Ansar-Ul-Haque Yasar

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.

Analyzing the efficiency of context-based grouping on collaboration in VANETs with large-scale simulation

Vehicle-to-vehicle and vehicle-to-infrastructure communication systems enable vehicles to share information captured by their local sensors with other interested vehicles. To ensure that this information is delivered at the right time and location, context-aware routing is vital for intelligent inter-vehicular communication. Traditional network addressing and routing schemes do not scale well for large vehicular networks. The conventional network multicasting and broadcasting cause significant overhead due to a large amount of irrelevant and redundant transmissions. To address these challenges, we first take into account contextual properties such as location, direction, and information interest to reduce the network traffic overhead. Second, to improve the relevancy of the received information we leverage the mobility patterns of vehicles and the road layouts to further optimize the peer-to-peer routing of the information. Third, to ensure our approach is scalable, we propose a context-based grouping mechanism in which relevant information is shared in an intelligent way within and between the groups. We evaluate our approach based on groups with common spatio-temporal characteristics. Our simulation experiments show that our context-based routing scheme and grouping mechanism significantly reduces the propagation of irrelevant and redundant information.

Optimizing information dissemination in large scale mobile peer-to-peer networks using context-based grouping

Nowadays novel embedded computing devices enable vehicles to form large scale mobile peer-to-peer networks in which they can assist each other to improve their driving experience. Therefore context-aware communication is considered to be vital for inducing inter-vehicular intelligence between groups of vehicles with similar interests. However, traditional network addressing schemes are not well suited for group-based communication in large scale vehicular networks. The classical network paradigms of multicasting and broadcasting to define groups are too limited. First, there is no way to optimize network traffic based on the contextual characteristics of the nodes. Second, the groups of nodes are highly dynamic with vehicles randomly joining and leaving multiple groups. We propose an information dissemination approach based on context grouping in which only relevant information is shared among nodes. We evaluate our approach in a large scale vehicular network where groups are formed based on the location and shared interests of the nodes. The experiments show that by inducing our context-based grouping mechanism we can significantly eliminate irrelevant information and reduce overall network traffic in a scalable way.

Simulation Model of Carpooling with the Janus Multiagent Platform

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 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 a that serves as a proof of concept. Our 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 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 based on the work presented in this paper. Furthermore, we also intend to carry out a validation study of our results with real data.

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.

Theory and Practice in Large Carpooling Problems

Abstract We address the carpooling problem as a graph-theoretic problem. If the set of drivers is known in advance, then for any car capacity, the problem is equivalent to the assignment problem in bipartite graphs. Otherwise, when we do not know in advance who will drive their vehicle and who will be a passenger, the problem is NP-hard. We devise and implement quick heuristics for both cases, based on graph algorithms, as well as parallel algorithms based on geometric/algebraic approach. We compare between the algorithms on random graphs, as well as on real, very large, data.

Enhancing experience prototyping by the help of mixed-fidelity prototypes

In this research review I undertook the problem related to the usage of a new concept known as the Mixed- Fidelity Prototype which is a mixture of its predecessors Low- and High- Fidelity Prototypes in Experience Prototyping. Experience Prototyping is a good way to explore, communicate and interact with the designs we develop like experiencing cycling on the ice, although the mood, snow conditions, bicycle type and many other factors really matter and tend to change with time. Experience Prototyping in itself is a very large domain to be explored separately but this research contains the idea to improve the ways we can achieve these factors with a minimal variation. Prototyping has the involvement of both the users and the stake holders so it is really important that correct experiments and the prototypes should get the desired results. With the effective usage of mixed-fidelity prototype in Experience Prototyping a better understanding of the real system can be developed as it gives a feeling of both the Low- and High- Fidelity Prototypes. It can unable an HCI developer to properly identify the requirements either functional or non-functional as identified by the proposed user. This paper has a comparison study between the uses of different prototyping models in Experience prototyping for better understanding of the system under development in detail and reasons for preferring Mixed-Fidelity Prototypes over the others.