We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

A relationship between people’s mobility and their social networks is presented based on an analysis of calling and mobility traces for one year of anonymized call detail records of over one million mobile phone users in Portugal. We find that about 80% of places visited are within just 20 km of their nearest (geographical) social ties’ locations. This figure rises to 90% at a ‘geo-social radius’ of 45 km. In terms of their travel scope, people are geographically closer to their weak ties than strong ties. Specifically, they are 15% more likely to be at some distance away from their weak ties than strong ties. The likelihood of being at some distance from social ties increases with the population density, and the rates of increase are higher for shorter geo-social radii. In addition, we find that area population density is indicative of geo-social radius where denser areas imply shorter radii. For example, in urban areas such as Lisbon and Porto, the geo-social radius is approximately 7 km and this increases to approximately 15 km for less densely populated areas such as Parades and Santa Maria da Feira.

Socio-geography of Human Mobility: A study using longitudinal mobile phone data

Population synthesis is concerned with the generation of synthetic yet realistic representations of populations. It is a fundamental problem in the modeling of transportation where the synthetic populations of micro-agents represent a key input to most agent-based models. In this paper, a new methodological framework for how to ‘grow’ pools of micro-agents is presented. The model framework adopts a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE). Compared to the previous population synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs sampling and traditional generative models such as Bayesian Networks or Hidden Markov Models, the proposed method allows fitting the full joint distribution for high dimensions. The proposed methodology is compared with a conventional Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary. It is shown that, while these two methods outperform the VAE in the low-dimensional case, they both suffer from scalability issues when the number of modeled attributes increases. It is also shown that the Gibbs sampler essentially replicates the agents from the original sample when the required conditional distributions are estimated as frequency tables. In contrast, the VAE allows addressing the problem of sampling zeros by generating agents that are virtually different from those in the original data but have similar statistical properties. The presented approach can support agent-based modeling at all levels by enabling richer synthetic populations with smaller zones and more detailed individual characteristics.

How to generate micro-agents? A deep generative modeling approach to population synthesis

While activity-based travel demand generation has improved over the last few decades, the behavioural richness and intuitive interpretation remain challenging. This paper argues that it is essential to understand why people travel the way they do and not only be able to predict the overall activity patterns accurately. If one cannot understand the “why?” then a model’s ability to evaluate the impact of future interventions is severely diminished. Bayesian networks (BNs) provide the ability to investigate causality and is showing value in recent literature to generate synthetic populations. This paper is novel in extending the application of BNs to daily activity tours. Results show that BNs can synthesise both activity and trip chain structures accurately. It outperforms a frequentist approach and can cater for infrequently observed activity patterns, and patterns unobserved in small sample data. It can also account for temporal variables like activity duration.

Activity-based travel demand generation using Bayesian networks

This study investigates flexible bus that provides door-to-door service with multiple passengers sharing the vehicle, which reduces congestion on the urban network. The service area is divided into zones, and flexible buses are assigned to zonal routes based on the historical demand characteristics before demand realization. Passengers are served by either regular service or ad hoc service after the demand realization to minimize the sum of ad hoc service cost and detour time cost. The elastic and stochastic natures of demand are captured in the formulation by the volume stochasticity of demand, detour time stochasticity, and elasticity of demand with respect to the flexible bus service price and quality. The profit of the flexible bus service is maximized while accounting for the detour time cost. To effectively solve the problem, volume and detour time reliability measures are introduced to separate the problem into a vehicle-to-route assignment problem and a passenger-to-vehicle assignment problem. A gradient-based solution approach is devised to determine the flexible bus routing plans by optimizing the associated reliability measures. The solution approach is further improved by combining the gradient-based approach with a greedy search solution approach and relaxing the formulations. The formulation and solution approaches are implemented using real data in Chengdu, China, with promising results.

Zonal-based flexible bus service under elastic stochastic demand

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.

Connected Population Synthesis for Transportation Simulation

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.

Peer pressure enables actuation of mobility lifestyles

Connected population synthesis for transportation simulation

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Connected Population Synthesis for Transportation Simulation

Peer pressure enables actuation of mobility lifestyles

Connected population synthesis for transportation simulation