Professor Ashish Bhaskar

Co-Leader, Business and Engineering Domain; Professor in Civil Engineering, School of Civil and Environmental Engineering

PhD (Ecole Polytechnique Federale de Lausanne), Master of Engineering (University of Tokyo)

Dr Ashish Bhaskar is a Professor in Civil Engineering (Transport discipline), at the Queensland University of Technology (QUT), Brisbane, Australia. His expertise includes transport big data analytics, modelling, simulation, and control. At this stage, the focus of his research is on addressing the issues and challenges related to road traffic congestion and its detrimental socioeconomic and environmental impacts. Together, with his team of researchers and collaborators from government, industry, and academia, he is working towards evidence-based decisions support, management, and control of multimodal transport system. He has a proven track record of working with government and industry including Queensland Department of Transport and Main Roads (TMR) on number of projects as lead chief investigator. Currently, three key themes of his research are:

• Transport big data analytics: His research team has established expertise in management, analysis, and visualization of real ‘big’ traffic and transit data from South-East Queensland. The research focus is on understanding data characteristics, accuracy, reliability, and fusion for different mobility applications. His team has the opportunity to get their hands dirty with multisource real traffic and transit data such as loop detectors, infrared beacons, radars, Bluetooth and WIFI MAC scanners, GTFS, transit Smart Card, 3rd party (Taxi, Intelematics, Here, Waze, etc.), traffic signals and emerging connected vehicle technology (SPATEM, MAPEM). A list of selected research projects aligned with transport data analytics are:
  • Field trial evaluation for Cooperative Intelligent Transport Systems: Signal use case availability and reliability (ongoing, expected completion 2022)
  • Vehicle detector technologies (loops, infrared and radar) evaluation (ongoing, expected completion 2022)
  • Data Visualization: Intersection and route level performance measure (completed 2020)
  • Interactive big data visualization for decision making: A prototype for mobility data (completed 2020)
  • Reverse engineering on Emergency Vehicle Pre-emption (EVP) and STREAMS data: Empirical insights and visualization (completed 2019)
  • Comparative overview of Addinsight and Intelematics data (completed 2017)
  • Insights on the comparison of Waze and STREAMS Incident Management System records (completed 2017)
  • Accuracy and reliability of the travel time estimates from Bluetooth MAC scanners (completed 2013)
  • Crowd monitoring using Bluetooth and Wifi scanners (completed 2013)
• Modelling and simulation: The availability of large-scale real data for research and training (acknowledgements to TMR, BCC, netBI, Black & White Cabs) establishes an evidence based foundation for the modelling and simulation of large urban networks. Aspro Bhaskar’s team is engaged in reverse engineering on the traffic and transit monitoring data sets for better situation awareness, multimodal modelling of the demand (origin destination matrices) and supply for large urban networks, predictive analytics for traffic state forecasting, and calibration and validation of large‑scale simulation models. A list of selected research projects aligned with modelling and simulation theme are:
  • Advanced data analytics: Real-time demand calibration/prediction (ongoing, expected completion 2023)
  • Queen’s Wharf Brisbane Phase-3: Longitudinal study on mobility patterns (ongoing, expected completion 2024)
  • Data driven traffic assignment: Exploiting Bluetooth vehicle trajectory data (ongoing, expected completion 2023)
  • AI for traffic and transit congestion forecasting (ongoing, expected completion 2024)
  • Data Evaluation Methods and Insights- phase 1 & 2 (ongoing, expected completion 2022)
  • Simulating public transit priority systems on Brisbane network (ongoing, expected completion 2023)
  • Transit spatial gap identification: Exploiting big traffic and transit data (completed 2021)
  • Network scale arterial traffic state prediction: Fusing multi-sensor traffic data (completed 2020)
  • Origin destination matrix estimation using big traffic data: A structural perspective (completed 2020)
  • Macroscopic model based urban network signal optimization (completed 2019)
  • Traffic flow modelling for non-lane and quasi lane disciplined traffic behavior (completed 2018)
  • Incorporating Bluetooth vehicle trajectories for OD estimation (completed 2019)
  • Predictive modelling of disturbances in public transit network (completed 2018)
  • Transit benchmarking: Modelling the productivity, effectiveness, and efficiency of transit routes (completed 2017)
  • Incident duration modelling and system optimal traffic re-routing (completed 2017)
  • Exploring properties of macroscopic fundamental diagram (completed 2014)
  • Multimodal and multi‑objective trip planner (completed 2014)
  • Motorway and arterial travel time estimation (completed 2014)
• Operations and Control: Real time traffic control is vital to reduce congestion on our urban networks. Aspro Bhaskar’s data analytics, modelling and simulation expertise from the above-mentioned pillars is further extended to develop algorithms to control the traffic flow on networks for efficient and safe mobility. Selected examples of the research project under this theme are:
  • Next generation of traffic signal control (ongoing, expected completion 2023)
  • Optimal Signal Control Strategy for Heterogeneous Less Lane-Disciplined Traffic Conditions (ongoing, expected completion 2023)
  • Extended cell transmission model for arterial traffic and its application to design a robust signal plan (completed 2020)
  • Automated Vehicle Trajectory Planning for Motorway On-Ramp Merging (completed 2019)
  • Coordinated ramp signaling factory testing and implementation in TMR’s STREAMS platform (completed 2018)
  • Incorporating fail safe feature for coordinated ramp signaling (completed 2018)
  • Review of Traffic Management Systems (completed 2017)
  • Utilizing big transit data for transfer coordination (completed 2015)

Aspro Bhaskar co-leads Business and Engineering Systems domain for the QUT Centre for Data Science (CDS), where he aims to establish a platform to initiate and incubate trans-disciplinary collaborations with a common interest towards cutting edge data driven solutions to the challenges faced by the business and engineering industries. Feel free to contact him to know more about the QUT data science and mobility expertise.

Aspro Bhaskar is serving as the Academic Lead International and Engagement for the School of Civil and Environmental Engineering at QUT. As member of the school executive committee, he is actively engaged in shaping the industry and international engagement strategies for the school. Feel free to contact him for engagement with School of Civil and Environmental Engineering.

Aspro Bhaskar is co-chair for the World Conference on Transport Research Society (WCTRS) SIG-C3 on Intelligent Transport Systems (ITS). This SIG objective is to facilitate closer links between the research communities, institutions and practitioners working in the area of ITS to develop and exchange ideas and methodologies. To join the group, or for more information, please feel free to contact him.

He holds a PhD in Intelligent Transport Systems from École polytechnique fédérale de Lausanne (EPFL),   Lausanne, Switzerland, Masters in Transport Engineering from the University of Tokyo, Tokyo, Japan, and a Bachelor of Technology (BTech) degree in Civil Engineering from the Indian Institute of Technology, Kanpur, India.  He has completed Graduate Certificate in Academic Practices (GCAP) from QUT which is focused on enabling academic staff professional development centered on learning and teaching in higher education.

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