An optimal downsampling procedure for microscopic simulation modeling of transportation networks: a proof-of-concept study

2007 ◽  
Vol 34 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Ciprian Alecsandru ◽  
Sherif Ishak ◽  
Yan Zhang
Keyword(s):  
Traffic Control ◽  
Transformation Process ◽  
General Motors ◽  
Proof Of Concept ◽  
Simulation Environment ◽  
Microscopic Simulation ◽  
Car Following ◽  
Real Time Traffic ◽  
Behavioral Parameters ◽  
Management Functions

This paper investigates and demonstrates the concept of scalability of microscopic traffic simulation systems as a means of reducing the associated computational requirements and maximizing their potential support for real-time traffic control and management functions. The primary goal of this research is to examine the feasibility of transforming the original simulation environment into a downsampled simulation environment, where fewer representative entities are simulated. This must be achieved, however, while retaining maximum fidelity to microscopic simulation properties and preserving most of the macroscopic characteristics. The methodology is presented as an optimization problem whose objective is to minimize the errors resulting from the transformation process and to seek optimal values of the behavioral parameters in the downsampled environment. In this proof-of-concept stage, experimental analysis was conducted on a homogeneous freeway segment using one of the well-known, and arguably sufficiently calibrated, car-following models developed by General Motors Laboratories (GM3). The results were promising and showed that optimal relationships between the behavioral parameters in both environments can be established to minimize the information loss associated with the transformation process.Key words: microscopic simulation, scalability, computational efficiency, downsampling, car-following models.

Remote Simulation to Evaluate Real-Time Traffic Control Strategies

2000 ◽  
Vol 1727 (1) ◽  
pp. 95-100 ◽  
Author(s):  
David E. Lucas ◽  
Pitu B. Mirchandani ◽  
K. Larry Head
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Control Systems ◽  
Real Time ◽  
Traffic Control ◽  
Control Strategies ◽  
Remote Access ◽  
Hardware In The Loop ◽  
Simulation Environment ◽  
Real Time Traffic

Simulation is a valuable tool for evaluating the effects of various changes in a transportation system. This is especially true in the case of real-time traffic-adaptive control systems, which must undergo extensive testing in a laboratory setting before being implemented in a field environment. Various types of simulation environments are available, from software-only to hardware-in-the-loop simulations, each of which has a role to play in the implementation of a traffic control system. The RHODES (real-time hierarchical optimized distributed effective system) real-time traffic-adaptive control system was followed as it progressed from a laboratory project toward actual field implementation. The traditional software-only simulation environment and extensions to a hardware-in-the-loop simulation are presented in describing the migration of RHODES onto the traffic controller hardware itself. In addition, a new enhancement to the standard software-only simulation that allows remote access is described. The enhancement removes the requirement that both the simulation and the traffic control scheme reside locally. This architecture is capable of supporting any traffic simulation package that satisfies specific input-output data requirements. This remote simulation environment was tested with several different types of networks and was found to perform in the same manner as its local counterpart. Remote simulation has all of the advantages of its local counterpart, such as control and flexibility, with the added benefit of distribution. This remote environment could be used in many different ways and by different groups or individuals, including state or local transportation agencies interested in performing their own evaluations of alternative traffic control systems.

Microscopic Simulation of Urban Traffic Based on Cellular Automata

1997 ◽  
Vol 08 (05) ◽  
pp. 1025-1036 ◽  
Author(s):  
J. Esser ◽  
M. Schreckenberg
Keyword(s):  
Cellular Automata ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management Strategies ◽  
Urban Traffic ◽  
Large Network ◽  
Microscopic Simulation ◽  
Real Time Traffic ◽  
Traffic Systems

Saturated capacities in traffic systems evoke increasing interest in simulations of complex networks serving as laboratory environment for developing management strategies. Especially for urban areas questions concerning overall traffic control have to be considered with regard to their impacts on the whole network. Modeling traffic flow dynamics using cellular automata allows us to run large network traffic simulations with only comparatively low computational efforts. We present a traffic simulation tool for urban road networks which is based on the Nagel–Schreckenberg Model. Arbitrary kinds of roads and crossings are modeled as combinations of only a few basic elements. Furthermore parking capacities are considered as well as circulations of public transports. The vehicles are driven corresponding to route plans or at random depending on the available data. The application of this network simulation covers investigations on the field of traffic planning as well as online simulations based on real-time traffic data as basis for dynamic traffic management systems.

Observation-Based Lane-Vehicle Assignment Hierarchy: Microscopic Simulation on Urban Street Network

2000 ◽  
Vol 1710 (1) ◽  
pp. 96-103 ◽  
Author(s):  
Heng Wei ◽  
Joe Lee ◽  
Qiang Li ◽  
Connie J. Li
Keyword(s):  
Traffic Control ◽  
Travel Behavior ◽  
Dynamic Traffic Assignment ◽  
Street Network ◽  
Microscopic Simulation ◽  
Lane Changing ◽  
Urban Street ◽  
Car Following ◽  
Assignment Model ◽  
Vehicle Assignment

A lane-assignment model in a vehicle-based microscopic simulation system describes a vehicle’s position during its journey on an urban street network. In other words, it is used to estimate an individual vehicle’s location, speed, routing plan, lane-choice plan, lane-changing plan, and car-following plan from its entrance to a street network until the end of the trip. From the authors’ observations and study of lanechoice and lane-changing behavior, it is concluded that a vehicle is assigned to a lane in a logical manner depending on the relationship between its route-planned motivation and traffic conditions in the current lane and other lanes. A lane-assignment model consists of three components: lane choice, car following, and lane changing. The lane-changing component is composed of three submodels—a decision model, a lane-changing condition model, and a lane-changing maneuver model. Rules are discussed for lane-choice and lane-changing modeling based on videotaped observations over four-lane urban streets. Then a heuristic structure of a lane-vehicle-assignment model is proposed, which exposes the inherent relationship between vehicle-based travel behavior and lane-vehicle assignment on an urban street network. With the addition of a lane-assignment model derived from observed data, a simulation may be developed to correctly represent travel behavior and dynamic traffic assignment at the lane level and provide a more effective tool for design and evaluation of the performance of strategies for traffic control, traveler information, and congestion alleviation.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

scholarly journals A NEW CAR-FOLLOWING MODEL CONSIDERING ACCELERATION OF LEAD VEHICLE

Transport ◽  
2014 ◽  
Vol 31 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Bingrong Sun ◽  
Na Wu ◽  
Ying-En Ge ◽  
Taewan Kim ◽  
Hongjun Michael Zhang
Keyword(s):  
General Motors ◽  
Weighted Sum ◽  
Relative Speed ◽  
New Model ◽  
Car Following ◽  
Stimulus Response ◽  
The Road ◽  
Car Following Model ◽  
On The Road ◽  
Lead Vehicle

For decades, the general motors (gm) car following model has received a great deal of attention and provided a basic framework to describe the interactions between vehicles on the road. It is based on the stimulus-response assumption that the following vehicle responds to the relative speed between the lead vehicle and itself. However, some of the empirical findings show that the assumption of gm model is not always true and need some modification. For example, the acceleration of the following vehicle is very sensitive to the sign of the relative speed and because of no term in the model that directly represents the leader’s acceleration, the follower’s response to the leader’s acceleration can be retarded. This paper offers a new car-following model that can be considered as a variant of the gm model that can better capture car following behavior. The new model treats the follower’s acceleration as a proportion of a weighted sum of the leader’s acceleration and the relative speed between the lead and following vehicles. This paper compares the new model with the original gm model numerically and the characteristics of the new parameters in the model are investigated. It is also shown that the new model overcomes the shortcomings of the original gm model identified in this paper and gives us more instruments to capture the real-world car-following behavior.

scholarly journals Transport modeling: An artificial immune system approach

2006 ◽  
Vol 16 (1) ◽  
pp. 3-30
Author(s):  
Dusan Teodorovic ◽  
Jovan Popovic ◽  
Panta Lucic
Keyword(s):  
Immune System ◽  
Real Time ◽  
Traffic Control ◽  
System Approach ◽  
Artificial Immune System ◽  
Control Strategies ◽  
Transportation Systems ◽  
Artificial Immune ◽  
Real Time Traffic ◽  
Wide Range

This paper describes an artificial immune system approach (AIS) to modeling time-dependent (dynamic, real time) transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions) that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies) for different antigens (different traffic "scenarios"). This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

2019 ◽  
Vol 2673 (12) ◽  
pp. 367-379 ◽  
Author(s):  
Yalda Rahmati ◽  
Mohammadreza Khajeh Hosseini ◽  
Alireza Talebpour ◽  
Benjamin Swain ◽  
Christopher Nelson
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Models ◽  
Driving Behavior ◽  
Data Driven ◽  
Microscopic Simulation ◽  
Car Following ◽  
The Road ◽  
Human Driver ◽  
Significant Difference ◽  
On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

scholarly journals The Dynamic Control of the Light Signalling Device in Real-Time

2019 ◽  
Vol 292 ◽  
pp. 03014
Author(s):  
Jan Mrazek ◽  
Lucia Duricova Mrazkova ◽  
Martin Hromada ◽  
Jana Reznickova
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Dynamic Control ◽  
Traffic Density ◽  
Light Signaling ◽  
Dynamic Traffic ◽  
Real Time Control ◽  
Real Time Traffic ◽  
Time Control ◽  
Control Dynamic

The article is focused on the issue of interval on a light signaling device. Light signaling devices operate on different systems by means of which they are controlled. The control problem is a very static setting that does not respond to real-time traffic. Important variables for dynamic real-time control are traffic density in a selected area along with average speed. These variables are interdependent and can be based on dynamic traffic control. Dynamic traffic control ensures smoother traffic through major turns. At the same time, the number of harmful CO2 emitted from the means of transport should be reduced to the air. When used in low operation, power consumption should be reduced.

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