Optimization model of bayonet layout for individual vehicle travel detection

QIAO Wenying; HUANG Min; ZHANG Xiaolan

doi:10.13471/j.cnki.acta.snus.2023B053

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Optimization model of bayonet layout for individual vehicle travel detection

Research articles | 更新时间：2024-01-25

- Optimization model of bayonet layout for individual vehicle travel detection
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 63, Issue 1, Pages: 137-144(2024)
- 作者机构：
  
  1.中山大学智能工程学院，广东深圳 518107
  2.广东省智能交通系统重点实验室，广东深圳 518107
  3.广东省交通环境智能监测与治理工程技术研究中心，广东深圳 518107
  4.广东工贸职业技术学院，广东广州 510510
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.2023B053
  CLC： U491
- Published：25 January 2024，
  
  Published Online：30 October 2023，
  
  Received：20 August 2023，
  
  Accepted：14 September 2023
扫描看全文
乔文瑛,黄敏,张小兰.面向车辆个体出行检测的卡口布设优化模型[J].中山大学学报(自然科学版)(中英文),2024,63(01):137-144.

QIAO Wenying,HUANG Min,ZHANG Xiaolan.Optimization model of bayonet layout for individual vehicle travel detection[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(01):137-144.
乔文瑛,黄敏,张小兰.面向车辆个体出行检测的卡口布设优化模型[J].中山大学学报(自然科学版)(中英文),2024,63(01):137-144. DOI： 10.13471/j.cnki.acta.snus.2023B053.

QIAO Wenying,HUANG Min,ZHANG Xiaolan.Optimization model of bayonet layout for individual vehicle travel detection[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(01):137-144. DOI： 10.13471/j.cnki.acta.snus.2023B053.

摘要

针对高清卡口检测器可以获取出行者个体级别的细粒度信息但难以实现路网全覆盖的问题，从缺失轨迹重构角度出发，研究了面向车辆个体出行检测的卡口布设优化方法。考虑相邻两次卡口检测序列间的缺失情况，提出缺失轨迹一次重构、二次重构方法。基于一次重构原理，创建流量捕获率、轨迹覆盖率以衡量卡口布设方案对路面交通信息的检测规模；基于二次重构原理，创建缺失轨迹离散度以衡量轨迹重构可靠度。以流量捕获率、轨迹覆盖率作为约束条件，以最大化缺失轨迹离散度作为卡口布设优化目标，构建了面向车辆个体出行检测的卡口布设优化模型，并采用粒子群算法求解。以广州市海珠区某区域路网为例，分全新布设和新增布设两种情况进行算例分析，结果表明：在全新布设场景中，优化后的卡口布设方案相流量捕获率提升了6.20%，轨迹覆盖率提升了2.76%，缺失轨迹离散度提升了139%，在车辆个体出行轨迹检测及重构方面比当前方案获得了更优的效果；在新增布设场景中，依次对新增的1-6个卡口进行优化求解，得到了新增位置和优化结果。

Abstract

Bayonet detector can obtain fine-grained information at individual traveler level， but it is difficult to achieve full coverage of the road network. Aiming at this problem， from the perspective of missing trajectory reconstruction， the bayonet layout optimization method for individual vehicle travel detection was studied. Considering the missing situation between two adjacent bayonet detection sequences， the methods of first reconstruction and second reconstruction of missing trajectory were proposed. Based on the principle of primary reconstruction， the flow capture rate and track coverage rate were created to measure the monitoring scale of road traffic information by bayonet layout scheme. Based on the principle of secondary reconstruction， the missing trajectory dispersion was created to measure the reliability of trajectory reconstruction. A bayonet layout optimization model for individual vehicle travel detection was constructed by taking traffic capture rate， trajectory coverage as constraints， and maximizing the dispersion of missing tracks as the optimization objective of bayonet layout. Particle swarm optimization algorithm was used to solve the optimization model. Taking a regional road network in Haizhu， Guangzhou as an example， the new layout and the added layout were analyzed respectively. The results showed that： in the new layout scene， the optimized bayonet layout scheme increased the phase flow capture rate by 6.20%， the track coverage rate by 2.76%， and the dispersion of missing track by 139%， which obtained better results than the current scheme in terms of individual vehicle travel track detection and reconstruction. In the added layout scene， the optimization solution of added 1-6 bayonets were carried out successively， and the added positions and optimization results were obtained.

关键词

城市交通卡口布设粒子群算法（PSO）个体出行检测缺失轨迹离散度

Keywords

urban trafficbayonet layoutparticle swarm optimization（PSO）individual travel detectiondispersion of missing trajectory

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