The Experts below are selected from a list of 141 Experts worldwide ranked by ideXlab platform
Isam Kaysi - One of the best experts on this subject based on the ideXlab platform.
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Commodity-Based Truck Origin-Destination Matrix Estimation Using Input-Output Data and Genetic Algorithms
Transportation Research Record, 2005Co-Authors: Omar Al-battaineh, Isam KaysiAbstract:A commodity-based model to estimate a truck origin-Destination (O-D) matrix is presented. The model takes advantage of the genetic algorithm global search method to find the best O-D matrix that when assigned to the network gives the minimum deviation between observed and estimated data. The model is flexible with respect to the type of data used in estimating the O-D matrix; however, the case study presented in this paper takes into consideration only two sets of information: commodity flow on specific links and column and row sums of the O-D matrix. Flows are treated as commodity dollar value; therefore, the estimated O-D matrix entries consist of the value of the commodity shipped by truck from the origin Zone to the Destination Zone. The method is composed of two submodels. The first submodel, the trip generation model, uses input-output data with employment and population data to estimate the zonal level of commodity attraction and production. The second submodel, the genetic algorithm model, searche...
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Commodity-Based Truck Origin–Destination Matrix Estimation Using Input–Output Data and Genetic Algorithms
Transportation Research Record: Journal of the Transportation Research Board, 2005Co-Authors: Omar Al-battaineh, Isam KaysiAbstract:A commodity-based model to estimate a truck origin–Destination (O-D) matrix is presented. The model takes advantage of the genetic algorithm global search method to find the best O-D matrix that when assigned to the network gives the minimum deviation between observed and estimated data. The model is flexible with respect to the type of data used in estimating the O-D matrix; however, the case study presented in this paper takes into consideration only two sets of information: commodity flow on specific links and column and row sums of the O-D matrix. Flows are treated as commodity dollar value; therefore, the estimated O-D matrix entries consist of the value of the commodity shipped by truck from the origin Zone to the Destination Zone. The method is composed of two submodels. The first submodel, the trip generation model, uses input–output data with employment and population data to estimate the zonal level of commodity attraction and production. The second submodel, the genetic algorithm model, searches globally for the optimum O-D matrix. The model and its application to a case study of a region in Ontario, Canada, are presented. Directions for future research are provided.
Paolo Santi - One of the best experts on this subject based on the ideXlab platform.
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LoSeRO: A Locality Sensitive Routing Protocol in Opportunistic Networks with Contact Profiles
IEEE Transactions on Mobile Computing, 2020Co-Authors: Gianpiero Costantino, Rajib Ranjan Maiti, Fabio Martinelli, Paolo SantiAbstract:Mobility trajectories of users contain personal information that when analyzed may reveal relevant data usable as message-sharing condition, e.g., interests in common or similar mobility patterns. In particular, leveraging on mobility patterns, in [12] we designed and presented a Geo-casting routing protocol called LoSeRO for opportunistic networks, which uses knowledge of the locations most frequently visited by a user to route messages. LoSeRO forwards messages—in a multi-casting way—to all users who have a mobility profile that intersects the packet's Destination Zone. LoSeRO presented a relative good performance value, however, to improve its performances, in this paper our contribution is to propose an upgraded version of our earlier proposed protocol, termed as LoSeRO v2 . In particular, it upgrades the traditional working fashion of LoSeRO by extending the knowledge of the most frequented locations to those users not only directly met, i.e., two-hops away. With this purpose, through simulations, we compare the performance of LoSeRO v2, with LoSeRO and other existing routing geo-casting protocols, and we illustrate how LoSeRO v2 achieves enhanced performances comparing precision, coverage and additional metrics.
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SAC - LoSeRO: a locality sensitive routing protocol in opportunistic networks
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016Co-Authors: Gianpiero Costantino, Rajib Ranjan Maiti, Fabio Martinelli, Paolo SantiAbstract:User trajectories can be used to extrapolate personal information such as interests and movement patterns. Extrapolating this information is especially important in the context of opportunistic networks, which take advantage of human mobility and their interactions to deliver messages to relevant users. In this paper, we propose a Geo-casting routing protocol called LoSeRO for opportunistic networks, which uses knowledge of the locations most frequently visited by a user to route messages. LoSeRO forwards messages---in a multicasting way---to all users who have a mobility profile that intersects the packet's Destination Zone. In particular, users' mobility profile is based on pre-defined Zones, and LoSeRO generates a mobility vector, called MobyZone, populated by the most n-frequented Zones. Thus, if the Destination Zone of the packet belongs to the MobyZone of a user, then the user is chosen as valid candidate for receiving the packet. Efficiency of our protocol has been evaluated using different metrics, such as coverage, precision and success rate, and compared to that of state-of-the-art geo-casting protocols. Simulations show that LoSeRO reaches the best compromise among the mentioned evaluation metrics.
Omar Al-battaineh - One of the best experts on this subject based on the ideXlab platform.
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Commodity-Based Truck Origin-Destination Matrix Estimation Using Input-Output Data and Genetic Algorithms
Transportation Research Record, 2005Co-Authors: Omar Al-battaineh, Isam KaysiAbstract:A commodity-based model to estimate a truck origin-Destination (O-D) matrix is presented. The model takes advantage of the genetic algorithm global search method to find the best O-D matrix that when assigned to the network gives the minimum deviation between observed and estimated data. The model is flexible with respect to the type of data used in estimating the O-D matrix; however, the case study presented in this paper takes into consideration only two sets of information: commodity flow on specific links and column and row sums of the O-D matrix. Flows are treated as commodity dollar value; therefore, the estimated O-D matrix entries consist of the value of the commodity shipped by truck from the origin Zone to the Destination Zone. The method is composed of two submodels. The first submodel, the trip generation model, uses input-output data with employment and population data to estimate the zonal level of commodity attraction and production. The second submodel, the genetic algorithm model, searche...
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Commodity-Based Truck Origin–Destination Matrix Estimation Using Input–Output Data and Genetic Algorithms
Transportation Research Record: Journal of the Transportation Research Board, 2005Co-Authors: Omar Al-battaineh, Isam KaysiAbstract:A commodity-based model to estimate a truck origin–Destination (O-D) matrix is presented. The model takes advantage of the genetic algorithm global search method to find the best O-D matrix that when assigned to the network gives the minimum deviation between observed and estimated data. The model is flexible with respect to the type of data used in estimating the O-D matrix; however, the case study presented in this paper takes into consideration only two sets of information: commodity flow on specific links and column and row sums of the O-D matrix. Flows are treated as commodity dollar value; therefore, the estimated O-D matrix entries consist of the value of the commodity shipped by truck from the origin Zone to the Destination Zone. The method is composed of two submodels. The first submodel, the trip generation model, uses input–output data with employment and population data to estimate the zonal level of commodity attraction and production. The second submodel, the genetic algorithm model, searches globally for the optimum O-D matrix. The model and its application to a case study of a region in Ontario, Canada, are presented. Directions for future research are provided.
Johannes Schlaich - One of the best experts on this subject based on the ideXlab platform.
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Generating Origin-Destination Matrices from Mobile Phone Trajectories
Transportation Research Record, 2010Co-Authors: Markus Friedrich, Katrin Immisch, Prokop Jehlicka, Thomas Otterstätter, Johannes SchlaichAbstract:This paper presents a method for generating origin–Destination (O-D) matrices with the use of floating phone data, that is, data generated from mobile phones moving through a study area. Mobile phone signals recorded in the cellular phone network are used to derive time–space trajectories of moving mobile phone devices. The start and end points of each trajectory determine the origin and Destination Zone. Link counts are used to project the sample of mobile phone movements to the broader movement of cars, trucks, and rail passengers. With results of a clustering process of traffic counts, O-D matrices for typical traffic days are computed. The resulting O-D matrices can be used for a long-term traffic state forecast.
Martin Oppermann - One of the best experts on this subject based on the ideXlab platform.
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Travel horizon: a valuable analysis tool?
Tourism Management, 1998Co-Authors: Martin OppermannAbstract:Abstract This paper tests the usefulness of Schmidhauser's (1976/1977) travel horizon concept in analysing travel behavior and accumulated travel experience. Based on a pilot study of New Zealand residents it shows that the concept appears valid, as the travel horizon of the respondents had expanded as proposed by Schmidhauser. However, it is here argued that the concept of travel horizons needs to be modified in order to reflect long-term travel patterns and, therefore, the concept of Destination Zone horizons is suggested, which recognizes that an individual can ascend as well as descend on the horizon ladder. Destination Zone horizons can be used by Destinations' marketing organizations in segmenting their market for a greater effectiveness of their marketing efforts.
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Expanding travel horizons
1998Co-Authors: Martin OppermannAbstract:This paper uses the proposed concept of travel horizons to analyze the travel behavior and accumulated travel experience of New Zealand residents. It reveals the growing familiarity of New Zealanders with overseas Destinations. However, it is also argued that the concept of travel horizons needs to be modified in order to reflect long-term travel patterns and, therefore, the concept of Destination Zone horizons is suggested which recognizes that an individual can ascend as well as descend on the horizon ladder.
