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Enhancing pedestrian mobility in Smart Cities using Big Data

Author

Listed:
  • Ebony Carter
  • Patrick Adam
  • Deon Tsakis
  • Stephanie Shaw
  • Richard Watson
  • Peter Ryan

Abstract

Smart City is an emerging concept in global urban development. A Smart City applies ICT technologies to provide greater efficiencies for its urban areas and civilian population. One of the key requirements for a Smart City is to exploit data from its ICT infrastructure (such as Internet of Things connected sensors) to improve city services and features such as accessibility and sustainability. To address this requirement, the City of Melbourne (COM) Smart City office maintains several hundred data sets relating to urban activity and development. These datasets address parking, mobility, land use, 3D data, statistics, environment, and major city developments such as rail projects. One promising dataset relates to pedestrian traffic. Data are obtained from sensors and updated on the COM website (City of Melbourne Open Data Platform: https://data.melbourne.vic.gov.au/.) at regular intervals. These data include the number of pedestrians passing 53 specific locations in the central business district and also their times and directions of travel. In a 24 h period, over 650,000 pedestrians were counted passing all locations. Peak rates of several thousand pedestrians per minute are regularly recorded during city rush hours at hotspots making the data amenable to Big Data analysis techniques. Results are obtained in graphical format as heatmaps and charts of city pedestrian traffic using both Microsoft Excel® for static analysis and PowerBI® for more advanced interactive visualisation and analysis. These findings can identify pedestrian hotspots and inform future locations of traffic lights and street configurations to make the city more pedestrian friendly. Further, the experience gained can be used to examine other data sets such as bicycle traffic that can be analysed to inform city infrastructure projects. Future work is suggested that could link these pedestrian flow data with social media data from smartphones and potentially wearable devices such as fitness monitors to correlate pedestrian satisfaction with traffic flow. The ‘happiness’ effect of pedestrians passing through green areas such as city parks can also be quantified. This research was undertaken with the assistance of Swinburne University under its Capstone Project scheme.

Suggested Citation

  • Ebony Carter & Patrick Adam & Deon Tsakis & Stephanie Shaw & Richard Watson & Peter Ryan, 2020. "Enhancing pedestrian mobility in Smart Cities using Big Data," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(2), pages 173-188, April.
  • Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:2:p:173-188
    DOI: 10.1080/23270012.2020.1741039
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    Citations

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    Cited by:

    1. Xueling Li & Xiaoyan Zhang & Yuan Liu & Yuanying Mi & Yong Chen, 2022. "The impact of artificial intelligence on users' entrepreneurial activities," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 597-608, May.
    2. Clement, Dr. Jessica & Crutzen, Prof. Nathalie, 2021. "How Local Policy Priorities Set the Smart City Agenda," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    3. Siqing Shan & Xin Wen & Yigang Wei & Zijin Wang & Yong Chen, 2020. "Intelligent manufacturing in industry 4.0: A case study of Sany heavy industry," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 679-690, July.
    4. Cheng-Jie Jin & Ke-Da Shi & Shu-Yi Fang, 2023. "Simulation of Single-File Pedestrian Flow under High-Density Condition by a Modified Social Force Model," Sustainability, MDPI, vol. 15(11), pages 1-15, May.
    5. Jing Ge & Feng Wang & Hongxia Sun & Liuliu Fu & Mingwei Sun, 2020. "Research on the maturity of big data management capability of intelligent manufacturing enterprise," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 646-662, July.
    6. Susie Ruqun WU & Gabriela Shirkey & Ilke Celik & Changliang Shao & Jiquan Chen, 2022. "A Review on the Adoption of AI, BC, and IoT in Sustainability Research," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    7. Katarzyna Sosik-Filipiak & Oleksandra Osypchuk, 2023. "Identification of Solutions for Vulnerable Road Users Safety in Urban Transport Systems: Grounded Theory Research," Sustainability, MDPI, vol. 15(13), pages 1-19, July.
    8. Dezhi Li & Wentao Wang & Guanying Huang & Shenghua Zhou & Shiyao Zhu & Haibo Feng, 2023. "How to Enhance Citizens’ Sense of Gain in Smart Cities? A SWOT-AHP-TOWS Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(3), pages 787-820, February.

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