Mohamed Wahba

Multiobjective Optimization for Multimodal Evacuation

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.

Safety models incorporating graph theory based transit indicators.

There is a considerable need for tools to enable the evaluation of the safety of transit networks at the planning stage. One interesting approach for the planning of public transportation systems is the study of networks. Network techniques involve the analysis of systems by viewing them as a graph composed of a set of vertices (nodes) and edges (links). Once the transport system is visualized as a graph, various network properties can be evaluated based on the relationships between the network elements. Several indicators can be calculated including connectivity, coverage, directness and complexity, among others. The main objective of this study is to investigate the relationship between network-based transit indicators and safety. The study develops macro-level collision prediction models that explicitly incorporate transit physical and operational elements and transit network indicators as explanatory variables. Several macro-level (zonal) collision prediction models were developed using a generalized linear regression technique, assuming a negative binomial error structure. The models were grouped into four main themes: transit infrastructure, transit network topology, transit route design, and transit performance and operations. The safety models showed that collisions were significantly associated with transit network properties such as: connectivity, coverage, overlapping degree and the Local Index of Transit Availability. As well, the models showed a significant relationship between collisions and some transit physical and operational attributes such as the number of routes, frequency of routes, bus density, length of bus and 3+ priority lanes.

Spatial Effects on Zone-Level Collision Prediction Models

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.

Integrated Multimodel Evaluation of Transit Bus Emissions in Toronto, Canada

This paper investigates transit bus emissions in the city of Toronto, Ontario, Canada, by linking the results of a microsimulation transit assignment model, MILATRAS (microsimulation learning-based approach to transit assignment), with emission factors derived from Mobile6.2C. Emissions were estimated at the level of individual buses during idling conditions at bus stops and on roadway links between stops during the morning peak period. The busiest routes were associated with the highest total emissions as a result of a combination of high ridership and lower speeds; this association confirmed the common wisdom that newer, low-emitting buses should be first allocated to these routes. The highest dwell emissions occurred at intermodal transfer stations (bus to subway and vice versa). On a passenger kilometer basis, the highest-emitting routes were not the busiest, but rather were those with the lowest ridership. In fact, the highest emissions per passenger kilometer were associated with the Airport Rocket, a route that provided service to the airport and was characterized by low ridership in the morning peak period. On average, bus trips in Toronto were about three times more fuel efficient than were private car trips and created 20 times less carbon monoxide pollution. The effects of changing fuel types and fleet age on transit bus emissions were assessed. Implications for bus operations are discussed relative to fleet allocation to minimize total emissions.

MILATRAS: a microsimulation platform for testing transit-ITS policies and technologies

As innovative technological solutions are integrated with transit services, it is useful to have a tool for evaluating and refining new strategies prior to the deployment. MILATRAS is an environment, where travel demand modelers, can experiment with possible transit-ITS technologies and policies, while dynamic microsimulation sub-models of dynamic departure time and transit path choices, passengers perception updating and passengers within-day and day-to-day travel choice dynamics are properly represented. The proposed framework can be used to generate and evaluate different transit-ITS policies. Currently, a full scale implementation of the City of Brampton transit system, Ontario, is under construction. The Brampton transit system operates around 150 buses on 32 routes, serving nearly 25,000 daily rides

Comparison of Agent-Based Transit Assignment Procedure with Conventional Approaches: Toronto, Canada, Transit Network and Microsimulation Learning-Based Approach to Transit Assignment

The public transportation system, a key part of a multimodal transportation network, has been widely viewed as an efficient way to reduce road congestion and pollution. Public transportation planners use transit assignment models to forecast travel demand and service performance. As technologies evolve and smart transit systems become more prevalent, it is important that assignment models adapt to new policies, such as traveler information provision. This paper investigates three transit assignment tools that represent three approaches to modeling transit trip distribution over a network of fixed routes. These tools are the EMME/2 Transit Assignment Module (Module 5.35), commonly used by planners; Toronto, Canada, Transit Commissions transit assignment tool, MADITUC; and the newly developed Microsimulation Learning-based Approach to Transit Assignment (MILATRAS). These approaches range from aggregate, strategy-based frameworks to fully disaggregate microscopic platforms. MILATRAS presents a stochastic process approach (i.e., nonequilibrium based) for modeling within-day and day-to-day variations in the transit assignment process in which aggregate travel patterns can be extracted from individual choices. Although MILATRAS presents a different standpoint for analysis in comparison with equilibrium-based models, it still gives the steady state run loads. MILATRAS performs comparatively well with EMME/2 and MADITUC. In addition, MILATRAS presents a policy-sensitive platform for modeling the effects of smart transit system policies and technologies on passengers’ travel behavior (i.e., trip choices) and transit service performance.