Development Of Accident Prediction Models And Improvement Strategies

This study is focused on the relationship between accidents and the traffic/geometric capacity of highways. By using regression analyses, this research is conducted to quantify the effects of traffic-to-capacity associations upon the number of accidents on different highway types. A concept of modified capacity is introduced where some variables such as surface rates, surface types, shoulder condition, inside shoulder type and conditions, and others are taken into consideration together with the main explanatory power, namely daily V$sb{-}$C ratios. An analysis of section lengths is conducted to investigate the influence of section lengths on the composition and explanatory power of the prediction models. For such, several section length cutoff points are analyzed and, based on the model's variations, a minimum section length is recommended for each highway type. The data sets are then constituted of sections with lengths equal or greater than the suggested cutoff points. Prediction models are generated based on four years of data analysis (1985, 1986, 1987, and 1988), where yearly, common-section, and averaged models are created. The averaged models are validated based on other averaged three-year models which are generated from the data sets for the years 1989, 1990, and 1991. The model validation is also based on existing models when prediction models for the specific highway types hereby analyzed exist from other authors. Following, daily V$sb{-}$C and six-year averaged models are created according to the capacity calculated on the Highway Capacity Manual and the geometric models developed for each highway type. Capacity is calculated on a daily basis to comply with the daily characteristics of the traffic flow. Geometric and Daily V$sb{-}$C accident prediction models are then recommended for rural and urban interstate highways, rural and urban two-lane highways, and urban multilane divided and undivided highways.

Modeling of Transport Demand explains the mechanisms of transport demand, from analysis to calculation and forecasting. Packed with strategies for forecasting future demand for all transport modes, the book helps readers assess the validity and accuracy of demand forecasts. Forecasting and evaluating transport demand is an essential task of transport professionals and researchers that affects the design, extension, operation, and maintenance of all transport infrastructures. Accurate demand forecasts are necessary for companies and government entities when planning future fleet size, human resource needs, revenues, expenses, and budgets. The operational and planning skills provided in Modeling of Transport Demand help readers solve the problems they face on a daily basis. Modeling of Transport Demand is written for researchers, professionals, undergraduate and graduate students at every stage in their careers, from novice to expert. The book assists those tasked with constructing qualitative models (based on executive judgment, Delphi, scenario writing, survey methods) or quantitative ones (based on statistical, time series, econometric, gravity, artificial neural network, and fuzzy methods) in choosing the most suitable solution for all types of transport applications. Presents the most recent and relevant findings and research - both at theoretical and practical levels - of transport demand Provides a theoretical analysis and formulations that are clearly presented for ease of understanding Covers analysis for all modes of transportation Includes case studies that present the most appropriate formulas and methods for finding solutions and evaluating results

In India, the primary mode of transport is road transport which plays an important role in the conveyance of goods and passengers and linking the centers of production, consumption and distribution. It is also a key factor for promoting socio-economic development in terms of social, regional and national integration. The motor vehicle population is growing at a faster rate than the economic and population growth.