About this Research Topic
Intelligent Transport Systems (ITS) rely on various sensing and communication technologies and data, have shown their capability of exploiting real-time and/or predicted data to enhance traffic safety, reduce the negative environmental impacts of transport systems, and increase the efficiency of transportation networks. However, performances of dynamic transportation models in reproducing network dynamics are strongly related to the quality and quantity of the available demand information. The last decades have witnessed an intensive research activity in this direction, but only a few frameworks have been successfully implemented on real-world instances.
This proposed research topic focuses on the new opportunities brought by new data collection, mobile sensing and communication technologies for developing and calibrating models for both private, Public Transport, shared and multimodal systems, including micro-mobility. Demand models enhanced by V2V and V2I communication, mobile and smartphone data, location data such as GPS, social networks, Shared Mobility, and Electronic ticketing are only a few examples. By leveraging on a more reliable, complete and large set of data, researchers can nowadays tackle many of the critical and systematic issues behind parameter estimation, develop new models based on a better understanding of complex mobility behaviour, forecast unusual patterns and introduce benchmarking practices to validate their approaches.
Topics welcome, but not limited to are:
• Online and offline demand estimation
• Dynamic Demand models
• Activity based models
• Traffic assignment
• Large scale estimation
• Calibration of traffic models
• Use of Big Data for model validation
Keywords: Big Data, Traffic Assignment, Online and offline demand estimation, Large scale estimation, Activity based models, Calibration of traffic models
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