160,560 research outputs found
Experiences with a simplified microsimulation for the Dallas/Fort Worth area
We describe a simple framework for micro simulation of city traffic. A medium
sized excerpt of Dallas was used to examine different levels of simulation
fidelity of a cellular automaton method for the traffic flow simulation and a
simple intersection model. We point out problems arising with the granular
structure of the underlying rules of motion.Comment: accepted by Int.J.Mod.Phys.C, 20 pages, 14 figure
Simulation Framework for Cooperative Adaptive Cruise Control with Empirical DSRC Module
Wireless communication plays a vital role in the promising performance of
connected and automated vehicle (CAV) technology. This paper proposes a
Vissim-based microscopic traffic simulation framework with an analytical
dedicated short-range communication (DSRC) module for packet reception. Being
derived from ns-2, a packet-level network simulator, the DSRC probability
module takes into account the imperfect wireless communication that occurs in
real-world deployment. Four managed lane deployment strategies are evaluated
using the proposed framework. While the average packet reception rate is above
93\% among all tested scenarios, the results reveal that the reliability of the
vehicle-to-vehicle (V2V) communication can be influenced by the deployment
strategies. Additionally, the proposed framework exhibits desirable scalability
for traffic simulation and it is able to evaluate transportation-network-level
deployment strategies in the near future for CAV technologies.Comment: 6 pages, 6 figure, 44th Annual Conference of the IEEE Industrial
Electronics Societ
iTETRIS Platform Architecture for the Integration of Cooperative Traffic and Wireless Simulations
The use of cooperative wireless communications can support driving through dynamic exchange of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) messages. Traffic applications based on such systems will be able to generate a safer, faster, cheaper and cleaner way for people and goods to move. In this context, the iTERIS project aims at providing the framework to combine traffic mobility and wireless communication simulations for large scale testing of traffic management solutions based on cooperative systems. This paper addresses the description and explanation of the implementation choices taken to build a modular and interoperable architecture integrating heterogeneous traffic and wireless simulators, and application algorithms supporting traffic management strategies. The functions of an “in-between” control system for managing correct simulation executions over the platform are presented. The inter-block interaction procedures identified to ensure optimum data transfer for simulation efficiency are also introduced
Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures
Reinforcement learning (RL) constitutes a promising solution for alleviating
the problem of traffic congestion. In particular, deep RL algorithms have been
shown to produce adaptive traffic signal controllers that outperform
conventional systems. However, in order to be reliable in highly dynamic urban
areas, such controllers need to be robust with the respect to a series of
exogenous sources of uncertainty. In this paper, we develop an open-source
callback-based framework for promoting the flexible evaluation of different
deep RL configurations under a traffic simulation environment. With this
framework, we investigate how deep RL-based adaptive traffic controllers
perform under different scenarios, namely under demand surges caused by special
events, capacity reductions from incidents and sensor failures. We extract
several key insights for the development of robust deep RL algorithms for
traffic control and propose concrete designs to mitigate the impact of the
considered exogenous uncertainties.Comment: 8 page
Bicycle traffic and its interaction with motorized traffic in an agent-based transport simulation framework
Cycling as an inexpensive, healthy, and efficient mode of transport for everyday traveling is becoming increasingly popular. While many cities are promoting cycling, it is rarely included in transport models and systematic policy evaluation procedures. The purpose of this study is to extend the agent-based transport simulation framework MATSim to be able to model bicycle traffic more realistically. The network generation procedure is enriched to include attributes that are relevant for cyclists (e.g. road surfaces, slopes). Travel speed computations, plan scoring, and routing are enhanced to take into account these infrastructure attributes. The scoring, i.e. the evaluation of simulated daily travel plans, is furthermore enhanced to account for traffic events that emerge in the simulation (e.g. passings by cars), which have an additional impact on cyclists’ decisions. Inspired by an evolutionary computing perspective, a randomizing router was implemented to enable cyclists to find realistic routes. It is discussed in detail why this approach is both feasible in practical terms and also conceptually consistent with MATSim’s co-evolutionary simulation approach. It is shown that meaningful simulation results are obtained for an illustrative scenario, which indicates that the developed methods will make real-world scenarios more realistic in terms of the representation of bicycle traffic. Based on the exclusive reliance on open data, the approach is spatially transferable
- …