Speaking

Managing safety and efficiency at multimodal intersections is a growing challenge in dense urban environments. This study examines multiple intersections along Washington and Jefferson Streets in Downtown Phoenix, a critical location encompassing the new Downtown Hub light rail transfer stations. This area sees higher-than-average pedestrian traffic and is home to several sports and special event venues, which can increase pedestrian traffic substantially. Using PTV Vissim and Viswalk, we analyzed existing conditions and evaluated treatments designed to reduce conflicts and improve operational performance.

Key strategies included pedestrian scrambles, leading pedestrian intervals (LPIs) and removal of permissive turn movements during red phases, all aimed at reducing vehicle conflicts. The pedestrian simulation utilizing Viswalk’s Social Force Model allowed for the inclusion of natural vehicle-pedestrian interactions and their influence on the performance of other modes in the network. Simulations incorporated future transit routing scenarios and multimodal interactions, ensuring recommendations align with long-term transit operations.

Results indicate improved pedestrian experience while maintaining acceptable traffic performance. The use of PTV Vissim and Viswalk provided a data-driven approach to evaluate complex interactions between vehicles, transit, and pedestrians, enabling the analysis of higher-resolution interactions between modes. This approach provides another essential layer of vehicle-pedestrian interaction to transportation models that helps contextualize traffic analysis results within the broader context of an integrated urban system.

AI copilots, agents and web tools powered by large language models are rapidly becoming standard across engineering disciplines. This session demystifies how these tools work and explains how they can be applied in transportation. We’ll begin with a clear overview of today’s AI assistants: how they generate predictions, ground responses in your documents and support a wide range of tasks. This foundation will help practitioners understand their strengths, limitations and where human judgment remains essential. We’ll also outline mainstream tools available today and what typically makes one option better suited to a particular need. 

From there, we’ll move from introductory concepts to intermediate practice with “models in series,” a technique that strengthens quality assurance and quality control. Throughout the session, we will emphasize responsible use—effective prompting, validation loops, records retention and transparency—so teams can gain speed and consistency without compromising trust. Attendees will leave with a clearer understanding of model capabilities, a checklist for safe and responsible use and a starter playbook for applying AI to common and mid-level transportation tasks. After the session, participants and their teams will be positioned to implement and refine AI solutions both personally and professionally. 

This presentation will provide an overview of a two-phase project focused on improving operations at a study intersection in the City of Surprise, Arizona. The first portion of the presentation will introduce the research conducted on how municipalities and transportation agencies determine when to add additional left-turn lanes or convert existing left-turn movements to protected-only phasing. Attendees will learn which standards, criteria and national practices were reviewed and how these guidelines were applied to evaluate existing conditions at the study intersection.

The second portion of the presentation will walk through the operational analysis performed using the findings from phase one. Several improvement scenarios—ranging from signal-phasing modifications to geometric enhancements—will be presented and compared. The audience will gain insight into how each alternative affects intersection performance, what operational tradeoffs were considered and how short-term needs were balanced with long-term growth.

The presentation will conclude with a discussion of the final two-part recommendation: an interim improvement strategy that can be implemented quickly to address immediate concerns and a more robust horizon-year solution that offers greater long-term benefits. Attendees will leave with a clearer understanding of how evaluating multiple scenarios supports informed decision-making and helps municipalities prioritize improvements effectively.

The Town of Gilbert, Arizona, is proactively addressing the challenges of at-grade trail crossings along arterial roadways through Phase 2 of its Trail Safety Crossing Improvement Project. The project includes treatment review and development of over 25 trail-crossing sites. A key component in the project was the development of evidence-based guidelines for Pedestrian Hybrid Beacon (PHB) placement. Recognizing the limitations of existing guidance, particularly the lack of specific minimum spacing criteria, the town initiated a rigorous study to assess the operational impacts of PHBs in proximity to signalized intersections.

This presentation details a comprehensive microsimulation analysis, utilizing Vissim to model PHB performance under a range of traffic conditions representative of Gilbert's arterial roadways. Scenarios encompassing Level of Service (LOS) D, E and F were evaluated to determine the sensitivity of vehicle queuing to varying PHB-signal spacing distances. The results of this analysis were translated into a user-friendly decision support tool, enabling engineers to efficiently screen potential PHB locations based on crossing distance and average daily traffic volume.

Attendees will gain valuable insights into the operational characteristics of PHBs, the complex interactions between trail crossings and adjacent signalized intersections and a replicable methodology for data-driven site selection. This presentation will demonstrate how Gilbert is leveraging advanced modeling techniques to optimize PHB placement, maximize safety benefits and foster a more walkable and bikeable community.