Gravity and scaling laws of city to city migration

Models of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration

Mobility as a Resource (MaaR) for resilient human-centric automation: a vision paper

With technological advances, mobility has been moving from a product (i.e., traditional modes and vehicles), to a service (i.e., Mobility as a Service, MaaS). However, as observed in other fields (e.g. cloud computing resource management) we argue that mobility will evolve from a service to a resource (i.e., Mobility as a Resource, MaaR). Further, due to increasing scarcity of shared mobility spaces across traditional and emerging modes, the transition must be viewed within the critical need for ethical and equitable solutions for the traveling public (i.e., research is needed to avoid hyper-market driven outcomes for society). The evolution of mobility into a resource requires novel conceptual frameworks, technologies, processes and perspectives of analysis. A key component of the future MaaR system is the technological capacity to observe, allocate and manage (in real-time) the smallest envisionable units of mobility (i.e., atomic units of mobility capacity) while providing prioritized attention to human movement and ethical metrics related to access, consumption and impact. To facilitate research into the envisioned future system, this paper proposes initial frameworks which synthesize and advance methodologies relating to highly dynamic capacity reservation systems. Future research requires synthesis across transport network management, demand behavior, mixed-mode usage, and equitable mobility

Effects of countdown displays in public transport route choice under severe overcrowding

The paper presents a route choice model for dynamic assignment in congested, i.e. overcrowded, transit networks where it is assumed that passengers are supported with real-time information on carrier arrivals at stops. If the stop layout is such that passenger congestion results in First-In-First-Out (FIFO) queues, a new formulation is devised for calculating waiting times, total travel times and route splits. Numerical results for a simple example network show the effect of information on route choice when heavy congestion is observed. While the provision of information does not lead to a remarkable decrease in total travel time, with the exception of some particular instances, it changes the travel behaviour of passengers that seem to be more averse to queuing at later stages of their journey and, thus, prefer to interchange at less congested stations

Interdependence between transportation system and power distribution system: a comprehensive review on models and applications

The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.fi=vertaisarvioitu|en=peerReviewed

Accounting for uncertainty, robustness and online information in transportation networks

textTransportation equilibrium problems with deterministic forecasts of O-D demand yield unsatisfactory results. Accurate estimation of transportation network performance helps in improving network resiliency and reducing network-wide congestion. Accounting for uncertainty and risk in transportation networks facilitate efficient evaluation and design of transportation networks and this has emerged as a recent topic of interest. Central problems in this area are quantifying network performance, designing robust networks, and modeling information recourse to optimize the performance of transportation systems. In this dissertation, different approaches for evaluating network performance under stochastic origin-destination (OD) demand conditions are presented. Specifically, two fundamentally different approaches - analytical expressions and single point approximations - for evaluating transportation network performance under uncertain demand are discussed. Computational results on multiple transportation test networks demonstrate the benefit of incorporating demand uncertainty in the model. A natural extension of the stochastic network evaluation model is the robust network design model. This model determines link improvement policies for the network considering not only the expected network performance but also its volatility under a budget constraint. A solution procedure based on a multiobjective evolutionary algorithm that computes the high performance network designs for a stochastic objective function is discussed. Computational results for the robust network design problem demonstrate the value of incorporating robustness. Accounting for dynamics and stochasticity based on user equilibrium conditions are studied by developing a linear programming based network model where the cell transmission model is used as the embedded traffic flow model. Computational results from this model are demonstrated. Finally, information recourse is proposed as one potential strategy for mitigating transportation network uncertainty. An online equilibrium model where travelers have the ability to take recourse enroute is developed as a fixed point formulation. A heuristic solution approach based on the method of successive averages (MSA) is proposed to solve this problem. Key findings from this problem relate to studying the benefit of online information provision as compared to off line network equilibrium problems. Further, opportunities for using these methodologies in other areas, and open problems of interest in this area are discussed. In the overall, this research is envisioned as an important first step in the development of fundamentally new network assignment models that account for uncertainty, robustness and information recourse in stochastic transportation networks.Civil, Architectural, and Environmental Engineerin

Approximation Techniques for Transportation Network Design Problem under Demand Uncertainty

Conventional transportation network design problems treat origin-destination (OD) demand as fixed, which may not be true in reality. Some recent studies model fluctuations in OD demand by considering the first and the second moment of the system travel time, resulting in stochastic and robust network design models, respectively. Both of these models need to solve the traffic equilibrium problem for a large number of demand samples and are therefore computationally intensive. In this paper, three efficient solution-approximation approaches are identified for addressing demand uncertainty by solving for a small sample size, reducing the computational effort without much compromise on the solution quality. The application and the performance of these alternative approaches are reported. The results from this study will help in deciding suitable approximation techniques for network design under demand uncertainty. DOI: 10.1061/(ASCE)CP.1943-5487.0000091. (C) 2011 American Society of Civil Engineers

Pareto Optimal Multiobjective Optimization for Robust Transportation Network Design Problem

A study was done to formulate and solve the multiobjective network design problem with uncertain demand. Various samples of demand are realized for optimal improvements in the network while the objectives of the expected total system travel time and the higher moment for total system travel time are minimized. A formulation is proposed for multiobjective robust network design, and a solution methodology is developed on the basis, of a revised fast and elitist nondominated sorting genetic algorithm. The developed methodology has been tested on the Nguyen-Dupuis network, and various Pareto optimal solutions are compared with earlier work on the single-objective robust network design problem. A real medium-size network was solved to prove efficacy of the model. The results show better solutions for the multiobjective robust network design problem with relatively less computational effort