Smart Planning with Big Data and AI

In the metropolitan regions of South-East Asia, rapid urban development can change parameters of transport systems over very few years. Under such conditions, traditional methods for data collection and processing are expensive and too slow to depict reality. Evidence informing the planning process is under a permanent backlog.

Modern digital technology can automate the collection and analysis of data in new cost-effective, quick, detailed and smart ways. For instance, footage of traffic surveillance cameras can be analysed with AI tools. For instance, footage of traffic surveillance cameras can be analysed with artificial intelligence. Equipment originally primarily installed for real-time traffic management, such as traffic surveillance cameras may find a second purpose in generating planning data. Other sources, such as mobile phones, vehicle monitoring, etc. add to big data streams. Machine learning facilitates the accumulation and interpretation of the data for Intelligent Transport Systems (ITS) modelling and forecasting.

This knowledge product is designed as a dialogue between different stakeholders. It aims to establish an understanding among the participants of the opportunities data can bring for transport modelling and intelligent traffic management and develop capacities to estimate the data requirements of such applications.

Participants shall be enabled to develop consistent approach to transport data sourcing and standardisation for ITS and data-driven evidence-based governance and planning. This may include understanding on measures for optimising public transport on metropolitan level to facilitate a modal shift. Showcasing widespread applications, the workshop will develop the participant’s capacity to recognise the data needs and potentials of different stakeholders and third-party resources.

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Using GPS Technology for Demand Data Collection

Travel demand data is a necessary basis for urban mobility planning, but especially in developing and emerging economies data availability is often weak or non-existing. The Global Positioning System (GPS) technology offers a cheap alternative for data collection to traditional diary or survey methods. This document elaborates on advantages and disadvantages of the approach and also discusses different aspects of the post-processing of GPS data in order to determine trips, mode choice and trip purposes.


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