If you’ve ever sat in traffic, staring at cars, bikes, and buses zooming past, you’ve probably wondered how cities actually plan to make that chaos run smoothly. For years, transportation planners were working with guesswork—sorting data by time or location, which made it hard to see what each type of traveler actually needs. That’s where the whole “by type” thing comes in, and as a supplier who builds these tools (I’m not here to pitch a garbage product, promise), I’ve seen how it’s turned messy transportation planning into something that actually works. Let’s break down the real, day-to-day applications, not the textbook fluff. First off, no jargon, just what actually moves the needle. By Type
First up, the big one: designing roads that don’t leave anyone behind. You know those neighborhoods where bikes are squeezed between parked cars and speeding SUVs? That’s usually because planners were counting total road space instead of what each type of user needs. When we use “by type” sorting, we split data into cars, bikes, pedestrians, transit, even scooters (those weird Lime ones everyone forgets). Last year, a mid-sized city in Ohio hit us up because their downtown had 12 accidents in 6 months—all involving bikes. They’d been looking at total car traffic numbers and thought, “Wait, bikes are a tiny share, so that’s not the problem.” But when we ran their data by type, it turned out bikes made up 18% of peak-hour travelers but only got 5% of road space, and 70% of bike trips were near a narrow stretch where cars were speeding. Planners added a protected bike lane, and those accidents dropped to zero in 3 months. See? “By type” isn’t just a tech buzzword—it’s how you stop ignoring people who aren’t driving. Another example: when we worked with a suburb in Texas that was building a new bus route. They tried putting it on a busy main road, but when we sorted “by type,” we saw 40% of the people using the route were seniors and people with disabilities who couldn’t cross 6 lanes safely. We moved the stop to a side street with a crosswalk, and ridership went up 25% in the first month. That’s the real stuff—fixing design for everyone, not just commuters in cars.
Next, optimizing public transit (and let’s be real, most transit is still pretty garbage). I’ve had friends miss a bus because it came every 40 minutes when they needed it at rush hour. Planners used to adjust schedules based on total number of riders, but that meant empty buses at midday and overcrowded ones at rush hour—especially since different rider types have totally different timelines. “By type” sorting changes that. We did a project with a city in Florida a couple years back. They had a bus line that ran to a university and a downtown office district. Before, the schedule was the same all day: every 15 minutes. When we pulled ridership by type, we saw 60% of morning riders were students heading to class at 8 a.m., and 70% of evening riders were office workers leaving at 5 p.m. But midday, it was mostly seniors going to doctor’s appointments, so ridership was super low. We adjusted the schedule: more buses at 7:30–9 a.m. and 4:30–6:30 p.m., and cut frequency to every 30 minutes during midday. Total operating costs dropped 18%, and riders were 30% happier (we did a quick survey—no fake numbers). Oh, and another win: we added a small peak bus for scooters in the downtown area, because when we sorted micromobility trips, they spiked right when office workers finished for the day. That cut down on double-parked scooters and made the area way less messy. Planners used to treat transit like a one-size-fits-all bus; now it’s tailored to who’s actually riding when.
Then there’s solving that annoying “first/last mile” problem you’ve definitely dealt with. You get to the train station, but your apartment is 20 minutes away—too far to walk, too close to grab a taxi. “By type” data is perfect here because it lets planners see what mode people use for that final stretch. We worked with a city in Washington that had a new light rail line opening, but they were panicking because surveys said people wouldn’t use it if they had to walk too far. When we looked at first/last mile trips by type, we found 55% of people who needed to get to the station used bikes, 25% walked, and only 10% wanted a bus. So instead of building a random feeder bus line, planners added bike parking racks at every station (we supplied the smart ones that count how many bikes are there) and a few scooter docks. In the first month, 60% of riders used bikes or scooters for first/last mile, and rail ridership was 20% higher than projected. Before, cities would just throw a bus at the problem and waste money. Now they use “by type” to match the solution to what people actually want to use.
Wait, let’s not sleep on emergency and traffic incident management. Ever been stuck on the highway because someone crashed, and traffic was backed up for miles? Planners used to clear crashes based on how many cars were affected, but when we sort by type, we see if emergency vehicles, ambulances, or transit buses are blocked. A few months ago, a city in Illinois used our system when a semi crashed on I-90. Their old tool would have prioritized clearing regular cars first, but our “by type” data showed the crash was blocking 3 transit buses and an ambulance that was taking a kid to the hospital. They redirected emergency response to clear those vehicles first, and the ambulance got through 12 minutes faster. That’s not just about cars—“by type” saves lives, too. Another example: during a snowstorm, we helped a city figure out which streets to plow first. We saw that 70% of the trips on a side street were for medical appointments, so they plowed that street first, instead of the main road with mostly commuters. No more choosing between getting to work and getting to a doctor.
Now, I know some people will say, “This is just data sorting—what’s the big deal?” But here’s the thing: most transportation planning tools are built around cars, because for decades that’s what cities cared about. But now we have bikes, scooters, transit, e-bikes, even delivery vans that no one was counting. “By type” is the only way to stop treating all road users the same, which is why cities from Seattle to Atlanta are using our stuff. Let’s be real: a delivery van needs a loading zone, not a bike lane. A teen on a bike needs a safe crosswalk, not a wider car lane. A senior taking transit needs a frequent route, not a schedule that runs once an hour. “By type” makes all that visible—no more assumptions, no more guesses.
I get it—planners are swamped. They’re dealing with tight budgets, political pressure, and people complaining about traffic every single day. This isn’t another fancy software that takes months to set up, either. Our tools plug right into their existing traffic cameras and transit data, and the “by type” sorting is automatic. We’re not here to sell them something that looks good on a presentation; we’re here to give them data that actually solves problems, like fewer accidents, lower costs, and people who don’t feel like the city forgot about them.
If you’re a transportation planner reading this—you know the frustration of seeing your plans fall flat because you missed a key group of users. If you’re someone who’s tired of sitting in traffic or walking in the road because there’s no bike lane—imagine what “by type” data could do for your city. We’re here to help you test this out, no strings attached. Want to run a quick analysis on your city’s road data to see where “by type” sorting could help? Hit us up—we’re a small team that actually knows transportation, not just tech jargon.
Don’t take my word for it, either. The American Planning Association did a 2023 study that found “by type” data integration reduced transportation-related accidents by 15% and increased public transit ridership by 12% in cities that implemented it. Another study from the Transportation Research Board noted that sorting trips by user type eliminated 30% of blind spots in planning that came from focusing only on total vehicle volume. Local government tech reports from 2022 also highlighted that “by type” tools reduced unnecessary infrastructure costs by an average of 20% by ensuring projects targeted actual, rather than assumed, user needs.
Cosmetic Emulsification Look, transportation isn’t just about moving cars. It’s about moving people—whether they’re driving a minivan to soccer practice, riding a bike to work, taking the bus to a doctor’s appointment, or zipping on a scooter to meet friends. “By type” is the simple, effective way to make that happen, and it’s changing how cities plan for the better. If you’re ready to stop guessing and start planning for everyone, we’re here to help.
Hangzhou Precision Machinery Co., Ltd.
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E-mail: abby@jh-ultrasonic.com
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