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The criticisms put forward against economic impact studies lead to a key question: “Is it possible to measure the impact of sporting properties and events in a holistic, conservative, and reliable way?” This research endeavors to build on the academic literature to add to the scope and rigor of economic impact research by proposing an impact assessment process model for practitioners that facilitates employment of a holistic, conservative and reliable impact study and seeks to address these concerns.

Using seven identified key realities that highlight the challenges facing impact studies, and adopting a collaborative self-ethnographic methodological approach, the work highlights lessons learned from four empirical economic impact studies undertaken by the authors over a five-year period.

The study provides a broad view of impact studies, which extend beyond financial implications and provides a more inclusive methodology. Particularly, the proposed impact assessment process model seeks to improve the credibility of impact studies by facilitating a holistic approach that incorporates direct, indirect and intangible impacts.

The proposed model has value to researchers and is designed to improve the overall credibility of economic impact methodology. It also provides a more accurate measure of direct impact while considering intangible and indirect impacts, including social/community impacts.

The proposed model has value to and practitioners and is designed to improve the overall credibility of economic impact methodology. It also provides a more accurate measure of direct impact while considering intangible and indirect impacts, including social/community impacts.

The proposed process model to measure the impact of a sports event is a needed element in the world of funding, managing and implementing events of all sizes.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

Designing a sustainable bituminous concrete with long-term performance is a challenging problem. In addition, strength of the subgrade has a crucial impact on pavement design. This paper aims to concentrate on subgrade soil stabilization with granite dust powder (GDP) and crumb rubber powder (CRP) to improve the engineering properties of the soil. Further design of bituminous concrete pavement with cement-treated layers in base and subbase course layers was carried out with life cycle cost analysis and life cycle assessment for 1 km of a four-lane national highway.

Subgrade soil stabilized with GDP and CRP is characterized as per Indian Standards (IS)-2720 to determine the optimum dosage. Further, the mechanistic-empirical pavement design was carried out using Indian Road Congress-37 (2018), analyzed using IITPAVE software and validated with ANSYS software. The life cycle cost analysis is carried out using the net present value method, and the life cycle assessment is performed according to the cradle-to-grave approach.

A soil mix comprising 10% GDP and 2.5% CRP yielded a soaked California bearing ratio value of 6.58%. In addition, the design of bituminous concrete pavement with cement-treated granular layers showed a 26.9% reduction in life cycle cost and 59.4% reduction in total carbon footprint per kilometer compared to the pavement with traditional aggregate layers.

The research on subgrade stabilization with sustainable materials like GDP and CRP incorporating mechanistic empirical pavement design, life cycle cost analysis and life cycle assessment is limited. Overall, the study recommends the use of GDP and CRP to stabilize soil for subgrade application and incorporate cement-treated granular layers, which offer economic and environmental benefits compared to traditional pavement construction.

Freeway work zones have been traffic bottlenecks that lead to a series of problems, including long travel time, high-speed variation, driver’s dissatisfaction and traffic congestion. This research aims to develop a collaborative component of connected and automated vehicles (CAVs) to alleviate negative effects caused by work zones.

The proposed cooperative component is incorporated in a cellular automata model to examine how and to what scale CAVs can help in improving traffic operations.

Simulation results show that, with the proposed component and penetration of CAVs, the average performances (travel time, safety and emission) can all be improved and the stochasticity of performances will be minimized too.

To the best of the authors’ knowledge, this is the first research that develops a cooperative mechanism of CAVs to improve work zone performance.

IN order to be able to discriminate with certainty between butter and such margarine as is sold in England, it is necessary to carry out two or three elaborate and delicate chemical processes. But there has always been a craving by the public for some simple method of determining the genuineness of butter by means of which the necessary trouble could be dispensed with. It has been suggested that such easy detection would be possible if all margarine bought and sold in England were to be manufactured with some distinctive colouring added—light‐blue, for instance—or were to contain a small amount of phenolphthalein, so that the addition of a drop of a solution of caustic potash to a suspected sample would cause it to become pink if it were margarine, while nothing would occur if it were genuine butter. These methods, which have been put forward seriously, will be found on consideration to be unnecessary, and, indeed, absurd.

This study aims to assist the project manager in minimising the material logistics cost of road project by planning the optimal movement of aggregate across three stages of supply chain: sourcing, processing and distribution.

The paper conceptualises the raw material consumption in a road project as a logistics network distribution problem. A linear programming (LP) formulation is constructed with appropriate decision variables by integrating the three stages of material movement. The series of LP scenarios are solved using an LP solver to decide the optimal movement of the aggregate to be consumed in different layers of road segments.

The results obtained from the model show that planning material logistics of an entire road project using optimisation provides substantial saving in logistics costs than using common sense. Further, the magnitude of cost saving improves as the complexity of the model increases in term of enormous feasible options.

The model shown in this paper may serve as a basis for planning the logistics of raw materials consumed in the road projects. The small improvement in material flows by optimising supply chain shows sensible cost benefit to the project manager and hence control and monitor the overall cost and activities of the project. The output of the model is also expected to help the project team as an input in the decision-making processes such as appropriate material sourcing contract, capacity assessment of material processing facility and transportation planning.

While the optimisation models are widely used and popular among the many industrial applications, this research shows distinct application of such a model in managing the logistics of the road construction project.