Traffic and related self-driven many-particle systems

Helbing, Dirk

doi:10.1103/revmodphys.73.1067

Cited by 2,931 publications

(2,112 citation statements)

References 543 publications

(825 reference statements)

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“…While finite reaction times have been investigated for more than 40 years [1] the HDM-IDM combination is, to our knowledge, the first car-following model allowing accident-free driving at realistic accelerations in all traffic situations for reaction times of the order of and even exceeding the time headway. A closer look at quantitative features of stop-and-go traffic or oscillations shows that, compared to simulations of the original IDM, the HDM extensions reduce the gradients of transitions between free and congested traffic and increase the wavelengths of stop-and-go waves, in agreement with empirical data.…”

Section: Discussionmentioning

confidence: 99%

“…The nature of human driving behaviour and the differences with the automated driving implemented in most micromodels is a controversial topic in traffic science [1,2,3,4,5,6,7,8]. Finite reaction times and estimation capabilities impair the human driving performance and stability compared to automated driving, sometimes called 'adaptive cruise control' (ACC).…”

Section: Introductionmentioning

confidence: 99%

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Delays, inaccuracies and anticipation in microscopic traffic models

Treiber

Kesting

Helbing

2006

Physica A: Statistical Mechanics and its Applications

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458

265

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We generalize a wide class of time-continuous microscopic traffic models to include essential aspects of driver behaviour not captured by these models. Specifically, we consider (i) finite reaction times, (ii) estimation errors, (iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal anticipation. The estimation errors are modelled as stochastic Wiener processes and lead to time-correlated fluctuations of the acceleration.We show that the destabilizing effects of reaction times and estimation errors can essentially be compensated for by spatial and temporal anticipation, that is, the combination of stabilizing and destabilizing effects results in the same qualitative macroscopic dynamics as that of the respectively underlying simple car-following model. In many cases, this justifies the use of simplified, physics-oriented models with a few parameters only. Although the qualitative dynamics is unchanged, multianticipation increase both spatial and temporal scales of stop-and-go waves and other complex patterns of congested traffic in agreement with real traffic data. Remarkably, the anticipation allows accident-free smooth driving in complex traffic situations even if reaction times exceed typical time headways.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delays, inaccuracies and anticipation in microscopic traffic models

Treiber

Kesting

Helbing

2006

Physica A: Statistical Mechanics and its Applications

Self Cite

458

265

View full text Add to dashboard Cite

show abstract

“…Besides traffic scientists, mathematicians and physicists have also contributed to the field of traffic flow. Because of the numerous publications, we refer the reader to (Helbing, 2001) for an overview. Briefly speaking, the main modeling approaches can be classified as follows: Car-following models deal with the non-linear interactions and dynamics of single vehicles (e.g.…”

Section: Introductionmentioning

confidence: 99%

On the distribution of urban road space for multimodal congested networks

Zheng

Geroliminis

2013

Transportation Research Part B: Methodological

119

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a b s t r a c tTransport systems in real cities are complex with many modes of transport sharing and competing for limited road space. This work intends to understand how space distributions for modes and interactions among modes affect network traffic performance. While the connection between performance of transport systems and general land allocation is the subject of extensive research, space allocation for interacting modes of transport is an open research question. Quantifying the impact of road space distribution on the performance of a congested multimodal transport system with a dynamic aggregated model remains a challenge. In this paper, a multimodal macroscopic fundamental diagram (MFD) is developed to represent the traffic dynamics of a multimodal transport system. Optimization is performed with the objective of minimizing the total passenger hours traveled (PHT) to serve the total demand by redistributing road space among modes. Pricing strategies are also investigated to provide a higher demand shift to more efficient modes. We find by an application to a bi-modal two-region city that (i) the proposed model captures the operational characteristics of each mode, and (ii) optimal dynamic space distribution strategies can be developed. In practice, the approach can serve as a physical dynamic model to inform space distribution strategies for policy makers with different goals of mobility.

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“…No matter how dense the traffic may become, some vehicles at the front of a congestion wave tend to escape from the bottleneck, with a certain minimum outflow speed v out min,i . This minimum speed v out min,i is an empirically determined variable (Helbing, 2001). This leads to the following mean value of the sending function :…”

Section: Updating the Number Of Vehicles In A Cellmentioning

confidence: 99%

A compositional stochastic model for real time freeway traffic simulation

Boel

Mihaylova

2006

Transportation Research Part B: Methodological

109

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Traffic flow on freeways is a nonlinear, many-particle phenomenon, with complex interactions between vehicles. This paper presents a stochastic model of freeway traffic at a time scale and of a level of detail suitable for on-line estimation, routing and ramp metering control. The freeway is considered as a network of interconnected components, corresponding to one-way road links consisting of consecutively connected short sections (cells). The compositional model proposed here extends the Daganzo cell transmission model by defining sending and receiving functions explicitly as random variables, and by also specifying the dynamics of the average speed in each cell. Simple stochastic equations describing the macroscopic traffic behavior of each cell, as well as its interaction with neighboring cells are obtained. This will allow the simulation of quite large road networks by composing many links. The model is validated over synthetic data with abrupt changes in the number of lanes and over real traffic data sets collected from a Belgian freeway.

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Traffic and related self-driven many-particle systems

Cited by 2,931 publications

References 543 publications

Delays, inaccuracies and anticipation in microscopic traffic models

Delays, inaccuracies and anticipation in microscopic traffic models

On the distribution of urban road space for multimodal congested networks

A compositional stochastic model for real time freeway traffic simulation

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