Computational Methods for Calculating Multimodal Multiclass Traffic Network Equilibrium: Simulation Benchmark on a Large-Scale Test Case

Table 4

Comparison between the performances of algorithms with homogeneous and heterogeneous users.

Demand profile; quality indicator/method	Monoclass users				Multiclass users
	(euros)	Improvement to; compared to prob.	CT (hours)	Improvement to CT; compared to smart SSP	(euros)	Improvement to; compared to GBP	CT (hours)	Improvement to CT compared to SSP
MSA ranking	0.47	−40.03%	105.62	−9.28%	1.38	−24.32%	132.26	−9.74%
Gap-based prob. (GBP)	0.42	−23.88%	101.81	−5.33%	1.11	—	137.52	−14.10%
Probabilistic (prob.)	0.34	—	98.12	−1.53%	1.23	−10.81%	143.19	−18.81%
Step-size prob. (SSP)	0.55	−64.78%	108.71	−12.48%	2.61	−135.14%	120.52	—
Smart step-size prob.	0.37	−10.45%	96.65	—	1.27	−14.41%	139.59	−15.81%
Simulated annealing	0.21	36.36%	85.62	11.41%	0.32	71.17%	91.30	24.25%
Genetic algorithm	0.27	18.48%	72.84	24.64%	0.54	51.35%	83.78	30.49%