Filtering by
- All Subjects: Transportation
- Genre: Academic theses
- Creators: Kaloush, Kamil
- Creators: Zhou, Xuesong
- Member of: Theses and Dissertations
This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.
Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.
The objective of the research is to test the use of 3D printed thermoplastic to produce fixtures which affix instrumentation to asphalt concrete samples used for Simple Performance Testing (SPT). The testing is done as part of materials characterization to obtain properties that will help in future pavement designs. Currently, these fixtures (mounting studs) are made of expensive brass and cumbersome to clean with or without chemicals.
Three types of thermoplastics were utilized to assess the effect of temperature and applied stress on the performance of the 3D printed studs. Asphalt concrete samples fitted with thermoplastic studs were tested according to AASHTO & ASTM standards. The thermoplastics tested are: Polylactic acid (PLA), the most common 3D printing material; Acrylonitrile Butadiene Styrene (ABS), a typical 3D printing material which is less rigid than PLA and has a higher melting temperature; Polycarbonate (PC), a strong, high temperature 3D printing material.
A high traffic volume Marshal mix design from the City of Phoenix was obtained and adapted to a Superpave mix design methodology. The mix design is dense-graded with nominal maximum aggregate size of ¾” inch and a PG 70-10 binder. Samples were fabricated and the following tests were performed: Dynamic Modulus |E*| conducted at five temperatures and six frequencies; Flow Number conducted at a high temperature of 50°C, and axial cyclic fatigue test at a moderate temperature of 18°C.
The results from SPT for each 3D printed material were compared to results using brass mounting studs. Validation or rejection of the concept was determined from statistical analysis on the mean and variance of collected SPT test data.
The concept of using 3D printed thermoplastic for mounting stud fabrication is a promising option; however, the concept should be verified with more extensive research using a variety of asphalt mixes and operators to ensure no bias in the repeatability and reproducibility of test results. The Polycarbonate (PC) had a stronger layer bonding than ABS and PLA while printing. It was recommended for follow up studies.
The high quality of system observability is the fundamental guarantee for accurately predicting and controlling the system. The rich information from the emerging heterogeneous data sources is making it possible. This research proposes a modeling framework to systemically account for the multi-source sensor information in urban transit systems to quantify the estimated state uncertainty. A system of linear equations and inequalities is proposed to generate the information space. Also, the observation errors are further considered by a least square model. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states, and its corresponding state estimate uncertainties are further quantified by calculating its maximum state range.
In addition to optimizing daily operations, the continuing advances in information technology provide precious individual travel behavior data and trip information for operational planning in transit systems. This research also proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. An agent-based single-level integer linear formulation is proposed and can be effectively by the Lagrangian decomposition.
The recently emerging trend of self-driving vehicles and information sharing technologies starts creating a revolutionary paradigm shift for traveler mobility applications. By considering a deterministic traveler decision making framework, this research addresses the challenges of how to optimally schedule household members’ daily scheduled activities under the complex household-level activity constraints by proposing a set of integer linear programming models. Meanwhile, in the microscopic car-following level, the trajectory optimization of autonomous vehicles is also studied by proposing a binary integer programming model.
Crumb rubber use in asphalt mixtures by means of wet process technology has been in place for several years in the United States with good performance record; however, it has some shortcomings such as maintaining high mixing and compaction temperatures in the field production. Organosilane (OS), a nanotechnology chemical substantially improves the bonding between aggregate and asphalt by modifying the aggregate structure from hydrophilic to hydrophobic contributing to increased moisture resistance of conventional asphalt mixtures. Use of Organosilane also reduces the mixing and compaction temperatures and facilitates similar compaction effort at lower temperatures. The objective of this research study was first to perform a Superpave mix design for Crumb Rubber Modified Binder (CRMB) gap-graded mixture with and without Organosilane; and secondly, analyse the performance of CRMB mixtures with and without Organosilane by conducting various laboratory tests. Performance Grade (PG) 64-22 binder was used to create the gap-graded Hot Mix Asphalt (HMA) mixtures for this study. Laboratory tests included rotational viscometer binder test and mixtures tests: dynamic modulus, flow number, tensile strength ratio, and C* fracture test. Results from the tests indicated that the addition of Organosilane facilitated easier compaction efforts despite reduced mixing and compaction temperatures. Organosilane also modestly increased the moisture susceptibility and resistance to crack propagation yet retaining equal rutting resistance of the CRMB mixtures.
This thesis formulates and solves the park-and-ride facility design problem for special events based on space-time network models. The general network design process with park-and-ride facilities location design is first elaborated and then mathematical programming formulation is established for special events. Meanwhile with the purpose of relax some certain hard constraints in this problem, a transformed network model which the hard park-and-ride constraints are pre-built into the new network is constructed and solved with the similar solution algorithm. In doing so, the number of hard constraints and level of complexity of the studied problem can be considerable reduced in some cases. Through two case studies, it is proven that the proposed formulation and solution algorithms can provide effective decision supports in selecting the locations and capabilities of park-and-ride facilities for special events.
The availability of cooled space, whether public or private, has been shown to greatly reduce health risks associated with extreme heat. However, a lack of fine-scale knowledge of which households have access to this infrastructure results in an incomplete understanding of the health risks associated with heat. This knowledge gap could result in the misallocation of resources intended to mitigate negative health impacts associated with heat exposure. Additionally, when discussing accessibility to public cooled space there are underlying questions of mobility and mode choice. In addition to captive riders, a growing emphasis on walking, biking and public transit will likely expose additional choice riders to extreme temperatures and compound existing vulnerabilities to heat.
In this research effort, a reliability framework is developed using Monte Carlo simulation for predicting the fatigue life of AC material using the S-VECD model. The reliability analysis reveals that the fatigue life prediction is very sensitive to the uncertainty in the input variables. FAM testing in similar loading conditions as AC, and upscaling of AC modulus and damage response using FAM properties from a relatively simple homogenized continuum approach shows promising results. The FAM phase fatigue life prediction and upscaling of FAM results to AC show more reliable fatigue life prediction than the fatigue life prediction of AC material using its experimental data. To assess the sensitivity of fatigue life prediction model to uncertainty in the input variables, a parametric sensitivity study is conducted on the S-VECD model. Overall, the findings from this research show promising results both in terms of upscaling FAM to AC properties and the reliability of fatigue prediction in AC using experimental data on FAM.