Bottleneck congestion and residential location of heterogeneous commuters

Takayama, Yuki; Kuwahara, Masao

doi:10.1016/j.jue.2017.05.001

Cited by 45 publications

(23 citation statements)

References 45 publications

(42 reference statements)

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“…Since then, many scholars performed related investigations contributing to the development of Vickrey's highway bottleneck model, in which commuters face a tradeoff between the schedule delay cost of arriving at work at a time other than the most preferred time, and the cost of time spent queuing behind a highway bottleneck [2][3][4][5][6][7][8][9][10][11][12]. Some scholars investigated other congestible facilities and traffic congestion situations [13][14][15][16].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Pricing and Service for the Peak-Period Bus Commuting Inefficiency of Boarding Queuing Congestion

et al. 2018

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This paper proposes an equilibrium bus boarding model to investigate optimal pricing and service for peak-period bus commuting inefficiency of boarding queuing congestion. Commuters are assumed to choose their optimal time-of-use decision from home or the workplace to the bus. We found that: (1) when the earliest commuter boards the bus as soon as the bus arrives at the bus station, the dynamic boarding queuing congestion toll that eliminates the boarding queuing congestion creates social optimal equilibrium and the optimal bus departure interval during the peak period; (2) the optimal bus departure interval during the peak period is the time that the preceding bus riders spend on boarding, which means the relationship between service frequency and ridership does not conform to the square root principle: the optimal bus frequency is proportional to the square root of the number of commuters.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Pricing and Service for the Peak-Period Bus Commuting Inefficiency of Boarding Queuing Congestion

et al. 2018

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“…Several studies have incorporated a spatial dimension into the bottleneck model by embedding the dynamic bottleneck congestion into a simple monocentric city model (Arnott, 1998;Gubins and Verhoef, 2014;Takayama and Kuwahara, 2017;Fosgerau et al, 2018;Fosgerau and Kim, 2019). Their models, however, consider homogeneous commuters, thereby being inapplicable to examining the long-run distributional impacts of peak-load pricing.…”

Section: Introductionmentioning

confidence: 99%

Who gains and who loses from congestion pricing in a monocentric city with a bottleneck?

Takayama

2020

Economics of Transportation

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This study develops a model in which heterogeneous commuters choose their residential locations and departure times from home in a closed monocentric city with a bottleneck located at the entrance to the central business district (CBD). We show that commuters sort themselves both temporally and spatially according to their income, value of time, and flexibility at the equilibria with and without an optimal congestion pricing. These two equilibria exhibit fundamentally different properties, indicating that congestion pricing alters the urban spatial structure. We then consider two cases wherein rich commuters are either flexible or inflexible and demonstrate that (a) rich commuters reside farther from the CBD in the former case and closer to the CBD in the latter case; (b) congestion pricing makes cities denser and more compact in the former, whereas it causes cities to become less dense and to expand spatially in the latter; and (c) in both cases, pricing helps rich commuters but hurts poor commuters. We further reveal that although expanding the capacity of the bottleneck generates a Pareto improvement when commuters do not relocate, it can lead to an unbalanced distribution of benefits among commuters: commuters residing closer to the CBD gain, while those residing farther from the CBD lose. This suggests that expanding capacity financed by the revenue from congestion pricing could be regressive in a city where rich commuters are inflexible.

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“…In particular when heterogeneity concerns both heterogeneity in value of time and in values of schedule delay, different possibilities arise. We distinguish three ideal types: ratio heterogeneity ( de Palma and Lindsey (2002) , van den Berg and Verhoef (2011a,b) ), proportional heterogeneity ( Vickrey (1973) , van den Berg and Verhoef (2011b) ), and more general heterogeneity ( Newell (1987) , Lindsey (2004) , Börjesson and Kristoffersson (2014) , Wu and Huang (2015) , Liu et al (2015) , Chen et al (2015) , , Takayama and Kuwahara (2017) ). 2 "Ratio heterogeneity" refers to heterogeneity in the ratio of the value of time over the value of schedule delay, or α i / β i , in the conventional notation.…”

Section: Introductionmentioning

confidence: 99%

Carpooling with heterogeneous users in the bottleneck model

Berg

Verhoef

2019

Transportation Research Part B: Methodological

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We investigate the effects of carpooling in a dynamic equilibrium model of congestion, which captures various dimensions of heterogeneity: heterogeneity in the preference for and cost of carpooling, heterogeneity in values of time and values of schedule delay. We investigate various policy scenarios: no-toll, first-best pricing, and subsidization of carpooling. The optimally differentiated subsidy equals each type of users' marginal external benefit (MEB) of switching to m-person carpooling or carpooling, which turns out to be heterogeneous for "ratio heterogeneity", where the ratios of the values of time and schedule delay vary, and homogeneous for "proportional heterogeneity", where these values vary in fixed proportion over the population. If such differentiation over users is impossible, the subsidy is a weighted average of the MEB's, with the weights reflecting the relative sensitivity of the group size of carpoolers to the subsidy. Using a numerical example, we investigate the welfare effects and distributional effects of different policies. The relative efficiency of the differentiated subsidization first increases and then falls with the degree of ratio heterogeneity, and decreases over the entire parameter range and more with the degree of proportional heterogeneity.

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Bottleneck congestion and residential location of heterogeneous commuters

Cited by 45 publications

References 45 publications

Optimal Pricing and Service for the Peak-Period Bus Commuting Inefficiency of Boarding Queuing Congestion

Optimal Pricing and Service for the Peak-Period Bus Commuting Inefficiency of Boarding Queuing Congestion

Who gains and who loses from congestion pricing in a monocentric city with a bottleneck?

Carpooling with heterogeneous users in the bottleneck model

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