Estimation of Freeway Density Based on Combination of Data Point Traffic Detector Data and Automatic Vehicle Identification Data

被引:1
|
作者
Qom, Somaye Fakharian [1 ]
Xiao, Yan [1 ]
Hadi, Mohammed [1 ]
Al-Deek, Haitham [2 ]
机构
[1] Florida Int Univ, Dept Civil & Environm Engn, 10555 West Flagler St, Miami, FL 33174 USA
[2] Univ Cent Florida, Dept Civil Environm & Construct Engn, 12800 Pegasus Dr,Suite 211,POB 162450, Orlando, FL 32816 USA
关键词
D O I
10.3141/2484-12
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This study compared the results from three freeway density estimation methods based on point detector data with the results obtained from the density estimation procedure of the Highway Capacity Manual 2010 (HCM 2010). The three methods were the cumulative volume based method, the occupancy-based method, and the fundamental relationship based method. The study also developed and tested a new method that integrated data from point traffic detectors and automatic vehicle identification readers to estimate the density of freeway segments for offline and real-time applications. The four density estimation methods were compared with each other and the HCM 2010 method by using two case studies based on simulation modeling and real-world data. Results showed that the density estimates based on the proposed segmentation method, cumulative volume method, and HCM 2010 method were generally closer to each other compared with the estimates based on the other two tested methods. The simulation case study showed that the density estimates from these three methods were also closer to density measurements obtained on the basis of vehicle trajectories from simulation. The two case study results indicate that the selection of the density estimation method affects mainly the level-of-service value during intermediate congested conditions.
引用
收藏
页码:110 / 118
页数:9
相关论文
共 50 条
  • [11] Vehicle trajectory reconstruction using automatic vehicle identification and traffic count data
    Feng, Yu
    Sun, Jian
    Chen, Peng
    JOURNAL OF ADVANCED TRANSPORTATION, 2015, 49 (02) : 174 - 194
  • [12] Fusing a Bluetooth Traffic Monitoring System With Loop Detector Data for Improved Freeway Traffic Speed Estimation
    Bachmann, Chris
    Roorda, Matthew J.
    Abdulhai, Baher
    Moshiri, Behzad
    JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2013, 17 (02) : 152 - 164
  • [13] A novel dynamic OD estimation approach based on automatic vehicle identification data
    Sun, Jian
    Feng, Yu
    Tongji Daxue Xuebao/Journal of Tongji University, 2013, 41 (09): : 1366 - 1371
  • [14] Estimation and prediction of freeway traffic congestion propagation using tagged vehicle positioning data
    Li, Yan
    Xu, Jinhua
    Li, Yuran
    Xue, Yubing
    Yao, Zhenxing
    TRANSPORTMETRICA B-TRANSPORT DYNAMICS, 2024, 12 (01)
  • [15] Developing Data-Driven Approaches for Traffic Density Estimation Using Connected Vehicle Data
    Aljamal, Mohammad A.
    Farag, Mohamed
    Rakha, Hesham A.
    IEEE ACCESS, 2020, 8 : 219622 - 219631
  • [16] Predicting traffic breakdown on expressways using linear combination of vehicle detector data
    Shigemi, Rikuto
    Ando, Hiroyasu
    Wada, Kentaro
    Mukai, Risa
    IEICE NONLINEAR THEORY AND ITS APPLICATIONS, 2023, 14 (02): : 416 - 427
  • [17] ESTIMATION OF ROADWAY TRAFFIC DENSITY ON FREEWAYS USING PRESENCE DETECTOR DATA
    KURKJIAN, A
    GERSHWIN, SB
    HOUPT, PK
    WILLSKY, AS
    CHOW, EY
    TRANSPORTATION SCIENCE, 1980, 14 (03) : 232 - 261
  • [18] Real-Time Flux and Density Estimation of Freeway Traffic with Decentralized Speed Data
    Zhang, Liguo
    Qi, Ruiying
    2017 CHINESE AUTOMATION CONGRESS (CAC), 2017, : 1091 - 1095
  • [19] Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System
    Tang, Jinjun
    Zou, Yajie
    Ash, John
    Zhang, Shen
    Liu, Fang
    Wang, Yinhai
    PLOS ONE, 2016, 11 (02):
  • [20] MODEL BASED ESTIMATION OF FREEWAY TRAFFIC FLOW FROM MULTIPLE DATA SOURCES
    Ngoduy, Dong
    TRANSPORT DYNAMICS, 2011, : 19 - 26