Increasing pressures caused by negative consequences of transportation and traffic problems in major cities, have resulted many attempts towards improvement of planning and management of transportation systems, according to sustainability objectives. In this paper, a comprehensive model is developed using a system dynamics approach to evaluate sustainable urban transportation. This model includ...

Traffic congestion causes huge economic loss worldwide in every year due to wasted fuel, excessive air pollution, lost time, and reduced productivity. Understanding how humans move and select the transportation mode throughout a large-scale transportation network is vital for urban congestion prediction and transportation scheduling. In this study, we collect big and heterogeneous data (e.g., G...

The paper discusses a method for modeling the operation of an urban transportation system. The proposed model models the bus operation in an urban transportation system with an equation system. The objective of the proposed model is to simulate the operation of an urban transport network. The network consists of a set of lines, a number of vehicles (buses) circulate on each line, where we consi...

Data mining methods have been widely and successfully used in many fields in the last decade. And geographic knowledge discovery and spatial data mining also have attracted more attentions recently. This paper presents an ART-MMAP neural network based spatio-temporal data mining method to simulate and predict urban expansion. The spatial matrices derived from different urban related features, i...

This paper deals with the real time regulation of traffic within a disturbed transportation system. We show the necessity of a decision support system that detects, analyzes and resolves the unpredicted disturbances. Due to the disturbed aspect of transportation system, we present a multi-agent approach for the regulation process. This approach includes an anytime algorithm, which permits to ac...

Data mining methods have been widely and successfully used in many fields in the last decade. And geographic knowledge discovery and spatial data mining also have attracted more attentions recently. This paper presents an ART-MMAP neural network based spatio-temporal data mining method to simulate and predict urban expansion. The spatial matrices derived from different urban related features, i...

The rapid development of intelligent transportation system technologies and the policy emphasis on their deployment have increased the importance of predictive dynamic network flow models, especially so-called dynamic network loading and dynamic traffic assignment models. In this chapter we provide a critical review of analytic models used in predicting time-varying urban network flows. Specifi...

Abstract. Understanding urban mobility is a fundamental question for institutional organizations (transport authorities, city halls) and it involves many different fields like social sciences, urbanism or geography. With the increasing number of probes tracking human locations, like magnetic pass for urban transportation, road sensors, CCTV systems or cell phones, mobility data are exponentiall...

This paper first studies the definition and connotation of transportation efficiency. From the viewpoint of different groups participating in urban transportation systems, different system functions and targets required by each group are analyzed. Then the corresponding system targets and evaluation rules required by the administrator are mainly studied. Four primary aspects which have great im...

Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient dist...