Los Angeles: A Tale of Two Bike Lanes

The scene of the battle: Figueroa Street runs for 30 miles north from the port of LA. Image: JM Rosenfeld via Flickr, re-used under creative commons.

It was the best of plans, it was the worst of plans. It was a plan hailed as a success, it was a plan that failed miserably. It was a plan that had won over those who’d been sceptical; it was a plan that once-supportive council members sent unceremoniously to the scrap heap. And, to top it all, both the biggest success and the biggest failure of Los Angeles’ plans for cycling infrastructure took place on the same street.

LA wasn’t always a driver’s town. In the 1920s, it had the longest urban rail network in the world, and innovative infrastructure was built for cyclists as well. Despite this, Angelenos fell in love with the car early on and moved for more highway projects, making it the road-based city it is today.

Lately, though, the city’s residents have become increasingly supportive of transportation projects that go beyond the car. In 2008, they voted for Measure R, which includes one of the most ambitious rail construction plans in the United States. Two years later, the city approved a bike plan that calls for 1,684 miles of bikeways.

All the same, implementing these plans has been slow going: voters who supported the creation of bike lanes in theory changed their mind when it came time to take away their precious car lanes or parking spaces. The Los Angeles Times estimates that, of the more than 1,600 miles of proposed bikeways, just 200 have been built.

One particularly acute case of this has occurred on one of the city’s most important roads, Figueroa Street. Though not as famous as other LA thoroughfares like Hollywood Boulevard, it’s a key artery for the city’s downtown, connecting the rolling hills of gentrifying Northeast Los Angeles with USC, the Coliseum, and the city’s distant port to the south.

The planned bike lane for Figueroa in Northeast Los Angeles has become a case study in exactly how much can go wrong with a seemingly good plan. In documents released in 2010, the area was listed as a priority. But after locals became hostile to the idea, councilman Gil Cedillo, who’d previously supported the plan, suddenly changed his mind; in July, the Los Angeles Times reported that Cedillo had halted all work on advancing the bike lane project. Citing concerns that adding bike lanes would restrict access to emergency vehicles, he added that cyclists are a “tiny but vocal segment of the population”.

Naturally, this didn’t go over well with the cycling community in Northeast LA. Josef Bray-Ali, owner of the well known Flying Pigeon bike shop and a vocal supporter of cycling infrastructure throughout the city, said of Cedillo, “We're going to have to get in his face non-stop, constantly…  I'm not going to back down.” Rick Risemberg, another advocate, accused Cedillo in a blog post of responding to pressure from those who don’t live in his district but do provide much of his financial backing.

As cycling advocates in Northeast LA regroup, perhaps they could learn from the tactics used to quell opposition to a scheme further south on Figueroa. In 2010, a plan for bike lanes along the two mile stretch between Downtown and the USC/Exposition Park complex, known as the MyFigueroa plan, began to take shape after a series of public meetings.

As with many other plans, the plan drew widespread, though diffuse, popular support. By contrast, its opponents were few, but dedicated – and, most importantly, rich. The website People for Bikes reported in April 2014 that the most visible face of opposition to the project was Darryl Holter, owner of eight car dealerships along the route, who vocally opined that the project would hurt his sales. But behind the scenes, other major local players, such as USC and the Natural History Museum, were dragging their feet, too. Though they publicly supported the plan, they also called for a traffic study that would jeopardise key funding for the project.

Fortunately for bike advocates, such opposition was overwhelmed by the strength of grassroots support. The Los Angeles County Bicycle Coalition mobilised supporters to put pressure on the city council. The plan won backing, too, from others in the local business community and all five local neighbourhood councils. In March, the campaign found another ally at the very top of the city’s government: mayor Eric Garcetti. By May, opinion had turned and construction was under way; even Holter backed down, and withdrew his case.

It’s unclear whether this strategy would work in Northeast LA. Though this area was included in Garcetti's “Great Streets” plan, the mayor has stayed silent on the issue. Maybe the shadowy interests accused of manipulating Cedillo are more powerful than those further south along Figueroa. Nevertheless, this example has important lessons for all cities looking to build bike infrastructure. Car dependent cities elsewhere should take note. 

This article was amended on 18 August to correct some inaccuracies concerning Mayor Garcettie's "Great Streets" plan.

 
 
 
 

Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

This article was originally published on The Conversation. Read the original article.