Scholarly Work - Civil Engineering
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/3460
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Item Two-Lane Highways: Indispensable Rural Mobility(MDPI AG, 2022-03) Al-Kaisy, AhmedTwo-lane highways refer to roadways consisting of two lanes in the cross section, one for each direction of travel. Occasionally, passing lanes may be added to one or two sides of the roadway extending the cross section to three or four lanes at those locations. In this entry, two-lane highways strictly refer to roads in rural areas meeting the previous definition and do not include urban and suburban streets.Item Capacity at All-Way Stop Control Intersections: Case Study(SAGE Publications, 2023-08) Al-Kaisy, Ahmed; Doruk, DorukhanThis paper presents an empirical investigation into the capacity of all-way stop-controlled (AWSC) intersections. Video data was collected over four days at an AWSC intersection site in Bozeman, Montana. The site is characterized by single-lane approaches and high level of vehicular and pedestrian traffic. Using strict protocols, video records were processed at the individual vehicle level and several information metrics were extracted for each vehicle in the data set on all approaches. Study results indicate that the total intersection capacity at the study site varied between 400 and 1,400 vehicles per hour. The study suggests that the wide range of capacity observations is largely associated with the pedestrian crossing activity at the study site. Statistical tests confirmed that both pedestrian crossing activity and the level of conflict have significant effects on intersection capacity at the 95% confidence level. For movement type, the right-turn movement was not found to have a significant effect on intersection capacity while left-turn movement was found to negatively affect the intersection capacity. The results presented in this paper offer valuable information on AWSC intersection capacity, given the limited amount of information in the literature and the dated nature of those empirical observations.Item A Novel Network Screening Methodology for Rural Low-Volume Roads(Scientific Research Publishing, Inc., 2023-01) Al-Kaisy, Ahmed; Raza, SajidLow-volume roads (LVRs) are an integral part of the rural transportation network providing access to remote rural areas and facilitating the movement of goods from farms to markets. These roads pose unique challenges for highway agencies including those related to safety management on the highway network. Specifically, traditional network screening methods using crash history can be effective in screening rural highways with higher traffic volumes and more frequent crashes. However, these traditional methods are often ineffective in screening LVR networks due to low traffic volumes and the sporadic nature of crash occurrence. Further, many of the LVRs are owned and operated by local agencies that may lack access to detailed crash, traffic and roadway data and the technical expertise within their staff. Therefore, there is a need for more efficient and practical network screening approaches to facilitate safety management programs on these roads. This study proposes one such approach which utilizes a heuristic scoring scheme in assessing the level of risk/safety for the purpose of network screening. The proposed scheme is developed based on the principles of US Highway Safety Manual (HSM) analysis procedures for rural highways and the fundamentals in safety science. The primary application of the proposed scheme is for ranking sites in network screening applications or for comparing multiple improvement alternatives at a specific site. The proposed approach does not require access to detailed databases, technical expertise, or exact information, making it an invaluable tool for small agencies and local governments (e.g. counties, townships, tribal governments, etc.).Item A New Approach for Identifying Safety Improvement Sites on Rural Highways: A Validation Study(MDPI AG, 2024-02) Dhakal, Bishal; Al-Kaisy, AhmedThe research presented in this paper examines a new proposed approach for identifying safety improvement sites on rural highways. Unlike conventional approaches, the proposed approach does not require crash history, but rather utilizes classified variables for traffic volume, geometric features, and roadside characteristics that do not require access to exact data or extensive technical expertise. The research validates the performance of the proposed approach using field data from a large sample of rural two-lane highway segments in the state of Oregon including traffic, roadway, and crash data. A mathematical model for the prediction of the EB expected number of crashes using multivariate regression analysis is developed and used as the network screening criterion. The model’s independent variables include roadway geometry, roadside characteristics, and traffic exposure, while the dependent variable is the EB expected number of crashes. Using observed crash history as a reference, the performance of the proposed approach was compared to two of the well-established methods in practice, namely, the Empirical Bayes (EB) and the potential for safety improvement (PSI) methods. The study results suggest that by using crash density for highway segments, the performance of the proposed method was lower than that of the EB and PSI methods. This is despite the high R-square value of the predictive model used in the proposed method. However, when using crash frequencies for highway segments, the performance of the proposed method was found comparable to the well-established EB and PSI methods.Item High-Level Assessment ofStatewide GNSS-RTN Business Models(2023-06) Al-Kaisy, Ahmed; Raza, SajidThe applications of geospatial technologies and positioning data embrace every sphere of modern-day science and industry where geographical positioning matters. Among all other fields, geospatial technology plays a remarkable role in the transportation sector and has the potential to play an even more critical role in future autonomous transportation systems. In this regard, the GNSS-Real-Time Network (GNSS-RTN) technology is promising in meeting the needs of automation in most advanced transportation applications. The GNSS-RTN is a satellite-based positioning system that uses a network of reference stations to provide centimeter-level accuracy in positioning data in real-time. The technical aspect and working technology of GNSS-RTN are widely studied, however, only limited research has been conducted on the various GNSS-RTN business models currently in use nationally and internationally. Therefore, this study aims at assessing the various GNSS-RTN business models currently used in practice as well as those that are deemed potentially viable but have not yet moved to practice. Eight different business models were cataloged and used in the current assessment. All business models were assessed using three criteria: state control, sustainability, and state/agency costs. The findings of this research are important in helping state agencies make informed decisions as they build, expand or manage their own GNSS-RTN systems.Item Empirical Bayes application on low-volume roads: Oregon case study(Elsevier BV, 2021-12) Al-Kaisy, Ahmed; Huda, Kazi Tahsinntroduction: This paper investigates the Empirical Bayes (EB) method and the Highway Safety Manual (HSM) predictive methodology for network screening on low-volume roads in Oregon. Method: A study sample of around 870 miles of rural two-lane roadways with extensive crash, traffic and roadway information was used in this investigation. To understand the effect of low traffic exposure in estimating the EB expected number of crashes, the contributions of both the observed and the HSM predicted number of crashes were analyzed. Results and Conclusions: The study found that, on low-volume roads, the predicted number of crashes is the major contributor in estimating the EB expected number of crashes. The study also found a large discrepancy between the observed and the predicted number of crashes using the HSM procedures calibrated for the state of Oregon, which could partly be attributed to the unique attributes of low-volume roads that are different from the rest of the network. However, the expected number of crashes for the study sample using the HSM EB method was reasonably close to the observed number of crashes over the 10-year study period. Practical Applications: Based on the findings, it can still be very effective to use network screening methods that rely primarily on risk factors for low-volume road networks. This is especially applicable in situations where accurate and reliable crash data are not available.