Page 53 of 171

Why are children getting hurt in the street because of “looming”?

Adults are better than children at detecting the speed of a car that’s traveling faster than 20 miles per hour and are more likely to avoid crossing, thus not getting hit. 

Director of New York City-based Transportation Alternatives Paul Steely-White asked on Twitter for a plain English translation of this three-year old journal article about vehicle speeds and something called “looming”.

The article is called “Reduced Sensitivity to Visual Looming Inflates the Risk Posed by Speeding Vehicles When Children Try to Cross the Road”.

Skip to the end if you want the plain English translation, but I’ve posted the abstract below followed by excerpts from Tom Vanderbilt’s Traffic.

ABSTRACT: Almost all locomotor animals respond to visual looming or to discrete changes in optical size. The need to detect and process looming remains critically important for humans in everyday life. Road traffic statistics confirm that children up to 15 years old are overrepresented in pedestrian casualties. We demonstrate that, for a given pedestrian crossing time, vehicles traveling faster loom less than slower vehicles, which creates a dangerous illusion in which faster vehicles may be perceived as not approaching. Our results from perceptual tests of looming thresholds show strong developmental trends in sensitivity, such that children may not be able to detect vehicles approaching at speeds in excess of 20 mph. This creates a risk of injudicious road crossing in urban settings when traffic speeds are higher than 20 mph. The risk is exacerbated because vehicles moving faster than this speed are more likely to result in pedestrian fatalities.

The full text is free to download, but I think Steely-White needs to learn more now, so I pulled out my favorite book about driving, Tom Vanderbilt’s “Traffic”.

Page 95-97:

For humans, however, distance, like speed, is something we often judge rather imperfectly. Unfortunately for us, driving is really all about distance and speed. Consider a common and hazards maneuver in driving: overtaking a car on a two-lane road another approaches in the oncoming lane. When objects like cars are within twenty or thirty feet, we’re good at estimating how far away they are, thanks to our binocular vision (and the brain’s ability to construct a single 3D image from the differing 2D views each eye provides). Beyond that distance, both eyes are seeing the same view in parallel, and so things get a bit hazy. The farther out we go, the worse it gets: For a car that is twenty feet away, we might be accurate to within a few feet, but when it is three hundred yards away [900 feet], we might be off by a hundred yards [300 feet]. Considering that it takes about 279 feet for a car traveling at 55 miles per hour to stop (assuming an ideal average reaction time of 1.5 seconds), you can appreciate the problem of overestimating how far away an approaching car is – especially when they’re approaching you at 55 miles per hour.

[Here comes the keyword used in the journal article, “looming”]

Since we cannot tell exactly how far away the approaching car might be we guess using spatial cues, like its position relative to a roadside building or the car in front of us. We can also use the size of the oncoming car itself as a guide. We know it is approaching because its size is expanding or looming on our retina.

But there are problems with this. The first is that viewing objects straight on, as with the approaching car, does not provide us with a lot of information.

[…]

If all this is not enough to worry about there’s also the problem of the oncoming cars speed. A car in the distance approaching 20 miles per hour makes passing easy, but what if it is doing 80 miles per hour? The problem is this: We cannot really tell the difference. Until, that is, the car gets much closer — by which time it might be too late to act on the information.

[the topic continues]

Plain English translation

However, nothing I found in Traffic relates children and “looming”. The bottom line is that children are worse than adults at detecting the speed of a car coming in the cross direction and thus decide wrongly on when to cross the street.

Update: Based on Vanderbilt’s writing, it seems that humans cannot really be taught how to compensate for looming, to build a better perceptual model in the brain to detect the difference between cars traveling 20 and 80 MPH. If this is true, and I’d like to see research of pedestrian marketing and education programs designed for children, it may be that we should stop trying this approach.

Smartphones replace cars. Cars become smartphones.

Teens’ smartphone use means they don’t want to drive. Car makers’ solution? Turn cars into smartphones.

The Los Angeles Times reported in March 2013, along with many other outlets, that “fewer 16-year-olds are rushing to get their driver’s licenses today than 30 years ago as smartphones and computers keep adolescents connected to one another.”

Smartphones maintain friendships more than any car can. According to Microsoft researcher Danah Boyd, who’s been interviewing hundreds of teenagers, “Teens aren’t addicted to social media. They’re addicted to each other.” (Plus not every teen needs a car if their friends have one. Where’s Uber for friends? That, or transit or safe cycle infrastructure, would help solve the “I need a ride to work at the mall” issue.)

Driving is on the decline as more people choose to take transit, bike, walk, or work from home (and not unemployment).

intel cars with bicycle parts

Marketing images from Intel’s blog post about cars becoming smartphones.

What’s a car maker to do?

The first thing a car maker does to fight this (losing) battle is to turn the car into a smartphone. It’s definitely in Intel’s interest, and that’s why they’re promoting the story, but Chevrolet will soon be integrating National Public Radio – better known as NPR – as an in-dash app. It will use the car’s location to find the nearest NPR affiliate. Yeah, my smartphone already does that.

The second thing they do is to market the product differently. Cars? They’re not stuck in traffic*, they’re an accessory to your bicycle. Two of the images used in Intel’s blog post feature bicycles in some way. The first shows a bicycle helmet sitting on a car dashboard. The second shows how everyone who works at a proposed Land Rover dealership is apparently going to bike there, given all the bikes parked at an adjacent shelter.

The new place to put your smartphone when you take the train.

* I’m looking at you, Nissan marketing staff. Your commercial for the Rogue that shows the mini SUV driving atop a train full of commuters in order to bypass road congestion (and got a lot of flack) is more ridiculous than Cadillac’s commercial showing a car blowing the doors of other cars, while their drivers look on in disbelief, in order to advertise the 400+ horsepower it has (completely impractical for driving in the urban area the commercial showcases).

Chicago Crash Browser, miraculously, has 2012 bicycle and pedestrian crash data

Screenshot shows that you can choose your own search radius. When researching, be sure to copy the permalink so you can revisit your results. 

I’ve upgraded the Chicago Crash Browser, my web application that gives you some basic crash and injury statitics for bicyclist and pedestrian crashes anywhere in Chicago, to include 2012 data. It took the Illinois Department of Transportation eight months to compile the data and it took me four months to finally get around to uploading it into my database. While I spent that time, I made some improvements to the usability of the app and output more information. Since the last major changes I made (back in February 2013) I’ve gained two code contributors (Richard and Robert) making this my first communal project on GitHub.

I know that it’s been used as part of research in the 46th Ward participatory budgeting process for 2013, and by residents in the 26th Ward to show Alderman Maldonado the problem intersections in the Humboldt Park area. Transitized recently included pedestrian crash stats obtained from the Crash Browser in a blog post about pedestrianizing Michigan Avenue in Streeterville.

The first change I made was adding another zoom level, number 19, so you can get closer to the data. I made some changes to count how many people were injured and total them. You can now choose your search distance in multiples of 50 feet between 50 and 200, inclusive. As is typical, I get sidetracked when I notice errors on the map. Thankfully I just fire up JOSM and correct them so the next person that looks at the map sees the correction. Future changes I want to make include upgrading to the latest jQuery, LeafletJS, and Leaflet plugins. I’d also like to migrate to Bootstrap to improve styling and add responsive design so it works better on small screens.

Sign up for the newsletter where I’ll send a couple emails each year describing new changes (I’ve so far only published one newsletter).

Developing a method to score Divvy station connectivity

A Divvy station at Halsted/Roscoe in Boystown, covered in snow after the system was shutdown for the first time to protect workers and members. Photo by Adam Herstein.

In researching for a new Streetsblog Chicago article I’m writing about Divvy, Chicago’s bike-share system, I wanted to know which stations (really, neighborhoods) had the best connectivity. They are nodes in a network and the bike-share network’s quality is based on how well (a measure of time) and how many ways one can move from node to node.

I read Institute for Transportation Development Policy’s (ITDP) report “The Bike-Share Planning Guide” [PDF] says that one station every 300 meters (984 feet) “should be the basis to ensure mostly uniform coverage”. They also say there should be 10 to 16 stations per square kilometer of the coverage area, which has a more qualitative definition. It’s really up to the system designer, but the report says “the coverage area must be large enough to contain a significant set of users’ origins and destinations”. If you make it too small it won’t meaningfully connect places and “the system will have a lower chance of success because its convenience will be compromised”. (I was inspired to research this after reading coverage of the report in Next City by Nancy Scola.)

Since I don’t yet know the coverage area – I lack the city’s planning guide and geodata – I’ll use two datasets to see if Chicago meets the 300 meters/984 feet standard.

Dataset 1

The first dataset I created was a distance matrix in QGIS that measured the straight-line distance between each station and its eight nearest stations. This means I would cover a station in all directions, N, S, E, W, and NW, NE, SE, and SW. Download first dataset, distance matrix.

Each dataset offers multiple ways to gauge connectivity. The first dataset, using a straight-line distance method, gives me mean, standard deviation, maximum value, and minimum value. I sorted the dataset by mean. A station with the lowest mean has the greatest number of nearby stations; in other words, most of its nearby stations are closer to it than the next station in the list.

Sorting the first dataset by lowest mean gives these top five best-connected stations:

  1. Canal St & Monroe St, a block north of Union Station (191), mean of 903.96 feet among nearest 8 stations
  2. Clinton St & Madison St, outside Presidential Towers and across from Northwestern Train Station (77), 964.19 feet
  3. Canal St & Madison St, outside Northwestern Train Station (174), 972.40
  4. Canal St & Adams St, north side of Union Station’s Great Hall (192), 982.02
  5. State St & Randolph St, outside Walgreens and across from Block 37 (44), 1,04.19

The least-connected stations are:

  1. Prairie Ave & Garfield Blvd (204), where the nearest station is 4,521 feet away (straight-line distance), or 8.8x greater than the best-connected station, and the mean of the nearest 8 stations is 6,366.82 feet (straight-line distance)
  2. California Ave & 21st St (348), 6,255.32
  3. Kedzie Ave & Milwaukee Ave (260), 5,575.30
  4. Ellis Ave & 58th St (328), 5,198.72
  5. Shore Drive & 55th St (247), 5,168.26

Dataset 2

The second dataset I manipulated is based on Alex Soble’s DivvyBrags Chrome extension that uses a distance matrix created by Nick Bennett (here’s the file) that estimates the bicycle route distance between each station and every other station. This means 88,341 rows! Download second dataset, distance by bike – I loaded it into MySQL to use its maths function, but you could probably use python or R.

The two datasets had some overlap (in bold), but only when finding the stations with the lowest connectivity. In the second dataset, using the estimated bicycle route distance, ranking by the number of stations within 2.5 miles, or the distance one can bike in 30 minutes (the fee-free period) at 12 MPH average, the following are the top five best-connected stations:

  1. Ogden Ave & Chicago Ave, 133 stations within 2.5 miles
  2. Green St & Milwaukee Ave, 131
  3. Desplaines St & Kinzie St, 129
  4. (tied) Larrabee St & Kingsbury St and Carpenter St & Huron St, 128
  5. (tied) Clinton St & Lake St and Green St & Randolph St, 125

Notice that none of these stations overlap with the best-connected stations and none are downtown. And the least-connected stations (these stations have the fewest nearby stations) are:

  1. Shore Drive & 55th St, 11 stations within 2.5 miles
  2. (tied) Ellis Ave & 58th St and Lake Park Ave & 56th St, 12
  3. (tied) Kimbark Ave & 53rd St and Blackstone Ave & Hyde Park Blvd and Woodlawn Ave & 55th St, 13
  4. Prairie Ave & Garfield Blvd, 14
  5. Cottage Grove Ave & 51st St, 15

This, the second dataset, gives you a lot more options on devising a complex or weighted scoring system. For example, you could weight certain factors slightly higher than the number of stations accessible within 2.5 miles. Or you could multiply or divide some factors to obtain a different score.

I tried another method on the second dataset – ranking by average instead of nearby station quantity – and came up with a completely different “highest connectivity” list. Stations that appeared in the least-connected stations list showed up as having the lowest average distance from that station to every other station that was 2.5 miles or closer. Here’s that list:

  1. Kimbark Ave & 53rd St – 13 stations within 2.5 miles, 1,961.46 meters average distance to those 13 stations
    Blackstone Ave & Hyde Park Blvd – 13 stations, 2,009.31 meters average
    Woodlawn Ave & 55th St – 13 stations, 2,027.54 meters average
  2. Cottage Grove Ave & 51st St – 15 stations, 2,087.73 meters average
  3. State St & Kinzie St – 101 stations, 2,181.64 meters average
  4. Clark St & Randolph St – 111 stations, 2,195.10 meters average
  5. State St & Wacker Dr – 97 stations, 2,207.10 meters average

Back to 300 meters

The original question was to see if there’s a Divvy station every 300 meters (or 500 meters in outlying areas and areas of lower demand). Nope. Only 34 of 300 stations, 11.3%, have a nearby station no more than 300 meters away. 183 stations have a nearby station no further than 500 meters – 61.0%. (You can duplicate these findings by looking at the second dataset.)

Concluding thoughts

ITDP’s bike-share planning guide says that “residential population density is often used as a proxy to identify those places where there will be greater demand”. Job density and the cluster of amenities should also be used, but for the purposes of my analysis, residential density is an easy datum to grab.

It appears that stations in Woodlawn, Washington Park, and Hyde Park west of the Metra Electric line fare the worst in station connectivity. The 60637 ZIP code (representing those neighborhoods) contains half of the least-connected stations and has a residential density of 10,468.9 people per square mile while 60642, containing 3 of the 7 best-connected stations, has a residential density of 11,025.3 people per square mile. There’s a small difference in density but an enormous difference in station connectivity.

However, I haven’t looked at the number of stations per square mile (again, I don’t know the originally planned coverage area), nor the rise or drop in residential density in adjacent ZIP codes.

There are myriad other factors to consider, as well, including – according to ITDP’s report – current bike mode share, transit and bikeway networks, and major attractions. It recommends using these to create a “demand profile”.

Station density is important for user convenience, “to ensure users can bike and park anywhere” in the coverage area, and to increase market penetration (the number of people who will use the bike-share system). When Divvy and the Chicago Department of Transportation add 175 stations this year – some for infill and others to expand the coverage area – they should explore the areas around and between the stations that were ranked with the lowest connectivity to decrease the average distance to its nearby stations and to increase the number of stations within 2.5 miles (the 12 MPH average, 30-minute riding distance).

N.B. I was going to make a map, but I didn’t feel like spending more time combining the datasets (I needed to get the geographic data from one dataset to the other in order to create a symbolized map). 

Divvy isn’t a real bike

Riding on Divvy in the snow.

Divvy, for the first time in its short, seven-month existence, shut down today at 12 PM on account of the weather and keeping members and workers – who move bikes, shovel snow, and drive vans around town – safe.

Every news media in town reported on the shutdown. Chicagoist, ABC 7, NBC 5, FOX 32, Chicago Tribune, Sun-Times – you name it they had it.

But nothing has been published, except parts of this story from DNAinfo Chicago, that discusses how bicycling – whether on Divvy or your own bike – is very difficult in Chicago winters because of the poor coordination between the Streets & Sanitation and Transportation departments’ snow removal efforts, and the slow pace at which CDOT gets around to removing snow from the protected bike lanes. (I was quoted, alongside someone I recommended the author get in touch with, and we have differing views on the matter.)

In winter the protected bike lanes are the only bikeable kind of bike lane as conventional bike lanes become snow storage areas because plows can’t reach further right when there are parked cars (to avoid knocking off car mirrors).

This problem is not unique to Chicago and other cities have solved it. The cold isn’t why people in Chicago stop biking: it’s that snow and ice make it even more difficult in a region with little, separated (meaning safe and desirable) cycling infrastructure. There are climes with similar and worse winters where a large portion of people who bike in the summer keep biking in the winter. Places like Boulder, Minneapolis, Montréal, and Copenhagen.

A well-plowed, separated bike lane in a Copenhagen winter. Stranded? Put your bike on the back of a taxi (their buses don’t allow bikes).

I think it’s good that news media have recognized Divvy’s position as a transit system in the area, which they do by holding it to the same, weird standard they do Chicago Transit Authority and Metra, and posting about it frequently. When the CTA or Divvy has some marginal or perceived issue with its finances or service, an article gets written. But when it comes to bicycle infrastructure they give the city a pass where it doesn’t deserve one.

The media cares about Divvy, but it doesn’t care about bicycling. It might be the 11,000 Divvy members (more than Active Transportation Alliance or The Chainlink), however, that gets the city to kick up its bike lane snow removal efforts up a notch and I anxiously await that day.