Improving Transport Demand Modeling with Volunteered Geographic Information

Travel demand modeling requires micro-data of populations at household and person levels as a key input. Unfortunately, due to privacy constraints and the high cost of surveys, such data is often unavailable. To overcome this limit, populations can be synthesized to create disaggregated and complete representations of the studied population from real samples. Within this framework, Volunteered Geographic Information (VGI) is very promising to enrich the input sample and consequently to improve the goodness-of-fit of the resulting synthetic populations. An approach based on an evolutionary algorithm (EPSA) was previously introduced to generate a synthetic population with progressive inputs fed by VGI. However, it does not perfectly address the problem of assigning results to their locations. To solve this problem, we present a new Improved Evolutionary Population Synthesis Approach (IEPSA) based on a three-dimensional stable matching algorithm. Experimental results show the high efficiency of the proposed solution.