Bicycle infrastructure and traffic congestion: Evidence from DC's Capital Bikeshare

Hamilton, Timothy L.; Wichman, Casey J.

doi:10.1016/j.jeem.2017.03.007

Cited by 136 publications

(56 citation statements)

References 26 publications

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“…At this point, smart multimodal intersection control (Portilla et al, 2016) can help to gain a better biking mobility while without any significant interruption to existing vehicle flows. The improvement in bike mobility might further promote more bike usage as a part of the green intermodal transportation system, and accelerate the traffic mode shift towards reducing VMT and mitigating traffic congestion as well as vehicle emissions (Hamilton and Wichman, 2018;Schrank et al, 2015).…”

Section: Mobilitymentioning

confidence: 99%

“…According to DDOT (2015b), although the cost recovery rate of CaBi is projected to increase from 75% (FY2016) to 87% (FY2021), its operating balance might stay in negative in the foreseeing future. Notice that BSS could bring us public benefits from social, environmental, economic, and health-related aspects that may not be shown in the operating balance (DDOT, 2015b;Fishman, 2016;Hamilton and Wichman, 2018;Shaheen et al, 2013). There are many usable business models as surveyed in Shaheen et al (2013).…”

Section: Implications For Data-driven Decision Supportsmentioning

confidence: 99%

“…Use of bicycles is hugely rewarding from social, environmental, economic, and health-related aspects for cities, communities and bike users. Benefits of alleviating congestion and mitigating associated environmental damages accrue from the vehicle miles traveled (VMT) in transportation reduced by bicycling (Hamilton and Wichman, 2018;Wang and Zhou, 2017). Accessibility to neighborhoods is enhanced by bicycling to boost economic opportunities to local businesses (Buehler and Hamre, 2015).…”

mentioning

confidence: 99%

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Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area

Xie¹,

Wang²

2018

Journal of Transport Geography

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Bikesharing has gradually become an adopted form of mobility in urban area recent years as one sustainable transportation mode to bring us many social, environmental, economic, and healthrelated benefits and rewards. There is increased research toward better understanding of bikesharing systems (BSS) in urban environments. However, our comprehension remains incomplete on the patterns and characteristics of BSS. In this paper, aiming to help improving sustainability in multimodal transportation through BSS, we perform a systematic data analysis to examine underlying patterns and characteristics of the system dynamics in a bikeshare network and to acquire implications of the patterns and characteristics for decision making. As a case study, we use trip history data from the Capital Bikeshare system in the Washington DC area and some additional data sources. The study covers seven important aspects of bikeshare transportation systems, which are respectively trip demand and flow, operating activities, use and idle times, trip purpose, origindestination flows, mobility, and safety. For these aspects, by using appropriate statistical methods and geographic techniques, we investigate travel patterns and characteristics of BSS from data to evaluate the qualitative and quantitative impacts of the inputs from key stakeholders on main measures of effectiveness such as trip costs, mobility, safety, quality of service, and operational efficiency, where key stakeholders include road users, system operators, and city. We also disclose some new patterns and characteristics of BSS to advance the knowledge on travel behaviors. Finally, we briefly summarize our findings and discuss the implications of the patterns and characteristics for data-driven decision supports from the relations between BSS and key stakeholders for promoting bikeshare utilization and transforming urban transportation to be more sustainable.

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Section: Mobilitymentioning

confidence: 99%

Section: Implications For Data-driven Decision Supportsmentioning

confidence: 99%

mentioning

confidence: 99%

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Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area

Xie¹,

Wang²

2018

Journal of Transport Geography

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“…They also find that the congestion-reducing impact of bikeshares is concentrated in highly congested areas [24].…”

Section: Congestionmentioning

confidence: 97%

“…In their study of the impact of bicycle-sharing infrastructure on urban transportation [24], find that the availability of a bikeshare reduces traffic congestion over 2-3% within a neighborhood. They also find that the congestion-reducing impact of bikeshares is concentrated in highly congested areas [24].…”

Section: Congestionmentioning

confidence: 99%

The Effects of Urban Public Transit Investment on Traffic Congestion and Air Quality

Beaudoin¹,

Lawell²

2017

Urban Transport Systems

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Traffic congestion is ubiquitous across urban roadways, and the adverse health effects accompanying deteriorating air quality are an ongoing concern. Beyond these local effects, transportation is also a major contributor of greenhouse gas emissions and is thus a significant element of the climate change debate. A contentious issue currently confronting transportation analysts and policy-makers is what the effects of public transit investment on traffic congestion and on air quality are and therefore what the appropriate level of public transit investment should be. While public transit receives plenty of political support for its "green" reputation and its contribution to sustainability, there have been relatively few studies examining the ex post-effects of public transit investment on traffic congestion or air quality. In this chapter, we review our theoretical and empirical research on the effects of public transit investment on congestion, the demand for automobile travel, and air quality.

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Free Wheel, Free Will! The Effects of Bikeshare Systems on Urban Commuting Patterns in the U.S.

Xu¹

2020

J Policy Anal Manage

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Urban bikeshare systems have become increasingly popular in the U.S. in recent years. In this paper, I examine the effects of bikeshare systems on patterns of commuting to and from work in U.S. cities. To study this, I link cities across 2008 through 2016 American Community Survey (ACS) data and estimate the effects of bikeshare systems using both individual‐level ACS records and city panel data. Event‐study estimates suggest that bikeshare systems lead to a rise in bicycle commuting to and from work, and the effects of bikeshare systems are statistically and economically significant. I also find evidence of modal shifts after the introduction of bikeshare systems: While bicycle commuting rates increase, there is a decline in automobile commuting to and from work.

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Bicycle infrastructure and traffic congestion: Evidence from DC's Capital Bikeshare

Cited by 136 publications

References 26 publications

Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area

Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area

The Effects of Urban Public Transit Investment on Traffic Congestion and Air Quality

Free Wheel, Free Will! The Effects of Bikeshare Systems on Urban Commuting Patterns in the U.S.

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