Enhancing pedestrian mobility in Smart Cities using Big Data. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Carter, Ebony; Adam, Patrick; Tsakis, Deon; Shaw, Stephanie; Watson, Richard; Ryan, Peter
• Is Part Of: Journal of management analytics. Volume 7:Issue 2(2020)
• Page Start: 173
• Page End: 188
• Publication Date: 2020-04-02
• Subjects: Internet of Things -- Smart Cities -- Big Data analytics; Management -- Mathematical models -- Periodicals; Management -- Periodicals; Management -- Mathematical models; Management; Periodicals; 658.4033
• DOI: 10.1080/23270012.2020.1741039
• Languages: English
• ISSNs: 2327-0012

Abstract

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.