Active traffic management on road networks: a macroscopic approach

2010 ◽  
Vol 368 (1928) ◽  
pp. 4607-4626 ◽  
Author(s):  
Alex A. Kurzhanskiy ◽  
Pravin Varaiya
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Control Strategies ◽  
Continuous Process ◽  
Measurement Data ◽  
Road Networks ◽  
Operations Planning ◽  
Traffic Measurement ◽  
Traffic State ◽  
Real Time Traffic

Active traffic management (ATM) is the ability to dynamically manage recurrent and non-recurrent congestion based on prevailing traffic conditions in order to maximize the effectiveness and efficiency of road networks. It is a continuous process of (i) obtaining and analysing traffic measurement data, (ii) operations planning, i.e. simulating various scenarios and control strategies, (iii) implementing the most promising control strategies in the field, and (iv) maintaining a real-time decision support system that filters current traffic measurements to predict the traffic state in the near future, and to suggest the best available control strategy for the predicted situation. ATM relies on a fast and trusted traffic simulator for the rapid quantitative assessment of a large number of control strategies for the road network under various scenarios, in a matter of minutes. The open-source macrosimulation tool A urora R OAD N ETWORK M ODELER is a good candidate for this purpose. The paper describes the underlying dynamical traffic model and what it takes to prepare the model for simulation; covers the traffic performance measures and evaluation of scenarios as part of operations planning; introduces the framework within which the control strategies are modelled and evaluated; and presents the algorithm for real-time traffic state estimation and short-term prediction.

scholarly journals Assessments of Real-Time Communications over TSN Automotive Networks

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 556
Author(s):  
Lucia Lo Bello ◽  
Gaetano Patti ◽  
Giancarlo Vasta
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Communication System ◽  
Traffic Management ◽  
Mac Layer ◽  
Automated Driving ◽  
Real Time Traffic ◽  
Management Scheme ◽  
Event Driven ◽  
Automotive Networks

The IEEE 802.1Q-2018 standard embeds in Ethernet bridges novel features that are very important for automated driving, such as the support for time-driven communications. However, cars move in a world where unpredictable events may occur and determine unforeseen situations. To properly react to such situations, the in-car communication system has to support event-driven transmissions with very low and bounded delays. This work provides the performance evaluation of EDSched, a traffic management scheme for IEEE 802.1Q bridges and end nodes that introduces explicit support for event-driven real-time traffic. EDSched works at the MAC layer and builds upon the mechanisms defined in the IEEE 802.1Q-2018 standard.

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 ENHANCED VANET ROUTING PROTOCOLS FOR DYNAMIC MAPPING IN REAL TIME TRAFFIC

2019 ◽  
Vol 01 (03) ◽  
pp. 139-147
Author(s):  
Wang Haoxiang ◽  
Smys S
Keyword(s):  
Real Time ◽  
Routing Protocols ◽  
Traffic Management ◽  
Path Optimization ◽  
Delivery Ratio ◽  
Digital Map ◽  
Real Time Traffic ◽  
Dynamic Mapping ◽  
Adhoc Network ◽  
Frame Work

The developments in the means of transportation along with the communication advancements has made the automotives to step into its next level of innovation by providing a safe, convenient and well-timed transportation. This is made possible by the introduction of the frame work that is particularly designed to establish connectivity between vehicles on road without any previous structure to support with. This paradigm formed particularly in organizing communication between vehicles is the vehicular Adhoc network (VANET) that causes a vehicles to vehicle connection for proper managing of the traffic flow to make the travel more safe and comfortable. The paper proposes a dynamic mapping of real time traffic with the acquisition of digital map by crowd mapping with clustering to offer path optimization to minimize the delay in the responses, for having an efficient traffic managing. The evaluation of the proposed methodology ensures the minimization of the delay in the communication and the improved delivery ratio incurred, when compared with the carry-forward based routings methods that cause more delay resulting in imperfect traffic management.

scholarly journals RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
High Speed ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Trajectory Data ◽  
Real Time Traffic ◽  
Histogram Estimation ◽  
Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

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.

Real-Time Traffic Measurement from Single Loop Inductive Signatures

2002 ◽  
Vol 1804 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Seri Oh ◽  
Stephen G. Ritchie ◽  
Cheol Oh
Keyword(s):  
Real Time ◽  
The United States ◽  
Traffic Data ◽  
Traffic Surveillance ◽  
Vehicle Class ◽  
Traffic Measurement ◽  
Single Loop ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Traffic Data Collection

Accurate traffic data acquisition is essential for effective traffic surveillance, which is the backbone of advanced transportation management and information systems (ATMIS). Inductive loop detectors (ILDs) are still widely used for traffic data collection in the United States and many other countries. Three fundamental traffic parameters—speed, volume, and occupancy—are obtainable via single or double (speed-trap) ILDs. Real-time knowledge of such traffic parameters typically is required for use in ATMIS from a single loop detector station, which is the most commonly used. However, vehicle speeds cannot be obtained directly. Hence, the ability to estimate vehicle speeds accurately from single loop detectors is of considerable interest. In addition, operating agencies report that conventional loop detectors are unable to achieve volume count accuracies of more than 90% to 95%. The improved derivation of fundamental real-time traffic parameters, such as speed, volume, occupancy, and vehicle class, from single loop detectors and inductive signatures is demonstrated.

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