bike stations added*
local policies in discussion
increase in bike use**
* In partnership with Lime, who provided the necessary infrastructure.
** Based on statistics from Lime relating to bike station use within a 5 mile radius of the UCLA campus.
Transforming Westwood into a sustainable, connected community by improving transport multimodality, efficiency, and equity.
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01
We've partnered with Lime to launch a pilot project that has seen the creation of 15 new bike stations in the Westwood area, with Lime providing the necessary infrastructure, including e-scooters and bikes, while we focused on optimizing deployment locations based on predictive analytics and facilitating the creation of designated pickup/drop-off zones.
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Despite LA's temperate climate, which is ideal for biking, the city's heavy reliance on cars has ingrained a culture that favors driving over cycling. Neighborhoods like Westwood and Downtown suffer from fragmented bike lane networks across hilly, demanding terrain, forcing riders to share congested roads with cars and increasing the likelihood of accidents. Parking and storage for bike-share bikes present another challenge. Parking and storage for bike-share bikes present another challenge: In densely populated areas, finding suitable locations for bike docks is problematic. Limited sidewalk space and competing demands for street space make them difficult to install without inconveniencing pedestrians and drivers.
Furthermore, the security of the bikes is a concern, with theft and vandalism posing significant risks. Socioeconomic disparities in LA also impact the success of bike-sharing programs. Many low-income communities, particularly in South LA and parts of Downtown, lack access to bike-sharing stations. These areas are often underserved by public transportation, and residents might benefit the most from affordable, convenient bike-sharing options. However, the initial costs of setting up bike-share infrastructure and the ongoing maintenance can be prohibitive without substantial investment and support.
To address the inadequate bike-sharing infrastructure in Los Angeles, we started with a problem analysis using GPS data from Kaggle, Strava, Google Maps, publicly available traffic data, population density data from the census, and existing bike lane and traffic data from the LA Dept. of Transportation. We also collected heat maps of existing bike usage patterns from city bike-sharing programs.
These data sources revealed significant gaps in bike-sharing coverage, particularly in high-density neighborhoods like Downtown LA and Westwood. We discovered that these areas had high traffic congestion and significant commuter populations but limited bike-sharing infrastructure, highlighting the urgent need for strategically placing bike stations.
Using K-Means clustering, we segmented the city into distinct clusters based on bike usage patterns and population density. This revealed high-demand areas like the Wilshire Corridor, where clustering showed significant unmet demand for bike-sharing. We then used Random Forest Regression to predict future bike-sharing demand, incorporating variables such as time of day, weather conditions, and proximity to amenities.
This model identified peak demand periods and highlighted the impact of weather on bike usage, showing that certain high-demand areas lacked sufficient bike-sharing infrastructure during critical times. To optimize the placement of bike stations, we used genetic algorithms to minimize the distance from high-demand clusters and maximizing accessibility to bike lanes and public transport. These insights guided our station placement strategy, ensuring both high usage and efficiency.
Using these insights, our outreach coordinator and policy manager reached out to Lime, a micro-mobility service that runs electric scooters, bikes and mopeds via GPS. We proposed integrating our findings into their existing infrastructure to establish new bike sharing stations strategically across LA, and Lime saw this as a viable expansion strategy.
The partnership began with a pilot project that has seen 15 new bike stations in the Westwood area, with Lime providing the necessary infrastructure, including e-scooters and bikes, while we focused on optimizing deployment locations based on predictive analytics and facilitating the creation of designated pickup/drop-off zones.
02
In partnership with UCLA Transportation, we're introducing micro-mobility integration by implementing bike sharing stations at key hotspots near the proposed Westwood/UCLA metro station.
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The D Line Extension project aims to expand the city's subway system westward, connecting downtown LA with Westwood. However, the proposed Westwood/UCLA Metro station faces major challenges stemming from its location where 3 major access roads to the university converge, creating congestion, safety, and efficiency concerns. More than 1.3 miles from the campus itself, the station is situated at a juncture where vehicular traffic, residential zones, university students, pedestrians, bus stations, pedestrian walkways, and bike lanes intersect.
This creates congestion during peak hours, especially along the shortest route leading uphill to campus, which is already crowded with scooters, creating infamous safety risks due to narrow paths and aggressive riding (it's common to see students' commutes taking 30+ minutes between classes). The station's proximity to campus, while beneficial for accessibility, presents an efficiency dilemma—too far to comfortably walk yet not far enough to justify waiting for a bus—posing a significant challenge for urban mobility.
Here, we used ML models and algorithms to optimize traffic flow around the station using publicly available databases from the LA DOT on traffic volumes, speeds, and congestion patterns in the area. Initially, we deployed convolutional neural networks (CNNs) to analyze traffic camera feeds and extract vehicle movement patterns, which were crucial for identifying congestion hotspots and predicting traffic densities during peak hours.
These CNN models were trained on large datasets comprising historical traffic data from the LA DOT and Metro Los Angeles, incorporating variables such as time of day, weather conditions, and special events. The use of RNNs to predict future traffic conditions also allowed us to simulate various scenarios and assess the impact of changes in traffic patterns, such as the introduction of alternative transportation modes like electric scooters and bicycles.
Using insights from the exploratory study, we implemented a proprietary algorithm to optimize shuttle schedules and coordinate them with Metro station timings at Westwood/UCLA. Using reinforcement learning, we analyzed these datasets to train our model to predict peak demand periods for shuttle services and coordinate them with predicted metro arrival and departure times, ensuring seamless connections and reducing waiting times for commuters. Historical data on rider preferences and transit patterns were integrated to fine-tune shuttle routes, identifying high-traffic corridors and optimizing pickup and drop-off points near the metro station.
Our outreach coordinator is currently contacting members associated with the project and traffic engineers to see if it could be something the can implement. For now, in partnership with UCLA Transportation, we're introducing micro-mobility integration by implementing bike sharing stations at key hotspots near the proposed Westwood/UCLA metro station. We're working to establish designated pickup/drop-off zones (shown below) and protected bike lanes were established to enhance safety and reduce sidewalk congestion, as well as incentivized parking schemes to encourage commuters to use bikes for short-distance travel.
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In partnership with the California OEHHA, we helped develop a mapping and screening tool called CalEnviroScreen, which helps the state's politicians identify communities for prioritized climate investments and construct more effective policies to address them, overcoming the shortcomings of current government solutions.
The challenge with advancing environmental justice lies in identifying inequities that disproportionately affect marginalized communities in the first place. President-elect Joe Biden's ambitious environmental justice plan, part of his Build Back Better agenda, aims to invest 40% of a $2 trillion clean energy plan into communities facing poverty and pollution. Effectively targeting these communities requires robust data tools like, which under previous administrations has suffered neglect and inadequate funding.
However, government solutions like EJSCREEN, designed to map environmental vulnerabilities and burdens across the U.S., lacks integration of essential datasets, usability for policymakers, and consistency in state-reported data. This hampers its ability to provide comprehensive snapshots necessary for informed policy decisions and equitable resource allocation. To achieve Biden's environmental justice goals, federal initiatives must overhaul existing data systems, integrate public health metrics, and ensure community input to accurately reflect and address intersectional challenges.
But state data in EJSCREEN is inconsistent, making it difficult at times to get comparable snapshots of communities in different regions. EJSCREEN is also hard to use, which means it’s difficult for policymakers, journalists, and advocates to access the information they need. The problem is not that the data isn’t there — the bigger issue is that it is unusable and fragmented.
EJSCREEN was launched after the Obama administration passed the American Recovery and Reinvestment Act of 2009. An effective equity mapping tool would have been immensely helpful, and could have helped policymakers screen investments to ensure they were targeting the areas of greatest need. Data drives policy, and the lack of data drives policy.
To begin addressing this challenge, we reached out to the California Office of Environmental Health Hazard Assessment (OEHHA), allowing us to help contribute to the 4th iteration of a mapping and screening tool called CalEnviroScreen, which helps the state identify communities for prioritized climate investments at the state level. The tool develops a cumulative impact score based on pollution burdens and population characteristics.
In the map shown, the historically Black and redlined neighborhoods of East Oakland appear in red, indicating a higher score and greater incidence of environmental injustice. This comes with significant implications for policymakers. Should, for example, lawmakers in Sacramento prioritize the more agricultural Central Valley — home to many migrant farmworkers — for investment and other policy interventions ahead of deindustrialized urban centers like Long Beach and Oakland?
Our partners
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Visionary LA is collaborating with UCLA Transportation to optimize bike sharing and integrate them with campus transit systems. By aligning shuttle schedules with UCLA's internal bus routes and leveraging shared data, Visionary LA is working to develop a comprehensive transit solution that reduced redundancy and maximized efficiency. This partnership also enabled joint promotional campaigns to encourage sustainable transportation choices among students and staff.
To enhance micro-mobility options around the Westwood/UCLA station, Visionary LA formed a partnership with Lime, an e-scooter and bike-sharing service. This partnership began with a pilot project proposal that highlighted the potential to reduce traffic congestion and improve last-mile connectivity. Lime hs provided the necessary infrastructure, including e-scooters and bikes, while Visionary LA focused on optimizing deployment locations based on predictive analytics, facilitating the creation of designated pickup/drop-off zones.
Visionary LA is partnering with Metro LA to access critical transportation data and ensure integration with the Metro system. This partnership was established through a series of strategic meetings and proposals demonstrating the mutual benefits of improved urban mobility. Through this collaboration, Visionary LA hopes to use the Metro’s real-time transit data to synchronize shuttle schedules with train arrivals and departures. This coordination effort will significantly reduce commuter wait times, allowing LA to scale its shuttle services on demand.
We worked with the California Office of Environmental Health Hazard Assessment to develop a mapping and screening tool called CalEnviroScreen, which helps the state's politicians identify communities for prioritized climate investments and construct more effective policies to address them, overcoming the shortcomings of current government solutions.
Leadership
Executive board
Olivia M.
Westwood
Priya P.
Westwood
Marcus R.
Brentwood
Elena G.
Culver City
