Hybrid multilane models for highway traffic

We study effects of lane changing rules on multilane highway traffic using the Nagel-Schreckenberg cellular automaton model with different schemes for combining driving lanes (lanes used by default) and overtaking lanes. Three schemes are considered: a symmetric model, in which all lanes are driving lanes, an asymmetric model, in which the right lane is a driving lane and the other lanes are overtaking lanes, a hybrid model, in which the leftmost lane is an overtaking lane and all the other lanes are driving lanes. In a driving lane vehicles follow symmetric rules for lane changes to the left and to the right, while in an overtaking lane vehicles follow asymmetric lane changing rules. We test these schemes for three- and four-lane traffic mixed with some low-speed vehicles (having a lower maximum speed) in a closed system with periodic boundary conditions as well as in an open system with one open lane. Our results show that the asymmetric model, which reflects the "Keep Right Unless Overtaking" rule, is more efficient than the other two models. An extensible software package developed for this study is free available.Comment: 7 pages, 9 figure

Macroscopic Dynamics of Multi-Lane Traffic

We present a macroscopic model of mixed multi-lane freeway traffic that can be easily calibrated to empirical traffic data, as is shown for Dutch highway data. The model is derived from a gas-kinetic level of description, including effects of vehicular space requirements and velocity correlations between successive vehicles. We also give a derivation of the lane-changing rates. The resulting dynamic velocity equations contain non-local and anisotropic interaction terms which allow a robust and efficient numerical simulation of multi-lane traffic. As demonstrated by various examples, this facilitates the investigation of synchronization patterns among lanes and effects of on-ramps, off-ramps, lane closures, or accidents.Comment: For related work see http://www.theo2.physik.uni-stuttgart.de/helbing.htm

Line-of-Sight Obstruction Analysis for Vehicle-to-Vehicle Network Simulations in a Two-Lane Highway Scenario

In vehicular ad-hoc networks (VANETs) the impact of vehicles as obstacles has largely been neglected in the past. Recent studies have reported that the vehicles that obstruct the line-of-sight (LOS) path may introduce 10-20 dB additional loss, and as a result reduce the communication range. Most of the traffic mobility models (TMMs) today do not treat other vehicles as obstacles and thus can not model the impact of LOS obstruction in VANET simulations. In this paper the LOS obstruction caused by other vehicles is studied in a highway scenario. First a car-following model is used to characterize the motion of the vehicles driving in the same direction on a two-lane highway. Vehicles are allowed to change lanes when necessary. The position of each vehicle is updated by using the car-following rules together with the lane-changing rules for the forward motion. Based on the simulated traffic a simple TMM is proposed for VANET simulations, which is capable to identify the vehicles that are in the shadow region of other vehicles. The presented traffic mobility model together with the shadow fading path loss model can take in to account the impact of LOS obstruction on the total received power in the multiple-lane highway scenarios.Comment: 8 pages, 11 figures, Accepted for publication in the International Journal of Antennas and Propagation, Special Issue on Radio Wave Propagation and Wireless Channel Modeling 201

Traffic flow modeling and forecasting using cellular automata and neural networks : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand

In This thesis fine grids are adopted in Cellular Automata (CA) models. The fine-grid models are able to describe traffic flow in detail allowing position, speed, acceleration and deceleration of vehicles simulated in a more realistic way. For urban straight roads, two types of traffic flow, free and car-following flow, have been simulated. A novel five-stage speed-changing CA model is developed to describe free flow. The 1.5-second headway, based on field data, is used to simulate car-following processes, which corrects the headway of 1 second used in all previous CA models. Novel and realistic CA models, based on the Normal Acceptable Space (NAS) method, are proposed to systematically simulate driver behaviour and interactions between drivers to enter single-lane Two-Way Stop-Controlled (TWSC) intersections and roundabouts. The NAS method is based on the two following Gaussian distributions. Distribution of space required for all drivers to enter intersections or roundabouts is assumed to follow a Gaussian distribution, which corresponds to heterogeneity of driver behaviour. While distribution of space required for a single driver to enter an intersection or roundabout is assumed to follow another Gaussian distribution, which corresponds to inconsistency of driver behavior. The effects of passing lanes on single-lane highway traffic are investigated using fine grids CA. Vehicles entering, exiting from and changing lanes on passing lane sections are discussed in detail. In addition, a Genetic Algorithm-based Neural Network (GANN) method is proposed to predict Short-term Traffic Flow (STF) in urban networks, which is expected to be helpful for traffic control. Prediction accuracy and generalization ability of NN are improved by optimizing the number of neurons in the hidden layer and connection weights of NN using genetic operations such as selection, crossover and mutation

A realistic two-lane traffic model for highway traffic

A two-lane extension of a recently proposed cellular automaton model for traffic flow is discussed. The analysis focuses on the reproduction of the lane usage inversion and the density dependence of the number of lane changes. It is shown that the single-lane dynamics can be extended to the two-lane case without changing the basic properties of the model which are known to be in good agreement with empirical single-vehicle data. Therefore it is possible to reproduce various empirically observed two-lane phenomena, like the synchronization of the lanes, without fine-tuning of the model parameters

Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning

This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced

Artificial potential functions for highway driving with collision avoidance

We present a set of potential function components to assist an automated or semi-automated vehicle in navigating a multi-lane, populated highway. The resulting potential field is constructed as a superposition of disparate functions for lane- keeping, road-staying, speed preference, and vehicle avoidance and passing. The construction of the vehicle avoidance potential is of primary importance, incorporating the structure and protocol of laned highway driving. Particularly, the shape and dimensions of the potential field behind each obstacle vehicle can appropriately encourage control vehicle slowing and/or passing, depending on the cars' velocities and surrounding traffic. Hard barriers on roadway edges and soft boundaries between navigable lanes keep the vehicle on the highway, with a preference to travel in a lane center

Order parameter model for unstable multilane traffic flow

We discuss a phenomenological approach to the description of unstable vehicle motion on multilane highways that explains in a simple way the observed sequence of the phase transitions "free flow -> synchronized motion -> jam" as well as the hysteresis in the transition "free flow synchronized motion". We introduce a new variable called order parameter that accounts for possible correlations in the vehicle motion at different lanes. So, it is principally due to the "many-body" effects in the car interaction, which enables us to regard it as an additional independent state variable of traffic flow. Basing on the latest experimental data (cond-mat/9905216) we assume that these correlations are due to a small group of "fast" drivers. Taking into account the general properties of the driver behavior we write the governing equation for the order parameter. In this context we analyze the instability of homogeneous traffic flow manifesting itself in both of the mentioned above phase transitions where, in addition, the transition "synchronized motion -> jam" also exhibits a similar hysteresis. Besides, the jam is characterized by the vehicle flows at different lanes being independent of one another. We specify a certain simplified model in order to study the general features of the car cluster self-formation under the phase transition "free flow synchronized motion". In particular, we show that the main local parameters of the developed cluster are determined by the state characteristics of vehicle motion only.Comment: REVTeX 3.1, 10 pages with 10 PostScript figure