Potential safety benefits of lane departure prevention technology

Motor vehicles with advanced safety technologies are rapidly entering the marketplace and the impact of new features are transforming safety on roadways. Among the several safety related technologies currently available in the market, this paper aims to forecast the reduction in crashes with gradual adoption of vehicles with lane departure prevention (LDP) technology. Crash data for the state of Alabama from 2014 to 2016 were used to evaluate the safety benefits of LDP technology. In Alabama, 75% of single-vehicle crashes are the result of lane departure. A 20% effective LDP system implies, whereby an LDP system would prevent a vehicle from exiting a roadway on 20% of applicable instances, would reduce 2.7% and 16.4% of the relevant single-vehicle lane departure (SVLD) crashes by 2020 and 2045 respectively. With increase in the effectiveness of the technology, a greater reduction in crashes was observed. With 100% effectiveness, this technology can prevent 66.5% of SVLD crashes by year 2045. This study presents the first estimations of the number of crashes that could be reduced using LDP and therefore could have significant impacts on public and industry adoption rates of the technology. The results of this study influence policy making and regulatory approaches to improving motor vehicle safety and further recommend education and outreach activities to increase awareness on the benefits of LDP technology.     

Estimating time dependent O‐D trip tables during peak periods

Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real‐time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin‐destination (O‐D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O‐D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O‐D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding‐off, and, 2) low ratio of total number of trips to total number of O‐D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O‐D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed.