<p> In January 2012, California adopted federal law requiring city's traffic engineers to decrease the pedestrian walking speeds at signalized intersections from 4fps to 3.5fps. Ten signalized intersections along Atlantic Avenue between Spring Street to Carson Street were selected to evaluate impacts due to pedestrian walking speed changes. One hour peak evening volumes were collected and entered into Synchro by Trafficware to compare intersections and approach delays on 75 and 100 seconds cycle lengths with combination of coordinated and uncoordinated systems. Volume growth rate effects, surveyed pedestrian walking speed, and various observed characteristics at signalized intersection crossing were evaluated. Converting pedestrian walking speed from 4-fps to 3.5fps caused the cycle length to increase from 75 seconds to 90 seconds for coordination purposes. The Synchro results, overall, showed more intersection/approach delay, vehicular growth rates data showed a small effect on the major intersections delay when comparing the two walking speeds, and sampled pedestrian walking speeds indicated that the 15^th percentile of pedestrians walked at a faster speed than 3.5fps.</p>

<p> Average annual daily traffic (AADT) is important in transportation engineering and planning, and although the State of Louisiana collects AADT on a regular basis on state-maintained highways, most parishes and smaller municipalities do not have the resources to collect AADT frequently. Because the roads under the jurisdiction of parishes and municipalities account for three-fourths of the entire state road network, a practical method to estimated AADT must be developed. Before model development, previous studies into AADT estimation and their results are to be further analyzed. Roadway, demographic, and economic data for selected parishes in Louisiana is collected and processed to remove any data not necessary in model development, and afterwards, parish-specific and combination data models using this data are developed to compare to the observed AADT at a particular count station. Parish selection is based on population, number of existing count stations within the parish, and if an Interstate Highway traverses the parish. Because of the varying characteristics among the data in the selected parishes, parish-specific models for the rural parish roads are developed, and Poisson is selected as the regression model due to discrete data. Results for all Poisson models developed show that the models tend to overestimate AADT for lower observed AADT and underestimate AADT for higher observed AADT. Because of this, support vector regression (SVR) was used, and this method greatly improved the estimation of AADT in comparison to the Poisson regression as shown using certain goodness-of-fit parameters.</p>

<p> Existing railroad crossing crash prediction and hazard index equations are analyzed and found to inadequately measure safety at light rail crossings. The operational characteristics of common carrier freight and commuter railroads are different enough from the operational characteristics of light rail to affect the ability of existing railroad equations to accurately predict the number of crashes that occur at light rail crossings. These operational differences require light rail specific crash prediction equations to better predict crash numbers at light rail crossings. The goal of this research is to develop a method to measure safety at light rail crossings. </p><p> Through review of the literature describing different statistical methodologies that have been used to develop railroad crossing crash prediction and hazard index equations, the use of a nonlinear regression method to predict initial crash values with an Empirical Bayes Method adjustment to account for the actual crash history at the light crossing is determined to be the optimum model development method. </p><p> Operational alignment and configuration of light rail crossings are analyzed, and each is found to have some effect on the prediction of the number of crashes that occur at light rail crossings in addition to light rail vehicle volume, motor vehicle volume, sight obstructions, presence of a residential area near the light rail crossing, and the number of motor vehicle lanes crossing the crossing. Statistically valid models are developed to predict crashes based on light rail crossing alignment type, configuration type, and method of crossing control including traffic signals, flashing lights with gates, and passive signing. Sufficient data to develop a prediction equation for flashing light control is not available for this study. </p><p> The use of Geographic Information Systems (GIS) models is determined to be a benefit in use of application of the light rail specific crash number prediction equations. GIS models can be used not only to predict the number of crashes expected to occur at a light rail crossing, but also can be used to identify and analyze light rail crossing crash trends.</p>

<p>Crash-prone drivers should be effectively targeted for various safety education and regulation programs because their involvement in crashes presents a big adverse effect on highway safety. To improvement highway safety from the key element of transportation system, this research investigates crash-prone driver problem in Louisiana. Through analyzing the crash data of seven years from LADOTD, this study identifies the gravity of the problem and characteristics of crash-prone drivers and crashes they committed. </p><p> The analysis results show that quite a few drivers repeatedly had crashes; seven drivers had 13 crashes in seven years; and the maximum number of crashes occurring in a single year to a single driver is eight. Actually, the 5% of drivers who had multiple crashes were responsible for 35% of the crashes that occurred in the seven years in Louisiana. Crash injury rate is also higher for drivers with multiple crashes. The results demonstrate that young male age 15 to 24 and old female age 65 to 74 have the highest recurring crash potential, and male crash-prone drivers are generally more likely to involve in more crashes than female crash-prone drivers. The probability of having crashes in any given year is closely related to a driver&rsquo;s crash history; The results of three probability matrix models give quantitative estimate on crash risk predict at three time interval modeling scenarios. The developed probability predicting model can be used for estimating an individual future crash risk. </p><p> Based on the results, several suggestions are made on how to improve roadway safety through reducing crashes committed by drivers with much higher crash risk as identified by the analysis. </p>

<p> The substitution of electrical energy for gasoline as a transportation fuel is an initiative both with a long history, and one made both pressing and important in today's policy discussion by renewed interest in plug-in vehicles. The research presented in this dissertation attempts to inform the policy discussion for governments, for electric utilities, for the makers of electric cars, and for the industries developing and planning charging infrastructure. To that end, the impacts of variations to several possible system design parameters, on several metrics of evaluation, are assessed. The analysis is based on a dataset of vehicle trips collected by Georgia Institute of Technology, tracking almost 500 vehicles that commute to, from or within the Atlanta city center, comprising Atlanta `commuter-shed'. By assuming that this dataset of trips defines the desired travel behavior of urban and suburban American populations, the effects of travel electrification in personal vehicles can be assessed. </p><p> Several significant and novel findings have emerged from this research. These include the conclusion that at-work charging is not necessarily the logical next step beyond home-charging, as it will in general add little to the substitutability of electric vehicles. In contrast, high power en-route charging, combined with modest power home charging is shown to be surprisingly effective, potentially requiring of EV drivers a total time spent at en-route recharging stations similar to that for liquid fueled cars. From the vehicle marketing perspective, a quantification of the hybrid household effect, wherein multi-vehicle households own one EV, showed that about a quarter of all households could adopt a vehicle with 80 miles of range with no changes to travel patterns. Of interest to grid management, this research showed an apparent maximum fleet-wide load from unregulated charging of about 1 kW per vehicle, regardless of EVSE power or EV battery size. This contrasts with a potential late night load spike an order of magnitude higher under certain time-of-use charging algorithm implementations. Finally, an EVSE and EV power capacity of 10-12 kW was shown to be a likely optimum if grid services from modulated charging are being considered.</p>

<p> Due to the declining purchasing power of fuel tax revenue, the Highway Trust Fund is insufficient to operate and maintain the surface transportation system in the U.S. Alternative sources of revenue, other than the fuel tax, should be considered to address the insolvency of the funding system. Mileage fees and value pricing have long been attractive options to researchers and decision-makers, but they often raise equity concerns. This paper aims to design and evaluate equitable and progressive distance-based user charge policies, and focuses specifically on income-based fee rate structures. Three variable-rate vehicle-miles traveled (VMT) fee scenarios with respect to income are introduced and all policy scenarios are tested with a statewide transportation model in Maryland. Results show that income-based VMT fees can well protect lower-income households while generating more revenue. However, a standard fee structure based on Ramsey pricing does not work as well as the fixed-percentage incremental fee structure. The latter is progressive across all income groups while ensuring that equity and revenue goals are met.</p>

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 3159. Adviser: Albert J. Valocchi. Includes bibliographical references (leaves 93-106) Available on microfilm from Pro Quest Information and Learning.

Thesis (M.S.)--Lehigh University, 2009. / Adviser: James E. Roberts.

<p> A method for accurate emissions estimation that will contribute to promoting public health has been increasingly important. The purpose of this study is to develop a novel method that is designed to make accurate real-time emissions estimation from individual vehicles on freeways possible. The benefit of this method is that it can overcome the weakness of macroscopic emissions estimation methods, which underestimated emissions. </p><p> The most distinguishing feature of the Speed Profile Estimation (SPE) method is that it uses a speed profile (SP) that is generated by the sum of a basic SP (BSP), which is calculated by the basic travel information of an individual vehicle obtained from vehicle reidentification (REID), and a residual SP (RSP), which is estimated by categorized traffic information. </p><p> In order to estimate RSP this research employs Autoregressive (AR) model and Fourier series (FS). And to find the parameters of RSP, the total absolute difference between actual SP emissions and estimated SP emissions was optimized by genetic algorithm. For this, parameters are calculated for all possible combinations of three categorizations and clusters by K-mean clustering. Individual vehicle trajectories from two freeways, US101 and I-80, were provided by the Next Generation Simulation (NGSIM) dataset. US101 was examined for calibration, and I-80 for validation. And then, transferability tests were conducted for various section distances to verify model transferability. Finally, REID is simulated with low vehicle signatures match rates to test its applicability to real situations. </p><p> Unlike previous methods, the SPE is notable for its real-time, transferable, reliable, and cost efficient emissions estimation. The calibration and validation account only 4.0 % and 4.1 % MAPEs, respectively. Moreover, transferability tests showed that MAPEs are lower than 4.4 % in both longer and shorter section distances. Furthermore, REID simulation increases only 0.2 % MAPE even in low vehicle signatures match rates, which is lower than 5 % MAPE in emissions estimation. </p><p> Any signal-like formulation other than AR or FS can perform better emissions estimation when it replaces the RSP. Also, in this research the SPE method was calibrated only for LOS F, when it is arguably of greatest value, but further research should be coordinated to extend the models in other possible traffic conditions such as LOS A~E.</p>