Visco‐elastic traffic flow model

To explore freeway tunnel effects on ring road traffic flow, a two-lane traffic model is put forward. The model adopts lane-changing time to describe the net lane-changing rate, assuming that the time is approximately equal to the relaxation time of traffic flow, but infinite when the absolute value of difference of traffic density between the two lanes is lower than 1 veh/km, as it is hard for car drivers to perceive such a small difference. Based on the two-lane traffic model, a simulation platform is built to predict traffic flow on a two-lane freeway ring with a tunnel of 0.3 km length having a speed limit of 80 km/h, and free flow speeds on lane I and II equal to 120 and 100 km/h, respectively. The platform uses a third-order Runge–Kutta scheme to handle the time derivative term, and a fifth-order weighted essentially non-oscillatory scheme to calculate numerical flux. Simulation results show that the freeway tunnel can trigger traffic shock originating at the entrance when the coming flow density is beyond a traffic density threshold that is dependent on the off-ramp flow just upstream the tunnel. The occurrence of traffic shock leads to the mean travel time through the tunnel is almost a constant when the initial density normalized by jam density is less than 0.5. When initial density is above the density threshold, generally vehicles need more fuel consumption to run through the ring road in comparison with the case without tunnel. But the situation is just the opposite for larger normalized initial density such as 0.5.

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“…where R 1 and R 2 can be expressed as: [38][39][40] The tunnel entrance and exit are, respectively, located at X t1 and X t2 .…”

Section: Tlm Equationsmentioning

confidence: 99%

Numerical exploration of freeway tunnel effects with a two-lane traffic model

Смирнова

Zhang

et al. 2022

SIMULATION

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“…Feedback as stated by [17] and [18] must be beneficial to transport intelligent planning. Contributors in [19] and [20] point out to improved traffic flows as solutions to congestion by involving route guidance agents.…”

Section: Avoiding Unjustified Re-routingmentioning

confidence: 99%

Intelligent and Predictive Vehicular Networks

Chintu¹,

Anthony

Roshanaei

et al. 2014

ICA

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Seeking shortest travel times through smart algorithms may not only optimize the travel times but also reduce carbon emissions, such as CO 2 , CO and Hydro-Carbons. It can also result in reduced driver frustrations and can increase passenger expectations of consistent travel times, which in turn points to benefits in overall planning of day schedules. Fuel consumption savings are another benefit from the same. However, attempts to elect the shortest path as an assumption of quick travel times, often work counter to the very objective intended and come with the risk of creating a "Braess Paradox" which is about congestion resulting when several drivers attempt to elect the same shortest route. The situation that arises has been referred to as the price of anarchy! We propose algorithms that find multiple shortest paths between an origin and a destination. It must be appreciated that these will not yield the exact number of Kilometers travelled, but favourable weights in terms of travel times so that a reasonable allowable time difference between the multiple shortest paths is attained when the same Origin and Destinations are considered and favourable responsive routes are determined as variables of traffic levels and time of day. These routes are selected on the paradigm of route balancing, re-routing algorithms and traffic light intelligence all coming together to result in optimized consistent travel times whose benefits are evenly spread to all motorist, unlike the Entropy balanced k shortest paths (EBkSP) method which favours some motorists on the basis of urgency. This paper proposes a Fully Balanced Multiple-Candidate shortest path (FBMkP) by which we model in SUMO to overcome the computational overhead of assigning priority differently to each travelling vehicle using intelligence at intersections and other points on the vehicular network. The FBMkP opens up traffic by fully balancing the whole network so as to benefit every motorist. Whereas the EBkSP reserves some routes for cars on high priority, our algorithm distributes the benefits of smart routing to all vehicles on the network and serves the road side units such as induction loops and detectors from having to remember the urgency of each vehicle. Instead, detectors and induction loops simply have to poll the destination of the vehicle and not any urgency factor. The minimal data being processed significantly

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“… studied the relation between the average traffic flow and the vehicle density on road networks as a two‐dimensional traffic fundamental diagram. Zhu and Yang developed a viscoelastic traffic flow model.…”

Section: Literature Reviewmentioning

confidence: 99%

Data mining using regularized adaptive B‐splines regression with penalization for multi‐regime traffic stream models

Sun

Pan

2013

J of Advced Transportation

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SUMMARYThis paper presents a new data mining method that integrates adaptive B-spline regression and traffic flow theory to develop multi-regime traffic stream models (TSMs). Parameter estimation is implemented adaptively and optimally through a constrained bi-level programming method. The slave programming determines positions of knots and coefficients of the B-spline by minimizing the error of B-spline regression. The master programming model determines the number of knots through a regularized function, which balances model accuracy and model complexity. This bi-level programming method produces the best fitting to speed-density observations under specific order of splines and possesses great flexibility to accommodate the exhibited nonlinearity in speed-density relationships. Jam density can be estimated naturally using spline TSM, which is sometimes hardly obtainable in many other TSM. Derivative continuity up to one order lower than the highest spline degree can be preserved, a desirable property in some application. A five-regime B-spline model is found to exist for generalized speed-density relationships to accommodate five traffic operating conditions: free flow, transition, synchronized flow, stop and go traffic, and jam condition. A typical two-regime B-spline form is also explicitly given, depending only on free-flow speed, optimal speed, optimal density, and jam density.

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Visco‐elastic traffic flow model

Cited by 26 publications

References 32 publications

Numerical exploration of freeway tunnel effects with a two-lane traffic model

Numerical exploration of freeway tunnel effects with a two-lane traffic model

Intelligent and Predictive Vehicular Networks

Data mining using regularized adaptive B‐splines regression with penalization for multi‐regime traffic stream models

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