Resources/Commute Optimization
Where Commutrics Began

The idea that became a platform.

Years before Commutrics was a product, it was a research question: could you optimize an entire organization's commute for two things at once, lower emissions and shorter time, and hand each employee a personalized, incentivized plan? This 2019 study answered yes, and built the engine that everything since has grown from.

System
B⁺COS
Objectives
Emissions + time
Case study
21 commuters
Published
IJST · 2019
The foundation

Most commute programs treat every employee the same. This research didn't.

The standard playbook, free parking, flat transit subsidies, one-size-fits-all perks, ignores a basic truth: a single employee with a long solo commute can produce as much emissions as several short-commute coworkers combined. Spending the same on everyone wastes money on people who would never change, and underpays the people who would.

This study introduced B⁺COS, the Business⁺ Commute Optimization System, to fix that. Instead of guessing, it models every feasible commute option for every person, calculates the real cost of each in emissions and time, and finds the optimal mix that cuts an organization's footprint while respecting each commuter's tolerance, preferences, and the employer's budget for incentives.

It is the conceptual ancestor of the Commutrics platform, and the principle still holds: personalize the plan, optimize the whole, and pay only where it moves the needle.

Core idea
2

Goals, optimized at once

B⁺COS minimizes emissions and commute time simultaneously, surfacing the trade-off curve between them rather than forcing a single answer.

Emissions
30%

Lower emissions cost achievable

In the case study, the social cost of emissions dropped by about 30% (from $3.45 to $2.40 per day) when commuters allowed up to 25 extra minutes.

Trade-off front
205

Optimal plans, not one answer

At the 25-minute tolerance, the model generated 205 distinct optimal plans, letting decision-makers pick the balance of time and emissions they want.

Mode shift
13

Of 21 shifted to biking

At the lowest-emissions plan, 13 of 21 commuters were routed to biking and 5 to transit, with only 3 driving alone, a near-inversion of the starting mix.

Incentives
$1.20

Priced to each commuter's time

Incentives were tied to the value of added time. A commuter who gained 4 minutes by carpooling was offered $1.20, calculated from their hourly rate.

Per option
6

Trip attributes, per choice

For every commute option, a GIS engine computed six attributes, time, distance, cost, and CO₂, NOₓ, and VOC emissions, from real street and transit networks.

The core idea

Two goals that pull against each other.

The greenest commute is rarely the fastest. Biking and transit cut emissions but add minutes; driving alone is quick but dirty. Forcing a single "best" answer hides that tension.

B⁺COS instead maps the whole Pareto front, the set of plans where you can't reduce emissions any further without adding time, and vice versa. Every point on the curve is optimal; the choice of which point is a policy decision left to the organization.

As the curve shows, small increases in allowed commute time unlock steep early emissions cuts, then taper, exactly the kind of insight a flat subsidy can never reveal.

Emissions cost vs. commute time
Each point is a Pareto-optimal plan, 25-minute tolerance case
$3.8$3.4$3.0$2.6$2.2Emissions social cost225300375450Total commute time (minutes)Existing commutefastestgreenest
How it works

Two engines, one plan.

B⁺COS pairs a geographic engine that measures reality with an optimizer that searches for the best possible commute mix across an entire organization.

Engine one

The GIS model measures.

Using real street, highway, and transit networks, it computes what each commute option actually costs, for every person, across eight modes from driving to biking to transit combinations.

  • Geocodes every home and workplace location
  • Computes the fastest realistic route per mode
  • Outputs six attributes: time, distance, cost, CO₂, NOₓ, VOC
  • Evaluates every feasible carpool pairing
Engine two

The optimizer decides.

It searches the full space of who-takes-what to find plans on the emissions-time trade-off curve, while respecting each commuter's tolerance, preferences, and the employer's incentive budget.

  • Minimizes emissions and time as twin objectives
  • Honors per-commuter time tolerance and mode preferences
  • Caps total incentives to a set budget
  • Produces an individualized action plan per commuter
Measure reality  →  search for the best mix  →  hand each person a plan
The case study

21 commuters, re-routed.

A real community of 21 students at CSU Fresno provided their actual commutes. Here's the starting mix versus the lowest-emissions plan the optimizer found.

Before
Existing commute
Drive alone
15
Walk
4
Bike
2
Carpool
2
Transit
0
After · lowest-emissions plan
Optimized commute
Bike
13
Transit
5
Drive alone
3
Carpool
0
Walk
0
The optimizer favored biking over walking wherever feasible, because it cuts emissions to zero while keeping commute time low. Where bikes weren't practical, transit and carpools filled the gap. Solo driving fell from the dominant mode to just three commuters.
Flexibility is leverage

A few extra minutes buys a lot of cleaner air.

The model was run at four levels of commuter tolerance, the maximum extra time a person will accept. The more flexibility commuters allow, the more options the optimizer can find.

5 min tolerance
51
optimal plans available
10 min tolerance
112
optimal plans available
15 min tolerance
175
optimal plans available
25 min tolerance
205
optimal plans available
The takeaway for program design: emissions reductions aren't only about which modes you offer, they're about how much schedule flexibility you can give people. A guaranteed-ride-home program or a flexible start window does more than feel generous; it widens the set of greener commutes that are actually feasible.
Then & now

From a research model to a working platform.

Every idea in this 2019 study has a direct descendant in the Commutrics platform today.

B⁺COS, 2019
A GIS model computing trip attributes from a desktop mapping tool
Commutrics today
A cloud platform pulling live routing and emissions data through mapping APIs
B⁺COS, 2019
A 21-person academic case study run on a single computer
Commutrics today
Programs that scale to thousands of commuters across many employers
B⁺COS, 2019
Incentives priced to each commuter's added time and hourly value
Commutrics today
Personalized, optimized incentives delivered and tracked automatically
B⁺COS, 2019
An action report describing each commuter's recommended plan
Commutrics today
Live dashboards and audit-ready reporting on cost, mode shift, and emissions
Peer-reviewed · IJST 2019

Read the foundational study, or cite it.

The Commutrics team's foundational research, including the full GIS methodology, the optimization formulation, the case study, and the sensitivity analysis, is published in the International Journal of Sustainable Transportation, Taylor & Francis.

Suggested citation
Abdallah, M., Tawfik, A. M., Monghasemi, S., Clevenger, C. M., and Adame, B. A. (2019)."Developing commute optimization system to minimize negative environmental impacts and time of business commuters."International Journal of Sustainable Transportation. DOI: 10.1080/15568318.2018.1531184.
University of Colorado Denver & California State University, Fresno. Supported by the California Department of Transportation (Caltrans) and the Mountain-Plains Consortium.

The research grew up. It's a platform now.

The two-engine idea behind B⁺COS, measure reality, optimize the whole, personalize the plan, is exactly what Commutrics runs for real programs today. See it in action.