are responsible for the majority of traffic congestion. Although the problem of bottleneck detection is not new, contemporary methods have not solved the problem thoroughly with regards to bottleneck locations, activation time, and related congestion tracking. These elements are essential for identifying and characterizing a bottleneck. This paper proposes a comprehensive framework for detecting and extracting these features of highway bottlenecks from traffic data. We particularly focus on questions (i) whether a bottleneck is the primary source of congestion or (ii) whether it is activated due to congestion caused by another downstream bottleneck. The underlying principles of the proposed method include the detection of congestion (in spatio-temporal patterns of traffic congestion), and the detection of speed discontinuities in traffic data (since this is an important indicator of a bottleneck activation). The method is data-driven and automatic therefore can be easily applied to different highways and used to obtain meaningful statistics of existing bottlenecks. We have tested the method on simulated data and also demonstrated it on real data from a busy highway section in the Netherlands. The results suggest that the method is robust to different implementations, i.e. locations, of loop-detectors which measure traffic at discrete locations.

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Robust receding horizon parameterized control for multi‐class freeway networks: A tractable scenario‐based approach

Liu

Sadowska

Frejo

et al. 2016

Intl J Robust & Nonlinear

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In this paper, we propose a tractable scenario-based receding horizon parameterized control (RHPC) approach for freeway networks. In this approach, a scenario-based min-max scheme is used to handle uncertainties. This scheme optimizes the worst case among a limited number of scenarios that are considered. The use of parameterized control laws allows us to reduce the computational burden of the robust control problem based on the multi-class METANET model w.r.t. conventional model predictive control. To assess the performance of the proposed approach, a simulation experiment is implemented, in which scenario-based RHPC is compared with nominal RHPC, standard control ignoring uncertainties, and standard control including uncertainties. Here, the standard control approaches refer to state feedback controllers (such as PI-ALINEA for ramp metering). A queue override scheme is included for extra comparison. The results show that nominal RHPC approaches and standard control ignoring uncertainties may lead to high queue length constraint violations, and including a queue override scheme in standard control may not reduce queue length constraint violations to a low level. Including uncertainties in standard control approaches can obviously reduce queue length constraint violations, but the performance improvements are minor. For the given case study, scenario-based RHPC performs best as it is capable of improving control performance without high queue length constraint violations.In contrast, in heterogeneous multi-class models, these differences are considered, and vehicles are divided into different classes, such as cars, trucks, vans, and so on. The density of vehicles can be maintained around a critical value corresponding to maximum flow with a single-class fundamental diagram for mixed traffic. However, for model predictive control (MPC), in which the objective function depends on the dynamics of the controlled system, a more accurate model implies better prediction of future characteristics of the controlled system; thus, the controller has better information for determining control inputs. In general, the heterogeneous multi-class models are more accurate than homogeneous sing-class models, without increasing the computing complexity significantly. Some researchers have investigated the advantages of multi-class models. Wong and Wong [5] extended the Lighthill-Whitham-Richards (LWR) model to a multi-class version and found that the multi-class LWR model can reproduce some traffic phenomena (e.g., two-capacity phenomenon, hysteresis phenomenon of phase transition and platoon dispersion) that the single-class case cannot reproduce. Schreiter et al.[6] developed a multi-class controller that rerouted the traffic class specifically and showed that a multi-class controller outperformed a single-class controller. Liu et al. [7] extended METANET to a multi-class version, in which each vehicle class is assumed to be limited within its assigned space of the road, subjecting to its own fundamental diagram. Note, however, t...

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Vehicle Class–Specific Route Guidance of Freeway Traffic by Model-Predictive Control

Cited by 8 publications

References 13 publications

Trend analysis of traffic management based on literature data mining and graph analysis tools

Trend analysis of traffic management based on literature data mining and graph analysis tools

An Automated Detection Framework for Multiple Highway Bottleneck Activations

Robust receding horizon parameterized control for multi‐class freeway networks: A tractable scenario‐based approach

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