Optimization for dynamic ride-sharing: A review

Agatz, Niels; Erera, Alan L.; Savelsbergh, Martin; Wang, Xing

doi:10.1016/j.ejor.2012.05.028

Cited by 846 publications

(454 citation statements)

References 41 publications

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“…They propose an evolutionary multi-objective route planning algorithm to solve the problem, and show that this approach can provide good quality solutions in reasonable runtime. The multi-hop ride-sharing problem is a lot more difficult when also considering the routing of the drivers (Agatz et al 2012). Herbawi and Weber (2012) extend the previous work to match multiple riders with multiple drivers having time windows and allowing a possible detour from their routes.…”

Section: Literature Reviewmentioning

confidence: 94%

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Multi-hop driver-parcel matching problem with time windows

2017

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Crowdsourced shipping can result in significant economic and social benefits. For a shipping company, it has a potential cost advantage and creates opportunities for faster deliveries. For the society, it can provide desirable results by reducing congestion and air pollution. Despite the great potential, crowdsourced shipping is not well studied. With the aim of using the spare capacities along the existing transportation flows of the crowd to deliver small-to-medium freight volumes, this paper defines the multi-driver multi-parcel matching problem and proposes a general ILP formulation, which incorporates drivers' maximum detour, capacity limits, and the option of transferring parcels between drivers. Due to the high computational complexity, we develop two heuristics to solve the problem. The numerical study shows that crowdsourced shipping can be an economic viable and sustainable option, depending on the spatial characteristics of the network and drivers' schedules. Furthermore, the added benefits increase with an increasing number of participating drivers and parcels.

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Section: Literature Reviewmentioning

confidence: 94%

“…Methodologically, our research belongs to the family of ride-sharing problems, and more specially the multiple driver, multiple rider arrangement (Agatz et al 2012). Gruebele (2008) describes such multi-hop and multi-passenger routing systems in detail.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-hop driver-parcel matching problem with time windows

2017

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“…A vehicle booked by an individual cannot be used simultaneous by other users, as Equation (15). There is only one single exception, when a group of users needs to use simultaneously the car sharing system and the DS and DT for the origin and destination are zero, then they can instantly carpool using the car sharing system.…”

Section: Collaborative Mobility Indicator For Assessing Car Sharingmentioning

confidence: 99%

“…The work in [15] focused on the optimization problem of finding efficient matches between passengers and drivers. Optimization algorithms for complex matching ride systems are also explored in [16].…”

mentioning

confidence: 99%

Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals

Toader

Sprumont

Faye

et al. 2017

IJGI

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Abstract:The potential of geospatial big data has been drawing attention for a few years. Despite the larger and larger market penetration of portable technologies (nomadic and wearable devices like smartphones and smartwatches), their opportunities for travel behavior analysis are still relatively unexplored. The main objective of our study is to extract the human mobility patterns from GPS traces in order to derive an indicator for enhancing Collaborative Mobility (CM) between individuals. The first step, extracting activity duration and location, is done using state-of-the-art automated recognition tools. Sensors data are used to reconstruct individual's activity location and duration across time. For constructing the indicator, in a second step, we defined different variables and methods for specific case studies. Smartphone sensor data are being collected from a limited number of individuals and for one week. These data are used to evaluate the proposed indicator. Based on the value of the indicator, we analyzed the potential for identifying CM among groups of users, such as sharing traveling resources (e.g., carpooling, ridesharing, parking sharing) and time (rescheduling and reordering activities).

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“…Despite the obvious potential economic gains offered by this solution, and the adoption of many policies and incentives in the past to foster its use, very few systems have been put into practice; such systems are still far from reaching the modal share sought. This issue is reviewed in detail in recent works such as Agatz et al (2012) and Furuhata et al (2013).…”

Section: Dynamic Assignment For Collaborative Modesmentioning

confidence: 99%

Equilibrium models in multimodal container transport systems

2015

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Optimizing the performance of multimodal freight transport networks involves adequately balancing the interplay between costs, volumes, times of departure and arrival, and times of travel. In order to study this interplay, we propose an assignment model that is able to efficiently determine flows and costs in a multimodal network. The model is based on a so-called user equilibrium principle, which is at the basis of Dynamic Traffic Assignment problems. This principle takes into account transport demands to be shipped using vehicles that transport single freight units (such as trucks) or multiple freight units (such as trains and barges, where demand should be bundled to reach efficient operations). Given a particular demand, the proposed model provides an assignment of the demand over the available modes of transport. The outcome of the model, i.e., the equilibrium point, minimizes users' generalized costs, expressed as a function of mode, travel time and related congestion, and waiting time for bundling sufficient demand in order to fill a vehicle. The model deals with these issues across a doubly-dynamic time scale and in an integrated manner. One dynamic involves a learning dynamic converging towards an equilibrium (day-to-day) situation, reflecting the reaction of the players towards the action of the others. Another dynamic considers the possible departure time that results in minimum expected costs, also due to the fact that players mutually influence each other on the choice of departure times, due to congestion effects and costs for early/late arrival of freight units. This is a choice within a given time horizon such as a day or a week. We present a study on the influence and sensitivity of different model parameters, in order to analyse the implications on strategic decisions, fostering a target modal share for freight transportation. We also study under which conditions the different modes can be substitutes for each other.

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Optimization for dynamic ride-sharing: A review

Cited by 846 publications

References 41 publications

Multi-hop driver-parcel matching problem with time windows

Multi-hop driver-parcel matching problem with time windows

Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals

Equilibrium models in multimodal container transport systems

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