Low-Stress Bicycle Network Mapping: The District of Columbia’s Innovative Approach to Applying Level of Traffic Stress

2017 ◽  
Vol 2662 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Conor Semler ◽  
Meredyth Sanders ◽  
Darren Buck ◽  
James Graham ◽  
Alek Pochowski ◽  
...  
Keyword(s):  
System Approach ◽  
Additional Data ◽  
Congestion Management ◽  
Proof Of Concept ◽  
Department Of Transportation ◽  
National Leader ◽  
Bicycle Infrastructure ◽  
Infrastructure Investments ◽  
Network Mapping ◽  
New Facilities

Washington, D.C., has been a national leader in the adoption of innovative bicycle facilities. However, with much of the low-hanging fruit already plucked (i.e., bicycle facilities already in place), the District Department of Transportation (DOT) needed a mechanism to prioritize investments. Thus the District DOT developed a bicycle level of traffic stress (LTS) map as part of a Multimodal Congestion Management Study. Existing roadway information, combined with an innovative geographic information system approach, was used to create the map and to prioritize and expedite the collection of supplemental roadway information. The results confirmed existing perceptions about the availability of bicycle facilities in the District and identified previously unidentified gaps in the overall bicycle network. In addition, the methodology used to develop the LTS network map provided a proof-of-concept for other jurisdictions to use as they look to develop their own LTS network maps. With this information, the District DOT can now prioritize future bicycle infrastructure investments. It also has a mechanism to update the LTS map as additional data are collected and new facilities are constructed.

The Keys to Connectivity: The District of Columbia’s Innovative Approach to Unlocking Low-Stress Bicycle Networks

2018 ◽  
Vol 2672 (36) ◽  
pp. 63-72 ◽  
Author(s):  
Conor Semler ◽  
Meredyth Sanders ◽  
Darren Buck ◽  
Stephanie Dock ◽  
Burak Cesme ◽  
...  
Keyword(s):  
Information System ◽  
State Of The Art ◽  
Network Connectivity ◽  
Data Driven ◽  
System Level ◽  
Proof Of Concept ◽  
Department Of Transportation ◽  
Bicycle Infrastructure ◽  
Infrastructure Investments ◽  
Insight Into

Washington, D.C. (the District) has led the way in the adoption of state-of-the-art bicycle facilities and the innovative application of bicycle level of traffic stress (LTS) mapping for the District. Although the District’s LTS network map provides insight into bicycle network accessibility at the street level, the District needs a data-driven mechanism for identifying and prioritizing future investments to improve cycling at the system level. As part of its ongoing District Mobility Project, District Department of Transportation (DDOT) applied a geographic information system (GIS)-based network analysis to its bicycle LTS map to quantify network connectivity on low-stress District streets. This analysis was used to develop a bicycle network accessibility rating for District neighborhoods. The results revealed that though DDOT has invested in a range of innovative projects to improve accessibility in the District, only 27% of the overall network has been connected. By taking the analysis a step further and identifying the bottom 10% of accessible census blocks, DDOT both confirmed existing perceptions about the presence of disconnected low-stress “islands” in the District, and identified key gaps in the overall bicycle network. Using this information, DDOT is able to prioritize future bicycle infrastructure investments, while possessing a data-driven mechanism for assessing and communicating the accessibility benefits offered by individual investments. In addition, the methodology for developing the network accessibility rating provides a proof-of-concept for other jurisdictions looking to maximize the utility of their own LTS network maps.

Quantifying changes in bicycle volumes using crowdsourced data

2022 ◽  
pp. 239980832110661
Author(s):  
Ali Al-Ramini ◽  
Mohammad A Takallou ◽  
Daniel P Piatkowski ◽  
Fadi Alsaleem
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Crowdsourced Data ◽  
Machine Learning Approach ◽  
Bicycle Infrastructure ◽  
The Difference ◽  
Infrastructure Investments ◽  
Using Data ◽  
The Impact ◽  
The City

Most cities in the United States lack comprehensive or connected bicycle infrastructure; therefore, inexpensive and easy-to-implement solutions for connecting existing bicycle infrastructure are increasingly being employed. Signage is one of the promising solutions. However, the necessary data for evaluating its effect on cycling ridership is lacking. To overcome this challenge, this study tests the potential of using readily-available crowdsourced data in concert with machine-learning methods to provide insight into signage intervention effectiveness. We do this by assessing a natural experiment to identify the potential effects of adding or replacing signage within existing bicycle infrastructure in 2019 in the city of Omaha, Nebraska. Specifically, we first visually compare cycling traffic changes in 2019 to those from the previous two years (2017–2018) using data extracted from the Strava fitness app. Then, we use a new three-step machine-learning approach to quantify the impact of signage while controlling for weather, demographics, and street characteristics. The steps are as follows: Step 1 (modeling and validation) build and train a model from the available 2017 crowdsourced data (i.e., Strava, Census, and weather) that accurately predicts the cycling traffic data for any street within the study area in 2018; Step 2 (prediction) use the model from Step 1 to predict bicycle traffic in 2019 while assuming new signage was not added; Step 3 (impact evaluation) use the difference in prediction from actual traffic in 2019 as evidence of the likely impact of signage. While our work does not demonstrate causality, it does demonstrate an inexpensive method, using readily-available data, to identify changing trends in bicycling over the same time that new infrastructure investments are being added.

Yellow Brick Roadmap to Demand-Based Parking Pricing: Findings from Washington, D.C.

2019 ◽  
Vol 2673 (12) ◽  
pp. 339-353
Author(s):  
Soumya S. Dey ◽  
Stephanie Dock ◽  
Alek Pochowski ◽  
Meredyth Sanders ◽  
Benito O. Pérez ◽  
...  
Keyword(s):  
Supply And Demand ◽  
Lessons Learned ◽  
Pilot Program ◽  
Department Of Transportation ◽  
National Leader ◽  
Federal Highway ◽  
Parking Management ◽  
Cost Efficient ◽  
Value Pricing ◽  
Parking Pricing

Washington, D.C. (the District) has been a national leader with its progressive approach to parking management. Owing to the District’s strong housing and employment growth over the past decade, the District Department of Transportation (DDOT) needs a program to balance the competing parking needs of residents, commuters, visitors, and businesses. Using Federal funding from the Federal Highway Administration (FHWA) Value Pricing Pilot Program, DDOT planned and implemented a demand-based parking pricing pilot program in the Penn Quarter and Chinatown neighborhoods. The results of the pilot program confirmed that demand-based pricing programs can be both cost-efficient and effective, and highlighted a path to expanding demand-based pricing Districtwide. Using lessons learned from this project, practitioners will be better prepared to plan their own demand-based pricing programs, positioning themselves to effectively balance parking supply and demand in their own communities. The paper discusses the impacts of demand based pricing on a range of metrics such as parking search times, cruising, occupancy, and length of stay. It also assesses the impacts of the strategies on the larger transportation system and the study area.

Inland waterway network mapping of AIS data for freight transportation planning

2022 ◽  
pp. 1-22
Author(s):  
Magdalena I. Asborno ◽  
Sarah Hernandez ◽  
Kenneth N. Mitchell ◽  
Manzi Yves
Keyword(s):  
Travel Demand ◽  
Performance Metrics ◽  
Automatic Identification ◽  
Identification System ◽  
Time Duration ◽  
Movement Data ◽  
Demand Models ◽  
Inland Waterway ◽  
Infrastructure Investments ◽  
Network Mapping

Abstract Travel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84⋅0% accuracy in detecting stops at ports and 83⋅5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.

Perceived Safety and Separated Bike Lanes in the Midwest: Results from a Roadway Design Survey in Michigan

2018 ◽  
Vol 2672 (36) ◽  
pp. 1-11 ◽  
Author(s):  
Rebecca L. Sanders ◽  
Belinda Judelman
Keyword(s):  
Past Research ◽  
Sample Survey ◽  
Driving Safety ◽  
Compelling Evidence ◽  
Department Of Transportation ◽  
Perceived Safety ◽  
Bicycle Infrastructure ◽  
Key Barrier ◽  
Roadway Design ◽  
Design Preferences

This article presents the results of an address-based sample survey ( n = 351) conducted in the fall of 2016 for the Michigan Department of Transportation (MDOT) as part of an effort to provide guidance for building sidepaths. The survey investigated attitudes toward bicycling among drivers and bicyclists, bicycling habits, barriers to bicycling, and roadway design preferences regarding bicycle infrastructure in Michigan. In particular, this survey explored design preferences while bicycling with children, bicycling by oneself, and driving. Safety emerged as a key barrier to bicycling, as did distance, weather, and the difficulty of carrying things or traveling with others. Roadway design preferences were clearly weighted toward greater separation when sharing the roadway whether as a bicyclist or a driver, and this trend was most pronounced ( p < 0.001) when considering bicycling with children. In all cases, ratings for one-way separated bike lanes were similar to those for sidepaths, suggesting that separated bike lanes could be a key part of addressing the safety and comfort concerns of more cautious riders. Preferences for separation were strongly associated with perceived safety as a barrier. These results were even stronger for non-transport-cyclists, although all groups, regardless of frequency or type of bicycling, preferred more separation. These results corroborate past research and add compelling evidence for separated facilities as a key part of expanding the potential for bicycling trips in general, and particularly with children. The survey findings will inform guidance about sidepath design for MDOT.

scholarly journals An economic analysis of the health-related benefits associated with bicycle infrastructure investment in three Canadian cities

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246419
Author(s):  
David G. T. Whitehurst ◽  
Danielle N. DeVries ◽  
Daniel Fuller ◽  
Meghan Winters
Keyword(s):  
Time Horizon ◽  
Assessment Tool ◽  
Decision Makers ◽  
World Health ◽  
Sensitivity Analyses ◽  
Premature Deaths ◽  
Bicycle Infrastructure ◽  
Health Related ◽  
Infrastructure Investments ◽  
Value Of Health

Objectives Decision-makers are increasingly requesting economic analyses on transportation-related interventions, but health is often excluded as a determinant of value. We assess the health-related economic impact of bicycle infrastructure investments in three Canadian cities (Victoria, Kelowna and Halifax), comparing a baseline reference year (2016) with the future infrastructure build-out (2020). Methods The World Health Organization’s Health Economic Assessment Tool (HEAT; version 4.2) was used to quantify the economic value of health benefits associated with increased bicycling, using a 10-year time horizon. Outputs comprise premature deaths prevented, carbon emissions avoided, and a benefit:cost ratio. For 2016–2020, we derived cost estimates for bicycle infrastructure investments (including verification from city partners) and modelled three scenarios for changes in bicycling mode share: ‘no change’, ‘moderate change’ (a 2% increase), and ‘major change’ (a 5% increase). Further sensitivity analyses (32 per city) examined how robust the moderate scenario findings were to variation in parameter inputs. Results Planned bicycle infrastructure investments between 2016 and 2020 ranged from $28–69 million (CAD; in 2016 prices). The moderate scenario benefit:cost ratios were between 1.7:1 (Victoria) and 2.1:1 (Halifax), with the benefit estimate incorporating 9–18 premature deaths prevented and a reduction of 87–142 thousand tonnes of carbon over the 10-year time horizon. The major scenario benefit:cost ratios were between 3.9:1 (Victoria) and 4.9:1 (Halifax), with 19–43 premature deaths prevented and 209–349 thousand tonnes of carbon averted. Sensitivity analyses showed the ratio estimates to be sensitive to the time horizon, investment cost and value of a statistical life inputs. Conclusion Within the assessment framework permitted by HEAT, the dollar value of health-related benefits exceeded the cost of planned infrastructure investments in bicycling in the three study cities. Depending on the decision problem, complementary analyses may be required to address broader questions relevant to decision makers in the public sector.

Developing an urban bikeability index for different types of cyclists as a tool to prioritise bicycle infrastructure investments

2020 ◽  
Vol 139 ◽  
pp. 310-334 ◽  
Author(s):  
Julián Arellana ◽  
María Saltarín ◽  
Ana Margarita Larrañaga ◽  
Virginia I. González ◽  
César Augusto Henao
Keyword(s):  
Different Types ◽  
Bicycle Infrastructure ◽  
Infrastructure Investments

Estimating Average Automobile Occupancy from Accident Data in New York State

1996 ◽  
Vol 1553 (1) ◽  
pp. 115-123
Author(s):  
Seth A. Asante ◽  
Louis H. Adams ◽  
John J. Shufon ◽  
Joseph P. McClean
Keyword(s):  
New York ◽  
New York State ◽  
Congestion Management ◽  
Time Of Day ◽  
System Level ◽  
York State Department ◽  
Department Of Transportation ◽  
York State ◽  
Accident Data ◽  
Occupancy Rates

Average automobile occupancy (AAO) data are valuable input to congestion management systems (CMS). Continuous field collection of these data at the system level has been lacking because of high costs associated with current data collection methodology. It is shown how the New York State Department of Transportation (NYSDOT) has built upon prior research by the Connecticut Department of Transportation, which uses traffic accident data to calculate estimates of vehicle occupancy, and has tailored the process to meet NYSDOT's CMS needs. Accident data covering a 3-year period are used to estimate AAOs by county, year of occurrence, month of year, day of week, and time-of-day intervals. Occupancy rates are calculated to be lowest during the morning peak traffic period and highest during the evening period between 6:00 and 11:00 p.m. Occupancy rates are highest for summer months and lowest for winter months. Occupancy rates are highest for the weekends and lowest for weekdays. Accident-based AAOs are compared to multiple-station roadside-observed AAOs. Adjustment factors are developed to convert the former to be comparable to the latter. It is concluded that using accident data to estimate AAO is a viable and economical approach to continuous monitoring of system-level AAO performance. NYSDOT is currently using accident-based AAO data as an integral part of its CMS.

Where Do Bikeshare Bikes Actually Go?: Analysis of Capital Bikeshare Trips with GPS Data

2017 ◽  
Vol 2662 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Jon Wergin ◽  
Ralph Buehler
Keyword(s):  
Information System ◽  
System Analysis ◽  
The United States ◽  
Gps Data ◽  
Department Of Transportation ◽  
Short Term ◽  
Mixed Use ◽  
Bicycle Infrastructure ◽  
Geographic Information System Analysis

Bikeshare systems with docking stations have gained popularity in cities throughout the United States—and have increased from six programs with 2,300 bikes in 2010 to 74 systems with 32,200 bikes in 2016. Even though bikeshare systems generate a wealth of data about bicycle checkout and check-in locations and times at docking stations, virtually nothing is known about routes and activities undertaken between checkout and check-in. Such information could greatly enhance expansion of bikeshare systems, placement of new docking stations, and location of new bike lanes and paths. In pursuit of such information, the District Department of Transportation, Washington, D.C., placed GPS trackers on 94 Capital Bikeshare (CaBi) bikes in the spring of 2015. On the basis of these data, this geographic information system analysis distinguished riders by type of CaBi membership, identified popular routes, analyzed bicycle infrastructure use, and examined stops and dwelling times at places of interest. Results showed strong differences in trip attributes between types of membership. Trips taken by short-term users were longer in distance, slower than long-term users’ trips, and concentrated in and around the National Mall, whereas long-term users’ trips were concentrated in mixed-use neighborhoods. Short-term users rode 12% of their miles on dedicated bicycle infrastructure, 61% in parks, and 27% on roadways with motorized traffic, whereas for long-term members the percentages were 33%, 17%, and 50%, respectively. On the basis of the routes taken in this study, potential locations were recommended for bicycle infrastructure improvements and new bikeshare stations.

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