Frequency Optimization Models for Reducing Overcrowding Discomfort

One of the most important aspects of improving public bus transport attractiveness is reducing overcrowding in buses. However, most of the mathematical models that focus on designing bus services minimize the total social cost without considering the overcrowding discomfort. Further, they are mostly non-linear in nature and use heuristic and meta-heuristic approaches. Thus, they are difficult to understand and use by practitioners. This work addresses these gaps through models that include overcrowding discomfort and are also easy to implement and contextualize by practitioners. The authors develop one non-linear and two linear models to determine the optimum frequency of buses and apply them over a network of 34 routes of Delhi. The results reveal that the existing number of buses in Delhi is not sufficient to cater to the existing travel demand of peak hours, even after their optimum allocation. The authors also present a step-by-step procedure to enable practitioners to determine the minimum additional number of buses required to reduce the target discomfort and waiting time.

Bi-Level Planning of Multi-Functional Vehicle Charging Stations Considering Land Use Types

Locating and planning charging stations for Low-Emission Vehicles (LEVs) such as Battery Electric Vehicle (BEV), Hydrogen Fuel-Cell Vehicle (HFCV), and Natural Gas Vehicle (NGV) are becoming increasingly important for LEV users, government, and the automobile industry. Conventional planning approach of charging station usually plans single functional charging station that can only serve one kind of LEVs, and other factors such as fuel type, driving range, initial fuel tank level, and refueling time of the LEV are less considered in the planning stage. In this article, we propose a bi-level planning model to locate and size Multi-Functional Charging Station (MFCS) which can recharge BEV, HFCV, and NGV at the same time in a medium-sized city with different functional areas (e.g., residential area, industrial area, CBD area). We also established a method for generating a daily route considering vehicle attributes and user habits, and we loaded these traveling data into the upper model to select a set of optimal combinations of refueling station locations with a relatively high success ratio. In the lower model, we introduced the mathematical relationship between number of chargers and average user waiting time, and set the total social cost factor, including investment cost and waiting time cost, to evaluate each optimal combination, and then identified the optimum locational result and defined the size of each station. In the case study, we verify the proposed model in several scenarios and conclude that multifunctional refueling station performs better in terms of investment cost and users’ satisfaction level.

OPTIMIZING LIMITED-STOP BUS SERVICES ALONG A PUBLIC TRANSIT CORRIDOR WITH A DIFFERENTIAL FARE STRUCTURE

Limited-stop bus services are a highly efficient way to release more potential of the public transit system to meet travel demand, especially under constraints on vehicle fleet size and transportation infrastructure. This work first proposes a visualized fare table for the design of limited-stop bus services along a public transit corridor, along which many lines of public transit carry a heavy load of demand back and forth every working day. Based on this proposed fare table, a set of fare strategies and desired aims of fare policy, a differentiated fare structure is established to improve social equity and increase revenue. The nature of the structure can help travellers understand how to be charged between their origins and destinations (e.g. flat, time-based, stop-based or quality-based pricing) and then plan their trips efficiently. Secondly, a model is formulated to minimize the total social cost in designing a fixed demand limited-stop bus service system with a differentiated fare structure. Thirdly, numerical results are carried out with sensitivity analysis within three scenarios of differentiated fare structures. It is found that a differentiated fare structure has a great effect on passenger path choice behaviour and resulting optimal design of bus services. An attractive feature of this differentiated fare structure is that it could not only enhance the operator’s revenue and social equity but also reduce passenger transfers and social cost.

Learning Curve Analysis of Wind Power and Photovoltaics Technology in US: Cost Reduction and the Importance of Research, Development and Demonstration

The strategic transition from fossil energy to renewable energy is an irreversible global trend, but the pace of renewable energy deployment and the path of cost reduction are uncertain. In this paper, a two-factor learning-curve model of wind power and photovoltaics (PV) was established based on the latest empirical data from the United States, and the paths of cost reduction and corresponding social impacts were explored through scenario analysis. The results demonstrate that both of the technologies are undergoing a period of rapid development, with the learning-by-searching ratio (LSR) being greatly improved in comparison with the previous literature. Research, development, and demonstration (RD&D) have contributed to investment cost reduction in the past decade, and the cost difference between high and low RD&D spending scenarios is predicted to be 5.5%, 8.9%, and 11.27% for wind power, utility-scale PV, and residential PV, respectively, in 2030. Although higher RD&D requires more capital, it can effectively promote cost reduction, reduce the total social cost of deploying renewable energy, and reduce the abatement carbon price that is needed to promote deployment. RD&D and the institutional support behind it are of great importance in allowing renewables to penetrate the commercial market and contribute to long-term social welfare.

An Analysis of Social Cost in School Level Education of Nepal

Social costs in education plays decisive role for education development and economic growth of a nation. In this context, this article tries to analyze the size, trend and growth rate of social cost in the education sector of Nepal by disaggregating it into total social cost, recurrent social cost, capital social cost, unit cost and marginal cost in community based school education of Nepal. The study is based on the secondary data obtained from official documents of Nepal’s Government such as flash report, budget speech, red book and so on. The time series data from 2011 AD to 2015 AD were collected for the analysis by using convenience non-random sampling method. The overall finding of the study shows that the share of the annual average social cost in education is 15.84 percent of the total cost of the government of Nepal. In other words, the government has allocated annual average Rs 68930697.6 thousands as social cost in educational sector during the study years. The average annual growth rate of this cost is 5.15 percent. The study also indicates that annual average total social cost is Rs 68930697.6 thousands, recurrent social cost is Rs 5,71,63,418.79 thousands and capital social cost is Rs 1,17,67,278.81 thousands in Nepal. Similarly, annual average total social cost is estimated to be Rs 3,33,35,928.80 thousands. The total social unit cost per teacher and total social unit cost per student are Rs 196.39 thousands and 5.51 thousands respectively. The annual average marginal social cost per teacher and marginal social cost per student are Rs 1308.08 thousands and -202.96 thousands respectively. The negative sign indicates that student enrolment has decreased over the study period. The findings of the study conclude that there is no any predictable relationship among student enrolment, teaching and non-teaching staffs and social cost of education in case of Nepal. However, UNESCO (1999) had analyzed public investment on education of 16 countries and it found that their average investment on education was 19.2 percent of GDP. Therefore, Nepal government should increase in educational investment from its current status of 15.80 percent to at least more than it in the coming years to develop educational sector of Nepal.

Bi-Level Planning Model of Charging Stations Considering the Coupling Relationship between Charging Stations and Travel Route

The major factors affecting the popularization of electric vehicles (EV) are the limited travel range and the lack of charging infrastructure. Therefore, to further promote the penetration of EVs, it is of great importance to plan and construct more fast charging stations rationally. In this study, first we establish a travel pattern model based on the Monte Carlo simulation (MCS). Then, with the traveling data of EVs, we build a bi-level planning model of charging stations. For the upper model, with an aim to maximize the travel success ratio, we consider the influence of the placement of charging stations on the user’s travel route. We adopt a hybrid method based on queuing theory and the greedy algorithm to determine the capacity of charging stations, and we utilize the total social cost and satisfaction index as two indicators to evaluate the optimal solutions obtained from the upper model. Additionally, the impact of the increase of EV ownership and slow charger coverage in the public parking lot on the fast charging demands and travel pattern of EV users are also studied. The example verifies the feasibility of the proposed method.