On the Study of Vehicle Density in Intelligent Transportation Systems

被引:14
|
作者
Sanguesa, Julio A. [1 ]
Naranjo, Fernando [1 ]
Torres-Sanz, Vicente [1 ]
Fogue, Manuel [1 ]
Garrido, Piedad [1 ]
Martinez, Francisco J. [1 ]
机构
[1] Univ Zaragoza, Comp Sci & Syst Engn Dept, C Atarazana 2, Teruel 44003, Spain
关键词
COMMUNICATION;
D O I
10.1155/2016/8320756
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Vehicular ad hoc networks (VANETs) are wireless communication networks which support cooperative driving among vehicles on the road. The specific characteristics of VANETs favor the development of attractive and challenging services and applications which rely on message exchanging among vehicles. These communication capabilities depend directly on the existence of nearby vehicles able to exchange information. Therefore, higher vehicle densities favor the communication among vehicles. However, vehicular communications are also strongly affected by the topology of the map (i.e., wireless signal could be attenuated due to the distance between the sender and receiver, and obstacles usually block signal transmission). In this paper, we study the influence of the roadmap topology and the number of vehicles when accounting for the vehicular communications capabilities, especially in urban scenarios. Additionally, we consider the use of two parameters: the SJ Ratio (SJR) and the Total Distance (TD), as the topology-related factors that better correlate with communications performance. Finally, we propose the use of a new density metric based on the number of vehicles, the complexity of the roadmap, and its maximum capacity. Hence, researchers will be able to accurately characterize the different urban scenarios and better validate their proposals related to cooperative Intelligent Transportation Systems based on vehicular communications.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] INTELLIGENT TRANSPORTATION SYSTEMS
    Dimitrakopoulos, George
    Demestichas, Panagiotis
    IEEE VEHICULAR TECHNOLOGY MAGAZINE, 2010, 5 (01): : 77 - 84
  • [32] California Statewide intelligent transportation systems plan evaluation - Case study of conformity with National Intelligent Transportation Systems Architecture
    Golob, JM
    Stecher, CC
    Felkins, C
    INTELLIGENT TRANSPORTATION SYSTEMS AND VEHICLE-HIGHWAY AUTOMATION 2003: HIGHWAY OPERATIONS, CAPACITY, AND TRAFFIC CONTROL, 2003, (1826): : 1 - 6
  • [33] Emergency Vehicle-Centered Traffic Signal Control in Intelligent Transportation Systems
    Cao, Miaomiao
    Shuai, Qiqi
    Li, Victor O. K.
    2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC), 2019, : 4525 - 4531
  • [34] Impact of Intelligent Transportation Systems on Vehicle Fuel Consumption and Emission Modeling: An Overview
    Faris, Waleed
    Rakha, Hesham
    Elmoselhy, Salah A. M.
    SAE INTERNATIONAL JOURNAL OF MATERIALS AND MANUFACTURING, 2014, 7 (01) : 129 - 146
  • [35] Deep learning-based algorithm for vehicle detection in intelligent transportation systems
    Qiu, Linrun
    Zhang, Dongbo
    Tian, Yuan
    Al-Nabhan, Najla
    JOURNAL OF SUPERCOMPUTING, 2021, 77 (10): : 11083 - 11098
  • [36] A survey of connected shared vehicle-road cooperative intelligent transportation systems
    Guo G.
    Xu Y.-G.
    Xu T.
    Li D.-D.
    Wang Y.-P.
    Yuan W.
    Kongzhi yu Juece/Control and Decision, 2019, 34 (11): : 2375 - 2389
  • [37] Advancing Electric Vehicle Battery Analysis With Digital Twins in Intelligent Transportation Systems
    Saba, Irum
    Ullah, Mukhtar
    Tariq, Muhammad
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024, 25 (09) : 12141 - 12150
  • [38] A pattern recognition and feature fusion formulation for vehicle reidentification in intelligent transportation systems
    Ramachandran, RP
    Arr, G
    Sun, C
    Ritchie, SG
    2002 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-IV, PROCEEDINGS, 2002, : 3840 - 3843
  • [39] Deep learning-based algorithm for vehicle detection in intelligent transportation systems
    Linrun Qiu
    Dongbo Zhang
    Yuan Tian
    Najla Al-Nabhan
    The Journal of Supercomputing, 2021, 77 : 11083 - 11098
  • [40] An Adaptive Vehicle Detection Algorithm Based on Magnetic Sensors in Intelligent Transportation Systems
    Xu, Bin
    Zheng, Jianying
    Wang, Qing
    Xiao, Yang
    Ozdemir, Suat
    AD HOC & SENSOR WIRELESS NETWORKS, 2017, 36 (1-4) : 211 - 232