Traffic management model for vehicle re-routing and traffic light control based on Multi-Objective Particle Swarm Optimization

Hatri, Chaimae El; Boumhidi, Jaouad

doi:10.3233/idt-170288

Cited by 16 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(Tahifa et al, 2015) showed that Swarm Q-learning performs better than standard Q-learning in increasing the speed of TSC. To alleviate traffic congestion and limit the effects of incidents on traffic flow, (El Hatri and Boumhidi, 2017) proposed a Q-learning based traffic management model, which simultaneously optimizes vehicle re-routing and TSC based on the Multi-Objective Particle Swarm Optimization (MOPSO) method.…”

Section: Methods' Contribution and Combinationmentioning

confidence: 99%

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Noaeen¹,

Naik²,

Goodman³

et al. 2021

Preprint

View full text Add to dashboard Cite

Improvement of traffic signal control (TSC) efficiency has been found to lead to improved urban transportation and enhanced quality of life. Recently, the use of reinforcement learning (RL) in various areas of TSC has gained significant traction; thus, we conducted a systematic literature review as a systematic, comprehensive, and reproducible review to dissect all the existing research that applied RL in the network-level TSC (NTSC) domain. The review only targeted the network-level articles that tested the proposed methods in networks with two or more intersections. We used natural language processing to define the search strings and searched Google Scholar, Web of Science, IEEE Xplore, ACM Digital Library, Springer Link, and Science Direct databases. This review covers 160 peer-reviewed articles from 30 countries published from 1994 to March 2020. The goal of this study is to provide the research community with statistical and conceptual knowledge, summarize existence evidence, characterize RL applications in NTSC domains, explore all applied methods and major first events in the defined scope, and identify areas for further research based on the explored research problems in current research.

show abstract

Section: Methods' Contribution and Combinationmentioning

confidence: 99%

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Noaeen¹,

Naik²,

Goodman³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In practice, as the road network is dynamic and information on the state of the network is imperfect, this equilibrium is never reached. Dynamic user equilibrium approaches [13,26] route and then re-route vehicles dynamically, responding in real-time to road conditions. These approaches, however, are not inherently collective as they optimize for each user rather than the overall system.…”

Section: Related Workmentioning

confidence: 99%

Collective shortest paths for minimizing congestion on temporal load-aware road networks

Conlan

Cunningham

Demirci

et al. 2021

Proceedings of the 14th ACM SIGSPATIAL International Workshop on Computational Transportation Science

View full text Add to dashboard Cite

Shortest path queries over graphs are usually considered as isolated tasks, where the goal is to return the shortest path for each individual query. In practice, however, such queries are typically part of a system (e.g., a road network) and their execution dynamically affects other queries and network parameters, such as the loads on edges, which in turn affects the shortest paths. We study the problem of collectively processing shortest path queries, where the objective is to optimize a collective objective, such as minimizing the overall cost. We define a temporal load-aware network that dynamically tracks expected loads while satisfying the desirable 'first in, first out' property. We develop temporal load-aware extensions of widely used shortest path algorithms, and a scalable collective routing solution that seeks to reduce system-wide congestion through dynamic path reassignment. Experiments illustrate that our collective approach to this NP-hard problem achieves improvements in a variety of performance measures, such as, i) reducing average travel times by up to 63%, ii) producing fairer suggestions across queries, and iii) distributing load across up to 97% of a city's road network capacity. The proposed approach is generalizable, which allows it to be adapted for other concurrent query processing tasks over networks.

show abstract

“…Similarly, size of bias vector for a layer l is b. In our case, for example, where |L| = 5, we will need four weight matrices and bias vectors and the weight matrix for layer l and l = 4, could be given as W 1×a where a is the number of neurons in the layer l. Now, suppose the output of a neuron or input parameter x i for a layer l(1 < l < |L|) is v l i , then its value could be defined using the equation defined in (2).…”

Section: Deep Learning Modelmentioning

confidence: 99%

“…Rapid transit is used in urban areas typically for transporting large numbers of passengers over small distances, at high frequencies, and are usually preferred over other transportation modes due to its several advantages. Road transportation annually costs 1.25 million deaths and trillions of dollars to the global economy due to congestion [1,2]. Train-based rapid transit is the safest and most dependable mode of transportation due to lack of congestion, and a significantly lower chance of accidents and vehicle/system failure.…”

Section: Introductionmentioning

confidence: 99%

Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs

et al. 2019

View full text Add to dashboard Cite

Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered appropriate holistic urban transport models and integrated use of cutting-edge technologies. This paper proposes a comprehensive approach toward large-scale and faster prediction of metro system characteristics by employing the integration of four leading-edge technologies: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). Using London Metro as a case study, and the Rolling Origin and Destination Survey (RODS) (real) dataset, we predict the number of passengers for six time intervals (a) using various access transport modes to reach the train stations (buses, walking, etc.); (b) using various egress modes to travel from the metro station to their next points of interest (PoIs); (c) traveling between different origin-destination (OD) pairs of stations; and (d) against the distance between the OD stations. The prediction allows better spatiotemporal planning of the whole urban transport system, including the metro subsystem, and its various access and egress modes. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for analysis of metro systems.

show abstract

Traffic management model for vehicle re-routing and traffic light control based on Multi-Objective Particle Swarm Optimization

Cited by 16 publications

References 13 publications

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Collective shortest paths for minimizing congestion on temporal load-aware road networks

Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs

Contact Info

Product

Resources

About