Quantifying and analyzing traffic emission reductions from ridesharing: A case study of Shanghai

Yan, Longxu; Luo, Xiao; Zhu, Rui; Santi, Paolo; Wang, Huizi; De, Wang; Zhang, Shangwu; Ratti, Carlo

doi:10.1016/j.trd.2020.102629

Cited by 42 publications

(30 citation statements)

References 25 publications

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“…Additionally, ridesharing provides several other environmental benefits for a nation grappling with surging environmental problems (Pandey et al, 2021;Saxena and Raj, 2021). The reduction in Vehicle Miles Travelled (VMT) (Yan et al, 2020), emission reduction (Tikoudis et al, 2021) and reduction in car ownership rate (Henao and Marshall, 2019), all these benefits provide ways to solve some of the environmental problems that the nation faces. Therefore, examining the determinants of ridesharing behavior in a developing country like India becomes necessary.…”

Section: On-demand Ridesharingmentioning

confidence: 99%

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Determinants of on-demand ridesharing: the role of awareness of environmental consequences

Sajid

Zakkariya

Peethambaran

et al. 2022

MEQ

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PurposeThe primary goal of this study is to determine the predictors of on-demand ridesharing intention in an emerging economy. For this purpose, the study uses the theoretical underpinnings of the theory of planned behavior (TPB).Design/methodology/approachThe study surveyed 347 frequent users of ridesharing services using a set of pre-validated scales. The resulting data were analyzed using covariance-based structural equation modeling (CB-SEM).FindingsThe results of SEM analysis disclosed that the significant factors contributing to ridesharing intention are awareness of environmental consequences, subjective norm, perceived behavioral control and attitude (towards ridesharing).Practical implicationsThis empirical research provides statistically robust insights for developing marketing strategies that attract more individuals toward ridesharing services.Originality/valueThis research has remarkable significance as it is one of the pioneering studies that critically examine the determinants of ridesharing intention from a South Asian emerging economy. Further, the extended TPB framework proposed in this study explains 71.4% variance in ridesharing intention, which is significantly higher than existing studies, with none of them explaining more than 70% variance.

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Section: On-demand Ridesharingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determinants of on-demand ridesharing: the role of awareness of environmental consequences

Sajid

Zakkariya

Peethambaran

et al. 2022

MEQ

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“…Ride-hailing is an on-demand transport service to match drivers and passengers intelligently through a platform or pp, which is believed to power up public transit in MaaS (Campisi et al, 2021). Ride-splitting allows passengers to share a common route within one car and is examined to reduce the fleet size of operated vehicles (Chan et al, 2012; Yan et al, 2020). Bike-sharing, even in a city without implementing MaaS, is naturally used to be a complementary transportation mode for public transit to solve the first/last mile problem (Kong et al, 2020a; Martin et al, 2014; Qiao et al, 2021).…”

Section: Overview Of the Infrastructurementioning

confidence: 99%

Mobility as a Service and urban infrastructure: From concept to practice

Qiao

Huang

Yeh

2022

Transactions in Urban Data, Science, and Technology

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The debate about how Mobility as a Service (MaaS) will revolutionize individual and collective mobility is gaining increasing attention from researchers, industries, and public sectors. MaaS is expected to create a techno-utopia with a new organization and operation of transport systems where residents have equal access to instant and ubiquitous mobility services. However, transport service is an artifact that is highly dependent on the construction of urban infrastructure. The success of MaaS requires the support of urban infrastructure that we categorize as (1) transport-flow infrastructure, (2) information-flow infrastructure, and (3) computing-flow infrastructure. The connotation of urban infrastructure here includes not only conventional concepts, such as transportation infrastructure, but also intelligent transportation concepts, such as high-speed communication networks and autonomous fleets. Moreover, travel behavior data collected by city sensors, communication networks, and intelligent vehicles require appropriate infrastructures to dynamically compute for fleet dispatch and demand-supply match. Based on the degree of integration of these infrastructures, MaaS projects will have different results in varying cities. From concept to practice, given that a vast disparity in infrastructure exists between cities, we need an inclusive, mobile, and global understanding of the MaaS concept to make it successful in different parts of the world.

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“…Shared mobility encourages the sharing of transportation resources and an increase in vehicle utilization, thereby reducing vehicles and associated emissions. Overall, emerging shared mobility models can be summarized into mainly three categories [ 5 , 23 , 24 , 25 ]:…”

Section: Related Workmentioning

confidence: 99%

“…Among them, automobiles are the main contributors to total motor vehicle pollutant emissions, emitting more than 90% of carbonic oxide (CO), hydrocarbon (HC), oxynitride (NOx), and particulate matter (PM). How to mitigate traffic emissions without compromising convenient mobility options and economic growth has become a realistic and urgent issue for urban planners, enterprises, and the public [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Community Shuttle Services on Traffic and Traffic-Related Air Pollution

Zhao

Fang²,

Tang³

et al. 2022

IJERPH

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Community shuttle services have the potential to alleviate traffic congestion and reduce traffic pollution caused by massive short-distance taxi-hailing trips. However, few studies have evaluated and quantified the impact of community shuttle services on urban traffic and traffic-related air pollution. In this paper, we propose a complete framework to quantitatively assess the positive impacts of community shuttle services, including route design, traffic congestion alleviation, and air pollution reduction. During the design of community shuttle services, we developed a novel method to adaptively generate shuttle stops with maximum service capacity based on residents’ origin–destination (OD) data, and designed shuttle routes with minimum mileage by genetic algorithm. For traffic congestion alleviation, we identified trips that can be shifted to shuttle services and their potential changes in traffic flow. The decrease in traffic flow can alleviate traffic congestion and indirectly reduce unnecessary pollutant emissions. In terms of environmental protection, we utilized the COPERT III model and the spatial kernel density estimation method to finely analyze the reduction in traffic emissions by eco-friendly transportation modes to support detailed policymaking regarding transportation environmental issues. Taking Chengdu, China as the study area, the results indicate that: (1) the adaptively generated shuttle stops are more responsive to the travel demands of crowds compared with the existing bus stops; (2) shuttle services can replace 30.36% of private trips and provide convenience for 50.2% of commuters; (3) such eco-friendly transportation can reduce traffic emissions by 28.01% overall, and approximately 42% within residential areas.

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Quantifying and analyzing traffic emission reductions from ridesharing: A case study of Shanghai

Cited by 42 publications

References 25 publications

Determinants of on-demand ridesharing: the role of awareness of environmental consequences

Determinants of on-demand ridesharing: the role of awareness of environmental consequences

Mobility as a Service and urban infrastructure: From concept to practice

The Impact of Community Shuttle Services on Traffic and Traffic-Related Air Pollution

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