Rethinking Corridor Travel Time Analysis: A Multi-Modal Method CorridorSmart360

GCP Method, Multimodal Travel Times, Signal Timing

Transportation is changing rapidly. Cities are evolving into multimodal hubs. Agencies face mounting pressure to reduce congestion, improve safety, and plan for a more connected future. At W & S Solutions, we are proud to stand at the intersection of technology and transportation planning and management, delivering powerful data-driven insights that lead to smarter, safer, and more sustainable mobility systems. Our work spans across Asia, the USA, Canada, and beyond, serving Federal, state agencies, MPOs, counties, and cities through advanced data collection, analytics, and modeling services. We are not just another data vendor-we are problem solvers, system integrators, data insiders, and innovation partners.

We will introduce:

• A Simple Hypothetical Case

• Multi-Modal Travel Time Estimation Method

• Applications and Insights

• CorridorSmart360 Advantage

1. Introduction

Travel time is a foundational metric in transportation planning and traffic operations. However, traditional methods—manual travel time runs conducted in three two-hour periods per day—are often limited in frequency, modal coverage (car mode only), and responsiveness to real-world variability. W & S Solution has conducted many such studies with its own camera-based GPS units in GIS since 2018.

Prepared by W & S Solutions

However, with the rise of cloud computing and data fusion, it's now possible to obtain multi-modal travel times across days, corridors, and transportation modes—using the vast location intelligence data sources with our SmartCorridor360 method. Now we can add more travel times for additional travel modes in addition to INRIX data sources, since it is mainly focused on vehicles and trucks with no transit, bike, and pedestrian-related data. Thus, we enrich the data contents significantly and reduce cost-effectively. This is what our CorridorSmart360 can do.  

At W & S Solutions, we have developed the SmartCorridor360 - a scalable workflow for setting up a cost-effective process of obtaining corridor-level travel times across vehicles, transit buses, bicycles, and pedestrians, using near real-time and/or saved historical data in a cloud data storage, and analytics tools. This post illustrates how the new method compares with conventional techniques and why it offers a breakthrough for public agencies, MPOs, and transportation professionals seeking deeper insights.

2. A Simple Hypothetical Case: Regional Corridor Improvement Monitoring

Consider multiple major corridors in a metropolitan region, serving mixed traffic flow of cars, buses, trucks, bikes, and pedestrians. Under the traditional method, we performed travel time runs four times in a period—such as AM peak, midday, PM peak, in both weekday and weekend—using probe vehicles with camera-based GPS units for both before and after improvement project conditions. While this yields spot-sample vehicle travel times, it misses transit, active modes, and variability between days.

In contrast, we can set up a custom CorridorSmart360 workflow for these corridors over multiple periods of AM, Midday, and PM for all three weekdays of Tuesdays, Wednesdays, and Thursdays, and weekend Saturday for one week or months before and after the project improvement. By accessing the custom saved historical data sets, SmartCorrido360 can generate travel time data across all major travel modes for all periods and days for predefined segments of these corridors at once, as defined.

3. CorridorSmart360: Multi-Modal Travel Time Estimation Method

W & S Solutions uses the following cloud-native stack:

• Cloud Platform: Query travel times by mode (driving, transit, walking, bicycling and Pedestrian) for specific corridor segments and routes; Schedule requests throughout the day (e.g., every 5 or 15 minutes); Save traffic data for context processing.

• Cloud Alerts: Integrate Waze incident and congestion alerts to identify bottlenecks, closures, and route disruptions.

• Cloud Functions: Perform data queries and store travel time outputs in a scalable data warehouse and enable SQL-based analysis for corridor performances across time, speed, distance and mode.

• Cloud Scheduler: Run batch jobs every day, collecting consistent time series over multiple weekdays.

• Cloud Data Fusion Visualization: Aggregate travel times into dashboards and tables by period, direction, mode, and corridor segment.

• Result Exports: Create custom tables for corridor evaluations in CSV and Excel format.

As a summary, the following table shows a comparison of the Traditional and CorridorSmart360 Method.

Metric	Traditional Method	CorridorSmart360 Method
Modal Coverage	Vehicles only	Vehicles, buses, bikes, and pedestrians
Data Collection	4 runs per corridor for two hours (continues recording) on a typical day	-/+100 samples/corridor for each 5-min interval over any day
Temporal Resolution	Two hours of periods of AM, Midday, and PM	Any hours of any periods, including AM, Mid-Day, and PM
Days of Coverage	1–2 typical study days for multiple corridors	Any number of study days with no field presence of travel time runs.
Disruption Capture	Rarely captured	Integrated from Waze alerts
Resource Requirement	Field crew, GPS, vehicles, hotels, 8 hours out	Data acquisition, no on-site staff, in-house data analytics
Spatial Accuracy	Route-by-route variability	Uses Google’s path-matching intelligence

Prepared by W & S Solutions

4. Applications and Insights

Our CorridorSmart360 enables:

• Day-to-day reliability analysis for each mode

• Peak period vs. off-peak comparison across the entire corridors

• Transit buses vs. passenger vehicle performance segmentation using the mode-specific queries

• Multimodal equity insights—longer walking/biking times compared to driving times

• Transit competitiveness analysis vs. auto

In one corridor study, we may find that bike travel times are less than car travel times during peak congestion, and that pedestrian delay patterns are aligned with signal timing issues at key intersections. These findings are not visible from traditional vehicle-based runs.

5. CorridorSmart360 Advantage

   
   

We bring together transportation modeling expertise with Emme, Cube and Aimsun, Synchro, and HCS, cloud-based data innovations with Google GCP engineering to deliver:

• Scalable and multimodal travel time data

• Cost-effective, field-free data collection

• High granularity and reproducibility

• Custom output table for before and after studies

• Model calibrations for HCS and simulation models

Whether for corridor performance reports, Complete Streets planning, or project funding applications, our multi-modal data approach meets public agency needs.

Contact Us to Learn More

Whether you're a public agency, a research institute, or a private consulting firm, we invite you to collaborate with us. Join us on this journey to make transportation smarter, safer, and more equitable. Ready to move beyond vehicle-only, single-day studies? Let us help you unlock rich, scalable travel time data using CorridorSmart360.

→ Contact us at sales@wssolutions.us or call us at 1-925-380-1320 to learn more about how we can assist with your project needs.