Smart Feeder/Shuttle Bus Service: Consumer Research and Design

Yim, Youngbin; Ceder, Avishai

doi:10.5038/2375-0901.9.1.5

Cited by 22 publications

(13 citation statements)

References 9 publications

(3 reference statements)

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“…The service uses an automated phone-in-system for reservations, computerized dispatching over a wireless communication channel to the bus driver and an automated callback system for customer notifications. Yim and Ceder (2006) surveyed customers of Bay Area Rapid Transit and designed routing strategies.…”

Section: Introductionmentioning

confidence: 99%

2-Vehicle zone optimal design for feeder transit services

Quadrifoglio

2011

Public Transp

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Feeder transit services perform the crucial first/last mile access to transit by connecting people within a residential area to a major transit network. In this paper, we address the optimal zone design problem faced by planners for feeder transit services with high demands and long length of service area, where a two-vehicle operation is assumed to be adopted in each zone. By balancing customer service quality and operating cost, we develop an analytical model of the system by assuming continuous approximations. Closed-form expressions and numerical procedures are employed to derive the optimal number of zones to aid decision makers in determining the best design as a function of the main parameters. Analytical expressions and results are then validated by simulation analysis. AbbreviationsThe following notations represent model parameters: λ average demand in the whole residential area (customer/hour) α fraction of customers traveling from the residential area to the city; 1 − α is the fraction of customers traveling from the city to the residential area L length of the residential service area (mile) W width of the residential service area (mile) d distance between FRT bus stations within a zone (mile) 90 X. Li, L. Quadrifoglio a k customer cost of walking between a FRT bus station and a house within a zone ($/customer/hour) a w customer cost of waiting at terminals or bus stations ($/customer/hour) a h w customer cost of waiting at houses ($/customer/hour) a v customer cost of traveling in an on-demand vehicle ($/customer/hour) a b customer cost of traveling in a fixed route bus in the zones ($/customer/hour) a B customer cost of traveling in a major transit vehicle between the city and terminals ($/customer/hour) F v total cost of an on-demand vehicle ($/vehicle/hour) F b total cost of a fixed route bus ($/bus/hour) v wk average speed of customer walking (mile/hour) v b average speed of an on-demand vehicle or a fixed route bus (mile/hour) v B average speed of a major transit vehicle (mile/hour) s dwelling time of a fixed route bus or an on-demand vehicle (hour) S dwelling time of a major transit vehicle at terminals (hour) The computed variables in the model, that are a function of n and N , are: E(T wk ) expected walking time in a zone for pick-up or drop-off customers E(T p wt ) expected waiting time for pick-up customers in a zone E(T p rd ) expected ride time for pick-up customers in a zone E(T p rd−B ) expected ride time for pick-up customers in a major transit vehicle E(T d wt ) expected waiting time for drop-off customers at a terminal E(T d rd ) expected ride time for drop-off customers in a zone E(T d rd−B ) expected ride time for drop-off customers in a major transit vehicle.

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Section: Introductionmentioning

confidence: 99%

2-Vehicle zone optimal design for feeder transit services

Quadrifoglio

2011

Public Transp

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“…With the increase in demand for public transit, the comfort experienced by passengers in public transit decreases because of over-crowdedness. This makes it challenging for transit organizations to encourage people towards continuous use of public transit [1].…”

Section: Introductionmentioning

confidence: 99%

“…With rising urban population 1 , there is a growing need for an efficient public transport system to make cities environmentally sustainable and economically competitive. Delhi, for example, introduced its metro network in 2002, owing to the increasing demands for better transit.…”

Section: Introductionmentioning

confidence: 99%

“…However, none of these existing approaches personalize commuter experience based on their convenience requirements. Recent works [1], [6]- [10] have highlighted the need to identify user convenience to improve public transit services. However, most of these works attempt to define "convenience" are objective in nature, and based on factors like time and crowdedness, for example, adjusting travel times to reflect convenience [8].…”

Section: Introductionmentioning

confidence: 99%

“…This leads to implementing the following improved services: (i) providing commuters with relevant and accurate information like schedules of transit, (ii) on-demand navigation support, and (iii) real-time traffic updates. To further improve the use 1 ti.me/1dpszfx 2 http://bit.ly/21bAEMt 3 http://apple.co/1JNivAl 4 http://bit.ly/1yXbumL of public transport, authors in [2]- [5] aim to personalize public transport services based on commuters preferences. Existing work to personalizing transport services can be broadly categorized into three approaches: (i) developing adaptive interfaces based on commuter context and historical data [3], (ii) developing algorithms for route recommendations based on commuter interests [2], [5], and (iii) passively identifying commuter preferences based on information stored in Automated Fare Collection Cards [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards building real-time, convenient route recommendation system for public transit

Bajaj

Agarwal

Bouloukakis

et al. 2016

2016 IEEE International Smart Cities Conference (ISC2)

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Abstract-Public transportation is essential for sustainable and economical development of cities. Several transport organizations aim to provide service information to commuters through web and mobile apps. This information includes possible routes between two stations, estimated travel and arrival times, and real-time updates about traffic conditions. However, this information is currently not personalized according to commuter preferences. In this work, we emphasize the need for personalized transit service information to commuters and present a vision of our work in this direction. Our final goal is to develop a fully-functional personalized route recommendation system for public transit commuters. This involves identifying commuter preferences and suitable recommendation techniques, and developing a platform to communicate this information to the commuters. We identify the requirements for the development of this platform, and propose an architecture for our system. As a proof of concept, we present an Android participatory sensing application -MetroCognition, which acquires feedback on convenience experienced by commuters in public transit.

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Use of Geolocation for the Management of Collective Transport of People in Stations and Airports

Cabezuelo

2020

Current Trends in Web Engineering

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Smart Feeder/Shuttle Bus Service: Consumer Research and Design

Cited by 22 publications

References 9 publications

2-Vehicle zone optimal design for feeder transit services

2-Vehicle zone optimal design for feeder transit services

Towards building real-time, convenient route recommendation system for public transit

Use of Geolocation for the Management of Collective Transport of People in Stations and Airports

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