Mason Gemar

Investigation of Centroid Connector Placement for Advanced Traffic Assignment Models with Added Network Detail

Advanced traffic assignment models, such as simulation-based dynamic traffic assignment, typically incorporate more detailed network representations than do traditional planning models. In this context, the placement of centroid connectors may have a significant effect on model performance, and attention must be paid to their number and location to avoid unrealistic congestion or low utilization of minor roadways by local traffic. Given that the manual inspection of centroid connector placement may be too time-consuming in large regional networks, this paper proposes two simple automatic centroid connector placement strategies for dynamic traffic assignment applications. The first approach radially distributes the connectors to the nearest nodes and is intended to exemplify some limitations of the most common techniques in practice. The second strategy involves dividing the centroid and subsequent demand into two parts, distributing the demand across one sub-centroid linked to nearby nodes and one linked to the periphery, and thus effectively establishing a bilevel distribution. A modification of this strategy involves eliminating nodes at signalized intersections as viable candidates for connection. As part of the evaluation of the methods, a new metric, the locality factor, has been introduced to describe the use of minor streets by local traffic. The numerical experiments, conducted on two real-world networks, exemplify the effects of the incorporation of local streets and the placement of centroid connectors on model results. Sensitivity testing and limited field data comparisons suggest that the bilevel centroid connector placement strategy achieves more realistic results.

A stochastic dynamic programming approach for the equipment replacement optimization under uncertainty

In this paper, a stochastic dynamic programming (SDP) based optimization model is formulated for the equipment replacement optimization (ERO) problem that can explicitly account for the uncertainty in vehicle utilization. The Bellman approach is developed and implemented to solving the ERO SDP problem. Particular attention is paid to the SDP state-space growth and special scenario reduction techniques are developed to resolve the “curse of dimensionality” issue that is inherent to the dynamic programming method to ensure that the computer memory and solution computational time required will not increase exponentially with the increase in time horizon. SDP software computer implementation techniques, functionalities and the Graphical User Interfaces (GUI) are discussed. The developed SDP-based ERO software is tested and validated using the current Texas Department of Transportation (TxDOT) vehicle fleet data. Comprehensive numerical results, such as statistical analyses, the software computational time and solution quality, are described and substantial cost-savings have been estimated by using this ERO software. Finally, future research directions are also suggested.

Optimization of Equipment Replacement: Dynamic Programming-Based Optimization

The purpose of this paper is to present an optimization model formulation based on deterministic dynamic programming (DDP) and to propose both the Bellman and Wagner approaches to solve the problem of equipment replacement optimization (ERO). The developed solution methodology is general and can be used to make optimal keep and replace decisions for new and used vehicles, with and without annual budget considerations. A simple numerical example illustrates and steps through the Bellman DDP solution process and demonstrates how DDP is used to solve the ERO problem through backward recursion. The developed DDP-based ERO software is tested and validated with Texas Department of Transportation vehicle fleet data. Comprehensive numerical results, such as the software computational time and solution quality, are described. Substantial cost savings are estimated with this ERO software. Further research directions are suggested.

Subnetwork Analysis for Dynamic Traffic Assignment Models: Strategy for Estimating Demand at Subnetwork Boundaries

Dynamic traffic assignment (DTA) can be used to model impacts of traffic control plan scenarios on travel behavior. However, using DTA for modeling construction project impacts is limited by the computational time required to simulate entire roadway networks. DTA modeling of a subarea surrounding these work zones extracted from a larger network can decrease the overall run time. A particular issue of interest is estimating dynamic demand along the boundary of the selected subnetwork. Often demand at the subnetwork boundary is based on flows extracted from a full, base network analysis. However, it is likely that impacts within the subnetwork caused by traffic control plans will extend beyond the boundary and affect this demand. The use of a logit model to reassign demand at subnetwork boundary centroids on the basis of differences in internal (subnetwork) travel times between base and impact scenarios is presented. The proposed methodology was implemented by using several software programs linked through automated scripts. The methods implementation and the results from a case study are presented. Implementing the logit formulation was found to provide better estimates of subnetwork demand, specifically along the boundary, when compared with use of a fixed demand table extracted from the vehicle trajectories of the full network model under base conditions. Ultimately, the findings were encouraging and suggest that the strategy could enhance the accuracy of subnetwork demand estimation.