Agent-based modeling in transportation problems requires detailed information on each of the agents that represent the population in the region of a study. To extend the agent-based transportation modeling with social influence, a connected synthetic population with both synthetic features and its social networks need to be simulated. However, either the traditional manually-collected household survey data (ACS) or the recent large-scale passively-collected Call Detail Records (CDR) alone lacks features. This work proposes an algorithmic procedure that makes use of both traditional survey data as well as digital records of networking and human behavior to generate connected synthetic populations. The generated populations coupled with recent advances in graph (social networks) algorithms can be used for testing transportation simulation scenarios with different social factors.

This paper explores the utility of peer pressure as an actionable mechanism to induce socially responsible and environmentally-conscious mobility habits. We adopt a two-stage game theoretic model of peer pressure to investigate feedback between social, geographic, and temporal dimensions of agent choices in a hyper-realistic micro-simulation of travel. The results show that peer pressure helps in achieving desirable equilibrium properties while reducing congestion and emissions due to sustained mode shift. With a way to initiate the required social norming and a proper concern for privacy and ethics, these cost-effective mechanisms may soon begin to find use in improving community welfare.

Urban modeling, including agent-based modeling of the coupled transportation and land use evolution, requires detailed information on each of the agents that represent the population in the region of a study. Traditional ways of obtaining this information include household surveys based on individual travel diaries. The surveys data provide a rich set of features, but they are limited in sampling size, geographical scope and frequency of updates. Moreover, they lack detail on inter-personal connections that give rise to social influences driving choice processes at a range of time scales. While manual surveying techniques are limited in their ability to collect social network data at scale, digital records of inter-personal communications provide an abundance of social networking information. This work proposes an algorithmic procedure that makes use of both traditional survey data as well as digital records of networking and human behaviours in generating connected synthetic populations for urban simulation.