2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)最新文献_第10页

Risk Evaluation of Railway Business Line Construction Plan Based on Analytic Hierarchy Process and Bayesian Network 基于层次分析法和贝叶斯网络的铁路业务线路建设计划风险评价

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2020-09-01 DOI: 10.1109/ICITE50838.2020.9231361

Xiaoxin Di, Xiangqian Li, L. Zhou, Wei Xiao, Y. Yue

{"title":"Risk Evaluation of Railway Business Line Construction Plan Based on Analytic Hierarchy Process and Bayesian Network","authors":"Xiaoxin Di, Xiangqian Li, L. Zhou, Wei Xiao, Y. Yue","doi":"10.1109/ICITE50838.2020.9231361","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231361","url":null,"abstract":"In order to improve the construction level of highspeed railway business lines, ensure the quality status of highspeed rail facilities, reduce the risk of business line construction, rational evaluating the business line construction plan risk is an urgent research. This paper combines Analytic Hierarchy Process (AHP) with Bayesian network(BN) and adapts AHP to determine the weight of each indicator; Based on BN theory, constructs a probabilistic model of business line construction risk evaluation and calculates the probability of different risk factors as low, medium and high risk. Finally takes the Beijing Railway Bureau's business line as an example to conduct a comprehensive evaluation, and concludes that the overall risk factor of the project is at a medium risk level, and the evaluation results are in good agreement with the actual site, therefore indicating that this evaluation method is more comprehensive exhaustive than the existing evaluation methods, it has certain practical significance for the railway site construction plan.)","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122066930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Railway Passenger Flow Forecast Based on Hybrid PVAR-NN Model 基于PVAR-NN混合模型的铁路客流预测

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2020-09-01 DOI: 10.1109/ICITE50838.2020.9231346

Ruiqi Zhu, Huiyu Zhou

引用次数: 2

The Past, Present and Future of Railway Interlocking System 铁路联锁系统的过去、现在和未来

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2020-09-01 DOI: 10.1109/ICITE50838.2020.9231438

Luji Huang

引用次数: 8

Application of LSTM in Short-term Traffic Flow Prediction LSTM在短期交通流预测中的应用

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2020-09-01 DOI: 10.1109/ICITE50838.2020.9231500

Chuanli Kang, Zhenyu Zhang

引用次数: 8

A Genetic Algorithm-based AutoML Approach for Large-scale Traffic Speed Prediction 基于遗传算法的大规模交通速度自动预测方法

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2020-09-01 DOI: 10.1109/ICITE50838.2020.9231486

Junwei You

{"title":"A Genetic Algorithm-based AutoML Approach for Large-scale Traffic Speed Prediction","authors":"Junwei You","doi":"10.1109/ICITE50838.2020.9231486","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231486","url":null,"abstract":"With the continuous innovation of computer science as well as the big data acquisition technology, machine learning (ML), developed as a state-of-art framework, now has been comprehensively applied in speed prediction tasks. However, ML methods usually require intensive hyper-parameter tuning, which hinders the practical deployment of ML models. In view of this, this paper proposes an automated machine learning (AutoML) framework for speed prediction, which enables the prediction work to be accomplished in a much more timesaving and convenient way as well as in high prediction accuracy. The proposed framework utilizes the Genetic Algorithm (GA) following its four major procedures: Genome coding, Crossover, Mutation and Selection to automatically search for the optimal neural network architectures and hyperparameters. The proposed framework is examined on a real-world large-scale dataset in the city of Berlin, Germany. The experimental results demonstrate that the proposed method outperforms other benchmarking methods by a significant margin. Sensitivity analysis is also conducted to show the robustness of the proposed method. This study demonstrates the great penitential of using AutoML in traffic speed prediction and other related transportation applications.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126965404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Factor Recognition of Regional Serious Pedestrian-vehicle Crash Using Big Data for Intelligent Vehicles 基于智能车辆大数据的区域严重人车碰撞因子识别

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2020-09-01 DOI: 10.1109/ICITE50838.2020.9231398

Yanyan Chen, Yuntong Zhou

引用次数: 1

Research on ATS Route Control Based on the Binary Tree Algorithm 基于二叉树算法的ATS路由控制研究

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2020-07-22 DOI: 10.1109/ICITE50838.2020.9231378

Yi Liu, Feijie Wang, Shengmao Xie

引用次数: 0

Centralised Versus Decentralised Traffic Optimisation of Urban Road Networks: A Simulation Study 城市道路网络的集中与分散交通优化:模拟研究

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2020-06-20 DOI: 10.1109/ICITE50838.2020.9231396

M. Vallati

{"title":"Centralised Versus Decentralised Traffic Optimisation of Urban Road Networks: A Simulation Study","authors":"M. Vallati","doi":"10.1109/ICITE50838.2020.9231396","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231396","url":null,"abstract":"One of the pivotal challenges presented to urban road traffic controllers is the effective utilisation of transport infrastructure, as a result of growing urbanisation, the finite network capacity, and of the increasing number of road vehicles. In this context, the arrival of connected autonomous vehicles (CAVs) represents a unique opportunity for a fundamental change in urban traffic optimisation, and urban traffic control should take an active role in integrating CAVs into the mobility ecosystem in order to maximise benefits. Traditional approaches, commonly exploited by SATNAVs, are based on a decentralised logic, where each vehicle decides the route to follow in isolation, possibly by considering the current network conditions. The arrival of connected vehicles would allow the exploitation of centralised traffic optimisation, where a central urban traffic controller can suggest routes to vehicles by taking into account the current network conditions, and predicted future evolution. This paper introduces a centralised approach for traffic optimisation of urban road networks, and presents an extensive evaluation of the capabilities of centralised and decentralised approaches. Evaluation is based on a validated and calibrated SUMO simulation model of the town centre of Milton Keynes, United Kingdom.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124332866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1