- Wu, Xin et al. “Hierarchical travel demand estimation using multiple data sources: A forward and backward propagation algorithmic framework on a layered computational graph.” Transportation Research Part C-emerging Technologies 96 (2018): 321-346.
- Video
-
The overarching rationale of transportation system intelligence is that developments in sensing, cyber-physical infrastructures, and crowdsouring big data technologies can be integrated to effecitive use for improving the performance of transportation systems.
-
This paper first proposes a multi-layered Hierarchical Flow Network (HFN) to structurally model different levels of travel demand variables using multiple data sourses.
Household travel surveys can provide trip production and abstraction from zones and sometimes OD trip tables between the zones.
A number of studies are devoted to finding how to incorporate Mobile phone sample data into activity-based models to estimate trip chain behavior.
GPS/Floating car Data can be used to estimate the observed travel time of links and establish urban traffic indexes.
Large volumes of observed link counts can be collected from sensors including inductive loops,radars,cameras,etc.
Wei Ma PolyU
-
Wu, Xin et al. “Hierarchical travel demand estimation using multiple data sources: A forward and backward propagation algorithmic framework on a layered computational graph.” Transportation Research Part C-emerging Technologies 96 (2018): 321-346.
-
Lu, C. et al. “Dynamic origin-destination demand flow estimation under congested traffic conditions.” Transportation Research Part C-emerging Technologies 34 (2012): 16-37.
-
Shi, Q. and M. Abdel-Aty. “Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways.” Transportation Research Part C-emerging Technologies 58 (2015): 380-394.
-
Antoniou, C. et al. “Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: Design, demonstration and validation.” Transportation Research Part C-emerging Technologies 66 (2016): 79-98.
-
Wu, Cathy et al. “Cellpath: Fusion of Cellular and Traffic Sensor Data for Route Flow Estimation via Convex Optimization.” Transportation research procedia 7 (2015): 212-232.
-
Seo, T. et al. “Traffic state estimation on highway: A comprehensive survey.” Annu. Rev. Control. 43 (2017): 128-151.
-
Cvetek, Dominik et al. “A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion.” Applied Sciences 11 (2021): 2306.
-
Chenyang, Yang et al. “A Modified Stochastic User Equilibrium Based Back-Propagation Method of Transportation Network State Estimation.” 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA) (2020): 442-447.
-
Fakhouri, Ashkan Ahmadi and R. Soltani. “Multi-Objective Robust Optimization for the Traffic Sensors Location Problem.” IEEE Access 9 (2021): 6225-6238.
-
Zhu, Senlai et al. “Integrating Optimal Heterogeneous Sensor Deployment and Operation Strategies for Dynamic Origin-Destination Demand Estimation.” Sensors (Basel, Switzerland) 17 (2017): n. pag.
-
Rodriguez-Vega, M. et al. “Location of turning ratio and flow sensors for flow reconstruction in large traffic networks.” Transportation Research Part B-methodological 121 (2019): 21-40.