We propose an intelligent tool for terrestrial transportation design based on the underlying concept of adaptive meshing. Vehicle routes are deformable visual patterns which cover the geographic area adapting shapes to distributed demands or specific places. In this way the system allows dynamic interaction and continuous visual feedback for the designer constructing and evaluating a transportation bus network. To assist the designer, an optimization framework merging neural networks and evolutionary algorithms permits one to position services and create routes among them automatically. The transport system optimizer built on a geographic information system is illustrated by application to a real life case of clustering and routing for the transportation of the 780 employees of an enterprise.

Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, identification, registration, and many derived tasks. In this paper, we address feature correspondence within the general framework of graph matching optimization and with the principal aim to contribute. We proposed two optimized algorithms: first-order and second-order for graph matching. On the one hand, a first-order normalized cross-correlation (NCC) based graph matching algorithm using entropy and response through Marr wavelets within the scale-interaction method is proposed. First, we proposed a new automatic feature detection processing by using Marr wavelets within the scale-interaction method. Second, feature extraction is executed under the mesh division strategy and entropy algorithm, accompanied by the assessment of the distribution criterion. Image matching is achieved by the nearest neighbor search with normalized cross-correlation similarity measurement to perform coarse matching on feature points set. As to the matching points filtering part, the Random Sample Consensus Algorithm (RANSAC) removes outliers correspondences. One the other hand, a second-order NCC based graph matching algorithm is presented. This algorithm is an integer quadratic programming (IQP) graph matching problem, which is implemented in Matlab. It allows developing and comparing many algorithms based on a common evaluation platform, sharing input data, and a customizable affinity matrix and matching list of candidate solution pairs as input data. Experimental results demonstrate the improvements of these algorithms concerning matching recall and accuracy compared with other algorithms.

We propose an extension of the self-organizing map by embedding it into an evolutionary algorithm to deal with a combination of the Euclidean vehicle routing problem (VRP) and clustering k-median problem. Vehicle routes are deformable visual patterns adapting shapes to distributed demands, considering capacity and time duration constraints. Here, routes are defined among bus stops which represent clusters of customers. First, we show that the approach outperforms on accuracy other neural networks applications to the VRP. Then, we apply it to a real life case of combined clustering and routing wit capacity constraint for the transportation of the 780 employees of an enterprise, by solving the problem in a unified way, as well as sequentially by clustering first and routing second.