Mobility and Congestion in Urban India

Akbar, Prottoy; Couture, Victor; Duranton, Gilles; Storeygard, Adam

doi:10.3386/w25218

Cited by 32 publications

(24 citation statements)

References 6 publications

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“…Angel and Franco (2018) theorizes about the impacts of urban shapes, and concludes that both population density and compactness should affect travel distances. This hypothesis is confirmed by Akbar et al (2018), who compare average commuting outcomes across cities in India. These authors find that the shape non-convexities induced by hilly terrain and water bodies are associated with slower traffic on average.…”

Section: Introductionmentioning

confidence: 63%

Physical geography and traffic delays: Evidence from a major coastal city

Sáiz

Wang

2022

Environment and Planning B: Urban Analytics and City Science

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Traffic congestion is a major environmental and social problem whose causes include urban sprawl, imbalanced home-job distributions, increased car ownership, and lack of public transportation. We focus on a relatively understudied factor: the existence of geographic barriers. We study traffic times and flows in the Boston metropolitan area, a major coastal city with substantial shape non-convexities. We show that natural barriers not only cause additional delays to the trips affected directly, but also worsen downtown congestion for everyone. Additionally, commuter flows between places separated by barriers decrease, generating additional traffic elsewhere. We also find that places next to geographic obstacles suffer from higher risks of congestion, due to their lower traffic-diffusion ability. Policymakers may consider specific solutions for congestion arising from constraining physical geographies.

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Section: Introductionmentioning

confidence: 63%

Physical geography and traffic delays: Evidence from a major coastal city

Sáiz

Wang

2022

Environment and Planning B: Urban Analytics and City Science

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“…The city is characterised by rising incomes, poor public services and severe traffic congestion. Akbar et al (2018) reported that Indian cities are congested throughout the day, not just during peak hours; in Bengaluru, this has led to leading firms in the city threatening to relocate elsewhere, since the city’s poor infrastructure and traffic jams are affecting employee productivity.…”

Section: Study Areamentioning

confidence: 99%

Determinants of Commute Time in an Indian City

Sridhar¹,

Nayka²

2022

Margin: The Journal of Applied Economic Research

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Given the conspicuous lack of adequate research on commute time in the Indian intra-urban context, the research questions we answer in this article are: what is the average commute time to work for residents? What does this imply for the city’s effective labour market? What are the determinants of commute time to work? This article studies the questions for a large Indian city, known for its traffic gridlocks. Given that published data on journey-to-work is not readily available for Indian cities, we report findings from primary surveys of commuters travelling to work in Bengaluru. The actual time taken by commuters to reach their workplace is 42.45 minutes one-way, implying an insignificant reduction in the city’s effective labour market since 2001. We use geographic information systems to map the sectoral flow of workers and their origins and destinations within the city. Estimating regressions of the determinants of commute time, we find that workers in government jobs travel longer to get to work, elevation has a negative effect on commute time, and commute distance positively impacts travel time. The contribution of the article is the report of an Indian city’s commute time and its determinants, which can be used to influence policy. JEL Classification: R12, R41, R42

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“…Fourth, the approach we develop here is an important stepping stone towards measur-ing accessibility, which is ultimately relevant to welfare. 7 In our companion paper (Akbar, Couture, Duranton, and Storeygard, 2018), we rely on the mobility (speed) index developed here as key component of an analogous accessibility (travel time) index. The other key component of accessibility is a proximity (distance to destinations) index, which also builds on the approach that we develop here.…”

Section: Introductionmentioning

confidence: 99%