Travelers experience different transportation-related problems on roadways ranging from congestion, delay, and crashes, which are partially due to growing background traffic and traffic generated by new developments. With regards to congestion, metropolitan planning organizations (MPOs) pursue a variety of plans for mitigating congestion. These plans include, amongst other measures, imposing impact fees. The current research evaluates how delay and safety can be incorporated in the mitigation process as special impact fees. This study also evaluates traffic projection methodologies used in traffic impact studies. Traffic volume is a critical factor in determining both current and future desired and undesired highway operations. Highway crashes are also influenced by traffic volume, as a higher frequency of crashes is expected at more congested locations and vice versa. Accurately forecasted traffic data is required for accurate future planning, traffic operations, safety evaluation, and countermeasures. Adhering to the importance of accurate traffic projection, this study introduces a simplistic traffic projection methodology for small-scale projection utilizing three parameters logistic function as a forecasting tool. Three parameters logistic function produced more accurate future traffic prediction compared to other functions. When validation studies were performed, the coefficient of correlation was found to be above 90 percent in each location. The t-values for the three parameters were highly significant in the projection. The confidence intervals have been calculated at a 95 percent confidence level using the delta method to address the uncertainty and reliability factor in the projection using logistic function. A delay mitigation fee resulting from increases in travel time is also analyzed in this research. In regular traffic flow, posted speed limit is the base of measuring travel time within the segment of the road. The economic concept of congestion pricing is used to evaluate the impact of this travel time delay per unit trip. If the relationship between the increase in time and trip is known, then the developer can be charged for the costs of time delays for travelers by using that relationship. The congestion pricing approach determines the average and marginal effect of the travel time. With the known values of time, vehicle occupancy, and number of travel days per year, the extra cost per trip caused by additional trips is estimated. This cost becomes part of the mitigation fee that the developer incurs as a result of travel time delays for the travelers due to the development project. Using the Bureau of Public Road (BPR) travel time function and parameters found in 2000 HCM (Highway Capacity Manual), the average and marginal travel times were determined. The value of time was taken as $7.50 per hour after reviewing different publications, which relate it to minimum wage. The vehicle occupancy is assumed as 1.2 persons per vehicle. Other assumptions include 261 working days per year and 4 percent rate of return. The total delay impact fee will depend on the number of years needed for the development to have effect. Since the developer is charged a road impact fee due to constructions cost for the road improvement, the delay mitigation fee should be credited to the road impact fee to avoid double charging the developer. As an approach to incorporate safety into mitigation fees, the study developed a crash prediction model in which all factors significantly influencing crash occurrences are considered and modeled. Negative binomial (NB) is selected as the best crash modeling distribution among other generalized linear models. The developed safety component of the mitigation fee equation considers scenarios in which the proposed new development is expected to increase crash frequency. The mitigation fee equation is designed to incorporate some roadway features and traffic characteristics generated by the new development that influence crash occurrence. Crash reduction factors are introduced and incorporated in the safety mitigation fees equation. The difference between crash frequency before and after the development is multiplied by the crash cost then divided by the trips to obtain crash cost per trip. Crash cost is taken as $28,000/crash based on literature review. To avoid double charging the developer, either the road impact fee is applied as a credit to the delay mitigation fee or vice versa. In summary, this study achieved and contributed the following to researchers and practitioners: ... Developed logistic function as a simplified approach for traffic projection ... Developed crash model for crash prediction ... Developed safety mitigation fee equation utilizing the crash modeling ... Developed delay mitigation fee equation using congestion pricing approach

About 20 percent of the U.S. National Highway System is under construction during the peak summer roadway season. Fifty percent of all highway congestion is attributed to nonrecurring conditions and work zones are estimated to account for nearly 24 percent of nonrecurring delay. Work zones account for two percent of roadway crashes and more than 1,000 fatalities per year. Motorists across the United States have increasingly voiced their displeasure with work zones and the associated delay. This has posed a challenge to transportation officials and contractors as they are faced with finding ways to reduce work zone delay. A key to addressing this challenge to minimize motorist delay during construction and maintenance operations is to recognize these impacts well in advance. In order to meet this challenge, work zone strategy evaluations are necessary to understand the type, severity, and extent of impacts associated with various strategies. One major tool used to aid in conducting these evaluations is computer simulation. There are many simulation packages in existence, some of which are designed specifically for work zone analysis. These packages include, for example, QUEWZ, QuickZone, and CA4PRS. This research focuses on the evaluation of these three simulation packages along work zones located on four interstate highway segments on I-91 and I-95 in New England. The evaluation consists of comparing simulation results to field observations in the work zones. The queue lengths estimated by QuickZone and QUEWZ are compared to queue lengths observed in the work zone. Maximum rehabilitation production rates estimated by CA4PRS will be compared to actual production rates recorded in the work zone. This evaluation will allow for a determination to be made as to whether or not these simulation packages produce accurate estimates. In addition to accuracy, the evaluation also sheds light on the user-friendliness of each simulation model as well as other parameters such as data requirements and analysis time. Major results of this evaluation include: • QUEWZ and QuickZone are user-friendly work zone simulation models. • The estimations of queue length provided by QuickZone and QUEWZ for the four sites considered in this research were found to be comparable to the field observations. • CA4PRS is a user-friendly simulation model. However, the data required to perform an analysis is not as always easy to obtain. In addition, these simulated results of maximum rehabilitation production rates are not easily compared to observed data which are not typically available. This research should be helpful to guide state and local officials in New England in the selection of simulation models to assess work zone strategies for roadway reconstruction and rehabilitation projects in New England.

This thesis examines the marginal cost of congested travel to a variety of businesses by observing time spent in congestion and estimating excess labor costs based upon the relevant value of time. The fleets in the scoping study represented commercial deliveries of goods and services, government agencies, and transit systems. Observations on limited-access expressways within the 13-county Atlanta metropolitan region were used in the analysis. Vehicles were monitored by using a passive GPS assembly that transmitted speed and location data in real-time to an off-site location. Installation and operation during the observation period required no interaction from the driver. Over 217 hours of good freeway movement during 354 vehicle-days was recorded. Rates of delay, expressed as a unit of lost minutes per mile traveled, were calculated by taking the difference in speeds observed during congestion from an optimal free-flow speed of 45 mph and dividing that by the distance traveled per segment. The difference between the 50th and 95th percentile delay rates was used as the measure for travel unreliability. Daily average values of extra time needed per fleet vehicle to ensure on-time arrivals were derived, and the median buffer across all fleets was 1.65 hours of added time per vehicle. Weekly marginal costs per fleet vehicle were estimated by factoring in the corresponding driver wages or hourly operation costs (for transit fleets). Equivalent toll rates were calculated by multiplying the 95th percentile delay rate by the hourly costs. The equivalent toll per mile traveled was representative of an equal relationship between the marginal costs of congestion experienced and a hypothetical state of free-flow travel (under first-best rules of marginal cost pricing). The median equivalent toll rates across all fleets was $0.43 per mile for weekday mornings, $0.13 per mile for midday weekdays, $0.53 per mile for afternoon weekdays and $0.01 per mile for weekday nights and weekends.