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Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.