Beyond the Gridlock: Smarter Corridor Analysis Agent with Google Data and Gemini AI

CorridorSmart360 Agent

Traffic congestion is more than just a daily annoyance; it’s a complex puzzle involving engineering, urban planning, and human behavior. For transportation professionals and city planners, analyzing how a specific corridor performs—whether it’s a busy downtown avenue or a critical commuter highway—has traditionally been a slow, expensive process relying on one-day counts or floating car runs with limited four-time runs within 2 hours.

But traffic isn't static. It breathes, changes by the hour, and shifts depending on whether you are driving, taking a bus, or cycling.

At W & S Solutions, we believe the future of transportation planning is dynamic, data-driven, and AI-assisted. That is why we developed the CorridorSmart360 agent.

1. The Challenge of Modern Corridor Analysis

If you need to know how a specific corridor performs every Tuesday at peak AM hours versus mid-day Saturday, traditional methods fall short. You need to answer questions like:

• How does transit travel time compare to driving at 8:00 AM?

• Exactly how many minutes are lost to congestion (vehicle delay) during the evening rush?

• Is bicycling a viable alternative on this route during specific windows?

• Is the traffic data collected on one or three days reliable as compared to other seasons or representative over a longer period?

Gathering this data and answering these questions is prohibitive with current methods. CorridorSmart360 solves this by tapping into the world's most comprehensive real-time traffic engine: Google Maps data.

2. Introduction to CorridorSmart360 Agent

CorridorSmart360 Agent is a streamlined, powerful analytics agent designed to provide rapid insights into specific travel corridors. Built on a robust Google AI platform with customized Python and Streamlit framework, it automates the heavy lifting of data collection of Google Map data and visualization for multimodal travel data based on the corridor OD locations and the data collection scheduling in a much more cost-effective way.

3. How-to-Use-Agent workflow

a)      Define Your Corridor and Schedule

Here are steps of working with input files for the app:

• Open the APP agent: click on CorridorSmart360

• Under Option 1: Run New Estimation

• Upload the OD Locations file on the left (Download a sample file here).

• Then you will see the corridor with a map as a background.

• Upload Schedule Config file on the left (Contact W&S for a demo)

• Under Option 2: Resume Analysis

• Upload previous Excel/CSV results (Download a sample file here.)

• Click the red button “Run Estimation.”

You will see the corridor map and results in Charts and Tables of this corridor, and can even download the output file for further analysis. In addition, with an engineering prompt, ask Google Gemini AI to see additional insights into the corridor as a reference and perform the engineering analysis of the results and insights obtained.

b)      Define Your Corridor and Schedule

Forget complex simulation setups. Simply upload a CSV defining your Origin and Destination points (using WKT coordinates) (OD Locations File.csv) and a schedule file you want to estimate (dates, times, and frequency) (Schedule Coding Config.csv) as shown below. File 1 shows the coordinates of origin (Point 1) and destination (Point 12) with a description of the corridor. These corridors are defined in the Google Map or any GIS Software. File 2 shows estimation periods with the start_date and end_date, start_time and end_time, and the number of executions of the estimation for each period. It is noted that these parameters can be customized for each project as needed.  

File 1: Origin and Destination points (Point 1 to Point 12)

WKT	name	description
POINT (-122.1209665 37.6999302)	Point 1	Alameda East 14 St-Mission Blvd
POINT (-122.0940478 37.6802839)	Point 12	Alameda East 14 St-Mission Blvd

File 2: Schedule Coding Configuration (Two periods for one day)

start_date	end_date	start_time	end_time	executions_per_period
1/5/2026	1/5/2026	8:00	9:45	8
1/5/2026	1/5/2026	17:00	18:45	8

c)      The Power of Google Data

The agent goes to work, querying the Google Maps traffic patterns for driving, transit, walking, and bicycling travel times across the corridor and the entire schedule automatically. It obtains the corridor distance and

• Baseline trave travel times based on the posted travel speed by mode;

• Free-flow travel time by mode;

• Congested travel time by mode;

• Delays by mode;

• Travel information by time and mode:

  • Transit line names;

  • # of (bus) stops;

  • Sched. Departure.

d)      Base-line travel  time

This value is Google's general baseline travel time for the corridor. It is derived from road network data (speed limits, average historical speeds) but does not factor in the specific traffic conditions of the requested time. It acts as a "standard reference" time.

e)      Free-Flow travel time= Congested Travel Time – min(Congested Travel Time, Baseline Average)

It is possible that in practice, the free-flow travel time can be estimated for an exact time-stamp moment (e.g., Tuesday at 11:00 AM). Because Google uses historical congestion data to predict this, it can sometimes be faster than the general baseline travel time.

f)      Congested Travel Time

This is the specific prediction for the travel time (e.g., Tuesday at 11:00 AM) based on the historical real-time travel time, which sometimes can be shorter than the baseline average time. Because Google Maps uses historical congestion data to predict this, it can sometimes be faster than the general base-line if the specific time chosen has historically lighter-than-average traffic (e.g., mid-day lulls). It noted that the Google Map does not provide any historical travel times.

g)      Delays by mode

This is the specific prediction for the travel time delay for vehicles only, which is the difference between the congested travel times and the free-flow travel time. There is no data for other modes.

h)      Instant Visualization

The data is immediately rendered into an interactive dashboard:

• 🗺️ Corridor Map: A clear, interactive visualization of the study corridor in blue as shown below.

• 📉 Time-Series Graphs: Dynamic line charts showing travel times across all modes, and a dedicated graph pinpointing vehicle delays over time.

• 📄 Detailed Data Table: A raw look at every data point collected.

 ...

i)      Auto-Archiving

Every run (Run Estimation) is valuable. The application automatically saves the complete dataset, including a timestamp, to your local folder and provides a one-click Excel file for further offline analysis. These historical data files can be used as part of the historical data for the corridor.

4. The Game Changer: Your AI Traffic Assistant

The Agent can save the historical data once it is initiated, and then the raw historical data is necessary for future analysis. But insights into these data are what matter. This is where CorridorSmart360 leaps ahead of traditional tools.

We have integrated Google's Gemini AI directly into the dashboard of this Agent.

The application feeds the freshly generated traffic data into the AI model, acting as a context-aware "Traffic Engineer Assistant." You don't need to be a data scientist to understand the results. You can simply use the chat interface to ask natural language questions (prompts), such as:

• “As a Signal Timing Engineer or as a Transport Planner, analyze this travel time pattern.”

• "Compare the reliability of transit versus driving during the evening commute based on this data."

• "What are potential traffic calming measures we should consider given these delay patterns?"

• and so on.

Gemini analyzes the specific dataset you just generated and provides informed, context-relevant answers quickly. The following is an example of the first question and a Gemini’s answer for our analysis reference only. It noted that this can not replace our traffic engineers and urban planners, but can support us with additional analytical information.

Question (Prompt): Analyze the travel pattern insight and compare them by date Jan. 6-Jan. 8, and periods AM (6:30-8:30), MID (11:00-13:00), and PM (16:30-18:30) with a discussion and table for each period and by mode for these dates and periods, and find any specific patterns. Use two decimals for all distances, times, and delays in the analysis and tables.

Gemini Answer: Here is the result of the Gemini answer with no changes, as it is below. We can use this answer as a reference, review, and check if they are reasonable. Then we can continue to perform our engineering analysis.

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1)      Executive Summary

This report provides a detailed analysis of travel patterns for a key transportation corridor based on data collected from Tuesday, January 13, to Thursday, January 15, 2026. The analysis focuses on three critical time periods: AM Peak (06:30-08:30), Mid-Day (11:00-13:00), and PM Peak (16:30-18:30).

The primary findings indicate that vehicular congestion and associated delays are most severe during the PM Peak period, with the highest average delay of 1.16 minutes observed on Thursday. The AM Peak shows a progressive increase in congestion from Tuesday to Thursday. In contrast, non-vehicular modes (Transit, Bicycling, Walking) demonstrate highly reliable and consistent travel times across all days and periods, unaffected by traffic congestion. Driving remains the fastest mode, but its time advantage diminishes significantly during peak periods due to delay, reducing its reliability.

3)     AM Peak Period (06:30 - 08:30)

The AM Peak period is characterized by the morning commute. The data reveals a clear trend of increasing traffic delay as the week progresses.

Discussion:

• Driving: While consistently the fastest option, driving travel times show increasing delays from Tuesday to Thursday. The average delay nearly quadruples from Tuesday (0.12 min) to Thursday (0.46 min), indicating worsening conditions towards the end of the week for the morning commute. The worst congestion within this period typically occurs between 08:00 and 08:15.

• Transit: Public transit (Line 9) offers a completely reliable travel time of 10.40 minutes every day during this period. It is slower than driving but provides certainty for commuters.

• Bicycling & Walking: These active transport modes are entirely consistent, with travel times of 16.53 minutes and 46.12 minutes, respectively. They are viable, predictable alternatives for this short-distance corridor.

Table 1: AM Peak Period (06:30 - 08:30) Average Travel Metrics by Mode

Date	Mode	Avg. Distance (mi)	Avg. Duration (min)	Avg. Delay (min)
Jan-13-Tue	Driving	2.00	6.42	0.12
	Transit	2.10	10.40	0.00
	Bicycling	2.90	16.53	0.00
	Walking	2.00	46.12	0.00
Jan-14-Wed	Driving	2.00	6.51	0.22
	Transit	2.10	10.40	0.00
	Bicycling	2.90	16.53	0.00
	Walking	2.00	46.12	0.00
Jan-15-Thu	Driving	2.00	7.03	0.46
	Transit	2.10	10.40	0.00
	Bicycling	2.90	16.53	0.00
	Walking	2.00	46.12	0.00

3)     PM Peak Period (16:30 - 18:30)

The PM Peak period consistently shows the highest levels of congestion and delay across the observed days, representing the most challenging travel time for drivers.

Discussion:

• Driving: This period is defined by significant traffic delays. Thursday is the most congested day, with an average delay of 1.16 minutes, peaking at a 1.68-minute delay for the 17:15 departure. Tuesday is also heavily congested (1.01 min avg. delay). Wednesday provides a notable reprieve, with delays less than half of the other two days.

• Transit: Transit performance is variable in the PM peak. On Tuesday and Wednesday, service is faster towards the end of the period (shifting from 10.40-10.93 min to 8.27 min). This suggests a change in schedule or operating conditions after 18:00. This improved speed makes transit highly competitive with driving during the 18:15-18:30 timeframe.

• Bicycling & Walking: Once again, these modes are unaffected by the severe congestion, offering predictable travel times.

Table 3: PM Peak Period (16:30 - 18:30) Average Travel Metrics by Mode

Date	Mode	Avg. Distance (mi)	Avg. Duration (min)	Avg. Delay (min)
Jan-13-Tue	Driving	2.00	7.61	1.01
	Transit	2.10	10.04	0.00
	Bicycling	2.90	16.53	0.00
	Walking	2.00	46.12	0.00
Jan-14-Wed	Driving	2.00	7.03	0.43
	Transit	2.10	10.04	0.00
	Bicycling	2.90	16.53	0.00
	Walking	2.00	46.12	0.00
Jan-15-Thu	Driving	2.00	7.76	1.16
	Transit	2.10	9.79	0.00
	Bicycling	2.90	16.53	0.00
	Walking	2.00	46.12	0.00

4)      Overall Patterns and Conclusions

• Congestion Peaks: The PM Peak is the most congested period, especially on Tuesday and Thursday. The AM Peak shows a clear pattern of increasing congestion as the week progresses.

• Mode Reliability: The most significant pattern is the dichotomy between driving and all other modes. Driving is the fastest but least reliable mode, with travel times fluctuating by over 1.5 minutes depending on the time of day. Transit, bicycling, and walking offer exceptional reliability, with consistent travel times that are immune to traffic, making them attractive for travelers who prioritize predictability over raw speed.

• Day-of-Week Pattern: Thursday appears to be the most congested day overall, driven by high delays in both the AM and PM peaks. Wednesday shows significantly lighter traffic, particularly in the PM. This may suggest different commuting patterns, such as more people working from home or leaving the office at different times mid-week.

• Transit Competitiveness: During heavy PM peak congestion (e.g., Thursday at 17:15), the driving time (8.28 min) is very close to the transit time (10.40 min). When accounting for the "last mile" problem and parking, transit becomes a highly competitive alternative. The faster transit service after 18:00 makes it the superior choice in the later parts of the PM peak.

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Based on the analysis on the Google travel times, we can continue to work on the corridor with more data, including traffic volumes, LOS data, other observations, and more discussions with the local jurisdictions.

5. Summary of Advanced Agent Features

• Multi-Modal Analysis: Compare transportation information by modes of Driving, Transit, Walking, and Bicycling.

• Dynamic Scheduling: Test varied timeframes, dates, and frequencies easily.

• Precise Delay Calculation: Isolates traffic-induced delay from free-flow travel time.

• Interactive Visualizations: Review traffic conditions with the maps, charts and data tables.

• AI-Powered Insights: Integrated Gemini Pro for instant data interpretation.

• Secure & Local: Runs securely with the API keys; data saves locally.

6. Making Corridors Smarter

CorridorSmart360 Agent, developed by W & S Solutions (W&S) and powered by extensive Google data, moves corridor studies from reactive historical snapshots to proactive, near real-time analysis. It empowers planners to make better decisions faster.

7. 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.