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Description:
To have a comprehensive overview of the current challenges in the transportation sector, it is essential to analyze traffic behavior and, particularly, to estimate the demand realistically. Therefore, traffic demand, represented by origin-destination (OD) matrices, is a vital key input for many traffic-related applications in traffic planning and management domains. Many studies have developed models to estimate single-modal traffic demand matrices. The conventional models use section traffic counts and traffic surveys as inputs. Unfortunately, it is highly expensive and time-consuming to carry out traffic survey campaigns as the process is not fully automated. A few studies have also developed models to estimate the traffic demand of multimodal shared systems, for example, public or freight transportation systems. However, these models depend on rich data provided by particular data sources, such as user smart cards of public transport systems. To the best of our knowledge, there are no models for estimating multimodal traffic demand of private transportation modes, such as driving, cycling, and walking. We argue that the significant hurdle to developing such models is the lack of reliable data. GPS modules allow the automatic collection of floating data (FD). FD are extensive trip records that provide location coordinates, timestamps, and speed values of devices equipped with active GPS modules. This kind of data can provide the required rich information and significantly reduce the disadvantages of traffic survey campaigns. This work aims to estimate multimodal traffic demand matrices of private transportation modes by fusing different data sources. Specifically, it develops a model to estimate OD matrices of driving, cycling, and walking using traffic counts and FD. To achieve this, the work is divided into three main parts. In the first part, we exploited the potential of floating car data (FCD) to enhance the quality and performance of the demand estimation process for vehicles. This was done by improving ...