The paper introduces an adaptive optimization framework that is used to calibrate and optimize multiple intelligent transportation strategies such as high occupancy toll (HOT), ramp metering, and variable speed limits. The framework is capable of estimating the OD (origin–destination) matrix, calibrating the model with the experimental data, and conducting system optimization in transportation. This capability is shown with simulation experiments based on the public data of I-95 Florida. The preliminary results show that the revenue increases with target speed up to a point, after which the revenue drops. Also, the throughput on the HOT increases with decreasing target speed. So, to maximize revenue and throughput together, the optimal values are observed at moderate levels of the feasible range. Also, in the presence of accidents, the total throughput is improved by decreasing the toll rate. Optimal level of vehicles on the HOT lanes is examined to minimize vehicle emissions.
GartnerNHStamatiadisCTarnoffPJ. Development of advanced traffic signal control strategies for intelligent transportation systems: multilevel design. Transp Res Record1995; 1494: 98–105.
2.
FigueiredoLJesusIMachadoJ. Towards the development of intelligent transportation systems. In Proceedings of IEEE Intelligent Transportation Systems, pp. 1206–1211.
3.
WangFY. Parallel control and management for intelligent transportation systems: concepts, architectures, and applications. IEEE Trans Intell Transport Syst2010; 11(3): 630–638.
4.
CetinNBurriANagelK. A large-scale agent-based traffic microsimulation based on queue model. In Proceedings of Swiss Transport Research Conference (STRC), March 19 21, 2003.
YunIParkBB. Estimation of dynamic origin destination matrix: a genetic algorithm approach. In Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 2005.
7.
ying MaGLiPWenY. Application of genetic algorithm in estimating origin–destination matrices of traffic network. In Proceedings of 2005 IEEE International Conference on Granular Computing, Vol. 1, pp. 209–212.
8.
BeraSRaoKVK. Estimation of origin–destination matrix from traffic counts: the state of the art. Eur Transp2011; 49: 3–23.
9.
OzbayKMudigondaSBartinB. Microscopic simulation and calibration of an integrated freeway and toll plaza model. In 85th Transportation Research Board Annual Meeting, 2005.
10.
Al-DeekHKlodzinskiJ. Simulation and Evaluation of the Orlando–Orange County Expressway Authority (OOCEA) Electronic Toll Collection Plazas using TPSIM. Technical report, Florida Department of Transportation Research Center, 2000.
11.
MorgulEF. Simulation based evaluation of dynamic congestion pricing algorithms and strategies. Technical report, New Brunswick Rutgers, The State University of New Jersey, 2010.
12.
ZheLHaoLKeZ. Calibration and validation of paramics for freeway using toll data. In Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, 2009.
13.
LowSHLapsleyDE. Optimization flow control—I: basic algorithm and convergence. IEEE/ACM Trans Netw1999; 7(6): 861–874. DOI:10.1109/90.811451.
14.
PapageorgiouMKotsialosA. Freeway ramp metering: an overview. In Proceedings of IEEE Intelligent Transportation Systems, pp. 228–239.
15.
HegyiADe SchutterBHellendoornJ. Parallel control and management for intelligent transportation systems: concepts, architectures, and applications. IEEE Trans Intell Transp Syst2005; 6(1): 102–112.
16.
NelderJA. A simplex method for function minimization. Computer J1965; 7: 308–313.
17.
YenJLeeB. A simplex genetic algorithm hybrid. In IEEE International Conference on Evolutionary Computation, 1997.
18.
SmithMCSadekAWHuangS. Large-scale microscopic simulation: toward an increased resolution of transportation models. J Transp Eng2008; 134(7): 273–281.
19.
SmallKA. Using the revenues from congestion pricing. Transportation1992; 19(4): 359–381.
20.
LouYYinYLavalJA. Optimal dynamic pricing strategies for high-occupancy/toll lanes. Transp Res C Emerg Technol2011; 19(1): 64–74.
21.
GolobaTFReckeraWWAlvarezVM. Freeway safety as a function of traffic flow. Accident Anal Prevent2004; 36(6): 933–946.
22.
ZhangGYanSWangY. Simulation-based investigation on high-occupancy toll lane operations for Washington State Route 167. J Transp Eng2009; 135(10): 677–686.
23.
ChuLNesamaniKSBenouarH. Priority based high occupancy vehicle lanes operation. In Proceedings Of Transportation Research Board Annual Meeting.
MeldrumDTaylorC. Algorithm design, user interface, and optimization procedure for a fuzzy logic ramp metering algorithm: A training manual for freeway operations engineers. Technical report, University of Washington, 2000.
WangLGordonMDZhuJ. Regularized least absolute deviations regression and an efficient algorithm for parameter tuning. In Proceedings Of Sixth International Conference on Data Mining, pp. 690–700.
28.
PrecheltL. Early stopping – but when? In Neural Networks: Tricks of the Trade (Lecture Notes in Computer Science, vol. 7700). New York: Springer, 2012, pp. 53–67.
29.
PerkVDeSalvoJRodriguesT. Improving value of travel time savings estimation for more. Effective transportation project evaluation. Technical report, Florida Department of Transportation, 2012.
