Urban Transportation Policy: A Guide and Road Map

The main transportation issues facing cities today fall into familiar categories--congestion and public transit. For congestion, there is now a far richer menu of options that are understood, technically feasible, and perhaps politically feasible. One can now contemplate offering roads of different qualities and prices. Many selected road segments are now operated by the private sector. Road pricing is routinely considered in planning exercises, and field experiments have made it more familiar to urban voters. Concerns about environmental effects of urban trucking have resulted in serious interest in tolled truck-only express highways. As for public transit, there is a need for political mechanisms to allow each type of transit to specialize where it is strongest. The spread of “bus rapid transit†has opened new possibilities for providing the advantages of rail transit at lower cost. The prospect of pricing and privatizing highway facilities could reduce the amount of subsidy needed to maintain a healthy transit system. Privately operated public transit is making a comeback in other parts of the world. The single most positive step toward better urban transportation would be to encourage the spread of road pricing. A second step, more speculative because it has not been researched, would be to use more environmentally-friendly road designs that provide needed capacity but at modest speeds, and that would not necessarily serve all vehicles.Transportation policy; Road pricing; Privatization; Product differentiation

Master of Science

thesisThis thesis is conducted to compare a crash-level severity model with an occupant-level severity model for single-vehicle crashes on rural, two-lane roads. A multinomial logit model is used to identify and quantify the main contributing factors to the severity of rural, two-lane highway, single-vehicle crashes including human, roadway, and environmental factors. A comprehensive analysis of 5 years of crashes on rural, two-lane highways in Illinois with roadway characteristics, vehicle information, and human factors will be provided. The modeling results show that lower crash severities are associated with wider lane widths, shoulder widths, and edge line widths, and larger traffic volumes, alcohol-impaired driving, no restraint use will increase crash severity significantly. It is also shown that the impacts of light condition and weather condition are counterintuitive but the results are consistent with some previous research. Goodness of fit test and IIA (independence of irrelevant alternatives) test are applied to examine the appropriateness of the multinomial logit model and to compare the fit of the crash-level model with the occupant-level model. It is found that there are consistent modeling results between the two models and the prediction of each severity level by crash-level model is more accurate than that of the occupant-level model

Determining the extent and characteristics of overrepresentation of large truck crashes in daytime and nighttime work zones

The growth of vehicle travel in the United States has accelerated wear on the interstate highway system leading to frequent pavement repair and rehabilitation projects. The presence of work zones not only causes traffic congestion and backup but also increases the crash risk. Therefore, the FHWA (Federal Highway Administration) has allotted a significant amount of funds to improve work zone traffic safety and operations. This thesis compares truck and automobile crash characteristics in work zones with those of non-work zones and thus identifies engineering countermeasures to improve work zone truck safety. The researcher used a contingency analysis approach in this study. First, he categorized the North Carolina crash data using different variables. Once categorized, the Breslow-Day test is used to compare the odds of truck and automobile crashes between work zones and non-work zones. Overall, the researcher did not find a significant difference between odds of truck and automobile crashes compared to previous studies. The researcher believes that the difference in results between the present study and the previous studies could either be due to differences in the approach used or better truck management techniques employed by the North Carolina DOT (Department of Transportation). The researcher also identified that the maintenance projects performed during the day had a significantly higher odds of truck crashes relative to that of automobiles in work zones compared to control sections when workers were present, either with a lane closure or without a lane closure. The researcher believes that the results from the day maintenance projects and its subcategories are the key findings of this study. Therefore, these key findings are used to identify the possible reasons and countermeasures for any disproportionate change in truck to automobile crashes. The identified list of countermeasures includes the use of law enforcement, a smart work zone system, a dynamic late merge system, CMS (Changeable Message Signs), speed display signs, and a CB (Citizen Band) Wizard. These countermeasures were checked for cost effectiveness using a benefit cost (B/C) analysis. The researcher found that law enforcement, smart work zones with costs lower than or equal to half a million dollars, CMS, speed display signs, and the CB Wizard have B/C ratios greater than one and seem to be worthwhile for deployment in work zones. Smart work zones with significantly higher costs of 2.5 million dollars, for example, could be deployed using a more detailed analysis of work zone characteristics. Finally, dynamic late merge system could be used if the site conditions indicate a crash reduction potential of at least 10 – 15 percent

GEOMETRIC AND ENVIRONMENTAL CONSIDERATIONS IN HIGHWAY ALIGNMENT OPTIMIZATION

The highway alignment optimization problem is modeled to identify the preferred alignment alternatives which minimize total cost and satisfy the highway design standards. Several mathematical models have been developed during the past decades, among which the Highway Alignment Optimization (HAO) model has been used in several practical highway design projects with satisfactory results. However, several major cost components, such as vehicle operating cost and environmental cost are estimated roughly, and should be improved to yield more precise cost estimates and to allow optimization of lane widths. These are the HAO model features which this thesis seeks to improve. Lane width is an important factor in highway design, which is related to the travel speed, safety, as well as earthwork cost. This thesis employs Newton's method and Finite Difference method to search for the appropriate lane width. The preferred lane width found in the case study is 10.6 feet, for which the total cost is $233 million, and 12.5% less than the total cost at 12 feet lane width. In addition, this thesis improves the vehicle operating cost prediction by calculating the vehicle resistance force and horsepower, and estimating the fuel consumption based on the fuel consumption rate (g/hp-hr). Moreover, the environmental cost, particularly the vehicle emissions cost is incorporated in the newly improved HAO model. It is found that the vehicle emission cost decreases by 9% after including the environmental cost component in the model objective function. The results of the case study and sensitivity analyses indicate that the improved HAO model can find good highway alignments efficiently in tough topographic environmental. Moreover, the model can jointly consider the social, economic and environmental consequences, and result in less fuel consumption and pollutant emissions

Bayesian multivariate poisson-lognormal regression for crash prediction on rural two-lane highways

textCivil, Architectural, and Environmental Engineerin

Determining Major Causes of Highway Work Zone Accidents in Kansas

Highway work zones constitute a major safety concern for government agencies, the legislature, the highway industry, and the traveling public. Despite the efforts made by government agencies and the highway industry, there is little indication that work zone crashes are on the decline nationwide. The main reason behind this is that current safety countermeasures are not working effectively in the work zones. Lack of effective countermeasures may be due to the fact that the characteristics of work zone crashes are not well understood. The primary objective of this research was to investigate the characteristics of fatal crashes and risk factors to these crashes in the work zones so that effective countermeasures could be developed and implemented in the near future. The objective was accomplished using a four-step approach. First, literature review on previous work zone crash studies was conducted to establish a solid understanding on this issue. Second, the research team collected the crash data from the KDOT accident database and the original accident reports. A total of 157 fatal crash cases between 1992 and 2004 were examined. Third, based on the collected data, the researchers systematically examined the work zone fatal crashes using statistical analysis methods such as descriptive analyses and regression analyses. At the end of analyses, the unique crash characteristics and risk factors in the work zones were determined. Finally, improvements on work zone safety were recommended

Multi-level Safety Performance Functions For High Speed Facilities

High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors. In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data. iii At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized iv appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users

Development of a multivariate logistic model to predict bicycle route safety in urban areas

In response to the renewed appreciation of the benefits of bicycling to the environment and public health, public officials across the nation are working to establish new bicycle routes. During the past two decades, a number of methods have been endorsed for the selection of suitable bicycle routes. These methods are limited in that they do not explicitly address bicycle safety nor do they reflect urban conditions. The purpose of this research is to develop an objective bicycle route safety rating model based on injury severity. The model development was conducted using a logistic transformation of Jersey City\u27s bicycle crash data for the period 1997-2000. The resulting model meets a 90% confidence level by using various operational and physical factors (traffic volume, lane width, population density, highway classification, the presence of vertical. grades, one-way streets and truck routes) to predict the severity of an injury that would result from a crash that occurred at a specific location. The rating of the bicycle route\u27s safety is defined as the expected value of the predicted injury severity. This rating is founded on the premise that safe routes produce less severe accidents than unsafe routes. The contribution of this research goes beyond the model\u27s predictive capacity in comparing the safety of alternative routes. The model provides planners with an understanding, derived from objective data, of the factors that add to the route\u27s safety, the factors that reduce safety and the factors that are irrelevant. The model often confirms widely held beliefs as evidenced by the finding that highways with steep grades, truck routes and poor pavement quality create an unfavorable environment for bicyclists. Conversely, the model has found that increased volume and reduced lane width, at least in urban areas, actually reduce the likelihood of severe injury. Planners are encouraged to follow the lead of experienced bicyclists in choosing routes that travel through the urban centers as opposed to diverting bicyclists to circuitous routes on wide, low volume roads at the periphery of cities