This paper proposes a multimodal optimization framework that combines vehicular traffic and mass transit for emergency evacuation. The multi-objective approach optimizes the multimodal evacuation framework by investigating three objectives: minimizing in-vehicle travel time, minimizing at-origin waiting time, and minimizing fleet cost in the case of mass transit evacuation. For auto evacuees, an optimal spatiotemporal evacuation (OSTE) formulation is presented for generating optimal demand scheduling, destination choice, and route choice simultaneously. OSTE implements dynamic traffic assignment techniques coupled with genetic optimization to achieve the objective functions. For transit vehicles, a multiple-depot, time-constrained, pickup and delivery vehicle routing problem (MDTCPD-VRP) is formulated to model the use of public transit shuttle buses during evacuation. MDTCPD-VRP implements constraint programming and local search techniques to achieve the objective function and satisfy constraints. The OSTE and MDTCPD-VRP platforms are integrated in one framework to replicate the impact of congestion caused by traffic on transit vehicle travel times. This paper presents a prototype implementation of the conceptual framework for a hypothetical medium-size network in downtown Toronto, Ontario, Canada. The results show that including the waiting time and the in-vehicle travel time in the objective function reduced the network clearance time for auto-evacuees by 40% compared with including only the in-vehicle travel time. For mass transit, when considering fleet cost, an increase of 13% in network clearance time for transit evacuees was observed with a decrease of 12% in fleet size. Mass transit was shown to provide latent transportation capacity that is needed in evacuation situations.
This paper presents an operational prototype of an innovative framework for the transit assignment problem, structured in a multiagent way and inspired by a learning-based approach. The proposed framework is based on representing passengers and their learning and decision-making activities explicitly. The underlying hypothesis is that individual passengers are expected to adjust their behavior (i.e., trip choices) according to their experience with transit system performance. A hypothetical transit network, which consists of 22 routes and 194 stops, has been developed within a microsimulation platform (Paramics). A population of 3,000 passengers was generated and synthesized to model the transit assignment process in the morning peak period. Using reinforcement learning to represent passengers’ adaptation and accounting for differences in passengers’ preferences and the dynamics of the transit network, the prototype has demonstrated that the proposed approach can simultaneously predict how passengers will choose their routes and estimate the total passenger travel cost in a congested network as well as loads on different transit routes.
This paper documents the efforts to operationalize the conceptual framework of MIcrosimulation Learning-based Approach to TRansit Assignment (MILATRAS) and its component models of departure time and path choices. It presents a large-scale real-world application, namely the multi-modal transit network of Toronto which is operated by the Toronto Transit Commission (TTC). This large-scale network is represented by over 500 branches with more than 10,000 stops. About 332,000 passenger-agents are modelled to represent the demand for the TTC in the AM peak period. A learning-based departure time and path choice model was adopted using the concept of mental models for the modelling of the transit assignment problem. The choice model parameters were calibrated such that the entropy of the simulated route loads was optimized with reference to the observed route loads, and validated with individual choices. A Parallel Genetic Algorithm engine was used for the parameter calibration process. The modelled route loads, based on the calibrated parameters, greatly approximate the distribution underlying the observed loads. 75% of the exact sequence of transfer point choices were correctly predicted by the offstop/on-stop choice mechanism. The model predictability of the exact sequence of route transfers was about 60%. In this application, transit passengers were assumed to plan their transit trip based on their experience with the transportation network; with no prior (or perfect) knowledge of service performance.
A recent study developed a set of zone-level negative binomial collision prediction models to investigate the relationship between various transportation and sociodemographic characteristics and overall roadway safety. The developed models used data from Metro Vancouver, British Columbia, Canada, and considered the effect of Poisson variations and heterogeneity (extra variation) on collision occurrence. This study aims to evaluate spatial effects on the occurrence of collisions and to check whether the inclusion of spatial variables can improve the goodness of fit and inference capability of those previously developed prediction models. Transit-reliant and application-based collision prediction models with spatial correlations were developed by using the WinBUGS software. The convergence of the developed models was tested by trace plots of the parameter estimated, the Brooks–Gelman–Rubin statistics, and ratios of Monte Carlo errors relative to the standard deviations of the estimates. The results showed that incorporation of the spatial correlations affected the parameter estimates, the values of dispersion parameters and intercepts, and also the t-statistics. The effect of the main exposure variable on all of the models for total, severe, and property-damage-only collisions was found to be smaller under spatial models. The smaller values of the exponents of the main exposure variable confirmed the assumption that spatial effects need to be considered in collision prediction models to mitigate any potential bias associated with model misspecification.
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