Powering Urban Smart Mobility with Data Analytics (PUBLIC)

中国广东的快速城市化和机动车的广泛采用增加了交通流量，导致了拥堵、生产力损失和对环境的负面影响。该项目旨在通过使用大数据分析来描述和预测旅客的时空流动性和交通模式，开发数据分析平台和应用程序，以增强智能流动性（SM），例如公共交通调度，以及公共交通和共享单车之间的无缝连接，来应对这些挑战。使用SM解决方案提高公共交通的效率/优化调度，将使依赖这些交通工具的工人阶级受益，减少他们的出行成本和出行时间。该项目还将提供环境效益，如减少拥堵，从而减少二氧化碳排放，从而改善空气质量，改善大城市居民的健康状况。

The rapid urbanisation and wide adoption of motor vehicles in Guangdong, China has increased traffic and resulted in congestions, loss of productivity, and negative effects to the environment. The project aims to respond to these challenges by using big data analytics to characterise and predict the spatial-temporal (ST) traveller mobility and traffic patterns, to develop data analytics platform and applications to enhance smart mobility (SM), e.g., public transportation scheduling, and seamless connection between public transportation and shared bikes. The improved efficiency/ optimised scheduling of public transports, using SM solutions, will benefit the working class who rely on such transports by reducing their cost on travel and trip time. This project will also provide environmental benefits such as reducing congestions and therefore CO2 emission, resulting in better air quality and improving health of the residents in large cities.