Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
Source abstract
Abstract
The reliability of urban transportation systems is crucial for ensuring smooth traffic flow and minimizing disruptions caused by external factors. This study focuses on improving the stability and efficiency of transportation systems through the calibration of a refined link performance function while building upon the U.S. Bureau of Public Roads (BPR) model. To achieve this, we propose three customized algorithms—Newton’s method, Bayesian optimization, and the differential evolutionary algorithm—to calibrate the key parameters. Additionally, we conducted a sensitivity analysis to assess the influences of the model parameters on link performance. Numerical experiments conducted in Yuyao City demonstrate the applicability and efficacy of the proposed model and solution algorithms. Our results reveal that the Newton approach is notably more efficient than the Bayesian optimization algorithm and the differential evolutionary algorithm.
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Research context
OpenAlex topics: Transportation Planning and Optimization · Traffic control and management · Traffic Prediction and Management Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratory for Traffic & Transport Planning Digitalization, Transport Planning and Research Institute, Ministry of Transport China, Beijing 100028, China
School of Instrument Science and Engineering, Wuxi Campus, Southeast University, Wuxi 214000, China
School of Transportation, Southeast University, Nanjing 211189, China
Funding
Laboratory for Traffic and Transport Planning Digitalization Program · Key Laboratory Open Fund of the Transportation Industry in Comprehensive Transportation Theory · National Natural Science Foundation of China Youth Program
References (52)
Show source-provided references
1. The taxi sharing practices: Matching, routing and pricing methods10.1016/j.multra.2022.100003 ↗
2. Design of Commute Carpooling Based on Fixed Time and Routes
3. Design of intelligent carpooling program based on big data analysis and multi-information perception10.1007/s10586-017-1274-9 ↗
4. Huang, K., Liu, F., Hu, Y., and Liu, Z. (2018). Green Intelligent Transportation Systems: Proceedings of the 7th International Conference on Green Intelligent Transportation System and Safety 7, Springer.10.1007/978-981-10-3551-7 ↗
5. Conical volume-delay functions10.1287/trsc.24.2.153 ↗
6. Link capacity functions: A review10.1016/0041-1647(76)90055-1 ↗
7. Bottleneck and queuing analysis: Calibrating volume–delay functions of travel demand models10.3141/2255-13 ↗
8. Delay-function-based link models: Their properties and computational issues10.1016/j.trb.2004.10.002 ↗
9. Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows10.1155/2017/4629792 ↗
10. Reverse assignment: Calibrating link cost functions and updating demand from traffic counts and time measurements10.1080/17415977.2011.565339 ↗
11. Road pricing for congestion control with unknown demand and cost functions10.1016/j.trc.2009.05.009 ↗
12. Volume Delay Functions Based on Stochastic Capacity10.3141/2421-11 ↗
13. Foytik, P., Cetin, M., and Robinson, R.M. (2013, January 13–17). Calibration of BPR function based on link counts and its sensitivity to varying demand. Proceedings of the Transportation Research Board 92nd Annual Meeting, Washington, DC, USA.
14. An s-shaped three-parameter (S3) traffic stream model with consistent car following relationship10.1016/j.trb.2021.09.004 ↗
15. Estimating link travel time functions for heterogeneous traffic flows on freeways10.1002/atr.1423 ↗
16. Development of travel time functions for disrupted urban arterials with microscopic traffic simulation10.1016/j.physa.2022.126961 ↗
17. Joint Data-Driven Estimation of Origin–Destination Demand and Travel Latency Functions in Multiclass Transportation Networks10.1109/TCNS.2022.3161200 ↗
18. Offline calibration of dynamic traffic assignment: Simultaneous de-mand-and-supply estimation10.3141/2003-07 ↗
19. Toward a class of link travel time functions for dynamic assignment models on signalized networks10.1016/S0191-2615(96)00036-7 ↗
20. Traffic Assignment Using a Density-Based Travel-Time Function for Intelligent Transportation Systems10.1109/TITS.2015.2503348 ↗
21. Estimating key traffic state parameters through parsimonious spatial queue models10.1016/j.trc.2022.103596 ↗
22. Xing, J., Liu, R., Zhang, Y., Choudhury, C.F., Fu, X., and Cheng, Q. (2023). Urban network-wide traffic volume estimation under sparse deployment of detectors. Transp. A Transp. Sci., 2197511.10.1080/23249935.2023.2197511 ↗
23. A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio10.1016/j.multra.2022.100017 ↗
24. A method for evaluating the balance of land use and multimodal transport system of new towns/cities using an integrated modeling framework10.1016/j.multra.2022.100063 ↗
25. An ant colony optimization approach to the multi-vehicle prize-collecting arc routing for connectivity problem10.1016/j.multra.2022.100033 ↗
26. Investigating machine learning for simulating urban transport patterns: A comparison with traditional macro-models10.1016/j.multra.2023.100085 ↗
27. Recent advances in understanding the impact of built environment on traffic performance10.1016/j.multra.2022.100034 ↗
28. Calibration and Validation of Psychophysical Car-Following Model Using Driver’s Action Points and Perception Thresholds10.1061/JTEPBS.0000264 ↗
29. Concurrent Estimation of Origin-Destination Flows and Calibration of Microscopic Traffic Simulation Parameters in a High-Performance Computing Cluster10.1061/JTEPBS.0000093 ↗
30. Lane Management with Variable Lane Width and Model Calibration for Connected Automated Vehicles10.1061/JTEPBS.0000283 ↗
31. (1985). Highway Capacity Manual, Transportation Research Board Special Report 209.
32. Davidson, K.B. (1966, January 5–9). A flow travel time relationship for use in transportation planning. Proceedings of the 3rd Australian Road Research Board (ARRB) Conference, Sydney, Australia.
33. Travel time functions for transport planning purposes: Davidson’s function, its time dependent form and alternative travel time function
34. Singh, R. (1993). Updating Speed-Flow and Speed-Capacity Relationships in Traffic Assignment: 1990 Mtc Regional Travel Demand Model Development Project: Draft, University of California.
35. Improved Speed-Flow Relationships for Planning Applications10.3141/1572-03 ↗
36. The theoretical basis of a flow-travel time relationship for use in transportation planning
37. Estimating travel time functions for urban roads: Options and issues10.1080/03081068908717414 ↗
38. Singh, R., and Dowling, R. (2002, January 7–11). Improved speed-flow relationships: Application to transportation planning models. Commonwealth of Massachusetts, Executive Office of Transportation and Construction; and Boston Metropolitan Planning Organization. Proceedings of the Seventh TRB Conference on the Application of Transportation Planning Methods Transportation Research Board, Boston, MA, USA.
39. Research on the theory and application of road impedance function
40. Impact of individual daily travel pattern on value of time10.1007/s11116-015-9654-6 ↗
41. Sensitivity analysis for non-linear mathematical models
42. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S. (2007). Global Sensitivity Analysis. The Primer, John Wiley & Sons, Ltd.10.1002/9780470725184 ↗
43. Inexact newton methods10.1137/0719025 ↗
44. Bayesian optimization for congestion pricing problems: A general framework and its instability10.1016/j.trb.2023.01.003 ↗
45. Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach10.1016/j.tre.2023.103108 ↗
46. Otsuka, T., Shimizu, H., Iwata, T., Naya, F., Sawada, H., and Ueda, N. (2019, January 27–30). Bayesian optimization for crowd traffic control using multi-agent simulation. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE, Auckland, New Zealand.10.1109/ITSC.2019.8917496 ↗
47. Applying Bayesian Optimization for Calibration of Transportation Simulation Models10.1177/0361198120936252 ↗
48. Heuristic computing with sequential quadratic programming for solving a nonlinear hepatitis B virus model10.1016/j.matcom.2023.04.034 ↗
49. Design of intelligent computing solver with Morlet wavelet neural networks for nonlinear predator–prey model10.1016/j.asoc.2022.109975 ↗
50. Neuro-Evolutionary Computing Paradigm for the SIR Model Based on Infection Spread and Treatment
51. Liu, J., and Lampinen, J. (2002, January 28–31). A fuzzy adaptive differential evolution algorithm. Proceedings of the IEEE TENCOM’02, 2002 IEEE Region 10 Conference on Computer, Communications, Control and Power Engineering, Beijing, China.
52. Quantify the Road Link Performance and Capacity Using Deep Learning Models10.1109/TITS.2022.3153397 ↗