scholarly journals Real-Time Predictive Control Strategy Optimization

2020 ◽  
Vol 2674 (3) ◽  
pp. 1-11 ◽  
Author(s):  
Samarth Gupta ◽  
Ravi Seshadri ◽  
Bilge Atasoy ◽  
A. Arun Prakash ◽  
Francisco Pereira ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Large Scale ◽  
Dynamic Traffic Assignment ◽  
Control Strategies ◽  
Urban Traffic ◽  
Computational Performance ◽  
Management Measures ◽  
Network States ◽  
Traffic Guidance

Urban traffic congestion has led to an increasing emphasis on management measures for more efficient utilization of existing infrastructure. In this context, this paper proposes a novel framework that integrates real-time optimization of control strategies (tolls, ramp metering rates, etc.) with the generation of traffic guidance information using predicted network states for dynamic traffic assignment systems. The efficacy of the framework is demonstrated through a fixed demand dynamic toll optimization problem, which is formulated as a non-linear program to minimize predicted network travel times. A scalable efficient genetic algorithm that exploits parallel computing is applied to solve this problem. Experiments using a closed-loop approach are conducted on a large-scale road network in Singapore to investigate the performance of the proposed methodology. The results indicate significant improvements in network-wide travel time of up to 9% with real-time computational performance.

scholarly journals The Feasibility of Opening Communities -- Based on the Wiedemann Car-Following Model

2018 ◽  
Vol 11 (3) ◽  
pp. 57
Author(s):  
Xiao-Yan Cao ◽  
Bing-Qian Liu ◽  
Bao-Ru Pan ◽  
Yuan-Biao Zhang
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Gated Communities ◽  
Urban Traffic ◽  
Average Delay ◽  
Traffic Demand ◽  
Car Following ◽  
Car Following Model ◽  
Urbanization In China ◽  
The Impact

With the accelerating development of urbanization in China, the increasing traffic demand and large scale gated communities have aggravated urban traffic congestion. This paper studies the impact of communities opening on road network structure and the surrounding road capacity. Firstly, we select four indicators, namely average speed, vehicle flow, average delay time, and queue length, to measure traffic capacity. Secondly, we establish the Wiedemann car-following model, then use VISSIM software to simulate the traffic conditions of surrounding roads of communities. Finally, we take Shenzhen as an example to simulate and compare the four kinds of gated communities, axis, centripetal and intensive layout, and we also analyze the feasibility of opening communities.

Research on Path Optimization of Urban Traffic Guidance System

2014 ◽  
Vol 624 ◽  
pp. 520-523
Author(s):  
Dan Ping Wang ◽  
Kun Yuan Hu
Keyword(s):  
Control Systems ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Design Patterns ◽  
Rapid Development ◽  
Urban Traffic ◽  
Guidance System ◽  
Traffic Signal Control ◽  
Traffic Guidance ◽  
Traffic Signal Control Systems

With the rapid development of economics and technology; the number of vehicles has largely increased. In this paper, traffic guidance and traffic control systems were researched as well as the Internet of Things (IOT). The author tried to combine these three parts to send traffic data to road users so as to let them choose the best route to travel. Meanwhile, traffic network optimization has been realized to reduce traffic congestion areas. This paper has optimized regional traffic signal control systems based on IOT, traffic guidance as well as traffic assignment, involved data sources, IOT design patterns, data collection as well as the relationship between guidance obeisance rate and traffic jam. It also involved the definition of ideal traffic shortest routes, planning and designing of traffic control systems. Results and researches could hope to combine with reality in order to reduce traffic congestion.

A new method for real-time evaluation of urban traffic congestion: a case study in Xi’an, China

2019 ◽  
Vol 35 (10) ◽  
pp. 1033-1048 ◽  
Author(s):  
Chaode Yan ◽  
Xiaobing Wei ◽  
Xiao Liu ◽  
Zhiguo Liu ◽  
Jinxi Guo ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Urban Traffic ◽  
New Method ◽  
Time Evaluation

scholarly journals Large-Scale Traffic Congestion Prediction Based on the Symmetric Extreme Learning Machine Cluster Fast Learning Method

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 730 ◽  
Author(s):  
Yiming Xing ◽  
Xiaojuan Ban ◽  
Xu Liu ◽  
Qing Shen
Keyword(s):  
Extreme Learning Machine ◽  
Traffic Congestion ◽  
Large Scale ◽  
High Efficiency ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Fast Learning ◽  
Congestion Prediction ◽  
Learning Machine ◽  
Scale Data

The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. Traditional neural network training takes a long time in addition to easily falling into the local optimal and overfitting. Accordingly, this inhibits the large-scale application of traffic prediction. On the basis of the theory of the extreme learning machine (ELM), the current paper puts forward a symmetric-ELM-cluster (S-ELM-Cluster) fast learning methodology. In this suggested methodology, the complex learning issue of large-scale data is transformed into different issues on small- and medium-scale data sets. Additionally, this methodology makes use of the extreme learning machine algorithm for the purpose of training the subprediction model on each different section of road, followed by establishing a congestion prediction model cluster for all the roads in the city. Together, this methodology fully exploits the benefits associated with the ELM algorithm in terms of accuracy over smaller subsets, high training speed, fewer parameters, and easy parallel acceleration for the realization of high-accuracy and high-efficiency large-scale traffic congestion data learning.

Real-Time Monitoring Unit of Traffic Flow

2012 ◽  
Vol 588-589 ◽  
pp. 1058-1061
Author(s):  
Ting Zhang ◽  
Zhan Wei Song
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Traffic ◽  
Traffic Information ◽  
Real Time Monitoring ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Transit Use ◽  
Control Traffic

With the sustained growth of vehicle ownerships, traffic congestion has become obstacle of urban development. In addition to developing public transport and accelerating the construction of rail transit, use scientific managing and controlling method in real-time monitoring traffic flow to divert the traffic stream is an effective way to solve urban traffic problems. In this paper, cross-correlation algorithm is used to obtain real-time traffic information, such as capacity and occupancy of a lane, so as to control traffic lights intelligently.

scholarly journals Evaluation of Reinforcement Learning Traffic Signalling Strategies for Alternative Objectives: Implementation in the Network of Nicosia, Cyprus

2020 ◽  
Vol 21 (4) ◽  
pp. 295-302
Author(s):  
Haris Ballis ◽  
Loukas Dimitriou
Keyword(s):  
Reinforcement Learning ◽  
Environmental Sustainability ◽  
Urban Areas ◽  
Traffic Management ◽  
Large Scale ◽  
Smart Cities ◽  
Control Strategies ◽  
Urban Traffic ◽  
Signal Control ◽  
Urban Network

AbstractSmart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.

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