Increasing traffic congestion and the advancements in technology have fostered the growth of alternative transportation modes such as dynamic ride-sharing. Smartphone technologies have enabled dynamic ride-sharing to thrive, as this type of transportation aims to establish ride matches between people with similar routes and schedules on short notice. Many automated matching methods are designed to improve system performance; such methods include minimizing process time, minimizing total system cost or maximizing total distance savings. However, the results may not provide the maximum benefits for the participants. This paper intends to develop an algorithm for optimizing matches when considering participants' gender, age, employment status and social tendencies. The proposed matching algorithm also splits unmatched parts of drivers' routes and creates new travel requests to find additional matches using these unmatched parts. Accordingly, this paper performs an extensive simulation study to assess the performance of the proposed algorithm. The simulation results indicate that route splits may increase the number of matches significantly when there is a shortage of drivers. Furthermore, the paper demonstrates the effects and potential benefits of utilizing a social compatibility score in the objective function.
Turkey is a developing country where the transportation sector receives a considerable economic share. Roads are the pioneer mode of transportation in Turkey and the common opinion is that increasing highway networks lead to major changes in economic development. This paper focuses on the growth impact of highway infrastructure on economic development and recommends a dynamic panel data approach which is not common in the transportation economics literature. This model is applied to local regions that are located in the eastern and northern parts of Turkey to measure the effects of highway capital stocks on Gross Domestic Product (GDP) change between 2004 and 2016. The analysis reveals that the relationship between GDP and highway capital stock is positive, and statistically significant for local regions in Turkey. It is indicated that the findings of this paper can be a guide for policy and decision-makers and implemented into different regions and locations.
The sustainability of transportation infrastructure depends on the adoption of new construction materials and technologies that can potentially improve performance and productivity. However, most agencies would like to evaluate these new materials and technologies at both the project and network levels before replacing the traditional ones. It also remains a challenge to reliably estimate the costs and lifetime performance of new construction materials and technologies because of limited implementation data. To address these issues, this paper presents a comprehensive bottom-up methodology based on Life Cycle Cost Analysis (LCCA) to integrate project- and network-level analysis that can fast-track the acceptance of new materials or technologies. Hypothesized improvement rates are applied to the deterioration functions of existing materials to represent the expected improved performance of a new material compared with a conventional material with relatively similar characteristics. This new approach with stochastic treatment allows us to probabilistically evaluate new materials with limited data for their future performance. Feasible maintenance and rehabilitation schedules are found for each facility at the project level and near-optimal investment strategies are identified at the network level by using a metaheuristic evolutionary algorithm while satisfying network-wide constraints. This provides an effective solution to many issues that have not been fully addressed in the past, including the trade-off between multiple objectives, effects of time, uncertainty, and outcome interpretation. A hypothetical bridge deck system from New Jersey’s bridge inventory database is used to demonstrate the applicability of the proposed methodology in constructing a planning and management decision-support procedure.
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