Improving the sustainability of passenger transportation systems : a spatial agent-based approach

Abstract

To alleviate fossil fuel use, mitigate traffic congestion, and reduce air emissions, it is necessary to find sustainable mobility alternatives and better adapt existing transportation modes to move people in a more environmentally sound and economically feasible way. In recent years, sustainable mobility systems have begun to emerge, encompassing a range of novel technologies and solutions such as high-speed railway, autonomous vehicles, and bike sharing. The current literature on the sustainability implications of transportation systems often neglect the interactions between these emerging mobility systems and existing transportation modes and the heterogeneous individual travel patterns that affect transportation sustainability. Therefore, to better understand these emerging transportation systems and inform decision making, an interdisciplinary approach tightly linking life-cycle analysis, agent-based modeling, and geographic information system are used to generate the behavioral rules of passengers choosing different transport modes, simulate the vehicles traveling in real-world road networks, and evaluate the economic, social, and environmental impacts of the multimodal transportation system. Three case studies focusing on three emerging mobility systems—high-speed railway, autonomous taxis, and bike sharing—are proposed to demonstrate the benefits of using this hybrid method. The high-speed railway study evaluates the life-cycle environmental performance of the existing multi-modal transportation system with the newly-built high-speed railway. Geographic information and psychology theory are integrated to construct the real-world intercity transportation maps and produce the passengers' mode choice behaviors influenced in part by passengers' social networks. Results from the high-speed railway study show that the occupancy rate of the high-speed rail should be maintained at 80% or more to lower the overall environmental impacts. The through train may need to be gradually shut down to mitigate the system environmental impacts by up to 30%. The autonomous taxi study evaluates the travel costs and environmental implications of substituting conventional personal vehicle travel with autonomous taxi travel. A spatial agent-based model is developed to simulate how commuters travel by autonomous taxi in real-world road networks. The autonomous taxi study demonstrates that to meet daily commute demand with wait times less than 3 minutes, the optimized autonomous taxi fleet size is only 20% of the conventional solo-commuting personal car fleet. The commuting cost decreases by 38%, but the environmental performance of autonomous taxis system is not positive, mainly due to the unoccupied vehicle travels and low ride sharing. Lastly, the bike sharing study simulates the environmental and human health impacts of bike sharing on travelers' usage of other transport modes in a multi-modal transportation system, considering their interactions through the modeling of the modal split based on the heterogeneous mode choice behaviors of travelers. Two scenarios are proposed for the development of a bike-sharing system: bike infrastructure extensions, and bike-sharing incentives. Two scenarios are evaluated along with the corresponding environmental and social impacts. The simulation results indicate that free use of bike-sharing to solve the first/last mile problem of the transit system can be most sustainable with 1.5 million US dollars in transportation damage cost saved per year, and 22 premature deaths further prevented per year due to mode shift to cycling and walking. In summary, these spatial agent-based life-cycle analysis models can be powerful tools to help policy-makers improve the environmental, economic, and social performances of multi-modal transportation systems.