These results show the potential of recently introduced spatial interaction models in trip distribution estimation.We propose a maximum probability model to estimate the origin-destination trip matrix in the networks, where the observed traffic counts of links and the target origin-destination trip demands are independent discrete random variables with known probabilities. Taking the huge volume of point data being regressed into account, this value shows a good agreement between modeling results and real trips. The results show that the rank-based model can predict trip distribution up to 67 percent according to Sorensen Similarity Index.Additionally, the r-square measure obtained from regression analysis is 0.32 that shows a good agreement between estimated and ground truth matrices. Finally, we used Sorensen Similarity Index (SSI), Regression Analysis, and a visual measure, termed sparsity pattern, to examine the model. To capture a more complete pattern, the trajectories of yellow- and white-colored taxis were combined together. These dataset are published monthly by NYC Taxi and Limousine Commission (TLC). For the purpose of evaluating the results, trajectory data of taxi vehicles within NYC was employed. In this paper, the Furness method has been used. Also, in order to balance the resulting matrix, another process is needed. Hyman method is a repetitive algorithm which uses secant procedure to minimize the difference between the real trips’ average distance and the modelled trips’ average distance. In order to calibrate the models in which adjustable parameters are appeared, the Hyman method was employed. Manhattan is one of the most important trade centers in the world and its mobility rate is remarkable.
As a case study, the rank-based model has been applied for Manhattan, New York City (NYC) and the results have been evaluated. In this paper, the applicability of PWO, radiation and rank-based models in OD matrix estimation is addressed. In addition, in these models, computational mechanisms of trip distribution are not limited and different behavioral and decision-making characteristics of people are also taken into account.
The probabilistic form of these models is the same as OD estimation models. These models can be applied to a broad range of scales, from within a house or stadium, to a city, country, or even the whole earth. Among them are the rank-based (parameterized), radiation (non-parametric), and PWO (non-parametric) models. Recently, some parameterized and non-parametric models of human mobility pattern prediction, also known as spatial interaction (SI) models, have been developed. Up to now, various models such as gravity have been introduced to estimate the trip distribution. The most essential part of trip distribution is the model used for OD matrix estimation. The elements of this matrix indicate the amount of trips departing from origin zones to destination zones.The OD matrix is considered as an important input in various spatial analyses in Geospatial Information Systems (GIS). The output of trip distribution step in this model is termed Origin-Destination (OD) matrix. Traditionally, in order to predict the travel demand, a four step model is used, of which the second step is called trip distribution. Understanding and modeling the travel demand has numerous applications in designing urban infrastructures, managing the spread of diseases, monitoring the dispersion of computer viruses, urban planning and policies, spatiotemporal analyses in GIS, and Location-Based Services (LBS). Travel demand forecasting is an important topic in transportation planning.