Urban network gridlock: Theory, characteristics, and dynamics

Transportation Research Part C: Emerging Technologies

Volumn 36, Issue , 2013, Pages 480-497

  Mahmassani, Hani S ^a   Saberi, Meead ^a   Zockaie, Ali ^a  

^a Northwestern University   (United States)

Author keywords

Adaptive driving; Congestion; Gridlock; Hysteresis; Network Fundamental Diagram

Indexed keywords

COMPUTER SYSTEM RECOVERY; DYNAMICS; FUNCTIONS; HYSTERESIS; INFORMATION MANAGEMENT; TRAFFIC CONTROL; TRANSPORTATION ROUTES;

ADAPTIVE DRIVING; CENTRAL BUSINESS DISTRICTS; CONGESTION; DYNAMIC TRAFFIC ASSIGNMENT(DTA); FUNDAMENTAL DIAGRAM; GRIDLOCK; STEADY-STATE BEHAVIORS; STEADY-STATE CONDITION;

COMPLEX NETWORKS;

CENTRAL BUSINESS DISTRICT; HYSTERESIS; NETWORK ANALYSIS; THEORETICAL STUDY; TRAFFIC CONGESTION; URBAN TRANSPORT;

CHICAGO; ILLINOIS; UNITED STATES;

EID: 84887138281     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2013.07.002     Document Type: Article

References (34)

1. Ardekani S.A., Herman R. Urban network-wide traffic variables and their relations. Transportation Science 1987, 21(1):1-16.
2. Buisson C., Ladier C. Exploring the impact of homogeneity of traffic measurements on the existence of macroscopic fundamental diagrams. Transportation Research Record: Journal of the Transportation Research Board 2009, 2124:127-136.
3. Daganzo C.F. The nature of freeway gridlock and how to prevent it. Proceedings of the 13th International Symposium on Transportation and Traffic Theory 1996, 629-646. Elsevier Science Publishing.
4. Daganzo C.F. Urban gridlock: macroscopic modeling and mitigation approaches. Transportation Research Part B 2007, 41(1):49-62.
5. Daganzo C.F., Gayah V., Gonzales E. Macroscopic relations of urban traffic variables: bifurcations, multivaluedness and instability. Transportation Research Part B 2011, 41(1):278-288.
6. Dong J., Mahmassani H.S., Lu C.C. How reliable is this route? Predictive travel time and reliability for anticipatory traveler information systems. Transportation Research Record: Journal of the Transportation Research Board 2006, 1980:117-125.
7. Fernandez J.E., de Cea J., Valverde G.G. Effect of advanced traveler information systems and road pricing in a network with non-recurrent congestion. Transportation Research Part A 2009, 43(5):481-499.
8. Gayah V., Daganzo C.F. Clockwise hysteresis loops in the macroscopic fundamental diagram: an effect of network instability. Transportation Research Part B 2011, 45(4):643-655.
9. Geroliminis N., Daganzo C.F. Existence of urban-scale macroscopic fundamental diagrams: some experimental findings. Transportation Research Part B 2008, 9:759-770.
10. Geroliminis N., Sun J. Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transportation Research Part B 2011, 45(3):605-617.
11. Geroliminis N., Sun J. Hysteresis phenomena of a macroscopic fundamental diagram in freeway networks. Proceedings of the 10th International Symposium on Transportation and Traffic Theory 2011, 213-228. Elsevier Science Publishing.
12. Gonzales, E., Chavis, C., Li, Y., Daganzo, C.F., 2011. Multimodal transport in Nairobi, Kenya: insights and recommendations with a macroscopic evidence-based model. In: Presented at the 90th Annual Meeting of Transportation Research Board, Washington, D.C., 2011.
13. Herman R., Prigogine I. A two-fluid approach to town traffic. Science 1979, 204(1979):148-151.
14. Jayakrishnan R., Mahmassani H.S., Hu T.Y. An evaluation tool for advanced traffic information and management systems in urban networks. Transportation Research Part C 1994, 2(3):129-147.
15. Ji Y., Daamen W., Hoogendoorn S., Hoogendoorn-Lanser S. Investigating the shape of the macroscopic fundamental diagram using simulation data. Transportation Research Record: Journal of the Transportation Research Board 2010, 2161:40-48.
16. Knoop, V.L., Van Lint, J.W.C., Hoogendoorn, S.P., 2011. Data requirements for traffic control on a macroscopic level. In: 2nd International Workshop on Traffic Data Collection and its Standardisation, 22-23 September 2011, Brisbane, Australia.
17. Knoop, V., Van Lint, J.W.C., Hoogendoorn, S., 2012. Routing strategies based on the macroscopic fundamental diagram. Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C, 1-10.
18. Mahmassani H.S., Williams J.C., Herman R. Investigation of network-level traffic flow relationships: some simulation results. Transportation Research Record: Journal of the Transportation Research Board 1984, 971:121-130.
19. Mahmassani H.S., Williams J.C., Herman R. Performance of urban traffic networks. Proceedings of the 10th International Symposium on Transportation and Traffic Theory 1987, 1-20. Elsevier Science Publishing.
20. Mahmassani H.S., Jayakrishnan R. System performance and user response under real-time information in a congested traffic corridor. Transportation Research Part A 1991, 25(5):293-308.
21. Mahmassani H.S., Peeta S. Network performance under system optimal and user equilibrium dynamic assignments: implications for ATIS. Transportation Research Record: Journal of the Transportation Research Board 1993, 1408:83-93.
22. Mahmassani, H., Hu, T., Peeta, S., Ziliaskopoulos, A., 1994. Development and Testing of Dynamic Traffic Assignment and Simulation Procedures for ATIS/ATMS Applications. Technical Report DTFH61-90-R-00074-FG, Center for Transportation Research, University of Texas at Austin.
23. Mahmassani, H.S., Kim, J., Hou, T., Zockaie, A., Saberi, M., Jiang, L., Verbas, O., Cheng, S., Chen, Y., Haas, R., 2012. Implementation and Evaluation of Weather Responsive Traffic Estimation and Prediction System. Final Report, Publication No. FHWA-JPO-12-055, FHWA, U.S. Department of Transportation.
24. Mazloumian A., Geroliminis N., Helbing D. The spatial variability of vehicle densities as determinant of urban network capacity. Philosophical Transactions of Royal Society A. 2010, 368:4627-4647.
25. Mendez G.A., da Silva L.R., Herrmann H.J. Traffic gridlock on complex networks. Physica A: Statistical Mechanics and its Applications 2012, 391(1):362-370.
26. Saberi, M, Mahmassani, H.S., 2012. Exploring the properties of network-wide flow-density relations in freeway networks. Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C, 2315, 153-163.
27. Saberi, M, Mahmassani, H.S., in press. Empirical characterization and interpretation of hysteresis and capacity drop phenomena in freeway networks. Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C.
28. Srinivasan K.K., Mahmassani H.S. Modeling inertia and compliance mechanisms in route choice behavior under real-time information. Transportation Research Record: Journal of the Transportation Research Board 2000, 1725:45-53.
29. Urban Mobility Report, 2011. Texas Transportation Institute.
30. U.S. Census Bureau, 2007. http://www.census.gov/compendia/smadb/TableB-01.pdf.
31. Verbas I.O., Mahmassani H.S., Zhang K. Time-dependent origin-destination demand estimation: challenges and methods for large-scale networks with multiple vehicle classes. Transportation Research Record: Journal of the Transportation Research Board 2011, 2236:45-56.
32. Williams J.C., Mahmassani H.S., Herman R. Urban traffic network flow models. Transportation Research Record: Journal of the Transportation Research Board 1987, 1112:78-88.
33. Williams J.C., Mahmassani H.S., Herman R. Sampling strategies for two-fluid model parameter estimation in urban networks. Transportation Research Part A 1995, 29(3):229-244.
34. Zhou X., Qin X., Mahmassani H.S. Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record: Journal of the Transportation Research Board 2003, 1831:30-38.