Towards Developing a Travel Time Forecasting Model for Location-Based Services: a Review

Travel time forecasting models have been studied intensively as a subject of Intelligent Transportation Systems (ITS), particularly in the topics of advanced traffic management systems (ATMS), advanced traveler information systems (ATIS), and commercial vehicle operations (CVO). While the concept of travel time forecasting is relatively simple, it involves a notably complicated task of implementing even a simple model. Thus, existing forecasting models are diverse in their original formulations, including mathematical optimizations, computer simulations, statistics, and artificial intelligence. A comprehensive literature review, therefore, would assist in formulating a more reliable travel time forecasting model. On the other hand, geographic information systems (GIS) technologies primarily provide the capability of spatial and network database management, as well as technology management. Thus, GIS could support travel time forecasting in various ways by providing useful functions to both the managers in transportation management and information centers (TMICs) and the external users. Thus, in developing a travel time forecasting model, GIS could play important roles in the management of real-time and historical traffic data, the integration of multiple subsystems, and the assistance of information management. The purpose of this paper is to review various models and technologies that have been used for developing a travel time forecasting model with geographic information systems (GIS) technologies. Reviewed forecasting models in this paper include historical profile approaches, time series models, nonparametric regression models, traffic simulations, dynamic traffic assignment models, and neural networks. The potential roles and functions of GIS in travel time forecasting are also discussed.

An integrated urban systems model with GIS

The purpose of the research is to develop an integrated urban systems model, which will assist in formulating a better land use-transportation policy by simulating the relationships between land use patterns and travel behavior, integrated with geographic information systems (GISs). In order to make an integrated land use-transportation model possible with the assistance of GISs technologies, the following four sub-systems have been developed: (1) an effective traffic analysis zone generation system; (2) an iterative land use and transportation modeling system; (3) efficient interfaces between GIS and land use, and GIS and transportation models; and (4) a user-friendly graphic user interface (GUI) system. By integrating these sub-systems, a variety of alternative land use-transportation policies can be evaluated through the modification of input parameters in each simulation. Eventually, the developed model using a GIS will assist in formulating an effective land use policy by obtaining robust simulation results for both land use-transportation planners and decision makers. The model has been applied to the Urbana-Champaign area as well as to the Seoul region in Korea for a demonstration of the workings of the model.

A combined land use-transportation model when zonal travel demand is endogenously determined

A combined transportation-land use model is proposed in this paper. Unlike other existing urban land use and transportation planning models in which a "fixed demand" for services is assumed to be known at the zonal level of an urban area, zonal travel demand is endogenously determined together with link congestion costs, optimal amounts of production and resulting efficient densities of land uses, once the transportation network is given. Some characteristics of alternative solutions are demonstrated. The proposed model represents progress over previous efforts in combining land use-transportation problems since the travel choice as to origin, destination and routes as well as amounts of goods to be produced at the optimal density of land uses are integrated into a consistent mathematical programming framework.

Expert systems : applications to urban planning

xiv,268hlm.;bib.;indek