Periodic Reporting for period 1 - ECOINTELS (The Economics of the Intelligent Transportation Services)
Reporting period: 2018-12-01 to 2020-11-30
Technological advancements have enabled new flexible and demand-responsive transportation services, whose significance on both urban and rural transportation systems have been increased rapidly due to the successful transport network companies such as Uber and Lyft and due to innovative public transportation operators developing automated demand-responsive services with public-private-partnerships. At the same time, there has been also increasing interest in these services in several academic disciplines. Recently, research has focused on modelling these services and related pricing policies, and on developing algorithms for optimal vehicle routing and demand management for improving passenger experience and sustainability of the new transport services.
The overall objective of the project was to achieve deeper understanding on the economics of the intelligent transportation services by extending the economic models of the automated demand responsive transportation (DRT) and by analyzing potential socio-economic impacts of the new transportation services.
The overall objective of the project was to achieve deeper understanding on the economics of the intelligent transportation services by extending the economic models of the automated demand responsive transportation (DRT) and by analyzing potential socio-economic impacts of the new transportation services.
For improving theoretical foundations of the economics of the automated DRT, we modelled DRT service where passengers cause external travel time costs for other passengers due to the route changes. We modelled optimal pricing and trip production policies both on the aggregate and network level, which enable more detailed analysis of pricing decisions based on the routes of the trips. The alternative modelling approaches were reviewed and considered in collaboration with the researchers of the Ecobus project and the Next Generation Mobility Research Group of the Max Planck Institute for Dynamics and Self-Organization. The modelling approach with the clearest link to the theory of microeconomics was chosen for further modelling work.
In the extended model, the occupancy rate was modelled as an endogenous variable, which depends on trip demand and capacity of the vehicle fleet. The developed model and the results on optimal polices were also illustrated with numerical examples. The main policy conclusions are that DRT operators could provide several service classes simultaneously (e.g. a door-to-door service and a stop-to-stop service) for satisfying passenger groups with varying needs and preferences. The optimal prices would be higher for service types requiring relatively more additional vehicle kilometers such as door-to-door trips and trips to the distant locations with low demand densities, and the optimal prices would be lower for more efficient service types such as feeder services and stop-to-stop services.
In addition, we developed both agent-based simulation models and discrete choice models describing passenger‘s choices between transport modes for analyzing potential demand for new flexible transport services. We studied passenger’s choices and transport behavior in Germany and India.
In order to summarize the existing knowledge on new intelligent transportation services and the usage of this knowledge in public policies, we studied governance practices for using cost-benefit analysis and other economic evaluation methods in decision making in the European Union with a special focus on Germany. We analyzed the most uncertain and challenging impacts of the new intelligent and flexible transport services, which require improvements in current evaluation methods and governmental practices. The main conclusions of the analysis were that transport investment decisions and related evaluation responsibilities are shared between the national level, federal states and local authorities, which can cause additional requirements for the projects applying funding from several levels of government. This can be challenging especially for the new flexible transport services if potential impacts are not well known (which is typical for disrupting technologies), and if there is a lack of common measurement practices for evaluating the new type of impacts such as the impact of replacing waiting time at the bus stop to adjustment time at home or at work enabling more comfortable and/or productive use of time due to real-time predictions for pick-up times. Moreover, there are many sources of uncertainty causing challenges for evaluation such as uncertainty on spatiotemporal future demand and impacts of the flexible services on the whole transport system. On the other hand, the flexible services reveal passenger’s needs and preferences more deeply than other transport services, and thereby, open possibilities for more comprehensive impact evaluation of investments and policies.
In the extended model, the occupancy rate was modelled as an endogenous variable, which depends on trip demand and capacity of the vehicle fleet. The developed model and the results on optimal polices were also illustrated with numerical examples. The main policy conclusions are that DRT operators could provide several service classes simultaneously (e.g. a door-to-door service and a stop-to-stop service) for satisfying passenger groups with varying needs and preferences. The optimal prices would be higher for service types requiring relatively more additional vehicle kilometers such as door-to-door trips and trips to the distant locations with low demand densities, and the optimal prices would be lower for more efficient service types such as feeder services and stop-to-stop services.
In addition, we developed both agent-based simulation models and discrete choice models describing passenger‘s choices between transport modes for analyzing potential demand for new flexible transport services. We studied passenger’s choices and transport behavior in Germany and India.
In order to summarize the existing knowledge on new intelligent transportation services and the usage of this knowledge in public policies, we studied governance practices for using cost-benefit analysis and other economic evaluation methods in decision making in the European Union with a special focus on Germany. We analyzed the most uncertain and challenging impacts of the new intelligent and flexible transport services, which require improvements in current evaluation methods and governmental practices. The main conclusions of the analysis were that transport investment decisions and related evaluation responsibilities are shared between the national level, federal states and local authorities, which can cause additional requirements for the projects applying funding from several levels of government. This can be challenging especially for the new flexible transport services if potential impacts are not well known (which is typical for disrupting technologies), and if there is a lack of common measurement practices for evaluating the new type of impacts such as the impact of replacing waiting time at the bus stop to adjustment time at home or at work enabling more comfortable and/or productive use of time due to real-time predictions for pick-up times. Moreover, there are many sources of uncertainty causing challenges for evaluation such as uncertainty on spatiotemporal future demand and impacts of the flexible services on the whole transport system. On the other hand, the flexible services reveal passenger’s needs and preferences more deeply than other transport services, and thereby, open possibilities for more comprehensive impact evaluation of investments and policies.
The research results can be exploited in the transport policies and in the planning of new business development activities. The research carried out provides knowledge that enhance innovation capacity of transport industry to develop services which improve efficiency and sustainability of transportation systems. Moreover, the provided knowledge enhances authorities to design transport polices which utilize more efficiently the possibilities of emerging technologies such as automated demand responsive transportation and related digital information services to achieve European policy objectives for sustainability.