The European Green Deal and the implementation of the “Sustainable and Smart Mobility Strategy – putting European transport on track for the future" set the ambitious goal for Europe to be the first Continent to reach carbon neutrality by 2050. This will require a profound transformation of passenger and freight transport in multiple dimensions. Transport technologies, alternative fuels, digital economy and servitization are contributing to paving the way towards zero emissions at a faster pace in comparison to the required transport mode shifts and changes in individual’s behaviour in most regions. Additionally, across the World, transportation systems are now facing unprecedented challenges as a result of the socioeconomic impacts caused by the pandemic of COVID-19, leading to questionable mobility patterns and uncertain futures. In this context, reshaping future mobility will require using enhanced modelling tools to understand prevailing short-term to long-term traffic dynamics, while accounting for disruptive events and anticipating alternative future states of the transportation system. As such, assessing mobility trends, identifying patterns of urban dynamics along with predicting transport passenger and freight demand and modal shifts are essential to transport planning means to enable policymakers to reach sustainability goals.
There exists a wide diversity of modelling approaches in transport planning that have been used to assess policy impacts, identify mobility patterns, predict transport demand and how the transport system is likely to perform in the future. The literature seems to reveal a recent shift from classical techno-economic models, mainly based on statistical and economic approaches, to hybrid and machine learning-based ones. Therefore, the goal of this special issue is to provide an integrated overview of how modelling tools, encompassing both qualitative and quantitative approaches, are being used (and how useful these are) to assess trends and changes in passenger and/or freight transport for policy analysis These include all transportation modes logistics, through focusing on different model types and purposes. Examples can include models based on traditional classical algorithms (e.g., Kalman based filtering methods), regression models and moving average for series with seasonality, machine learning models (e.g. backpropagation learning trained artificial neural network), system dynamic models, hybrid models (combination of methods), foresight analysis methods, behavioural supply chain (logistic) models, agent-based models, other simulation, probabilistic methods, multi-objective/nonlinear/stochastic optimization methods and emerging techniques from big data analytics and artificial intelligence methods (e.g. evolutionary computing, machine learning).
Articles shall address passenger and/or freight transportation modelling theory and case studies with policy contributions to shape future mobility. These can cover all transportation modes (road, rail, maritime transport, air, cross-modal), geographies and spatial analysis. Examples of themes and applications are as follows:
• Assessing trends in passenger and/or freight transport demand;
• Assessing policy impacts and the effects of transport measures;
• Data mining applications, focusing on knowledge discovery from big data of emerging mobility patterns;
• Modelling sustainability transitions to meet sustainable mobility goals and treatment of uncertainties in transitions modelling;
• Bottom-up travel demand scenarios modelling, including spatiotemporal analysis;
• Supply-chain (logistics) modelling and scenarios including digitalised institutions applying e.g. blockchain, crowdsourcing, whole supply chain management;
• Prediction of long-term passenger traffic flows and/or freight flows at the macro and/or micro levels;
• Optimization of traffic flows and urban logistics services (multi-stakeholder networks);
• Optimization for scheduling and planning of transport services to meet user (passenger) and/or business (logistic) needs;
• Resilience assessment of transport networks;
• Modelling future CCAM* scenarios and their socioeconomic impacts;
• Static-probabilistic behavioural demand models (e.g. developed within random utility theory) ;
• Modeling and assessing the stakeholders' acceptability of actions to implement in the urban context.
(*) CCAM: Cooperative, Connected and Autonomous Mobility
The European Green Deal and the implementation of the “Sustainable and Smart Mobility Strategy – putting European transport on track for the future" set the ambitious goal for Europe to be the first Continent to reach carbon neutrality by 2050. This will require a profound transformation of passenger and freight transport in multiple dimensions. Transport technologies, alternative fuels, digital economy and servitization are contributing to paving the way towards zero emissions at a faster pace in comparison to the required transport mode shifts and changes in individual’s behaviour in most regions. Additionally, across the World, transportation systems are now facing unprecedented challenges as a result of the socioeconomic impacts caused by the pandemic of COVID-19, leading to questionable mobility patterns and uncertain futures. In this context, reshaping future mobility will require using enhanced modelling tools to understand prevailing short-term to long-term traffic dynamics, while accounting for disruptive events and anticipating alternative future states of the transportation system. As such, assessing mobility trends, identifying patterns of urban dynamics along with predicting transport passenger and freight demand and modal shifts are essential to transport planning means to enable policymakers to reach sustainability goals.
There exists a wide diversity of modelling approaches in transport planning that have been used to assess policy impacts, identify mobility patterns, predict transport demand and how the transport system is likely to perform in the future. The literature seems to reveal a recent shift from classical techno-economic models, mainly based on statistical and economic approaches, to hybrid and machine learning-based ones. Therefore, the goal of this special issue is to provide an integrated overview of how modelling tools, encompassing both qualitative and quantitative approaches, are being used (and how useful these are) to assess trends and changes in passenger and/or freight transport for policy analysis These include all transportation modes logistics, through focusing on different model types and purposes. Examples can include models based on traditional classical algorithms (e.g., Kalman based filtering methods), regression models and moving average for series with seasonality, machine learning models (e.g. backpropagation learning trained artificial neural network), system dynamic models, hybrid models (combination of methods), foresight analysis methods, behavioural supply chain (logistic) models, agent-based models, other simulation, probabilistic methods, multi-objective/nonlinear/stochastic optimization methods and emerging techniques from big data analytics and artificial intelligence methods (e.g. evolutionary computing, machine learning).
Articles shall address passenger and/or freight transportation modelling theory and case studies with policy contributions to shape future mobility. These can cover all transportation modes (road, rail, maritime transport, air, cross-modal), geographies and spatial analysis. Examples of themes and applications are as follows:
• Assessing trends in passenger and/or freight transport demand;
• Assessing policy impacts and the effects of transport measures;
• Data mining applications, focusing on knowledge discovery from big data of emerging mobility patterns;
• Modelling sustainability transitions to meet sustainable mobility goals and treatment of uncertainties in transitions modelling;
• Bottom-up travel demand scenarios modelling, including spatiotemporal analysis;
• Supply-chain (logistics) modelling and scenarios including digitalised institutions applying e.g. blockchain, crowdsourcing, whole supply chain management;
• Prediction of long-term passenger traffic flows and/or freight flows at the macro and/or micro levels;
• Optimization of traffic flows and urban logistics services (multi-stakeholder networks);
• Optimization for scheduling and planning of transport services to meet user (passenger) and/or business (logistic) needs;
• Resilience assessment of transport networks;
• Modelling future CCAM* scenarios and their socioeconomic impacts;
• Static-probabilistic behavioural demand models (e.g. developed within random utility theory) ;
• Modeling and assessing the stakeholders' acceptability of actions to implement in the urban context.
(*) CCAM: Cooperative, Connected and Autonomous Mobility
