A Simulation Sandbox to Compare Fixed-Route, Semi-flexible Transit, and On-demand Microtransit System Designs

Yoon, Gyugeun; Chow, Joseph Y.J.; Rath, Srushti

doi:10.1007/s12205-022-0995-3

Cited by 11 publications

(5 citation statements)

References 84 publications

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“…For example, in general a train and a bus operator would not enter such an agreement because the trains cannot be shared with the bus operator if one of the latter's route was disrupted even if the reverse can be done via bus bridging (Kepaptsoglou and Karlaftis 2009). Primary users of this framework would be different road-bound public transport systems (Banister and Mackett 1990), including both fixed route and semi-flexible transit operators (Yoon, Chow, and Rath 2022).…”

Section: Problem Statementmentioning

confidence: 99%

Mobility operator service capacity sharing contract design to risk-pool against network disruptions

Pantelidis

Chow

Cats

2023

Transportmetrica A: Transport Science

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We propose a new mechanism to design risk-pooling contracts between operators to improve service resilience during disruptions. We formulate a novel two-stage stochastic multicommodity flow model to determine the cost savings of a coalition under different disruption scenarios and solve it using L-shaped method along with sample average approximation. Computational tests are conducted for network instances with up to 1024 scenarios. The proposed model is applied to a regional multi-operator network in the Randstad area of the Netherlands, for four operators, 40 origin-destination pairs, and over 1400 links where disruption data is available. Using the proposed method, we identify stable cost allocations that could yield a 66% improvement in overall network performance over not having any risk-pooling contract in place. We illustrate the sensitivity of the HTM operator's bargaining power to different network structures and disruption scenarios.

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Section: Problem Statementmentioning

confidence: 99%

Mobility operator service capacity sharing contract design to risk-pool against network disruptions

Pantelidis

Chow

Cats

2023

Transportmetrica A: Transport Science

Self Cite

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“…Research on ride-pooling shares several key similarities with on-demand public transport (for a recent review of the latter, see Vansteenwegen et al (2022)) that are worth highlighting here. The mathematical complexity is similarly challenging and supply-side and demand-side constraints are similar (Yoon et al, 2022), in both systems a critical mass is needed to take off (Papanikolaou and Basbas, 2021), yet the strategic planning perspective in the design of on-demand public transport is more central than in the ride-pooling context (Narayan et al, 2020). Several recent studies have made initial steps in contributing toward bridging between these two research streams.…”

Section: Introductionmentioning

confidence: 99%

Hyper-pool: pooling private trips into high-occupancy transit-like attractive shared rides

Kucharski,

Cats

2024

Preprint

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Ride-pooling offers a promising alternative in the context of urban mobility, being an intermediate mode between private rides and public transport. However, its full potential to contribute towards sustainability and efficiency has arguably not been yet unlocked neither in practice nor in theory. We propose a novel hyper-pool algorithm for addressing the computational complexity issues associated with pooling passengers into high-occupancy rides. In our Amsterdam 2000 trip case-study we identify 40 hyper-pooled rides with an average occupancy 5.8 passengers per vehicle. Hyper-pool is an analytical, offline, utility-driven ride-pooling algorithm for bundling individual trip requests into attractive shared rides of high-occupancy. We depart from the ride-pooling ExMAS algorithm where single rides are pooled into attractive door-to-door rides and propose two novel demand-side algorithms for further bundling individual demand towards more compact pooling. First, we generate stop-to-stop rides, with a single pick-up and drop off points optimal for all the travellers. Second, we bundle the stop-to-stop rides again, resulting with hyper-pooled rides which are compact enough to resemble public transport operations. The algorithm is publicly available and reproducible. It is applicable for real-size demand datasets and opens new opportunities for unlocking the potential of ride-pooling.

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“…In recent years, many public transit agencies around the world have launched or replaced their FRT service with an ODT. There are, however, several instances where the adoption of ODT services was not suitable or cost-effective, and the services had to be suspended 12 – 15 —examples include the dedicated fleet ODT in Helsinki, Finland, and Boston, Massachusetts, as well as the crowdsourced ODT in Manhattan and Brooklyn, New York 12 . Therefore, understanding where and when each ODT type is most efficient and cost-effective is essential for their sustainable adoption and operation.…”

Section: Introductionmentioning

confidence: 99%

“…Using simulation, prior studies have shown that ODT services can provide better service quality than the FRT in low-to-medium transport demand areas 10 , 20 – 23 . The replacement can significantly reduce walking as well as total travel time for passengers 10 , 12 . This is critical for passengers travelling in harsh weather conditions, at night, and in unsafe neighbourhoods.…”

Section: Introductionmentioning

confidence: 99%

Sustainability analysis framework for on-demand public transit systems

Alsaleh,

Farooq

2023

Sci Rep

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There is an increased interest from transit agencies to replace fixed-route transit services with on-demand public transits (ODT). However, it is still unclear when and where such a service is efficient and sustainable. To this end, we provide a comprehensive framework for assessing the sustainability of ODT systems from the perspective of overall efficiency, environmental footprint, and social equity and inclusion. The proposed framework is illustrated by applying it to the Town of Innisfil, Ontario, where an ODT system has been implemented since 2017. It can be concluded that when there is adequate supply and no surge pricing, crowdsourced ODTs are the most cost-effective transit system when the demand is below 3.37 riders/km2/day. With surge pricing applied to crowdsourced ODTs, hybrid systems become the most cost-effective transit solution when demand ranges between 1.18 and 3.37 riders/km2/day. The use of private vehicles is more environmentally sustainable than providing public transit service at all demand levels below 3.37 riders/km2/day. However, the electrification of the public transit fleet along with optimized charging strategies can reduce total yearly GHG emissions by more than 98%. Furthermore, transit systems have similar equity distributions for waiting and in-vehicle travel times.

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A Simulation Sandbox to Compare Fixed-Route, Semi-flexible Transit, and On-demand Microtransit System Designs

Cited by 11 publications

References 84 publications

Mobility operator service capacity sharing contract design to risk-pool against network disruptions

Mobility operator service capacity sharing contract design to risk-pool against network disruptions

Hyper-pool: pooling private trips into high-occupancy transit-like attractive shared rides

Sustainability analysis framework for on-demand public transit systems

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