• Energy Research

Sustainable urban transport requires smart and environmentally-friendly technical solutions. It also needs to meet the demands of different user groups, including current and potential future users, in order to avoid opposition of the citizens and to support sustainable development decisions. While these requirements are well-known, conducting full surveys of user needs and preferences are tedious and costly, and the interests of different user groups may be contradictory. We therefore developed a methodology based on the prevalent Analytic Hierarchy Process (AHP), which is capable of dealing with the inconsistencies and uncertainties of users’ responses by applying an Interval Analytic Hierarchy Process (IAHP) through comparing the results of passengers to reference stakeholder groups. For a case study in Mersin, a coastal city in southern Turkey with 1.7 Million inhabitants, three groups were surveyed with questionnaires: 40 users of the public transport system, 40 non-users, and 17 experts. Based on interval pairwise comparison matrices, consisting of whole judgments of all groups, the IAHP methodology could attain a consensual preference ranking for a future public transportation system between the three groups. A sensitivity analysis revealed that the factor ranking was very stable.

In any public service development decision, it is essential to reach the stakeholders’ agreement to gain a sustainable result, which is accepted by all involved groups. In case this criterion is violated, the impact of the development will be less than expected due to the resistance of one group or another. Concerning public urban transport decisions, the lack of consensus might cause lower utilisation of public vehicles, thus more severe environmental damage, traffic problems and negative economic impacts. This paper aims to introduce a decision support procedure (applying the current MCDM techniques; Fuzzy and Interval AHP) which is capable of analysing and creating consensus among different stakeholder participants in a transport development problem. The combined application of FAHP and IAHP ensures that the consensus creation is not only based on an automated computation process (just as in IAHP) but also on the consideration of specific group interests. Thus, the decision makers have the liberty to express their preferences in urban planning, along with the consideration of numerical results. The procedure has been tested in a real public transport improvement decision as a follow-up project, in an emerging city, Mersin, Turkey. Results show that by the application of the proposed techniques, decision-makers can be more aware of the conflicts of interests among the involved groups, and they can pay more attention to possible violations.

Beyond the direct hazards of earthquakes, the deposited mass of earthquake-induced landslide (EQIL) in the riverbeds causing the river to thrust upward. The EQIL inventories are generated mostly by traditional or semi-supervised mapping approaches which required parameter's tuning or binary threshold decision in practical application. In this study, we investigated the impact of optical data from the PlanetScope sensor and topographic factors from the ALOS sensor on EQIL mapping using a deep-learning convolution neural network (CNN). Thus, six training datasets were prepared and used to evaluate the performance of the CNN model using only optical data and using this data along with each and all topographic factors across the west coast of the Trishuli River in Nepal. For the first time, the Dempster—Shafer (DS) model was applied for combining the resulting maps from each CNN stream that trained with different datasets. Finally, seven different resulting maps were compared against a detailed and accurate inventory of landslide polygons by a mean intersection-over-union (mIOU). Our results confirm that using the training dataset of the spectral information along with the topographic factor of the slope is helpful to distinguish the landslide bodies from other similar features such as barren lands and consequently increase the mapping accuracy. The improvement of the mIOU was a range from approximately zero to more than 17%. Moreover, the DS model can be considered as an optimizer method to combine the results from different scenarios.