The TravTek Traffic Management Center and Traffic Information Network

1991 ◽  
Author(s):  
Robert L. Rupert
Keyword(s):  
Traffic Management ◽  
Traffic Information ◽  
Information Network

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 Macroscopic Traffic State Estimation: Understanding Traffic Sensing Data-Based Estimation Errors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Paul B. C. van Erp ◽  
Victor L. Knoop ◽  
Serge P. Hoogendoorn
Keyword(s):  
State Estimation ◽  
Traffic Management ◽  
Estimation Error ◽  
Ground Truth ◽  
Traffic Information ◽  
Estimation Errors ◽  
Sensing Data ◽  
Traffic Conditions ◽  
Traffic State ◽  
Traffic State Estimation

Traffic state estimation is a crucial element in traffic management systems and in providing traffic information to road users. In this article, we evaluate traffic sensing data-based estimation error characteristics in macroscopic traffic state estimation. We consider two types of sensing data, that is, loop-detector data and probe speed data. These data are used to estimate the mean speed in a discrete space-time mesh. We assume that there are no errors in the sensing data. This allows us to study the errors resulting from the differences in characteristics between the sensing data and desired estimate together with the incomplete description of the relation between the two. The aim of the study is to evaluate the dependency of this estimation error on the traffic conditions and sensing data characteristics. For this purpose, we use microscopic traffic simulation, where we compare the estimates with the ground truth using Edie’s definitions. The study exposes a relation between the error distribution characteristics and traffic conditions. Furthermore, we find that it is important to account for the correlation between individual probe data-based estimation errors. Knowledge related to these estimation errors contributes to making better use of the available sensing data in traffic state estimation.

Performance Measurement for Traffic Management Systems

1998 ◽  
Vol 1634 (1) ◽  
pp. 39-45 ◽  
Author(s):  
James H. Banks
Keyword(s):  
Performance Measurement ◽  
Traffic Management ◽  
Evaluation Studies ◽  
Traffic Information ◽  
Management Systems ◽  
Management Actions ◽  
Institutional Infrastructure ◽  
Before And After ◽  
Incident Duration ◽  
Traffic Volumes

Performance measurement refers to attempts to quantify some aspect of the performance of an organization. A study was conducted to analyze needs, opportunities, and techniques for measuring performance of transportation management centers (TMCs). Opportunities and needs were identified by analyzing the interrelationships among performance measurement objectives, objects, and study designs. This analysis suggests that before-and-after evaluation studies of traffic management actions and monitoring of traffic data to detect system changes are the most appropriate forms of performance measurement for TMCs. Important potential measures of effectiveness for traffic management systems include travel time and related measures, ramp delay, traffic volumes, accident rates, traffic information accuracy, incident duration, and equipment status. Techniques are available for quantifying these measures, although there are a number of concerns with data accuracy, especially where travel times are estimated from loop-detector data. Case studies of two California TMCs suggest that, although sophisticated data collection systems are available or planned, the institutional infrastructure to carry out performance measurement may be lacking.

Distributed Link-Layer Control for Automated Highway Systems

1997 ◽  
Vol 119 (2) ◽  
pp. 192-197 ◽  
Author(s):  
Huei Peng
Keyword(s):  
Traffic Management ◽  
Control Algorithm ◽  
Traffic Information ◽  
Link Layer ◽  
Automated Highway Systems ◽  
Traffic Distribution ◽  
Automated Highway ◽  
Distributed Control Algorithm ◽  
Layer Control

The modeling and design of a link-layer control algorithm for automated highway systems are presented in this paper. Link-layer control systems address the coordination of traffic on a stretch of highway, and serve as the intermediate layer between traffic management (ATMS) and vehicle control (AVCS) systems. The key role of the link layer control system is to use macroscopic traffic information for improved traffic flow. A distributed control algorithm is developed based on optimal control theory. The proposed control law is implemented in a simulation program which keeps track of the motions of each individual vehicles on the highway. Simulation results under three perturbed conditions—uneven traffic distribution, broken vehicle, and traffic merging—are presented.

Operator Support in Traffic Management: A Heuristics Model and Experimental Study

1996 ◽  
Vol 40 (18) ◽  
pp. 872-876
Author(s):  
John Murray ◽  
Yili Liu
Keyword(s):  
Traffic Management ◽  
Contextual Information ◽  
Information Modeling ◽  
Superior Performance ◽  
Traffic Information ◽  
Control Center ◽  
Modeling Process ◽  
Quantitative Data Analysis ◽  
Human Operators ◽  
Real Traffic

The identification of problems from numeric traffic measurements is an important part of control center activities in ATMS (Advanced Traffic Management Systems). However, an information modeling process that relies solely upon ‘traditional’ quantitative data analysis does not reflect faithfully the actual methods used by human operators. In addition to common-sense knowledge and specific contextual information, operators also use various heuristics and rules-of-thumb to supplement the numerical analysis. This paper describes an experiment to examine the effectiveness of an expert system that integrates quantitative and qualitative traffic information using a human-centered knowledge system design. The system's performance was investigated using a data suite of real traffic scenarios; the statistically significant results showed that the integrated process had superior performance compared to the ‘traditional’ quantitative analysis running alone.

Keep Moving, Stay Tuned

Transfers ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. 67-86 ◽  
Author(s):  
Marith Dieker
Keyword(s):  
Media Studies ◽  
Traffic Control ◽  
Traffic Management ◽  
Sociotechnical Systems ◽  
The Other ◽  
Traffic Information ◽  
Traffic Jams ◽  
Information Updates ◽  
The 1960S

With the rise of privatized automobility and the increase of traffic jams, new sociotechnical systems have emerged that aim at traffic control. Radio traffic information has been a key element in these systems. Through a qualitative analysis of historical radio broadcasts of the largest Dutch news station between 1960 and 2000, this article explores the changing format and content of traffic information updates. I will show how the rather formal, detailed, and paternalistic narratives of the traffic reports in the 1960s gave way to more informal, witty, yet flow-controlling traffic information discourse in later decades. I will explain the dynamics involved by drawing on mobility and media studies and by developing two distinct notions of flow, one of which builds conceptually on Raymond Williams’s work on mobile privatization, the other is grounded in the field of traffic management. In so doing, this article aims to contribute to a better understanding of the role of public radio broadcasts in our world of privatized automobility.

scholarly journals Day-to-Day Traffic Assignment Model considering Information Fusion and Dynamic Route Adjustment Ratio

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Manman Li ◽  
Jian Lu ◽  
Jiahui Sun
Keyword(s):  
Fixed Point ◽  
Traffic Management ◽  
Traffic Assignment ◽  
Influence Factors ◽  
Traffic Information ◽  
Assignment Model ◽  
Double Exponential ◽  
The Difference ◽  
Double Exponential Smoothing ◽  
Dynamic Route

A new day-to-day traffic assignment model is proposed to describe travelers’ day-to-day behavioral changes with advanced traffic information system. In the model, travelers’ perception is updated by a double exponential-smoothing learning process combining experience and traffic information that is explicitly modelled. Route adjustment ratio is dynamically determined by the difference between perceived and expected utilities. Through theoretical analyses, we investigate the existence of its fixed point and the influence factors of uniqueness of the fixed point. An iterative-based algorithm that can solve the fixed point is also given. Numerical experiments are then conducted to investigate effects of several main parameters on its convergence, which provides insights for traffic management. In addition, we compare the system efficiencies under the static route adjustment ratio and dynamic route adjustment ratio and show the application of the model.

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