Multimodal Public Transit Trip Planner with Real-time Transit Data

Borole, Nilesh; Rout, Dillip; Goel, Nidhi; Vedagiri, P.; Mathew, Tom V.

doi:10.1016/j.sbspro.2013.11.172

Cited by 37 publications

(13 citation statements)

References 8 publications

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“…GA has been used sporadically in bus network optimization problems. Borole et al (2013), for example, developed route-planners using K-shortest path algorithm with GPS data, to obtain multi-criterion shortest path. Nanayakkara et al (2007) developed GA-based route-planners for large urban street networks in Singapore.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Trip Planner for Public Transport Using Genetic Algorithm

et al. 2020

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This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles real-time Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem’s computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The “walk mode” and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner’s capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper.

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Section: Methodsmentioning

confidence: 99%

Dynamic Trip Planner for Public Transport Using Genetic Algorithm

et al. 2020

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“…Kamargianni et al [11] give a further review of studies that have investigated partially integrated services; studies on fully integrated concepts are, however, lacking as of yet. Most of the published studies that include IMM platforms cover the concept rather superficially [2,9,25] or focus on the technical implementation of IMM platforms [26][27][28][29][30], which is challenging due to the necessary integration of multiple transportation systems and complex user preferences. Others have analysed the usage and users of intermodal transport in general, finding that a small percentage of citizens are using those opportunities of intermodal travel that are already available, but that intermodal trips are usually longer and of a routine nature compared to unimodal trips [31,32].…”

Section: Integrated Multimodal Mobility Platformsmentioning

confidence: 99%

Car Sharers’ Interest in Integrated Multimodal Mobility Platforms: A Diffusion of Innovations Perspective

2018

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Integrated multimodal mobility (IMM) platforms are being discussed as a promising solution to facilitate the transition to sustainable transport in both urban and rural areas. The idea behind such platforms is to provide a one-stop-shop offering information, booking and payment options for multiple means of transport. The aim of this paper, based on diffusion of innovations theory (DoI), is to investigate the interest in IMM platforms, as well as the factors in potential user groups that may influence the intention to use them. A sample of 711 car-sharing users responded to an online questionnaire containing items on DoI variables, as well as on the specific requirements concerning IMM platforms. The results show that few members of car-sharing schemes have used IMM platforms before, but that the interest in them is generally high. Perceived advantage and personal compatibility show the strongest associations with the intention to adopt IMM platforms followed by innovativeness, observability of use (social visibility of the innovation) and perceived technology security. Additionally, a target group was defined for the efficient and effective promotion of IMM platforms in an early market phase. Measures to promote IMM platforms were suggested based on this group’s perceptions and their specific requirements.

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“…Summaries of research contributions on passengers pretrip route advisory systems are in [8]- [10], [44], and [45]. Numerous projects were devoted to systems that recommend travelers a combination of transportation means for door to door journeys [6], [13], [16]- [19], [23], [24], [29], [32], [37], [39], [44]. However, all these works neither consider shared public nor pooled private vehicles.…”

Section: B Related Work and Paper Positioningmentioning

confidence: 99%

A Multi-Agent Advanced Traveler Information System for Optimal Trip Planning in a Co-Modal Framework

Dotoli

Zgaya

Russo³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

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We present an advanced traveler information system (ATIS) for public and private transportation, including vehicle sharing and pooling services. The ATIS uses an agentbased architecture and multi-objective optimization to answer trip planning requests from multiple users in a co-modal setting, considering vehicle preferences and conflicting criteria. At each set of users' requests, the transportation network is represented by a co-modal graph that allows decomposing the trip planning problem into smaller tasks: the shortest routes between the network nodes are determined and then combined to obtain possible itineraries. Using multi-objective optimization, the set of uservehicle-route combinations according to the users' preferences is determined, ranking all possible route agents' coalitions. The ATIS is tested for the real case study of the Lille metropolitan area (Nord Pas de Calais, France).

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Multimodal Public Transit Trip Planner with Real-time Transit Data

Cited by 37 publications

References 8 publications

Dynamic Trip Planner for Public Transport Using Genetic Algorithm

Dynamic Trip Planner for Public Transport Using Genetic Algorithm

Car Sharers’ Interest in Integrated Multimodal Mobility Platforms: A Diffusion of Innovations Perspective

A Multi-Agent Advanced Traveler Information System for Optimal Trip Planning in a Co-Modal Framework

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