A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted

A stochastic user-operator assignment game for microtransit service evaluation: A case study of Kussbus in Luxembourg

This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users' travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.Comment: arXiv admin note: substantial text overlap with arXiv:1912.0198

A Prototype for a Real-time Mobile-based Ridesharing System

Tänapäeval otsivad aina enam inimesi uusi võimalusi, kuidas kasutada olemasolevaid ressursse efektiivsemalt. Arenenud maades eksisteerib üks auto iga 1-2 inimese kohta, aga autode keskmine mahutavus on viis reisijat. Seega on ilmne, et sõidujagamisel on suur potentsiaal käesoleva probleemi lahendamisel. Sõidujagamine säästab palju aega ja kütust, sest sel puhul leitakse parim tasakaal sõiduaja, sõidudistantsi ja veetud reisijate vahel. Selline balansseerimine nõuab dünaamilist algoritmi, et lahendada tekkivat rändkaupmehe probleemi ning pakkuda kasutajatele kõige optimeeritumaid sõite. Käesolev töö keskendub reaalajalise mobiilirakenduse prototüübi loomisele, mis hõlmab endas funktsionaalsusi nii sõitjatele kui juhtidele. Juhtidel on võimalik postitada sõite, näha süsteemi poolt pakutud võimalusi koos teekonnaga ja näha sõitjaid, kes on reserveerinud koha nende sõitudes. Sõitjad saavad üles postitada oma soove sõitmiseks punktist A punkti B, reserveerida kohti sõitudes ning näha nende endi staatuseid kõikide süsteemi poolt välja pakutud sõitudele. Lisaks toetab süsteem rakendusesisest teadaannete saatmist ja asukohapõhiseid teenuseid, et pakkuda maksimaalset kasutajakogemust kõigile kasutajatele.Nowadays, more and more people are looking for ways how to use existing resources more effectively. In developed countries there is about 1-2 persons per car while the average car capacity is five people. Therefore, it is evident that ridesharing has a huge efficiency potential in this matter. Ridesharing saves a lot of time and fuel by finding the right balance between travel time, travel distance and carried passengers. Such balancing needs a dynamic TSP-type algorithm to offer the most optimized rides to the users. This thesis concentrates on creating a real-time mobile app which includes necessary functionalities for riders and drivers. Drivers can post their rides, see possible options with routes and riders who have booked a seat for their ride. Whereas riders can post their wishes to ride from A to B, book places in rides and see their statuses for all suitable rides. Additionally, the system will have support for push notifications and Google Maps API to maximize the user experience

Optimal Online Dispatch For High-Capacity Shared Autonomous Mobility-on-Demand Systems

Shared autonomous mobility-on-demand systems hold great promise for improving the efficiency of urban transportation, but are challenging to implement due to the huge scheduling search space and highly dynamic nature of requests. This paper presents a novel optimal schedule pool (OSP) assignment approach to optimally dispatch high-capacity ride-sharing vehicles in real time, including: (1) an incremental search algorithm that can efficiently compute the exact lowest-cost schedule of a ride-sharing trip with a reduced search space; (2) an iterative online re-optimization strategy to dynamically alter the assignment policy for new incoming requests, in order to maximize the service rate. Experimental results based on New York City taxi data show that our proposed approach outperforms the state-of-the-art in terms of service rate and system scalability