Stochastic Modeling of Lane Distribution and Dynamic Control of Managed Lanes for Highways

Recurrent congestion during increasingly extended daily peak periods is an escalating phenomenon with multidimensional impact.Congestion mitigation in highways is polarized between invasive infrastructure interventions and Intelligent Transportation Systems (ITS) that promote ameliorated performance with sustainable economic and spatiotemporal requirements.Nevertheless, traffic intra-class variability, adaptability of drivers’behavior to ITS and inter-lane behavior variation challenge the strategies’performance and advocate congestion that could be anticipated by timely operation of designated control policies.In this scope, a novel multi-level algorithm is introduced that provides unbiased definition of peak periods and prevailing traffic regimes, through stochastic clustering; forecasts lane traffic distribution in congested and uncongested traffic regimes through lane-scale parameterisation; optimises managed lanes (ML) systems control through multi-objective functions with traffic and economic interdependencies, so as to balance LOS and operational costs.In the first level, separate stochastic clustering procedures capture spatial patterns of lane stream dynamics and temporal patterns of time span during which max capacity is attained, which unbiasedly define prevailing traffic regimes and peak periods.Data mining reveals underlying spatial association between lane traffic distribution and traffic regimes emergence that is assessed in the subsequent level, through spatiotemporal parameterization per lane.Multivariate modeling is integrated to anticipate the sequence between separate regimes, as ensued by clustering, namely to capture patterns of lanes vehicle allocation during free flow and congested regimes, and forecast impending traffic behavior that could proactively trigger the efficient implementation of control policies.Static models are developed, to ensure simple feasible implementation to reactive control management systems, and dynamic models for integration into real-time proactive systems A novel introduced parameter, the lane density distribution ratio (LDDR), and the density of the destination-lane for congested conditions or of the origin-lane for uncongested, are addressed as promising determinant response variables Both are proven site-independent and occur intermittently during congestion and free flow conditions At the lower level, multi-objective optimisation of a ML’s system operation is conducted, on account of the explanatory variables of the developed forecasting models and the operational costs of such systems, so as to ensure timely operation of a policy and so congestion alleviation A reactive ML system is subject to the proposed optimisation scheme, where observed underutilization of the ML, undermines the system’s performance The integrated approach is assessed based on the efficiency of the designated control policy in preventing congested conditions, and it concludes with the set of Pareto optimal operation thresholds appointment, through maximisation of throughput per lane and minimisation of operational costs The procedure is considered innovative, as relevant frameworks are not acknowledged in literature for ML systems, and decisions for their timely operation are solely empirically driven The proposed algorithm is integrated at a reactive hard shoulder running (HSR) system, and is evaluated through a developed API and simulation.Finally,discrete choice ordered probit models of users’ adaptation a