Traffic signal control and route choice: A new assignment and control model which designs signal timings

Smith, Mike

doi:10.1016/j.trc.2015.02.002

Cited by 44 publications

(16 citation statements)

References 32 publications

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“…The capacity maximising effect arises because both models utilise the P 0 signal control policy. This policy has been studied previously (see Smith (1979aSmith ( , b, c, 1987, Smith et al (1987), Smith and Ghali (1990), Smith and van Vuren (1993), Smith and Mounce (2011), Smith (2015) and Liu and Smith (2015). Both the performance and stability of the re-routeing -control interactions is considered.…”

Section: It Is Importantmentioning

confidence: 99%

Traffic control and route choice: Capacity maximisation and stability

Smith

Liu

Mounce

2015

Transportation Research Part B: Methodological

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This paper presents idealised natural general and special dynamical models of day-to-day rerouteing and of day to day green-time response. Both green-time response models are based on the responsive control policy P 0 introduced in Smith (1979aSmith ( , b, c 1987. Several results are proved. For example, it is shown that, for any steady feasible demand within a flow model, if the general day to day re-routeing model is combined with the general day to day green-time response model then under natural conditions any (flow, green-time) solution trajectory cannot leave the region of supply-feasible (flow, green-time) pairs and costs are bounded. Throughput is maximised in the following sense.Given any constant feasible demand; this demand is met as any routeing / green-time trajectory evolves (following either the general or the special dynamical model). The paper then considers simple "pressure driven" responsive control policies, with explicit signal cycles of fixed positive duration. A possible approach to dynamic traffic control allowing for variable route choices is outlined. It is finally shown that modified Varaiya (2013) and Le at al (2013) pressure-driven responsive controls may not maximise network capacity, by considering a very simple one junction network. It is shown that (with each of these two modified policies) there is a steady demand within the capacity of the network for which there is no Wardrop equilibrium consistent with the policy. In contrast, responsive P 0 on this simple network does maximise throughput at a quasi-dynamic user equilibrium consistent with P 0 ; queues and delays remain bounded in natural dynamical evolutions in this case. It is to be expected that this P 0 result may be extended to allow for certain time-varying demands on a much wider variety of networks; to show that this is indeed the case is a challenge for the future.

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Section: It Is Importantmentioning

confidence: 99%

Traffic control and route choice: Capacity maximisation and stability

Smith

Liu

Mounce

2015

Transportation Research Part B: Methodological

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“…Smith (1979), Allsop (1974), andBentley andLambe (1980) were the first to combine models of route choice and traffic signal control to find optimal controls taking account of routing reactions. Recently, Liu and Smith (2015), and Smith (2015) investigated the combined traffic assignment model taking into account the route choice and traffic signal control at the same time. Specifically, Liu and Smith (2015) introduced a new combined traffic assignment process known as the restricted proportional adjustment process (RPAP).…”

Section: Introductionmentioning

confidence: 99%

“…The signal control model may be regarded as a model of a system periodically updated either by an operator or by an automatic system. Smith (2015) showed how route choice and signal control work simultaneously. Initially, green times give rise to travel times that influence travelers choice of route.…”

Section: Introductionmentioning

confidence: 99%

“…He showed that in the dynamical model of route choice, traffic flowflows converge to the set of Wardrop equilibria with time, and proved that the global stability of traffic assignment is stable if path costs are differentiable and monotone. Subsequently, Lee (1995), Ghali (1995), Meneguzzer (1996), He, Guo, and Liu (2010), Liu and Smith (2015), Smith (2015), Smith and Watling (2016), and other numerous researchers used, developed, and modified the Lyapunov theorem in the stability test of the dynamical model of route choice. Zhang, Ge, and Hang (2000), Boyer, Blandin, and Wynter (2015), and Jia and Ngoduy (2016) used the Lyapunov theorem to prove the stability of microscopic models (ACC, CACC, V2X), and routing strategies.…”

Section: Introductionmentioning

confidence: 99%

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Stability analysis on a dynamical model of route choice in a connected vehicle environment

Lee

Heydecker

Kim

et al. 2018

Transportation Research Part C: Emerging Technologies

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Research on connected vehicle environment has been growing rapidly to investigate the effects of real-time exchange of kinetic information between vehicles and road condition information from the infrastructure through radio communication technologies. A fully connected vehicle environment can substantially reduce the latency in response caused by human perception-reaction time with the prospect of improving both safety and comfort. This study presents a dynamical model of route choice under a connected vehicle environment. We analyze the stability of headways by perturbing various factors in the microscopic traffic flow model and traffic flow dynamics in the car-following model and dynamical model of route choice. The advantage of this approach is that it complements the macroscopic traffic assignment model of route choice with microscopic elements that represent the important features of connected vehicles. The gaps between cars can be decreased and stabilized even in the presence of perturbations caused by incidents. The reduction in gaps will be helpful to optimize the traffic flow dynamics more easily with safe and stable conditions. The results show that the dynamics under the connected vehicle environment have equilibria. The approach presented in this study will be helpful to identify the important properties of a connected vehicle environment and to evaluate its benefits.

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“…Van den Berg et al (2008) proposed a hybrid approach using methods from both optimal control and mixed integer linear programming. However, Smith (2015) summarizes the situation as follows: "At the moment, in practice, traffic signal timings are designed or optimized without systematically seeking to influence route choices beneficially." Substantially, there are two approaches in modelling signal settings and link flows in this combined problem.…”

Section: Introductionmentioning

confidence: 99%

A robust algorithm to solve the signal setting problem considering different traffic assignment approaches

Adacher

Gemma

2017

International Journal of Applied Mathematics and Computer Science

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In this paper we extend a stochastic discrete optimization algorithm so as to tackle the signal setting problem. Signalized junctions represent critical points of an urban transportation network, and the efficiency of their traffic signal setting influences the overall network performance. Since road congestion usually takes place at or close to junction areas, an improvement in signal settings contributes to improving travel times, drivers' comfort, fuel consumption efficiency, pollution and safety. In a traffic network, the signal control strategy affects the travel time on the roads and influences drivers' route choice behavior. The paper presents an algorithm for signal setting optimization of signalized junctions in a congested road network. The objective function used in this work is a weighted sum of delays caused by the signalized intersections. We propose an iterative procedure to solve the problem by alternately updating signal settings based on fixed flows and traffic assignment based on fixed signal settings. To show the robustness of our method, we consider two different assignment methods: one based on user equilibrium assignment, well established in the literature as well as in practice, and the other based on a platoon simulation model with vehicular flow propagation and spill-back. Our optimization algorithm is also compared with others well known in the literature for this problem. The surrogate method (SM), particle swarm optimization (PSO) and the genetic algorithm (GA) are compared for a combined problem of global optimization of signal settings and traffic assignment (GOSSTA). Numerical experiments on a real test network are reported.

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Traffic signal control and route choice: A new assignment and control model which designs signal timings

Cited by 44 publications

References 32 publications

Traffic control and route choice: Capacity maximisation and stability

Traffic control and route choice: Capacity maximisation and stability

Stability analysis on a dynamical model of route choice in a connected vehicle environment

A robust algorithm to solve the signal setting problem considering different traffic assignment approaches

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