We will create wider lanes for bikes. In Fenway, bikers are not accounted for in our road network. There is not enough space to ride or store bikes. The best option is to create a painted bike lane with dividers to separate them from ongoing traffic and separate them from pedestrians. We also need the addition of more bike racks for public use. Hubways would also be installed in many different locations to make bikes more accessible to the public. Bike shops will also be more prominent in the Fenway area, along with biking events.
As of now, bikes oftentimes fail to follow traffic lights. This endangers not only the safety of the cyclist, but the vehicles around them. It must be enforced that they follow street lights for the well-being of the community. Sensors should also be deployed for two reasons: to help humans get a better sense of the traffic flow and for AI to do the same. The humans will use the information for planning where to develop more bike lanes and AI will use it to better control traffic flow through the lights. The control parameters of the traffic lights will be the input of the cost function and the congestion of traffic will be the output. When the cost function is minimized with the help of agents at each light contributing data, traffic flow will be optimized providing a smoother transportation experience for everyone on the streets.
The photographs above show an intersection we chose as an example - the intersection of Huntington Avenue and Opera Place on Northeastern's campus. It has car lanes, the Green Line, heavy foot traffic, and a bike lane. As shown on the left, cars often drive in the bike lane when they are not supposed to. We propose a redesign of this intersection and street area, that will include the building of a barrier between the cars and the bike lane. Our proposed redesign (using the software Street Mix) is shown below. The width of Huntington, measured using Google Maps, is approximately 85 feet wide.