Design, Implementation and Testing of a New Mobile Multi Function Sensing Device for Identifying High-Risk Areas for Bicyclists in Highly Congested Urban Streets

被引:1
作者
Bernardes, Suzana Duran [1 ]
Kurkcu, Abdullah [2 ]
Ozbay, Kaan [2 ]
机构
[1] NYU, Dept Civil & Urban Engn, C2SMART, Brooklyn, NY 11201 USA
[2] NYU, Ctr UrbanSci & Progress CUSP, Dept Civil & Urban Engn, C2SMART, Brooklyn, NY USA
来源
16TH INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS AND PERVASIVE COMPUTING (MOBISPC 2019),THE 14TH INTERNATIONAL CONFERENCE ON FUTURE NETWORKS AND COMMUNICATIONS (FNC-2019),THE 9TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY | 2019年 / 155卷
关键词
Ultrasonic sensor; Bicycle safety; Traffic; IoT; SAFETY; BEHAVIOR;
D O I
10.1016/j.procs.2019.08.032
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The number of bicycle riders in New York City has been increasing steadily in the past few years. These numbers include private and shared bicycles. NYC bicycle network has been expanded to accommodate this new volume. Although this new infrastructure has reduced the number of cyclists killed or seriously injured (KSI) in some areas, in other areas similar improvements were not observed. This inconsistency of how the number of bicycle crashes varies from one region to another in the city is the primary motivation of this paper. A highly portable and inexpensive sensing device for measuring the distance between a bicycle and lateral objects is designed from scratch and developed. The developed mobile sensing device can also map bicycle trajectories to highlight critical segments where the safe distance from passing vehicles is not respected. This device which is powered by a portable power source is comprised of two ultrasonic sensors namely, a Global Positioning System (GPS) receiver, and a real-time clock (RTC). The sensor is secured inside a custom design 3D printed case. The case can be easily attached to any bicycle including shared Citi Bike bicycles for testing. The final prototype is entirely functional and used to collect sample data to demonstrate its effectiveness to address safety-related problems mentioned above. Finally, a dashboard is created to display collected key information. This key information can be used by researches and agencies for a better understanding of the factors contributing to the safety of bicycle routes. (C) 2019 The Authors. Published by Elsevier B.V.
引用
收藏
页码:218 / 225
页数:8
相关论文
共 28 条
  • [1] Performance comparison of Infrared and Ultrasonic sensors for obstacles of different materials in vehicle/robot navigation applications
    Adarsh, S.
    Kaleemuddin, Mohamed S.
    Bose, Dinesh
    Ramachandran, K. I.
    [J]. INTERNATIONAL CONFERENCE ON ADVANCES IN MATERIALS AND MANUFACTURING APPLICATIONS (ICONAMMA-2016), 2016, 149
  • [2] Allen-Munley C, 2004, TRANSPORT RES REC, P107
  • [3] Raspberry Pi as a low-cost data acquisition system for human powered vehicles
    Ambroz, Miha
    [J]. MEASUREMENT, 2017, 100 : 7 - 18
  • [4] "Safety in Numbers" re-examined: Can we make valid or practical inferences from available evidence?
    Bhatia, Rajiv
    Wier, Megan
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2011, 43 (01) : 235 - 240
  • [5] Evaluating the Safety Effects of Bicycle Lanes in New York City
    Chen, Li
    Chen, Cynthia
    Srinivasan, Raghavan
    McKnight, Claire E.
    Ewing, Reid
    Roe, Matthew
    [J]. AMERICAN JOURNAL OF PUBLIC HEALTH, 2012, 102 (06) : 1120 - 1127
  • [6] Dozza M., 2013, INT CYCL SAF C HELM
  • [7] Dozza M., 2017, OPEN SOURCE DATA LOG
  • [8] Understanding Bicycle Dynamics and Cyclist Behavior From Naturalistic Field Data (November 2012)
    Dozza, Marco
    Fernandez, Andre
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2014, 15 (01) : 376 - 384
  • [9] Getman A, 2017, Safer cycling: bicycle ridership and safety in New York City
  • [10] Guse C., 2019, NEW YORK DAILY NEWS