Helen Sin Ee Chuo, Yuan Han Swa, M. K. Tan, Kit Guan Lim, L. Barukang, Kenneth Tze Kin Teo
{"title":"离散城市交通网络系统的元胞自动机建模","authors":"Helen Sin Ee Chuo, Yuan Han Swa, M. K. Tan, Kit Guan Lim, L. Barukang, Kenneth Tze Kin Teo","doi":"10.1109/SCOReD53546.2021.9652709","DOIUrl":null,"url":null,"abstract":"The main objective of this project is to model and simulate a discrete based urban traffic network system. Nowadays, the congestion in traffic network is affecting the citizen’s standard of living especially for those who lives in urban area. This phenomenon cannot be avoided due to the increment of population in urban area. The effective and fast way to reduce this problem is by applying traffic lights optimiser at urban traffic network. Hence, high accuracy of traffic model is required to optimise the traffic lights signalisation. Cellular Automata is proposed to model the multiple intersections of urban traffic network system. Microscopic traffic network model is used to model the traffic flow of the traffic network. The vehicle parameters are determined for the computation of traffic condition. The purpose of applying these parameters is to estimate the traffic flow at the intersection more accurately. There are differences in the traffic flow when the vehicles are transferring from one intersection to another. The vehicles tend to decelerate for turning to left or right and accelerate for going straight. Besides, the probability of vehicles transferring to other lanes is required to model the real traffic network condition. Therefore, the modelling of the lane changing behaviour at the intersections has enhanced the precision of the developed model. Traffic light signalisation for the developed model is controlled by Petri Net. This traffic network model with consideration of more vehicle parameters such as time delay, maximum velocity, acceleration and deceleration has achieved a higher accuracy of traffic condition estimation.","PeriodicalId":6762,"journal":{"name":"2021 IEEE 19th Student Conference on Research and Development (SCOReD)","volume":"46 1","pages":"472-477"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Modelling Discrete Urban Traffic Network System with Cellular Automata\",\"authors\":\"Helen Sin Ee Chuo, Yuan Han Swa, M. K. Tan, Kit Guan Lim, L. Barukang, Kenneth Tze Kin Teo\",\"doi\":\"10.1109/SCOReD53546.2021.9652709\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The main objective of this project is to model and simulate a discrete based urban traffic network system. Nowadays, the congestion in traffic network is affecting the citizen’s standard of living especially for those who lives in urban area. This phenomenon cannot be avoided due to the increment of population in urban area. The effective and fast way to reduce this problem is by applying traffic lights optimiser at urban traffic network. Hence, high accuracy of traffic model is required to optimise the traffic lights signalisation. Cellular Automata is proposed to model the multiple intersections of urban traffic network system. Microscopic traffic network model is used to model the traffic flow of the traffic network. The vehicle parameters are determined for the computation of traffic condition. The purpose of applying these parameters is to estimate the traffic flow at the intersection more accurately. There are differences in the traffic flow when the vehicles are transferring from one intersection to another. The vehicles tend to decelerate for turning to left or right and accelerate for going straight. Besides, the probability of vehicles transferring to other lanes is required to model the real traffic network condition. Therefore, the modelling of the lane changing behaviour at the intersections has enhanced the precision of the developed model. Traffic light signalisation for the developed model is controlled by Petri Net. This traffic network model with consideration of more vehicle parameters such as time delay, maximum velocity, acceleration and deceleration has achieved a higher accuracy of traffic condition estimation.\",\"PeriodicalId\":6762,\"journal\":{\"name\":\"2021 IEEE 19th Student Conference on Research and Development (SCOReD)\",\"volume\":\"46 1\",\"pages\":\"472-477\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 19th Student Conference on Research and Development (SCOReD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCOReD53546.2021.9652709\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 19th Student Conference on Research and Development (SCOReD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCOReD53546.2021.9652709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modelling Discrete Urban Traffic Network System with Cellular Automata
The main objective of this project is to model and simulate a discrete based urban traffic network system. Nowadays, the congestion in traffic network is affecting the citizen’s standard of living especially for those who lives in urban area. This phenomenon cannot be avoided due to the increment of population in urban area. The effective and fast way to reduce this problem is by applying traffic lights optimiser at urban traffic network. Hence, high accuracy of traffic model is required to optimise the traffic lights signalisation. Cellular Automata is proposed to model the multiple intersections of urban traffic network system. Microscopic traffic network model is used to model the traffic flow of the traffic network. The vehicle parameters are determined for the computation of traffic condition. The purpose of applying these parameters is to estimate the traffic flow at the intersection more accurately. There are differences in the traffic flow when the vehicles are transferring from one intersection to another. The vehicles tend to decelerate for turning to left or right and accelerate for going straight. Besides, the probability of vehicles transferring to other lanes is required to model the real traffic network condition. Therefore, the modelling of the lane changing behaviour at the intersections has enhanced the precision of the developed model. Traffic light signalisation for the developed model is controlled by Petri Net. This traffic network model with consideration of more vehicle parameters such as time delay, maximum velocity, acceleration and deceleration has achieved a higher accuracy of traffic condition estimation.