Effects of user adaption on traffic-responsive signal control in agent-based transport simulations

All traffic-responsive approaches have in common that they directly influence waiting times of travelers (users) at intersections and, thereby, influence user reaction, e.g. route choice. On the other hand, users route choice directly influences sensor data and, thereby, the signal settings controlled by traffic-responsive signals. Thus, the interference of route choice and traffic-responsive signals constitutes a combined problem. This work focuses on a detailed simulation-based analysis of the effects of route choice on the performance of different traffic-responsive signal algorithms implemented in an inner-city area of a real-world scenario. It Is found that the effects of induced traffic matter a lot, especially for the inner-city area: A significantly higher number of agents travel through the inner city, increasing travel time, delay and noise levels in this area (in comparison to the case without user adaption), whereas overall traveled distances decrease, i.e. more direct routes are used and by-pass routes around the city become less congested. Furthermore, the effects of different levels of saturation on the interaction of route choice and signal control are analyzed

Braess's Paradox in an Agent-based Transport Model

Braess's paradox states that adding a link to the network can increase total travel time in a user equilibrium. In this paper, Braess's paradox is analyzed in the agent-based transport simulation MATSim. It can be observed, that two different types of the paradox occur: In the absence of spill back effects, the delay per agent caused by adding a new link is bounded, i.e. the delay per agent will not increase by extending the time span during which agents depart and, therefore, increasing the number of agents. In the presence of spill back effects, the delay per agent is unbounded. The same holds for the price of anarchy in both cases which gets unbounded if spill back effects are considered. As a consequence, Braess's paradox tends to be underestimated in models that do not capture spill back effects

Adaptive traffic signal control for real-world scenarios in agent-based transport simulations

This study provides an open-source implementation of a decentralized, adaptive signal control algorithm in the agent-based transport simulation MATSim, which is applicable for large-scale real-world scenarios. The implementation is based on the algorithm proposed by Lämmer and Helbing (2008), which had promising results, but was not applicable to real-world scenarios in its published form. The algorithm is extended in this paper to cope with realistic situations like different lanes per signal, small periods of overload, phase combination of non-conflicting traffic, and minimum green times. Impacts and limitations of the adaptive signal control are analyzed for a real-world scenario and compared to a fixed-time and traffic-actuated signal control. It can be shown that delays significantly reduce and queue lengths are lower and more stable than with fixed-time signals. Another finding is that the adaptive signal control behaves like a fixed-time control in overload situations and, therefore, ensures system-wide stability

The structure of user equilibria: Dynamic coevolutionary simulations vs. cyclically expanded networks

A variety of approaches exist that model traffic time-dependently. While all approaches have their advantages and disadvantages but have to find a balance between modeling traffic as realistic as possible and being still manageable in combinational terms. While transport simulations are efficient in evaluating user equilibria in large scale scenarios, their potential to be used for optimization is limited. On the other hand, analytical formulations like models based on cyclically time-expanded networks can be used to optimize traffic flow, but are not suitable for large scale scenarios. By optimizing the network structure in a mathematical model and evaluating its effect in a more realistic transport simulation, two models can benefit from each other. Detailed knowledge about model properties and differences in traffic flow behavior help to understand results and potential difficulties of such a model combination. In this paper, properties of two such models are compared regarding traffic flow modeling. It is shown that the set of user equilibria in both models and, therefore, the resulting route distributions can be structurally different

Towards a robust and wide-area traffic signal control for inner-city areas

This paper examines different signal approaches to control inner-city traffic and compares them with respect to two use cases - an illustrative gridlock scenario and a more complex real-world scenario. Based on the idea of back-pressure, it aims to develop an appropriate signal control method for inner-city traffic that reacts to current traffic and considers route choice, oversaturation, and spillback. For evaluation, the agent-based transport simulation MATSim is used. First positive results are presented. Difficulties that occur while applying the approach to the more complex scenario are analyzed. The outlook discusses suggestions to address these difficulties in the future

Optimization and simulation of fixed-time traffic signal control in real-world applications

This paper contributes to the question how to optimize fixed-time traffic signal coordinations for real-world applications. Therefore, two models are combined: An analytically model that optimizes fixed-time plans based on a cyclically time-expanded network formulation, and a coevolutionary transport simulation that is able to evaluate the optimized fixed-time plans for large-scale realistic traffic situations. The coupling process of both models is discussed and applied to a real-world scenario. Steps that were necessary to align the models and improve the results are presented. The optimized fixed-time signals are compared to other signal approaches in the application. It is found, that they also help to improve the performance of actuated signal control

Implementing an adaptive traffic signal control algorithm in an agent-based transport simulation

This paper describes the implementation of the fully traffic adaptive signal control algorithm by Lämmer in the agent-based transport simulation MATSim. The implementation is tested at an illustrative, single intersection scenario and compared to the results of Lammers MATLAB simulation. Plausibility of the self-controlled signals and overall results can be confirmed. Small deviations can be explained by differences in flow simulation and resolution of simulation time steps. In the simulation of the illustrative intersection, the adaptive control is proved to be stable and, overall, superior to a fixed-time control. Constant vehicle arrivals are simulated to show the performance of the control and its underlying sub-strategies. The expected behavior of the algorithm and its implementation are validated by analyzing queue lengths over time. The adaptive control significantly outperforms the fixed-time control for stochastic demand, where its ability to dynamically react to changes in flow becomes important

Traffic Signals and Lanes

This chapter reviews concepts, usage and restrictions of the traffic signal control extension for MATSim. The chapter is particularly interesting for MATSim users, who plan to simulate traffic signals microscopically. If one wishes to capture signalization effects on a rather coarse level, consider the approach presented in Charypar (2008, pp. 139), that can be realized with the time variant network feature ofMATSim (Lämmel et al., 2010). Before we go into detail on motivating traffic signals with MATSim, a case study is reviewed