Sungjin Cho

A Conceptual Design of an Agent-based Interaction Model for the Carpooling Application

Abstract Carpooling is an emerging alternative transportation mode that is eco-friendly and sustainable as it enables commuters to save time, travel resource, reduce emission and traffic congestion. The procedure of carpooling consists of a number of steps namely (i) create a motive to carpool, (ii) communicate this motive with other interested agents, (iii) negotiate a plan with the interested agents, (iv) execute the agreed plans and (v) provide a feedback to all concerned agents. The state-of-the-art research work on agent-based modeling is limited to a number of technical and empirical studies that are unable to handle the complex agent behavior in terms of coordination, communication and negotiations. In this paper we present a conceptual design of an agent-based model (ABM) for the carpooling application that serves as a proof of concept. Our agent-based model for the carpooling application is a computational model that is used for simulating the interactions of autonomous agents and to analyze the effects of change in factors related to the infrastructure, behavior and cost. In our agent-based carpooling application we use agent profiles and social networks to initiate our agent communication model and then employ a route matching algorithm and a utility function to trigger the negotiation process between agents. We plan to, as a part of the future work, develop a prototype of our agent-based carpooling application on the basis of the work presented in this paper. Furthermore, we also intend to carry out a validation study of our results with real data.

An agent-based model to evaluate carpooling at large manufacturing plants

Abstract Carpooling is thought to be part of the solution to resolve traffic congestion in regions where large companies dominate the traffic situation because coordination and matching between commuters is more likely to be feasible in cases where most people work for a single employer. Moreover, carpooling is not very popular for commuting. In order for carpooling to be successful, an online service for matching commuter profiles is indispensable due to the large community involved. Such service is necessary but not sufficient because carpooling requires rerouting and activity rescheduling along with candidate matching. We advise to introduce services of this kind using a two step process: (1) an agentbased simulation is used to investigate opportunities and inhibitors and (2) online matching is made available. This paper describes the challenges to build the model and in particular investigates possibilities to derive the data required for commuter behavior modeling from big data (such as GSM, GPS and/or Bluetooth).

Exploiting graph-theoretic tools for matching in carpooling applications

An automatic service to match commuting trips has been designed. Candidate carpoolers register their personal profile and a set of periodically recurring trips. The Global CarPooling Matching Service shall advise registered candidates how to combine their commuting trips by carpooling. Planned periodic trips correspond to nodes in a graph; the edges are labeled with the probability for for success while negotiating to merge two planned trips by carpooling. The probability values are calculated by a learning mechanism using on one hand the registered person and trip characteristics and on the other hand the negotiation feedback. The probability values vary over time due to repetitive execution of the learning mechanism. As a consequence, the matcher needs to cope with a dynamically changing graph both with respect to topology and edge weights. In order to evaluate the matcher performance before deployment in the real world, it will be exercised using a large scale agent based model. This paper describes both the exercising model and the matcher.

Estimating Scalability Issues While Finding an Optimal Assignment for Carpooling

Abstract An automatic service to match commuting trips has been designed. Candidate carpoolers register their personal profile and a set of periodically recurring trips. The Global CarPooling Matching Service (GCPMS) shall advise registered candidates on how to combine their commuting trips by carpooling. Planned periodic trips correspond to nodes in a graph; the edges are labeled with the probability for negotiation success while trying to merge planned trips by carpooling. The probability values are calculated by a learning mechanism using on one hand the registered person and trip characteristics and on the other hand the negotiation feedback. The GCPMS provides advice by maximizing the expected value for negotiation success. This paper describes possible ways to determine the optimal advice and estimates computational scalability using real data for Flanders.

Analysis of the Co-routing Problem in Agent-based Carpooling Simulation

Abstract Carpooling can cut costs and help to solve congestion problems but does not seem to be popular. Behavioral models allow to study the incentives and inhibitors for carpooling and the aggregated effect on the transportation system. In activity based modeling used for travel forecasting, cooperation between actors is important both for schedule planning and revision. Carpooling requires cooperation while commuting which in turn involves co-scheduling and co-routing . The latter requires combinatorial optimization. Agent-based systems used for activity based modeling, contain large amounts of agents. The agent model requires helper algorithms that deliver high quality solutions to embedded optimisation problems using a small amount of resources. Those algorithms are invoked thousands of times during agent society evolution and schedule execution simulation. Solution quality shall be sufficient in order to guarantee realistic agent behavior. This paper focuses on the co-routing problem.

Scalability issues in optimal assignment for carpooling

Carpooling for commuting can save cost and helps in reducing pollution. An automatic Web based Global CarPooling Matching Service (GCPMS) for matching commuting trips has been designed. The service supports carpooling candidates by supplying advice during their exploration for potential partners. Such services collect data about the candidates, and base their advice for each pair of trips to be combined, on an estimate of the probability for successful negotiation between the candidates to carpool. The probability values are calculated by a learning mechanism using, on one hand, the registered person and trip characteristics, and on the other hand, the negotiation feedback. The problem of maximizing the expected value of carpooling negotiation success was formulated and was proved to be NP-hard. In addition, the network characteristics for a realistic case have been analyzed. The carpooling network was established using results predicted by the operational FEATHERS activity based model for Flanders (Belgium).

An Activity-based Carpooling Microsimulation Using Ontology☆

Abstract This study aims to show an ability of Ontology, which is a formal explicit description of concepts in a domain of interest, in an activity-based microsimulation. Thus, an agent-based carpooling application using ontology techniques is presented as a case study with a focus on three functions of the Ontology. First, Ontology facilitates integrating between heterogeneous databases by defining the relationship between their concepts. Second, Ontology verifies the compatibility and consistency between the different angles to combine varied models in a common structure by providing shared knowledge between different domains modelling with the definition of objects and concepts. Lastly, Ontology is useful for modelling agent communication by means of making explicit the parsed message between agents with the shared knowledge. This paper introduces related studies and basic knowledge about using methodologies, and supports an example of using Ontology in an agent-based carpooling simulation.

Seoul activity-based Model: An Application of Feathers Solutions to Seoul Metropolitan Area

Seoul metropolitan area; Transportation demand model; Activity-based model; Agent-based model; Feathers

Validation of Activity-based Travel Demand Model using Smart-card Data in Seoul, South Korea

This study aims to validate an activity-based travel demand model, FEATHERS, using smart-card data which is collected in Seoul, South Korea, and to discuss some limits and challenges in the prediction of public traffic demands. To achieve the goal, global/local trip pattern indices and a hot-spot analysis were applied for the validation test as a comparison method in this study. Using those methods, the public traffic demands predicted by the simulation in the study area were evaluated comparing with ones in the smart-card data. As a result, FEATHERS Seoul shows the enough performance in predicting the global pattern of the public traffic demands, but a low performance in a local pattern, particularly in some areas with a mixture land-use type and/or a frequent public transit. This is because the current model does not handle such a complicate type of land-use and also a multi-modal trip in the simulation process. In conclusion, this study addressed the limits of the current model through the validation test using smart-card data and suggested some solution to the improvement in the specific models As a future work, we will apply smart-card data for the validation of the models operated in FS, such as a location choice model and a trip mode choice model.

Integrating GIS and FEATHERS: A Conceptual Design

Abstract This study proposes integrating GIS and FEATHERS in order to improve the capability of data management, spatial analysis and visualization in the model framework. GIS provides geodatabase for effectively storing and edit (non-)spatial data, useful functions of spatial analysis for defining spatial interactions between phenomena simulated by the modeling system, and interactive visualization tools. Thus, this study mainly focuses on three topics: i) why FEATHERS needs a GIS module, ii) how the GIS module is designed, and iii) what functions can be supported by the GIS module in the FEATHERS system. Moreover, we overview some transportation software adopting GIS to catch up a general trend in GIS and transportation and also suggest data schema for creating geodatabase and a source code for making map layers and map tools in python. At the end, we conclude this paper with summary and plan for the future work.