Integrating a traffic router and microsimulator into a land use and travel demand model

Abstract

This paper describes one of the first known attempts at integrating a dynamic and disaggregated land-use model with a traffic microsimulator and compares its predictions of land use to those from an integration of the same land-use model with a more traditional four-step travel demand model. For our study area of Chittenden County, Vermont, we used a 40-year simulation beginning in 1990. Predicted differences in residential units between models for 2030 broken down by town correlated significantly with predicted differences in accessibility. The two towns with the greatest predicted differences in land use and accessibility are also the towns that currently have the most severe traffic bottlenecks and poorest route redundancy. Our results suggest that this particular integration of a microsimulator with a disaggregated land-use model is technically feasible, but that in the context of an isolated, small metropolitan area, the differences in predicted land use are small.     

A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting

Short-term traffic volume forecasting represents a critical need for Intelligent Transportation Systems. This paper develops a novel forecasting approach inspired by human memory, called the spinning network (SPN). The approach is then used for short-term traffic volume forecasting, utilizing a data set compiled from real-world traffic volume data obtained from the Hampton Roads traffic operations center in Virginia. To assess the accuracy of the SPN approach, its performance is compared to two other approaches, namely a back propagation neural network and a nearest neighbor approach. The transferability of the SPN approach and its ability to forecast for longer time periods into the future is also assessed. The results of the performance testing conducted in this paper demonstrates the superior predictive accuracy and drastically lower computational requirements of the SPN compared to either the neural network or the nearest neighbor approach. The tests also confirm the ability of the SPN to predict traffic volumes for longer time periods into the future, as well as the transferability of the approach to other sites.     

采用沿岸动力概念以减少埃及红海海域高速船燃料消耗与降低排放(英文)

The progress of economic globalization,the rapid growth of international trade,and the maritime transportation has played an increasingly significant role in the international supply chain.As a result,worldwide seaports have suffered from a central problem,which appears in the form of massive amounts of fuel consumed and exhaust gas fumes emitted from the ships while berthed.Many ports have taken the necessary precautions to overcome this problem,while others still suffer due to the presence of technical and financial constraints.In this paper,the barriers,interconnection standards,rules,regulations,power sources,and economic and environmental analysis related to ships,shore-side power were studied in efforts to find a solution to overcome his problem.As a case study,this paper investigates the practicability,costs and benefits of switching from onboard ship auxiliary engines to shore-side power connection for high-speed crafts called Alkahera while berthed at the port of Safaga,Egypt.The results provide the national electricity grid concept as the best economical selection with 49.03 percent of annual cost saving.Moreover,environmentally,it could achieve an annual reduction in exhaust gas emissions of CO2,CO,NOx,P.M,and SO2by 276,2.32,18.87,0.825 and 3.84 tons,respectively.     

Assessing the impact of the built environment on travel behavior: a case study of Buffalo,New York

Assessing the impact of characteristics of the built environment on travel behavior can yield valuable tools for land use and transportation planning. Of particular interest are planning models that can estimate the effects of ‘smart growth’ planning. In this paper, a post-processor method of quantifying and searching for relationships among many aspects of travel behavior and the built environment is developed and applied to the Buffalo, NY area. A wide scope of travel behavior is examined, and over 50 variables, many of which are based on high-detail data sources, are examined for potentially quantifying the built environment. Linear modeling is then used to relate travel behavior and the built environment, and the resulting models may be applied in a post-processor fashion to travel models to provide some measure of sensitivity to built environment modifications. The study’s findings demonstrate that mode choice is highly correlated to measures of the built environment, and that many of the principles of smart growth appear to be a valid way to encourage non-vehicle travel. Home-based VHT and VMT appear to be affected by the built environment to a lesser degree.