--- title: "Build The Most Effective Transportation Model: Better Data to Reflect Changing Mobility" description: Transportation modeling depends on the quality of data inputs. Learn how analytics ensure planners have the best tools to adapt to changing demographic, economic, and spatial conditions. image: https://learn.streetlightdata.com/hubfs/LINKEDIN%20template%20Aug%202022_TDM%20webinar%20social.png --- [![SLD-logo-homepage](https://learn.streetlightdata.com/hubfs/SLD-logo-homepage.png)](https://www.streetlightdata.com/) Transportation Essentials Series # Build The Most Effective Transportation Model: Better Data to Reflect Changing Mobility ##### The pandemic showed how quickly travel behaviors can change and the challenge this poses for effective transportation modeling. To account for rapidly shifting travel behaviors, you need quick access to up-to-date information. But traditional data collection methods are time-consuming and costly, resulting in data gaps that limit the efficacy of your model. Join our webinar to learn how your agency can use on-demand data for **sketch planning models, screenline and O-D calibration, mode shift analysis, weaving analysis, VMT analysis**, and more. ##### Speakers will share:  - Examples of how industry peers have future-proofed transportation networks - How analytics support microsimulations, travel demand models, and more - Demos showing how to leverage analytics for transportation modeling ##### Who is this webinar for? - Transportation leaders who need to get ahead of shifting travel patterns - Transportation practitioners looking for tools to build models that reflect real-world conditions ### Watch Now \*indicates a required field ![Transportation Modeling Webinar](https://learn.streetlightdata.com/hubfs/Webinars%20and%20Video/Transportation%20Modeling%20Webinar/TDM-Webinar_LaptopImage.png) ##### "Many Cities and MPOs do not have access to regular traffic counts on low-activity corridors such as local roads. StreetLight is a next-generation data source, offering a more comprehensive traffic picture than we’ve ever had before. The applications are endless." ![](https://learn.streetlightdata.com/hubfs/Webinars%20and%20Video/Transportation%20Modeling%20Webinar/edmonton-logo-%20(1).png) **Sandeep Datla, Senior Transportation Modeling Engineer** The City of Edmonton, Canada ##### Panelist Bios Xinbo Mi is a Senior Transportation Engineer at Evansville Metropolitan Planning Organization, with duties including travel demand model and microsimulation model development and analysis, ArcGIS and Python tool development, traffic impact study review, individual analysis for local public agencies, ITS architecture maintenance, etc. Xinbo always finds ways to improve the efficiency of doing things with the utilization of state-of-art technologies such as Big Data and AI. Ted Reinhold is a Senior Solutions Engineer at StreetLight based in Raleigh, NC. Prior to joining StreetLight in 2018, Ted worked as a transportation planning and travel demand forecasting consultant in the Washington, DC metro area where he supported the development of regional transportation models and the evaluation of multi-modal transportation projects. ## No more old data inputs. Access real-world data to enable a more reliable and robust transportation model. ![location pins](https://learn.streetlightdata.com/hubfs/Icons%20for%20Landing%20Pages/location-pins%20(1).svg) Vehicle volumes that can be segmented by daypart and weight class ![metrics](https://learn.streetlightdata.com/hubfs/Icons%20for%20Landing%20Pages/metrics-1%20(2).svg) O-D, speed, trip length, and trip duration data to understand up-to-date travel patterns ![pedestrian](https://learn.streetlightdata.com/hubfs/Icons%20for%20Landing%20Pages/pedestrian.svg) Layer traveler characteristics over your analysis to ensure models account for demographic population shifts ### Powering 10,000+ Projects Every Month ![Siemens Logo](https://learn.streetlightdata.com/hubfs/Siemens%20logo.jpg) ![New York City](https://learn.streetlightdata.com/hubfs/new%20york%20city%20Department%20of%20transportation%20logo.jpg) ![arup](https://learn.streetlightdata.com/hubfs/arup%20logo.jpg) ![Toronto](https://learn.streetlightdata.com/hubfs/Toronto%20logo.jpg) ![Arcadis](https://learn.streetlightdata.com/hubfs/arcadis%20logo.jpg) ![Fehr Peers](https://learn.streetlightdata.com/hubfs/Fehr%20Peers%20logo.jpg) ![VHB](https://learn.streetlightdata.com/hubfs/VHB%20logo.jpg) ![sandag logo](https://learn.streetlightdata.com/hubfs/sandag%20logo.jpg) ![NVTA](https://learn.streetlightdata.com/hubfs/NVTA%20logo.jpg) ![SSTI](https://learn.streetlightdata.com/hubfs/SSTI%20logo.jpg) ![VDOT logo](https://learn.streetlightdata.com/hubfs/VDOT%20logo.jpg) ![Stantec logo](https://learn.streetlightdata.com/hubfs/Stantec%20logo.jpg) ### Access recent transportation modeling metrics across any road, any season, any location. [Watch Now](https://learn.streetlightdata.com/transportation-modeling-analytics-webinar#form-section)