Joakim Wernberg (p)

Justin Delloye

ABTRACT: There is a large body of empirical evidence that indicate increasing returns to scale with city size, including more productive workers and firms, higher wages and increased innovation. At face value, these results are attributed to the city as a whole. However, this does not resonate with the theoretical framework underpinning agglomeration economies or with the growing body of empirical evidence of highly localized externalities within cities, in particular those consistent with learning or informational spillovers. In this paper, I develop a model that builds on complexity and complex systems features to provide a coherent interpretation for empirical regularities associated to both within-city variations in agglomeration externalities and increasing productivity gains with city size. The model provides a conceptual explanation of how individual interactions may self-organize into city-wide modular information processors and a theoretical bridging of the literatures on agglomeration economies and complex systems.

Justin Delloye (p), Isabelle Thomas

Itzhak Benenson

Migrations are an essential driver of spatial population dynamics, and their understanding is crucial to forecast the evolution of urban and regional systems. Researchers in human geography have proposed several theoretical frameworks to catch the underlying rules that are seemingly shared by migration processes around the world. This paper anchors in the migration system approach that has been developing since the fifties. We argue that, in line with the general direction of migration research, these developments did not emphasize the timing of migration decisions. This shortcoming remains a gap in our understanding of migration dynamics, and it has contributed to the current call for the renewal of migration system theory. In that perspective, we propose a new stochastic model of complex migration system. In this model, individual migration decisions are represented by a nested Logit model, and they are aggregated in a nonlinear stochastic system that represents the collective migration dynamics. The model includes a “migration propensity” parameter, which describes the migration rate that remains after having controlled for regional utility differentials. This migration propensity parameter is calibrated on aggregate migration data from Belgium. With respect to the evolution of migration systems theory, our model reconciles existing dynamic approaches and it is suitable to empirical calibration. It contributes to the renewal of migration systems theory by providing a new dynamic tool.

Nir Fulman , Itzhak Benenson (p)

Joakim Wernberg

Long parking search time is a perpetual problem of every big city, and quantitative estimation of cruising time is a long-standing challenge for transportation research. The imbalance between the occupation rate, and the rates of arrivals and departures is typical for the central office/commercial/residential areas of every big city and results in notorious estimate of 30% urban traffic as entailed by the parking search
For the travelers, parking search time and parking price are the major factors that define the choice of the transportation mode, and, in a longer run, the decision on car ownership. For the city transportation planners and managers parking prices and constraints are the easiest tool for affecting travelers’ behavior and, eventually, urban traffic.
We demonstrate that the phenomenon of the parking search is only loosely connected to the other components of the urban traffic and, thus, can be investigated separately from it. Moreover, every component of the parking phenomenon – parking demand and supply, drivers’ arrival, departures and, also, drivers’ search behavior and decision making can be estimated based on the standard high-resolution urban data that are available in the majority of the large Western cities. In this way, dynamics of the urban parking pattern becomes a unique example of the complex subsystem of urban traffic system that can be explicitly and formally represented at resolution of a single driver and single parking spot, studied in theoretical and applied fashion. Furthermore, the results of this study are critical for urban transportation policy making
In the paper we investigate several analytical and simulation models of urban parking dynamics and propose easy and practical algorithms for estimating parking search time and establishing parking prices that guarantee predetermined level of parking occupation. All these solutions are built based on the standard high-resolution urban GIS data and, if necessary, simple field studies. Based on the models results, we propose parking policies that improve the state of urban transportation and discuss the ability of society to implement these policies.