Mustafa Gadah , Xuesong Zhou , Mohammad Abbasi , Vamshi Yellisetty
{"title":"Traffic flow theory-based modeling of bike-vehicle interactions for enhanced safety and mobility","authors":"Mustafa Gadah , Xuesong Zhou , Mohammad Abbasi , Vamshi Yellisetty","doi":"10.1016/j.multra.2025.100202","DOIUrl":null,"url":null,"abstract":"<div><div>This paper introduces an innovative approach to enhancing active transportation analysis and decision support by addressing the notable research gap of integrating traffic flow analysis, spatio-temporal trajectory models, and an input-output (moving queue) diagram. We establish a unique four-stage method for assessing bike-vehicle traffic interaction on designated road links: 1) Given the input of volume, we convert it to speed and density using the fundamental diagram and Q-K curves under different congestion conditions. 2) We analyze vehicle trajectories and utilize an input-output (moving queue) diagram to calculate the total exposures between bikes and vehicles as a function of speed difference and the product of bike and vehicle volume, ensuring the balance equations for both vehicle and bike exposure individually. 3) Beginning at the moment a vehicle enters a shared facility, we apply an illustrative method to determine the duration of individual exposure time, adjusting Newell’s car-following model to accommodate for various phases of driver reactions, transitioning from anticipation to overtaking/yield phase. 4) We measure the overall impact of exposure on mobility and safety using a multimodal semi-dynamic traffic assignment that focuses on both delay and exposure-based utility across various facility types and development scenarios. Our research underscores that controlling the flow of bikes and vehicles is a pivotal factor in determining the relative bike exposure to risk, offering valuable insights for the future development of transportation models and safety improvement strategies using a case study from Gilbert, AZ.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100202"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multimodal Transportation","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772586325000164","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper introduces an innovative approach to enhancing active transportation analysis and decision support by addressing the notable research gap of integrating traffic flow analysis, spatio-temporal trajectory models, and an input-output (moving queue) diagram. We establish a unique four-stage method for assessing bike-vehicle traffic interaction on designated road links: 1) Given the input of volume, we convert it to speed and density using the fundamental diagram and Q-K curves under different congestion conditions. 2) We analyze vehicle trajectories and utilize an input-output (moving queue) diagram to calculate the total exposures between bikes and vehicles as a function of speed difference and the product of bike and vehicle volume, ensuring the balance equations for both vehicle and bike exposure individually. 3) Beginning at the moment a vehicle enters a shared facility, we apply an illustrative method to determine the duration of individual exposure time, adjusting Newell’s car-following model to accommodate for various phases of driver reactions, transitioning from anticipation to overtaking/yield phase. 4) We measure the overall impact of exposure on mobility and safety using a multimodal semi-dynamic traffic assignment that focuses on both delay and exposure-based utility across various facility types and development scenarios. Our research underscores that controlling the flow of bikes and vehicles is a pivotal factor in determining the relative bike exposure to risk, offering valuable insights for the future development of transportation models and safety improvement strategies using a case study from Gilbert, AZ.