Space-time modeling of traffic flow

A key concern in transportation planning and traffic management is the ability to forecast traffic flows on a street network. Traffic flows forecasts can be transformed to obtain travel time estimates and then use these as input to travel demand models, dynamic route guidance and congestion management procedures. A variety of mathematical techniques have been proposed for modeling traffic flow on a street network. Briefly, the most widely used theories are: -Kinetic models based on partial differential equations that describe waves of different traffic densities, -deterministic models that use nonlinear equations for the estimation of different car routes, -large scale simulation models such as cellular automata and, -stochastic modeling of traffic density at distinct points in space. One problem with these approaches is that the traffic flow process is characterized by nonstationarities that cannot be taken into account by the vast majority of modeling strategies. However, recent advances in statistical modeling in fields such as econometrics or environmetrics enable us to overcome this problem. The aim of this work is to present how two statistical techniques, namely, vector autoregressive modeling and dynamic space-time modeling can be used to develop efficient and reliable forecasts of traffic flow. The former approach is encountered in the econometrics literature, whereas the later is mostly used in environmetrics. Recent advances in statistical methodology provide powerful tools for traffic flow description and forecasting. For a purely statistical approach to travel time prediction one may consult Rice and van Zwet (2002). In this work, the authors employ a time varying coefficients regression technique that can be easily implemented computationally, but is sensitive to nonstationarities and does not take into account traffic flow information from neighboring points in the network that can significantly improve forecasts. According to our approach, traffic flow measurements, that is count of vehicles and road occupancy obtained at constants time intervals through loop detectors located at various distinct points of a road network, form a multiple time series set. This set can be described by a vector autoregressive process that models each series as a linear combination of past observations of some (optimally selected) components of the vector; in our case the vector is comprised by the different measurement points of traffic flow. For a thorough technical discussion on vector autoregressive processes we refer to Lutkerpohl (1987), whereas a number of applications can be found in Ooms (1994). Nowadays, these models are easily implemented in commercial software like SAS or MATLAB; see for example LeSage (1999). The spatial distribution of the measurement locations and their neighboring relations cannot be incorporated in a vector autoregressive model. However, accounting for this information may optimize model fitting and provide insight into spatial correlation structures that evolve through time. This can be accomplished by applying space-time modeling techniques. The main difference of space-time models encountered in literature with the vector autoregressive ones lies in the inclusion of a weight matrix that defines the neighboring relations and places the appropriate restrictions. For some early references on space-time models, one could consult Pfeifer and Deutsch (1980 a,b); for a Bayesian approach, insensitive to nonstationarities we refer to Wikle, Berliner and Cressie (1998). In this work, we discuss how the space-time methodology can be implemented to traffic flow modeling. The aforementioned modeling strategies are applied in a subset of traffic flow measurements collected every 15 minutes through loop detectors at 74 locations in the city of Athens. A comparative study in terms of model fitting and forecasting accuracy is performed. Univariate time series models are also fitted in each measurement location in order to investigate the relation between a model's dimension and performance. References: LeSage J. P. (1999). Applied Econometrics using MATLAB. Manuscript, Dept. of Economics, University of Toronto Lutkerpohl H. (1987). Forecasting Aggregated Vector ARMA Processes. Lecture Notes in Economics and Mathematical Systems. Springer Verlag Berlin Heidelberg Ooms M. (1994). Empirical Vector Autoregressive Modeling. Springer Verlag Berlin Heidelberg Pfeifer P. E., and Deutsch S. J. (1980a). A three-stage iterative procedure for Space-Time Modeling. Technometrics, 22, 35-47 Pfeifer P. E., and Deutsch S. J. (1980b). Identification and Interpretation of First-Order Space-Time ARMA models. Technometrics, 22, 397-408 Rice J., and van Zwet E. (2002). A simple and effective method for predicting travel times on freeways. Manuscript, Dept. of Statistics, University of California at Berkeley Wikle C. K., Berliner L. M. and Cressie N. (1998). Hierarchical Bayesian space-time models. Environmental and Ecological Statistics, 5, 117-154

Urban Atlas, land use modelling and spatial metric techniques

Recently, through the GMES program of ESA the Urban Atlas dataset was released. The Urban Atlas is providing pan-European comparable land use and land cover data for Large Urban Zones with more than 100.000 inhabitants as defined by the Urban Audit. The production of the various datasets started in 2009 and is expected to be completed by the end of 2011. At presently datasets for more than 150 urban areas have been released. Most importantly the datasets can be freely downloaded and distributed. The availability of such a huge dataset produced with the same standards will have a major impact on the development of urban transportation models and the comparative analysis of the urban areas across Europe. Combined with the data sets that will be developed from the various Census of population it could become the basis for the application of various models in the next ten years. In this paper two major themes are discussed. First, how the current state of art in urban modeling (behavioral, cellular automata and statistical) can use these models, what type of additional data might be needed and how these datasets can be combined with other data for developing land use transportation models. Second, spatial metric techniques are used to define indicators for the landscape that could be used for comparing the structure and the form of the various cities. In the last ten years there has been an increasing interest in applying spatial metric techniques analysis of urban environments, to examine unique spatial components of intra-and inter-city urban structure, as well as, the dynamics of change. The landscape perspective assumes abrupt transitions between individual patches that result in distinct edges. These measures provide a link between the detailed spatial structures that result from urban change processes. The spatial metric indicators were developed for several cities and are then used for a comparative study of city typologies and urban fabric characteristics.

Spatial Time-Series Modeling: A review of the proposed methodologies

This paper discusses three modelling techniques, which apply to multiple time series data that correspond to different spatial locations (spatial time series). The first two methods, namely the Space-Time ARIMA (STARIMA) and the Bayesian Vector Autoregressive (BVAR) model with spatial priors apply when interest lies on the spatio-temporal evolution of a single variable. The former is better suited for applications of large spatial and temporal dimension whereas the latter can be realistically performed when the number of locations of the study is rather small. Next, we consider models that aim to describe relationships between variables with a spatio-temporal reference and discuss the general class of dynamic space-time models in the framework presented by Elhorst (2001). Each model class is introduced through a motivating application.spatial time-series, space-time models, STARIMA, Bayesian Vector Autoregressions

Applying a CA-based model to explore land-use policy scenarios to contain sprawl in Thessaloniki, Greece

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.This study addresses the issue of urban sprawl through the application of a Cellular Automata (CA) based model in the area of Thessaloniki, Greece. To link macro-scale to micro-dynamic processes the model integrates a statistical model at the regional level with a CA model at the local level. The model is used to compare two scenarios of growth of Thessaloniki to year 2030; the first one assuming a continuation of existing trends, whereas the second one assuming the enactment of various land use regulations in order to contain urban sprawl. The comparison of the results demonstrate that in the second scenario there is a smaller degree of leapfrog growth, with high percentage of new developed land being inside the existing city plans with development in areas outside the plans and in agricultural areas being minimized

Ontology-based Land Degradation Assessment from Satellite Images

International audienceIn this paper, we introduce the idea of documenting operational chains for land degradation assessment using ontologies. We believe that this will help end-users in better understanding the land degradation characteristics and evaluate the results of the assessment process. Since the application domain is wide, various operational chains for land degradation assessment and their associated documentation exist, according to different options. This parameterization causes the development of different ontologies, which, nonetheless are to a certain extent linked because of the common software components of the corresponding operational chains. We therefore propose a hierarchical structure of these ontologies; so that several requirements such as understanding of expert knowledge interconnections and application domain variety, documentation, assimilation of new expert knowledge, and reusability of software components become feasible

Allocation of Transportation Investments and Regional Growth: A Multilevel Optimization Framework

274 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1981.This study is concerned with the allocation of the transportation investments and their impact on the regional economy. Because the relationship between regional growth and the transport sector is highly complex, evaluation of alternative transportation plans should be performed with the aid of models which include among the decision variables not only the supply of transport, but also the level of national and regional economy.An economy-wide multilevel is developed with these characteristics. It is multiregional, multisectoral and dynamic. It consists of three different submodels--multiregional, distribution and network design--connected through an information flow consisting of shadow prices and allocative decisions.In addition to the allocation of the transportation budget the model estimates the magnitude of regional production, investment and consumption of the non-transportation sectors. Entropy maximizing techniques, resulting into gravity type distribution functions, are employed to determine the magnitude of the interregional flows.The large size of the model and its nonlinear character--zero-one variables, nonlinear entropy constraints--do not permit the application of direct solution methods. However, the model can be modified, so that decomposition algorithms can be applied. It is shown that the global optimal solution can be obtained by applying in two stages the Bender's Partitioning Algorithm. The computational experience with three test problems points out to a relatively rapid convergence.To test the applicability of the model to a real world problem the complete model was applied to Greece, a country with acute regional problems. The results of the model show that during the period 1975-1990 the interregional income differentials, as well as the national rate of growth will diminish. Since many of the input data are of questionable quantity additional tests are needed to confirm these trends.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

A multiregional optimization model for allocating trasnportation investments

This paper presents a multiregional optimization model which explicitly considers the direct and indirect relationships between regional growth and investments in transportation infrastructure. Consumption, demand and investments for each sector and region are derived endogenously. Trade flows are simulated by a gravity function and transportation network investment decisions are represented by 0-1 integer variables. Despite its complex structure the model can be estimated by applying in two stages the Benders Partitioning Algorithm. The model is applied to Greece to obtain a comprehensive investment plan for the transportation system.