The bus network design problem refers to a determination of optimizing the network of bus routes, usually in urban areas. The optimal routes must comply with a given passenger demand matrix and attain a compromise best solution from the user, the operator, and the community. This paper proposes the use of a genetic algorithm as a tool to handle the complexity of the bus network design problem. The methodology developed considers a mechanism to maintain as many satisfactory routes of the existing network as possible and, at the same time, to incorporate experience-based suggestions, such as minimizing of the number of transfers required by a passenger, into the revised bus network. The solution method, using genetic algorithm, has four steps: (1) generating a set of potential routes, (2) designing the bus network, (3) checking the routes for implementation, and (4) examining the extension of routes for improvement. The proposed method is validated through a benchmark bus network and a case study. The result of the case study, with a bus network serving a city with a population of 3.2 million, shows an improvement of 26.36% in the objective function value over the existing bus network. This improvement was realized by modifying only 36% of the routes while the remaining 74% of the existing network remained intact.

In order to better inform transportation decision makers of the criticality of transportation infrastructure, this paper explores an accessibility-based criticality prioritization methodology to identify and prioritize critical transportation infrastructure. In particular, the methodology evaluates the network-wide impacts of infrastructure degradation based on the increase in travel cost taking origin importance, destination attractiveness, and traffic congestion into account. The methodology is applied to the road network of Hillsborough County, Florida, threatened by flood risk from storm surge, sea-level rise, and intense precipitation. Light detection and ranging digital elevation data, transportation infrastructure and network data, and zone-based population data of the county are processed for analysis. The approach yields results of not only the criticality of transportation infrastructure under flooding impact but also the most vulnerable zones as a result of infrastructure inundation. The results show that some infrastructure is critical to adjacent areas, while some becomes important to a much broader region. The results further demonstrate that the infrastructure is more critical if it serves more people.

In this paper, a probabilistic fuzzy logic control is developed for the signalized control of a diamond interchange to improve the traffic flow on the surface streets and highways. The signal phasing, green-time extension, and ramp metering are decided in response to real-time traffic conditions. The probabilistic fuzzy logic for diamond interchange (PFLDI) includes the following three modules: probabilistic fuzzy phase timing that controls the green-time extension process of the current running phase, phase-selection logic that decides the next phase based on the presetup phase logic by the local transport authority, and probabilistic fuzzy ramp metering that determines the on-ramp-metering rate based on the traffic conditions of the arterial streets and highways. The advanced interactive microscopic simulator for urban and nonurban network software was used to model the diamond interchange and measure the effectiveness of the proposed PFLDI algorithm. The performance of the PFLDI was compared with that of an actuated diamond interchange (ADI) control based on the asservissement linéaire d'entré autoroutière (ALINEA) algorithm and a conventional fuzzy logic control for a diamond interchange (FLDI) algorithm. Simulation results show that the PFLDI lowers system total travel time and average delay, improves the downstream average speed, and lowers the downstream average delay compared with ADI and FLDI.

Incident detection algorithms, which are an essential part of traffic management systems, have been studied for several decades, but the research focus has primarily been on algorithms for incident detection on freeways and other free-flowing roads. When applied on arterial roads, the achievement of stable performance and scalability are major challenges when developing an effective incident detection algorithm. In this research, the authors propose an incident detection algorithm that utilizes travel time and traffic volume samples generated from a Bluetooth-based wireless vehicle reidentification system that has been implemented on arterial roads. The proposed algorithm is based on a moving average over time, which can recognize sample travel time and traffic volume patterns resulting from incidents. The use of a moving average overcomes limitations resulting from sparse travel time sample data collected. Within the algorithm, a threshold strategy is applied that makes the algorithm easy to implement and transfer, which is an important requirement for practitioners. The proposed algorithm is evaluated using reported accident data and the insight of two traffic engineers, and provides a good balance between detection rate and false-alarm rate.

Transportation planning is multidimensional, complex, and dynamic in nature. The decision-making process often involves multiple stakeholders with conflicting preferences. Effective decision outcomes can only be reached by explicitly addressing such conflicts. Over the last several decades, optimization techniques have been used for project-selection decisions to achieve maximized overall returns on investments. The existing methods for project selection capable of conducting trade-off analyses mainly focus on assessing trade-offs between project construction time, duration, and cost, as well as swapping between transportation agency costs and user costs. However, they have largely not addressed impacts on the overall economic returns by changing a few important decision factors such as differentiating relative importance of various transportation performance goals and measures, and different types of highway facilities, and further relaxing the budget constraints by management programs dealing with physical facilities and system operations while keeping the total budget unchanged. This paper introduces a trade-off analysis approach that uses a multicommodity minimum-cost network (MMCN) model to establish traffic details for the transportation network needed for estimating the benefits of implementing a single project or multiple projects jointly, and a surrogate worth trade-off (SWT) method for multiobjective project selection based on the estimated project benefits. A computational study has revealed that the proposed trade-off approach can generate noninferior solutions and increase the total benefits by 18–20%.

During military contingency operations, aircraft are required to land, taxi, and takeoff on unpaved surfaces. In some cases, operational time limitations do not allow for the construction of paved surfaces to establish airfield operations. The original flexible pavement design procedure for paved surfaces, which is based on the California Bearing Ratio (CBR) and the $\alpha$-factor (Alpha-factor), was extended and applied to the design and evaluation of aggregate-surfaced pavements. With the reformulation of the CBR-Alpha for the design of flexible pavements, efforts were also directed at defining a new equation for the design of aggregate-surfaced airfields. This paper focuses on the development of a new CBR-Beta procedure for the design and evaluation of aggregate-surfaced airfields. Data from previous studies conducted on aggregate-surfaced full-scale test sections were used for this purpose. The new performance curve proposed in this paper for aggregate-surfaced airfields has the same format as the equation that was proposed and accepted for flexible pavements.