Satisfaction of Istanbul Citizens with Urban Public Transportation

Transportation is one of the most challenging urban subsystems to transform in an environmentally friendly and futuristic way. Many city dwellers use a variety of modes of transportation. An efficient and sustainable urban transportation system must include many modes of transportation for a single trip. Intermodal combinations are essential for urban transportation efficiency. Public transportation and commuting are essential elements of multimodal travel. In urban areas, a mix of bicycles, vehicles, and public transportation is prevalent, while in rural areas, car, and public transportation are more prevalent. By examining the characteristics that lead customers to prefer water transportation over Metrobus and Marmaray, we hope to gain a better understanding of how the Asian and European sides of Istanbul are traversed. The number of participants in the "Maritime Transportation Satisfaction Survey" was 2,343. During this period, a model was built using the survey item "frequency of use" (dependent variable). Numerous survey examines and evaluation methodologies were utilized to determine the effectiveness of this strategy. The study examines the intermodal travel motivations and the evaluation of transportation options by multimodal users. For a successful urban transportation system, urban planning must take into account multimodal travel behavior and user expectations. There are initiatives to improve water transportation in Istanbul. Conventional maritime transportation is inadequate from start to finish. An integrated route optimization method is needed to increase the efficiency of maritime transportation. We believe that by strengthening maritime transportation links will increase water consumption. Before the coronavirus pandemic, 2,343 maritime carriers were evaluated on March 8, 2020. (Different surveys were conducted among the passengers of City Lines, Private Motors, Metrobus, and Marmaray to compare their choices and reasons.) SPSS will be used for data analysis. Multivariate Statistical Analysis relies on Multinomial Logistic Regression and Discriminant Analysis models, both of which use the K-fold and Leave-one-out criteria to decide which attributes are valid in the regression model and which are valid in the discriminant approach. The Hosmer-Lemeshow test criteria yielded a p-value greater than 0.05 for MLR characteristics

Evaluation Of Lane Use Management Strategies

The limited funding available for roadway capacity expansion and the growing funding gap, in conjunction with the increasing congestion, creates a critical need for innovative lane use management options. Various cost-effective lane use management strategies have been implemented in the United States and worldwide to address these challenges. However, these strategies have their own costs, operational characteristics, and additional requirements for field deployment. Hence, there is a need for systematic methodologies to evaluate lane use management strategies. In this thesis, a systematic simulation-based methodology is proposed to evaluate lane use management strategies. It involves identifying traffic corridors that are suitable for lane use management strategies, and analyzing the strategies in terms of performance and financial feasibility. The state of Indiana is used as a case study for this purpose, and a set of traffic corridors is identified. From among them, a 10-mile stretch of the I-65 corridor south of downtown Indianapolis is selected as the study corridor using traffic analysis. The demand volumes for the study area are determined using subarea analysis. The performance of the traffic corridor is evaluated using a microsimulation-based analysis for alleviating congestion using three strategies: reversible lanes, high occupancy vehicle (HOV) lanes and ramp metering. Furthermore, an economic evaluation of these strategies is performed to determine the financial feasibility of their implementation. Results from the simulation based analysis indicate that the reversible lanes and ramp metering strategies improve traffic conditions on the freeway in the major flow direction. Implementation of the HOV lane strategy results in improved traffic flow conditions on the HOV lanes but aggravated congestion on the general purpose lanes. The HOV lane strategy is found to be economically infeasible due to low HOV volume on these lanes. The reversible lane and ramp metering strategies are found to be economically feasible with positive net present values (NPV), with the NPV for the reversible lane strategy being the highest. While reversible lanes, HOV lanes and ramp metering strategies are effective in mitigating congestion by optimizing lane usage, they do not generate additional revenue required to reduce the funding deficit. Inadequate funds and worsening congestion have prompted federal, state and local planning agencies to explore and implement various congestion pricing strategies. In this context, the high occupancy toll (HOT) lanes strategy is explored here. Equity concerns associated with pricing schemes in transportation systems have garnered increased attention in the recent past. Income inequity potentially exists under the HOT strategy whereby higher-income travelers may reap the benefits of HOT lane facilities. An income-based multi-toll pricing approach is proposed for a single HOT lane facility in a network to simultaneously maximize the toll revenue and address the income equity concern, while ensuring a minimum level-of-service on the HOT lanes and that the toll prices do not exceed thresholds specified by a regulatory entity. The problem is modeled as a bi-level optimization formulation. The upper level model seeks to maximize revenue for the tolling authority subject to pre-specified upper bounds on toll prices. The lower level model solves for the stochastic user equilibrium solution based on commuters\u27 objective of minimizing their generalized travel costs. Due to the computational intractability of the bi-level formulation, an approximate agent-based solution approach is used to determine the toll prices by considering the tolling authority and commuters as agents. Results from numerical experiments indicate that a multi-toll pricing scheme is more equitable and can yield higher revenues compared to a single toll price scheme across all travelers

Real-time seat allocation for minimizing boarding/alighting time and improving quality of service and safety for passengers

Rail is considered as one of the most important ways of transferring passengers. High passenger loads has implications on train punctuality. One of the important parameters affecting punctuality is the average boarding/alighting time. Organizing boarding/alighting flows not only reduces the risk of extended dwell time, but also minimizes the risk of injuries and improves the overall service quality. In this paper, we investigate the possibility of minimizing the boarding/alighting time by maintaining a uniform load on carriages through systematic distribution of passengers with flexible tickets, such as season or anytime tickets where no seat information are provided at the time of reservation. To achieve this, the proposed algorithm takes other information such as passenger final destination, uniform load of luggage areas, as well as group travelers into account. Moreover, a discrete event simulation is designed for measuring the performance of the proposed method. The performance of the proposed method is compared with three algorithms on different test scenarios. The results show the superiority of the proposed method in terms of minimizing boarding/alighting time as well as increasing the success rate of assigning group of seats to group of passengers

Deep learning for real-time traffic signal control on urban networks

Real-time traffic signal controls are frequently challenged by (1) uncertain knowledge about the traffic states; (2) need for efficient computation to allow timely decisions; (3) multiple objectives such as traffic delays and vehicle emissions that are difficult to optimize; and (4) idealized assumptions about data completeness and quality that are often made in developing many theoretical signal control models. This thesis addresses these challenges by proposing two real-time signal control frameworks based on deep learning techniques, followed by extensive simulation tests that verifies their effectiveness in view of the aforementioned challenges. The first method, called the Nonlinear Decision Rule (NDR), defines a nonlinear mapping between network states and signal control parameters to network performances based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past. The NDR is implemented and tested within microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization procedure aiming to reduce delay, CO2 and black carbon emissions. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in optimizing different traffic and environmental objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The second method, called the Advanced Reinforcement Learning (ARL), employs the potential-based reward shaping function using Q-learning and 3rd party advisor to enhance its performance over conventional reinforcement learning. The potential-based reward shaping in this thesis obtains an opinion from the 3rd party advisor when calculating reward. This technique can resolve the problem of sparse reward and slow learning speed. The ARL is tested with a range of existing reinforcement learning methods. The results clearly show that ARL outperforms the other models in almost all the scenarios. Lastly, this thesis evaluates the impact of information availability and quality on different real-time signal control methods, including the two proposed ones. This is driven by the observation that most responsive signal control models in the literature tend to make idealized assumptions on the quality and availability of data. This research shows the varying levels of performance deterioration of different signal controllers in the presence of missing data, data noise, and different data types. Such knowledge and insights are crucial for real-world implementation of these signal control methods.Open Acces

Integrating Electric Buses in Conventional Public Transit: A First Appraisal

O caos urbano, a instabilidade dos preços de combustível e os efeitos cada vez mais evidentes da emissão de poluentes atmosféricos têm tornado o transporte individual privado frequentemente desagradável e dispendioso. Sistemas de transporte público são uma resposta possível para a redução do número de automóveis nas estradas; Em particular, autocarros são uma alternativa atraente, visto dependerem maioritariamente de infraestruturas pre-existentes sem necessitarem de mudanças significativas. Adicionalmente, o uso de autocarros elétricos na rede significaria uma maior redução de emissões poluentes e consumos de combustível mais baixos, quando comparado com frotas de autocarros exclusivamente convencionais.No entanto, veículos elétricos também apresentam desvantagens, tais como menor potência e autonomia, um escasso número de pontos de recarga em grande parte das redes urbanas e um desempenho altamente dependente das caraterísticas da via onde circulam. Isto poderá ser resolvido recorrendo a uma abordagem mais conservadora - frotas híbridas, constituídas por autocarros elétricos e convencionais.Nesta dissertação pretende-se abordar duas questões importantes neste tipo de frotas: como estimar o desempenho dos autocarros elétricos integrados na frota e como obter o equilíbrio ótimo entre os dois tipos de veículos. Para atingir estes objetivos, são analisados dados reais e simulados de uma rede de autocarros no Porto, Portugal, e são aplicadas abordagens heurísticas para obter a composição ideal das frotas híbridas.Este estudo, suportado por dados reais que cobrem uma grande parte da rede de autocarros, para além de sugerir configurações de frotas para a rede em consideração, também formula recomendações gerais para o planeamento e gestão de redes urbanas sustentáveis.Private individual transportation is becoming cumbersome and expensive, as urban traffic turns more chaotic, fuel prices increase and the effects of pollutant emissions become evident. Public transit systems are an answer to reducing the number of cars on the road. Particularly, buses are an attractive alternative, as they mostly depend on pre-existent infrastructure, having no need for complex changes. Making some of these buses electric would mean even less tailpipe emissions and cheaper consumption costs, when compared to fully conventional fleets.However, electric vehicles have disadvantages, such as lower power and autonomy, scarce recharge points on most urban networks and vehicle performance greatly dependent on route characteristics. We can solve this with a more conservative approach - using hybrid fleets, comprised by both electric and conventional buses.This dissertation intends on tackling two main aspects with this kind of fleets: estimating the performance of the integrated electric buses and obtaining optimal balances of both kinds of vehicles. To fulfil these goals, real and simulated data of a bus network in Porto, Portugal, is analysed and heuristic approaches are used to devise hybrid fleet arrangements.This study, supported by real data covering a large scope of the public transit network, in addition to suggesting fleet configurations for the specific network under consideration, also formulates general recommendations towards sustainable urban network planning and management

Proactive model to determine information technologies supporting expansion of air cargo network

Shippers and recipients expect transportation companies to provide more than just the movement of a package between points; certain information must be available to them as well, to enable forecasts and plans within the supply chain. The transportation companies also need the information flow that undergirds a transportation grid, to support ad-hoc routing and strategic structural re-alignment of business processes. This research delineates the information needs for an expanding air cargo network, then develops a new model of the information technologies needed to support expansion into a new country. The captured information will be used by shippers, recipients, and the transportation provider to better guide business decisions. This model will provide a method for transportation companies to balance the tradeoffs between the operating efficiencies, capital expenditures, and customer expectations of their IT systems. The output of the model is a list of technologies – optimized by cost – which meet the specific needs of internal and external customers when expanding air cargo networks into a new country

Multi-Criteria Evaluation in Support of the Decision-Making Process in Highway Construction Projects

The decision-making process in highway construction projects identifies and selects the optimal alternative based on the user requirements and evaluation criteria. The current practice of the decision-making process does not consider all construction impacts in an integrated decision-making process. This dissertation developed a multi-criteria evaluation framework to support the decision-making process in highway construction projects. In addition to the construction cost and mobility impacts, reliability, safety, and emission impacts are assessed at different evaluation levels and used as inputs to the decision-making process. Two levels of analysis, referred to as the planning level and operation level, are proposed in this research to provide input to a Multi-Criteria Decision-Making (MCDM) process that considers user prioritization of the assessed criteria. The planning level analysis provides faster and less detailed assessments of the inputs to the MCDM utilizing analytical tools, mainly in a spreadsheet format. The second level of analysis produces more detailed inputs to the MCDM and utilizes a combination of mesoscopic simulation-based dynamic traffic assignment tool, and microscopic simulation tool, combined with other utilities. The outputs generated from the two levels of analysis are used as inputs to a decision-making process based on present worth analysis and the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) MCDM method and the results are compared