scholarly journals Incorporating Queueing Dynamics into Schedule-Driven Traffic Control

2021 ◽  
Author(s):  
Hsu-Chieh Hu ◽  
Allen M. Hawkes ◽  
Stephen F. Smith
Keyword(s):  
Traffic Control ◽  
Planning Process ◽  
Heavy Traffic ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Time Model ◽  
Real Time Traffic ◽  
Online Planning ◽  
Queueing Delay ◽  
Pass Through

Key to the effectiveness of schedule-driven approaches to real-time traffic control is an ability to accurately predict when sensed vehicles will arrive at and pass through the intersection. Prior work in schedule-driven traffic control has assumed a static vehicle arrival model. However, this static predictive model ignores the fact that the queue count and the incurred delay should vary as different partial signal timing schedules (i.e., different possible futures) are explored during the online planning process. In this paper, we propose an alternative arrival time model that incorporates queueing dynamics into this forward search process for a signal timing schedule, to more accurately capture how the intersection’s queues vary over time. As each search state is generated, an incremental queueing delay is dynamically projected for each vehicle. The resulting total queueing delay is then considered in addition to the cumulative delay caused by signal operations. We demonstrate the potential of this approach through microscopic traffic simulation of a real-world road network, showing a 10-15% reduction in average wait times over the schedule-driven traffic signal control system in heavy traffic scenarios.

Real-Time Traffic Signal Control Based on Kalman Filtering Theory

2015 ◽  
Vol 713-715 ◽  
pp. 915-918
Author(s):  
Yuan Xin Xu ◽  
Wan Ying Yang ◽  
Wen Shi
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Control Method ◽  
Urban Traffic ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Traffic Flows ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Stable Signal

Aiming at the problem that individual control of urban traffic lights and stable signal timing. This paper proposed a real timing control method of traffic lights which based on Kalman filter. This method use Kalman filter to predict the next time traffic flows and then update the signal timing. By field researching the traffic flow of intersection in peak hour and predicting the traffic flow. Then update the signal timing. Meanwhile using the VISSIM to simulate the intersection. The result of the simulation shows that the length of vehicle queue decreased significantly and the number of stops dropped. The efficiency of access has been greatly improved.

Research on Calculating the Parameters of Signal Timing for TSP Based on Enumeration Method

2013 ◽  
Vol 756-759 ◽  
pp. 3094-3098
Author(s):  
Tian Hong Gu ◽  
Hui Jian Cao
Keyword(s):  
Communication Technology ◽  
Delay Time ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Transit Signal Priority ◽  
Average Delay ◽  
Real Time Traffic ◽  
Simulation Results ◽  
The Impact

It is of importance to calculate the parameters of signal timing for TSP (Transit Signal Priority). However, most studies computing the delay are provided based on formula of triangle area. With communication technology developing precisely calculating the delay time of buses can be achieved. The kernel algorithm of TSP still has room for improvement. In this paper, the algorithmic flow of the most of functions is presented based on Enumeration Method.Meanwhile the study uses the VISSIM simulation model to evaluate the impact of a number of alternative priority strategies on both the prioritized buses and general traffic. The priority logic that is considered in the study provides signal timing parameters within a real-time traffic signal control environment. A case study was conducted to validate the model results. Simulation results shows that this method effectively reduces average delay time of the travelers.

scholarly journals Context-Aware Intelligent Traffic Light Control through Secure Messaging

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Mükremin Özkul ◽  
Ilir Capuni ◽  
Elton Domnori
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Heavy Traffic ◽  
Waiting Times ◽  
Traffic Information ◽  
Context Aware ◽  
Traffic Flows ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Simulation Results

In this paper, we propose STCM, a context-aware secure traffic control model to manage competing traffic flows at a given intersection by using secure messages with real-time traffic information. The vehicle is modeled as a virtual sensor which reports the traffic state, such as its speed and location, to a traffic light controller through a secure and computationally lightweight protocol. During the reporting process, a vehicle’s identity and location are kept anonymous to any other vehicle in the system. At an intersection, the traffic light controller receives the messages with traffic information, verifies the identities of the vehicles, and dynamically implements and optimizes the traffic light phases in real-time. Moreover, the system is able to detect the presence of emergency vehicles (such as ambulances and fire fighting trucks) in the communication range and prioritize the intersection crossing of such vehicles to in order to minimize their waiting times. The simulation results demonstrate that the system significantly reduces the waiting time of the vehicles in both light and heavy traffic flows compared to the pretimed signal control and the adaptive Webster’s method. Simulation results also yield effective robustness against impersonating attacks from malicious vehicles.

scholarly journals DELAYS AT SIGNALIZED INTERSECTIONS WITH EXHAUSTIVE TRAFFIC CONTROL

2012 ◽  
Vol 26 (3) ◽  
pp. 337-373 ◽  
Author(s):  
M.A.A. Boon ◽  
I.J.B.F. Adan ◽  
E.M.M. Winands ◽  
D.G. Down
Keyword(s):  
Closed Form ◽  
Traffic Control ◽  
Heavy Traffic ◽  
Interarrival Time ◽  
Traffic Signal Control ◽  
Stay Green ◽  
Traffic Lights ◽  
Traffic Intersection ◽  
Poisson Arrivals ◽  
The Mean

In this paper, we study a traffic intersection with vehicle-actuated traffic signal control. Traffic lights stay green until all lanes within a group are emptied. Assuming general renewal arrival processes, we derive exact limiting distributions of the delays under heavy traffic (HT) conditions. Furthermore, we derive the light traffic (LT) limit of the mean delays for intersections with Poisson arrivals, and develop a heuristic adaptation of this limit to capture the LT behavior for other interarrival-time distributions. We combine the LT and HT results to develop closed-form approximations for the mean delays of vehicles in each lane. These closed-form approximations are quite accurate, very insightful, and simple to implement.

scholarly journals A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoke Zhou ◽  
Fei Zhu ◽  
Quan Liu ◽  
Yuchen Fu ◽  
Wei Huang
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Learning Algorithm ◽  
Control Model ◽  
Traffic Signal ◽  
Signal Timing ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Signal Timing

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

Based on Road Green Wave Effect of Collaborative Strategy of Signal Timing Fuzzy Control

2013 ◽  
Vol 321-324 ◽  
pp. 1836-1841
Author(s):  
Yi Zhang ◽  
Geng Sheng Huang
Keyword(s):  
Fuzzy Control ◽  
Traffic Control ◽  
Road Traffic ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Wave Effect ◽  
Density Correlation ◽  
Dynamic Coordination

With growth of Urban Road Traffic Volume and the increase of Road Network Density, correlation between adjacent road intersections is becoming more and more obvious. An intersection traffic signal adjustment tends to affect the health of a number of adjacent intersections road traffic flow. Its congestion may over time gradually spread to within a few blocks and regions all around the intersection. Therefore increasingly high demands of urban traffic signal control make a variety of advanced control technology integration, achieve the purpose to adjust a control parameter, in order to achieve dynamic coordination within the city - wide traffic control, to satisfy traffic demands, and then let the road traffic and the transport demand make a new balance. And This article introduces is the use of the green wave effect collaborative strategies adjacent green extension of fuzzy control in order to solve the problem of coupling between intersections road. This algorithm makes Signal Timing to be more flexible.

scholarly journals Softpressure: A Schedule-Driven Backpressure Algorithm for Coping with Network Congestion

2017 ◽  
Author(s):  
Hsu-Chieh Hu ◽  
Stephen F. Smith
Keyword(s):  
Hybrid Method ◽  
Queuing Theory ◽  
Hybrid Approach ◽  
The Other ◽  
Traffic Signal Control ◽  
Network Congestion ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Pass Through ◽  
The Stability

We consider the problem of minimizing the the delay of jobs moving through a directed graph of service nodes. In this problem, each node may have several links and is constrained to serve one link at a time. As jobs move through the network, they can pass through a node only after they have been serviced by that node. The objective is to minimize the delay jobs incur sitting on queues waiting to be serviced. Two popular approaches to this problem are backpressure algorithm and schedule-driven control. In this paper, we present a hybrid approach of those two methods that incorporates the stability of queuing theory into the schedule-driven control. We then demonstrate how this hybrid method outperforms the other two in a real-time traffic signal control problem, where the nodes are traffic lights, the links are roads, and the jobs are vehicles. We show through simulations that, in scenarios with heavy congestion, the hybrid method results in 50% and 15% reductions in delay over schedule-driven control and backpressure respectively. A theoretical analysis also justifies our results.

Assessment of Arterial Signal Timings Based on Various Operational Policies and Optimization Tools

2021 ◽  
pp. 036119812110111
Author(s):  
Suhaib Al Shayeb ◽  
Nemanja Dobrota ◽  
Aleksandar Stevanovic ◽  
Nikola Mitrovic
Keyword(s):  
Travel Time ◽  
Heavy Traffic ◽  
Future Research ◽  
Signal Timing ◽  
Highway Capacity ◽  
Average Delay ◽  
Highway Capacity Manual ◽  
Simulation And Optimization ◽  
Fort Lauderdale ◽  
Design Engineers

Traffic simulation and optimization tools are classified, according to their practical applicability, into two main categories: theoretical and practical. The performance of the optimized signal timing derived by any tool is influenced by how calculations are executed in the particular tool. Highway Capacity Software (HCS) and Vistro implement the procedures defined in the Highway Capacity Manual, thus they are essentially utilized by traffic operations and design engineers. Considering its capability of timing diagram drafting and travel time collection studies, Tru-Traffic is more commonly used by practitioners. All these programs have different built-in objective function(s) to develop optimized signal plans for intersections. In this study, the performance of the optimal signal timing plans developed by HCS, Tru-Traffic, and Vistro are evaluated and compared by using the microsimulation software Vissim. A real-world urban arterial with 20 intersections and heavy traffic in Fort Lauderdale, Florida served as the testbed. To eliminate any bias in the comparisons, all experiments were performed under identical geometric and traffic conditions, coded in each tool. The evaluation of the optimized plans was conducted based on average delay, number of stops, performance index, travel time, and percentage of arrivals on green. Results indicated that although timings developed in HCS reduced delay, they drastically increased number of stops. Tru-Traffic signal timings, when only offsets are optimized, performed better than timings developed by all of the other tools. Finally, Vistro increased arrivals on green, but it also increased delay. Optimized signal plans were transferred manually from optimization tools to Vissim. Therefore, future research should find methods for automatically transferring optimized plans to Vissim.

scholarly journals Vehicular Crowdsourcing for Congestion Support in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 662-685
Author(s):  
Stephan Olariu
Keyword(s):  
Smart City ◽  
Large Scale ◽  
Smart Cities ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Adaptive Traffic Signal Control ◽  
Traffic Conditions ◽  
Direct Benefits ◽  
Traffic Signal Control Systems ◽  
Computational Resources

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

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