Link Flow Evaluation Using Loop Detector Data: Traveler Response to Variable-Message Signs

1996 ◽  
Vol 1550 (1) ◽  
pp. 58-64 ◽  
Author(s):  
Youngbin Yim ◽  
Jean-Luc Ygnace
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Preliminary Investigation ◽  
Traffic Information ◽  
Mean Flow ◽  
Variable Message Signs ◽  
Real Time Traffic ◽  
Operational Test ◽  
Paris Region ◽  
Message Signs

Système d'Information Routière Intelligible aux Usagers (SIRIUS) is the largest urban field operational test of the advanced traveler information and automated traffic management system in Europe. With variable-message signs, SIRIUS has been in operation in the Paris region for 3 years. A preliminary investigation of the effectiveness of the SIRIUS system in traffic management is presented. The extent to which drivers respond to real-time traffic information and the consequential changes in link flow under SIRIUS is also presented. Time-series traffic data were analyzed to measure changes in mean flow rates at a selected link. It was found that variable-message signs influence drivers to choose less congested routes when drivers are provided with real-time traffic information, and that a driver's decision to divert is closely associated with the information pertaining to the level of congestion. In the Paris region, drivers received information on the length of the queue at the time of this study. As congestion becomes heavier, drivers are more likely to respond to variable-message signs. According to the data analysis, a queue length of 3 km seems to be a threshold at which a significant number of drivers choose to use an alternative route.

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 Cross-Vendor and Cross-State Analysis of GPS Probe Data Latency

2018 ◽  
Vol 2672 (42) ◽  
pp. 180-191 ◽  
Author(s):  
Zhongxiang Wang ◽  
Masoud Hamedi ◽  
Elham Sharifi ◽  
Stanley Young
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Ground Truth ◽  
Traffic Information ◽  
Incident Detection ◽  
Dynamic Message Signs ◽  
Real Time Traffic ◽  
Outsourced Data ◽  
Message Signs

Crowd sourced GPS probe data have become a major source of real-time traffic information applications. In addition to traditional traveler advisory systems such as dynamic message signs (DMS) and 511 systems, probe data are being used for automatic incident detection, integrated corridor management (ICM), end of queue warning systems, and mobility-related smartphone applications. Several private sector vendors offer minute by minute network-wide travel time and speed probe data. The quality of such data in terms of deviation of the reported travel time and speeds from ground-truth has been extensively studied in recent years, and as a result concerns over the accuracy of probe data have mostly faded away. However, the latency of probe data—defined as the lag between the time at which disturbance in traffic speed is reported in the outsourced data feed, and the time at which the traffic is perturbed—has become a subject of interest. The extent of latency of probe data for real-time applications is critical, so it is important to have a good understanding of the amount of latency and its influencing factors. This paper uses high-quality independent Bluetooth/Wi-Fi re-identification data collected on multiple freeway segments in three different states, to measure the latency of the vehicle probe data provided by three major vendors. The statistical distribution of the latency and its sensitivity to speed slowdown and recovery periods are discussed.

scholarly journals Methodology for Calculating Latency of GPS Probe Data

2017 ◽  
Vol 2645 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Zhongxiang Wang ◽  
Masoud Hamedi ◽  
Stanley Young
Keyword(s):  
Real Time ◽  
Ground Truth ◽  
Transportation Systems ◽  
Traffic Information ◽  
Reference Source ◽  
Traveler Information ◽  
Ground Truth Data ◽  
Real Time Traffic ◽  
Message Signs ◽  
Road Segmentation

Crowdsourced GPS probe data, such as travel time on changeable-message signs and incident detection, have been gaining popularity in recent years as a source for real-time traffic information to driver operations and transportation systems management and operations. Efforts have been made to evaluate the quality of such data from different perspectives. Although such crowdsourced data are already in widespread use in many states, particularly the high traffic areas on the Eastern seaboard, concerns about latency—the time between traffic being perturbed as a result of an incident and reflection of the disturbance in the outsourced data feed—have escalated in importance. Latency is critical for the accuracy of real-time operations, emergency response, and traveler information systems. This paper offers a methodology for measuring probe data latency regarding a selected reference source. Although Bluetooth reidentification data are used as the reference source, the methodology can be applied to any other ground truth data source of choice. The core of the methodology is an algorithm for maximum pattern matching that works with three fitness objectives. To test the methodology, sample field reference data were collected on multiple freeway segments for a 2-week period by using portable Bluetooth sensors as ground truth. Equivalent GPS probe data were obtained from a private vendor, and their latency was evaluated. Latency at different times of the day, impact of road segmentation scheme on latency, and sensitivity of the latency to both speed-slowdown and recovery-from-slowdown episodes are also discussed.

Artificial Intelligence-Based Architecture for Real-Time Traffic Flow Management

1998 ◽  
Vol 1651 (1) ◽  
pp. 53-58 ◽  
Author(s):  
Adel W. Sadek ◽  
Brian L. Smith ◽  
Michael J. Demetsky
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Traffic Flow ◽  
Traffic Management ◽  
Traffic Information ◽  
Case Based Reasoning ◽  
Flow Management ◽  
Real Time Traffic ◽  
Highway Network ◽  
Traffic Flow Management

Real-time traffic flow management has recently emerged as one of the promising approaches to alleviating congestion. This approach uses real-time and predicted traffic information to develop routing strategies that attempt to optimize the performance of the highway network. A survey of existing approaches to real-time traffic management indicated that they suffer from several limitations. In an attempt to overcome these, the authors developed an architecture for a routing decision support system (DSS) based on two emerging artificial intelligence paradigms: case-based reasoning and stochastic search algorithms. This architecture promises to allow the routing DSS to ( a) process information in real time, ( b) learn from experience, ( c) handle the uncertainty associated with predicting traffic conditions and driver behavior, ( d) balance the trade-off between accuracy and efficiency, and ( e) deal with missing and incomplete data problems.

scholarly journals Real time traffic information system-A step towards total traffic management at Kamakura

2001 ◽  
Vol 18 ◽  
pp. 887-894
Author(s):  
Shinji KAJITANI ◽  
Kunihiro SAKAMOTO ◽  
Hisashi KUBOTA ◽  
Youji Takahashi
Keyword(s):  
Information System ◽  
Real Time ◽  
Traffic Management ◽  
Traffic Information ◽  
Real Time Traffic ◽  
System A

Four-Dimensional Interactive Visualization System for Transportation Management and Traveler Information

2005 ◽  
Vol 1937 (1) ◽  
pp. 152-158 ◽  
Author(s):  
Michael L. Pack ◽  
Phillip Weisberg ◽  
Sujal Bista
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Traffic Management ◽  
Prototype System ◽  
Traveler Information ◽  
Visualization System ◽  
Dynamic Message Signs ◽  
Traffic Sensors ◽  
Real Time Traffic ◽  
Message Signs

This research developed a system for visualizing four-dimensional (4-D), real-time transportation data for the major road networks of Washington, D.C., Northern Virginia, and the entire state of Maryland. The effort employed a combination of OpenGL and other modeling techniques to develop a scalable, highly interactive 4-D model using available geographic information system (GIS) and transportation infrastructure data in conjunction with real-time traffic management center data. The prototype system interacts with real-time traffic databases to show animations of real-time traffic data (volume and speed) along with incident data (accident locations, lane closures, responding agencies, etc.). A user can “fly” or “drive” through the region to inspect conditions at an infinite number of angles and distances. The program also allows users to monitor the status of and interact with traffic control devices such as dynamic message signs, closed-circuit television feeds, and traffic sensors and even view the location of emergency response vehicles equipped with Global Positioning System transceivers. Because the system uses standard GIS data and relatively standard transportation databases to derive traffic measures, it can be scaled to incorporate other states and agencies.

Cloud IoT-Based Mobile Agent Framework for Real-Time Traffic Information Acquisition, Storage, and Retrieval

2019 ◽  
pp. 14-33
Author(s):  
Md Abdullah al Forhad ◽  
Md Nadim ◽  
Md. Rahatur Rahman ◽  
Shamim Akhter
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Management System ◽  
Traffic Information ◽  
Weather Data ◽  
Storage And Retrieval ◽  
Traffic Routing ◽  
Real Time Traffic ◽  
Traffic Management System ◽  
Vehicle Information

Traffic is an inevitable problem for metro cities around the globe. Intelligent traffic management system helps to improve the traffic flow by detecting congestions or incidents and suggesting appropriate actions on traffic routing. A new and dynamic internet-based decision-making tool for traffic management system was proposed and implemented in authors' previous works. The tool needs weather, road, and vehicle-related integrated information from different data repositories. Several online web portals host real-time weather data streams. However, road and vehicle information are missing in those portals. In addition, their coverage is limited to city-level congregate information but precise road segment-based information is necessary for real-time TMS decision. Internet of things (IoT)-based online sensors can be a solution for this circumstance. As a consequence, in this chapter, an IoT-based framework is proposed and implemented with several remote mobile agents. Agents are securely interconnected to the cloud, and able to collect and exchange data through wireless communication.

Finding Near-Optimal Locations for Variable Message Signs for Real-Time Network Traffic Management

2003 ◽  
Vol 1856 (1) ◽  
pp. 34-53 ◽  
Author(s):  
Nathan Huynh ◽  
Yi-Chang Chiu ◽  
Hani S. Mahmassani
Keyword(s):  
Real Time ◽  
Network Traffic ◽  
Traffic Management ◽  
Dynamic Traffic Assignment ◽  
A Priori ◽  
Optimal Solution ◽  
Solution Procedure ◽  
Variable Message Signs ◽  
Message Signs ◽  
Short Time

This study addressed the problem of finding the best locations for portable variable message signs to divert traffic to alternative paths when an incident occurs so that impact on the network is minimized. The study proposed and evaluated a solution procedure for finding such locations in the context of real-time network traffic management. In this context, it was essential that the procedure find the solution to the formulated mathematical program in a relatively short time. The procedure relied on a heuristic to guide the search and a simulation-based dynamic traffic assignment program to evaluate the solution. The proposed heuristic combined principles of greedy and drop heuristics. To evaluate the proposed solution procedure, four sets of experiments were conducted on the Fort Worth, Texas, network. The results from the proposed solution procedure are compared with those obtained by other methods—( a) an a priori solution to a stochastic programming formulation, and ( b) the optimal solution with an exact (but slow to execute) procedure. It is found that the solutions obtained from the proposed solution procedure consistently outperform the a priori solutions and that they are consistently within 15% of the optimal solutions.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

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