Active traffic management on road networks: a macroscopic approach

In this paper, we propose a traffic network flow model particularly suitable for qualitative analysis as a dynamical system. Flows at a junction are determined by downstream supply of capacity (lack of congestion) as well as upstream demand of traffic wishing to flow through the junction. This approach is rooted in the celebrated Cell Transmission Model for freeway traffic flow, and we analyze resulting equilibrium flows and convergence properties. I. INTRODUCTIONCompartmental systems are a broad modeling paradigm to study fluid-like flow of a single substance among interconnected "compartments" [1], [2]. The main contribution of this paper is to propose and analyze a compartmental model of freeway traffic networks that is amenable to analysis as a dynamical system. Existing approaches are often well-suited for simulations or for validation/fitting with empirical data, but the available literature often gives little insight into the network-level, qualitative properties of the dynamics. For example, models such as [3], [4] and the celebrated Cell Transmission Model (CTM) [5], [6] were primarily developed for simulation with few analytical results available. The primary exception is [7] which provides a thorough investigation of the CTM when modeling a stretch of highway with onramp queues but does not consider more general networks.We propose a model that encompasses the CTM and extends the model to general nonlinear supply and demand functions and to more general network topologies. In our proposed model, we consider a traffic network composed of road links interconnected at junctions. In keeping with the philosophy of the CTM, the flow of traffic through a junction is determined by the available supply of downstream road space into which vehicles can flow and upstream demand of vehicles wishing to flow into a given link.Our work is related to the dynamical flow networks recently proposed in [8], [9] and further studied in [10]. In [8], [9], downstream supply is not considered and therefore the flow exiting a link is equal to the link's demand. Thus downstream congestion does not affect upstream flow, an unrealistic assumption for traffic modeling. In [10], the authors allow flow to depend on the density of downstream links, but the paper focuses on throughput optimality of a particular class of routing policies that does not accomodate most models of traffic flow, including the proportional-priority, first-in-first-out rule considered in this paper, and limited theoretical results are given for general routing policies.

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“…We employ the proportional priority, first-in-first-out(PP/FIFO) rule for junctions adapted from [14]:…”

Section: Dynamic Modelmentioning

confidence: 99%

Dynamical properties of a compartmental model for traffic networks

Coogan

Arcak

2014

2014 American Control Conference

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“…On highways, the goal is to keep the traffic near the free-flow condition by controlling the in-and outflow on the on-and off-ramps and by assigning the velocity with the help of variable message signs (Carlson et al 2010). When implementing such control strategies, usually the equilibrium FD in figure 3a is used and the system is kept on the left-hand side of the maximum (Kurzhanskiy & Varaiya 2010). On the other hand, one may use non-equilibrium FDs, like those in figure 4c,h, and try to keep the system on the equilibrium branch even in the overlapping region-this would allow higher flux, that is, higher efficiency.…”

Section: Traffic Controlmentioning

confidence: 99%

Traffic jams: dynamics and control

Orosz

Wilson

Stépàn

2010

Phil. Trans. R. Soc. A.

344

215

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This introductory paper reviews the current state-of-the-art scientific methods used for modelling, analysing and controlling the dynamics of vehicular traffic. Possible mechanisms underlying traffic jam formation and propagation are presented from a dynamical viewpoint. Stable and unstable motions are described that may give the skeleton of traffic dynamics, and the effects of driver behaviour are emphasized in determining the emergent state in a vehicular system. At appropriate points, references are provided to the papers published in the corresponding Theme Issue.Keywords: vehicular traffic; congestion; stop-and-go waves; Hopf bifurcation; driver reaction time; unstable waves I asked Fermi whether he was not impressed by the agreement between our calculated numbers and his measured numbers. He replied, 'How many arbitrary parameters did you use for your calculations?' I thought for a moment about our cut-off procedures and said, 'Four'. He said, 'I remember my friend Johnny von Neumann used to say, with four parameters I can fit an elephant, and with five I can make him wiggle his trunk '. (Dyson 2004, p. 297) Background and challengesThe introduction of the assembly line in the automotive industry about a century ago allowed the mass production of automobiles, which, in turn, revolutionized land transportation. At the same time, a problem was also generated that has not yet been resolved: traffic congestion. Since then, researchers from many different disciplines (mathematics, physics and engineering) have targeted this problem, *Author for correspondence (gabor@engineering.ucsb.edu).One contribution of 10 to a Theme Issue 'Traffic jams: dynamics and control'.This journal is © 2010 The Royal Society 4455 on May 10, 2018 http://rsta.royalsocietypublishing.org/ Downloaded from 4456 G. Orosz et al. often using sophisticated mathematical tools brought from their own area of expertise. Also, analogies between traffic flow and other flows (fluid flow, gas flow and granular flow) were established. Although such analogies may help scientists to gain understanding of vehicular systems, it is becoming more and more obvious that traffic flows like no other flow in the Newtonian universe.To date, a vast number of different models have been constructed, but still no first principles have been established to guide the modelling procedure (if such principles exist at all). In many cases, authors claimed that the developed model described traffic better than models prior to that point, and such claims were often justified by fitting the models to empirical data. The quote at the beginning of this paper tries to illustrate, without questioning the importance of any specific model, that the above approach may easily lead to research capturing, but also missing, some essential characteristics. We believe that another way to conduct research in traffic can be by studying general classes of models and classifying their qualitative dynamical features (including 'hidden' unstable motions) when varying model parameters. To...

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“…In [40], a real-time traffic management framework is presented in which a traffic simulator is used for selecting best dynamic operations strategies with the estimation of current and predicted short-term road scenarios. Adverse road weather is a major cause of nonrecurring congestion and accounts for 25% of total delay in the U.S., which is equivalent to one billion hours of productivity loss [41].…”

Section: B Active Traffic Managementmentioning

confidence: 99%

Potential of Intelligent Transportation Systems in Mitigating Adverse Weather Impacts on Road Mobility: A Review

Dey

Mishra

Chowdhury

2015

IEEE Trans. Intell. Transport. Syst.

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Adverse weather conditions for roads, which cause transportation systems to perform far below capacity, can severely affect society's economic output. As elimination of road weather events is not possible, transportation agencies perform proactive and reactive maintenance activities to minimize adverse impacts to keep roadways in optimum condition. While reactive maintenance activities are conducted to clear roadways after the occurrence of extreme weather events, proactive activities minimize these impacts beforehand. The success of proactive activities solely depends on the availability of accurate road weather information, however. Traditional road weather forecasting techniques rely on governmental weather services, which are not appropriate to predict route-specific road weather conditions. In this paper, the authors reviewed current intelligent transportation systems (ITS)-based solutions for minimizing road weather impacts and possible ITS innovations to incorporate diverse data sources to improve road weather management activities. ITS-based initiatives, such as road weather information system (RWIS), allow transportation agencies obtain accurate road weather assessments. Location-specific infrastructures such as RWIS are cost prohibitive for system-wide deployments. Connected vehicles equipped with weather sensors could enhance mobile road weather data collection. This strategy could improve proactive maintenance programs and reduce adverse effects of weather to the surface transportation system.

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Active traffic management on road networks: a macroscopic approach

Cited by 84 publications

References 10 publications

Dynamical properties of a compartmental model for traffic networks

Dynamical properties of a compartmental model for traffic networks

Traffic jams: dynamics and control

Potential of Intelligent Transportation Systems in Mitigating Adverse Weather Impacts on Road Mobility: A Review

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