Freeway Traffic Management and Control

The advent of computer-controlled electronic freeway surveillance and control systems in the past decade represents a potentially significant new operational tool for traffic engineers in large urban areas. These systems are capable of responding to rapidly changing traffic conditions and in various demonstration projects they have proven useful in maintaining an acceptable level of service for freeway operations, reducing the extent and duration of traffic congestion, minimizing the adverse effects of accidents and other incidents on traffic operations, and reducing accident experience. Surveillance system goals and techniques for achieving these goals are reviewed in this paper with particular emphasis on the problem of managing unexpected capacity-reducing incident situations. Recent research has demonstrated the feasibility of detecting incidents from traffic flow data, which is desirable for surveillance purposes because this information can be used to implement control strategies which attempt in real-time to divert some freeway traffic to alternate routes. Some of the operational problems involved with freeway traffic management are discussed, particularly the generation of false alarms by detection algorithms and driver willingness to be diverted to alternate routes.

Download Full-text

Freeway Traffic Management and Control

Encyclopedia of Complexity and Systems Science ◽

10.1007/978-3-642-27737-5_232-3 ◽

2017 ◽

pp. 1-28 ◽

Cited By ~ 4

Author(s):

Andreas Hegyi ◽

Tom Bellemans ◽

Bart De Schutter

Keyword(s):

Traffic Management ◽

Freeway Traffic ◽

And Control

Download Full-text

A Novel Approach for Mixed Manual/Connected Automated Freeway Traffic Management

Sensors ◽

10.3390/s20061757 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1757

Author(s):

Duo Li ◽

Peter Wagner

Keyword(s):

Fixed Point ◽

Traffic Management ◽

Variable Speed Limit ◽

Freeway Traffic ◽

Novel Approach ◽

Data Source ◽

Sensor Density ◽

Interval Type ◽

And Control

Freeway traffic management and control often rely on input from fixed-point sensors. A sufficiently high sensor density is required to ensure data reliability and accuracy, which results in high installation and maintenance costs. Moreover, fixed-point sensors encounter difficulties to provide spatiotemporally and wide-ranging information due to the limited observable area. This research exploits the utilization of connected automated vehicles (CAVs) as an alternative data source for freeway traffic management. To handle inherent uncertainty associated with CAV data, we develop an interval type 2 fuzzy logic-based variable speed limit (VSL) system for mixed traffic. The simulation results demonstrate that when more 10% CAVs are deployed, the performance of the proposed CAV-based system can approach that of the detector-based system. It is demonstrated in addition that the introduction of CAVs may make VSL obsolete at very high CAV-equipment rates.

Download Full-text

Freeway Traffic Management and Control

Complex Dynamics of Traffic Management ◽

10.1007/978-1-4939-8763-4_232 ◽

2019 ◽

pp. 167-193

Author(s):

Andreas Hegyi ◽

Tom Bellemans ◽

Bart De Schutter

Keyword(s):

Traffic Management ◽

Freeway Traffic ◽

And Control

Download Full-text

Risk Evaluation for Conflicts between Crossing Pedestrians and Right-Turning Vehicles at Intersections

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211031885 ◽

2021 ◽

pp. 036119812110318

Author(s):

Feifei Xin ◽

Xiaobo Wang ◽

Chongjing Sun

Keyword(s):

Traffic Management ◽

Traffic Signals ◽

Control Measures ◽

Video Information ◽

Fuzzy Clustering Method ◽

Different Types ◽

Micro Level ◽

Conflict Risk ◽

And Control ◽

Deceleration Time

In recent years, conflicts between crossing pedestrians and right-turning vehicles have become more severe at intersections in China, where right-turning vehicles are usually not controlled by traffic signals. This study proposes a quantitative method for evaluating the conflict risk between pedestrians and right-turning vehicles at intersections based on micro-level behavioral data obtained from video detection. A typical intersection in Shanghai was selected as the study site. In total, 670 min of video were recorded during the peak hours from 7:30 a.m. to 9:30 p.m on one day. After processing the video information, vehicle and pedestrian tracking data were obtained, including the velocity, acceleration, deceleration, time, and location coordinates. Based on these data, several conflict indicators were proposed and these indicators were extracted automatically using MATLAB to identify pedestrian–right-turning vehicle conflicts and to determine the severity of the conflicts identified. This process identified 93 examples of such conflicts. The conflict risks were quantitatively classified using the K-means fuzzy clustering method and all of the conflicts were assigned to five grades. The characteristics of the conflict distribution and the severity of different types of conflict were also analyzed, which showed that conflicts on different areas on the crosswalk differed in their severity. Based on the conclusions, practical traffic management and control measures are proposed to reduce the risk on pedestrian crossings.

Download Full-text