Implementing kinematic wave theory to reconstruct vehicle trajectories from fixed and probe sensor data

Transportation Research Part C: Emerging Technologies

Volumn 20, Issue 1, 2012, Pages 144-163

  Mehran, Babak ^a   Kuwahara, Masao ^a   Naznin, Farhana ^a  

^a INSTITUTE OF INDUSTRIAL SCIENCE   (Japan)

Author keywords

Data fusion; Kinematic wave theory; Probe data; Travel time; Variational theory; Vehicle trajectory

Indexed keywords

DATA FUSION; KINEMATICS; PROBES; TRAJECTORIES; TRAVEL TIME; VEHICLES;

INPUT DATAS; KINEMATIC-WAVE THEORY; PROBE SENSOR; SENSOR DATA; SIGNAL TIMING; SOLUTION METHODS; VARIATIONAL THEORY; VEHICLE TRAJECTORIES;

SENSOR DATA FUSION;

DATA PROCESSING; KINEMATICS; SENSOR; THEORETICAL STUDY; TRAVEL TIME;

EID: 80054777132     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2011.05.006     Document Type: Article

References (17)

1. Berkow, M., Monsere, C.M., Koonce, P., Bertini, R.L., Wolfe, M., 2009. Prototype for data fusion using stationary and mobile data sources for improved arterial performance measurement. In: Proceedings of TRB 88th Annual Meeting, Washington, DC.
2. Choi K., Chung Y. A data fusion algorithm for estimating link travel time. ITS Journal: Intelligent Transportation Systems Journal 2002, 7:235-260.
3. Coifman B. Estimating travel times and vehicle trajectories on freeways using dual loop detectors. Transportation Research Part A 2002, 36:351-364.
4. Daganzo C.F. The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research Part B 1994, 28:269-287.
5. Daganzo C.F. A variational formulation of kinematic waves: basic theory and complex boundary conditions. Transportation Research Part B 2005, 39:187-196.
6. Daganzo C.F. A variational formulation of kinematic waves: solution methods. Transportation Research Part B 2005, 39:934-950.
7. Daganzo C.F. On the variational theory of traffic flow: well-posedness, duality and applications. Networks and Heterogeneous Media 2006, 1(1):601-619.
8. EL Faouzi, N.E., 2004. Data fusion in road traffic engineering: an overview. In: Proceedings of Multisensor. Multisource Information Fusion: Architectures, Algorithms, and Applications, Orlando, vol. 5434, pp. 360-371.
9. EL Faouzi, N.E., 2006. Bayesian and evidential approaches for traffic data fusion: methodological issues and case study. In: Proceedings of TRB 85th Annual Meeting, Washington, DC.
10. El Faouzi, N.E., Klein, L.A, De Mouzon, O., 2009. Improving travel time estimates from loop and toll collection data with Dempster-Shafer data fusion. In: Proceedings of TRB 88th Annual Meeting, Washington, DC.
11. Ivan J.N., Schofer J.L., Koppelman F.S., Massone L.L.E. Real-time data fusion for arterial street incident detection using neural networks. Transportation Research Record 1995, 1497:27-35.
12. Kuwahara M., Akamatsu T. Dynamic user optimal assignment with physical queues for a many-to-many OD pattern. Transportation Research Part B 2001, 35:461-479.
13. Lighthill, M.J., Whitham, G.B., 1955. On kinematic waves. I: flood movement in long rivers. II: a theory of traffic flow on long crowded roads. In: Proceedings Royal Society, London, vol. A229, pp. 281-345.
14. Newell G.F. A simplified theory on kinematic wave in highway traffic, part I: general theory. Transportation Research Part B 1993, 27(4):281-287.
15. Newell G.F. A simplified theory on kinematic wave in highway traffic, part II: queuing at freeway bottlenecks. Transportation Research Part B 1993, 27(4):289-303.
16. Newell G.F. A simplified theory on kinematic wave in highway traffic, part III: multi-destination flows. Transportation Research Part B 1993, 27(4):305-313.
17. Richards P. Shockwaves on the highway. Operations Research 1956, 4:42-51.