Improving Utilization Efficiency and Drivers’ Degree of Satisfaction in Urban Complex Parking Lot through Modelling the Dynamic Allocation of Parking Spaces

There are significant differences in the utilization efficiency of parking spaces in different spatial locations within the complex parking lots, which reduces the utilization efficiency of parking resources. For the above problem, a parking spaces supply demand characteristics indexes system was constructed. The Metro City complex was taken as an example, and its parking demand utilization characteristics were analyzed to judge the problem of parking spaces utilization. On this basis, a model of the dynamic allocation of parking spaces for parking spaces was constructed to improve drivers’ degree of degree of satisfaction and balance the occupancy rates for parking spaces in different zones. The simulation results show that after the implementation of the dynamic allocation of parking spaces, the differences of the parking spaces’ demand characteristic indexes between two different parking zones are significantly reduced. It was specifically observed that the differences between parking zones A and B in terms of turnover number, total parking time and average parking time were reduced from 2.24 times to 0.03 times, 1.3 h to 0.6 h and 2.2 h to 0.1 h, respectively, and the average interval time of parking spaces became smaller and more evenly distributed. It can be seen that this model can improve the overall utilization efficiency of the complex parking lot and drivers’ degrees of satisfaction.

Whither the Third Quarter Phenomenon?

INTRODUCTION: In 1991, Bechtel and Berning proposed that a decrement in morale and well-being affects people working in isolated and confined environments during the third quarter of their mission. Studies conducted during such conditions have suggested that whereas some people may experience such a phenomenon, it is not a typical occurrence in space or space simulation environments. Possible reasons for varying outcomes include demand characteristic bias, individual personality traits, training omissions, experimental methodological issues, and the impact of mission events on crewmember well-being. Research related to a future Mars expedition needs to investigate the impact of these factors.Kanas N, Gushin V, Yusupova A. Whither the third quarter phenomenon? Aerosp Med Hum Perform. 2021; 92(8):689691.

Artificial intelligence based prediction models: sales forecasting application in automotive aftermarket

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance.

Exploring Equity in Public Transportation Planning Using Smart Card Data

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.

Auxiliary Stopping Area Layout Method for High-Speed Maglev Operated Bidirectionally on Single Track

Auxiliary stopping area (ASA) is the necessary emergency facility for train safety of the normal high-speed maglev. The study addresses the ASA layout problem of the high-speed maglev operated bidirectionally on single track. First, an optimization model of the ASA layout for unidirectional double-track lines considering train safety, operation efficiency, and construction cost is established, and two basic methods of the ASA layout are investigated based on the distance demand characteristic of ASAs. Then, the ASA layout problem of bidirectional single-track lines is analyzed, and an ASA two-way coordination layout algorithm (ASA-TWCLA) is proposed. Finally, a numerical experiment is carried out. The results suggest that under the premise of train safety and operation efficiency, compared with using the basic methods separately on the two directions, adopting the ASA-TWCLA algorithm can obtain a more economical ASA layout scheme for the same scenario.

Data-Driven Evaluation for Demand Flexibility of Segmented Electric Vehicle Chargers in the Korean Residential Sector

The rapid spread of renewable energy resources has increased need for demand flexibility as one of the solutions to power system imbalance. However, to properly estimate the demand flexibility, demand characteristics must be analyzed first and the corresponding flexibility measures must be validated. Thus, in this study, a novel approach is proposed to evaluate the demand flexibility provided by Electric Vehicle Chargers (EVC) in the residential sector based upon a new process of electric charging demand characteristic data analysis. The proposed model estimates the frequency, consistency, and operation scores of the flexible demand resource (FDR) during identified ramp-up/down intervals presented in our previous work. The scores are included in the components that calculate the flexibility score referring that the closer it is to 1, the higher utilization as an FDR. A case study was conducted by considering EV user segmentation based on their demand characteristic analysis. The results confirm that flexibility scores of segmented EVC groups are about 0.0273 in ramp-up and ramp-down intervals. Based on the experimental results, the flexibility score can be utilized for multi-dimensional analysis and verification in perspectives of seasonality, participation time interval, customer group classification, and evaluation. Thus, the proposed method can be used as an indicator to determine how a segmented EVC group is adequate to participate as an FDR while suggesting meaningful implications through EVC demand data analysis.

Attribute accessibility, normative influence, and the effect of classical and country music on willingness to pay for social identity and utilitarian products

Previous studies indicate that background classical music is associated with customers in retail and leisure premises being prepared to pay more for various products and services. This online experiment tests whether these effects are due to music increasing the salience of valued product attributes (attribute accessibility hypothesis) or to a demand characteristic wherein music implies a norm to purchase expensive items (normative behavior hypothesis). A 3 (type of music—classical, country, no music, between subjects) × 2 (type of product—social identity or utilitarian, within subjects) × 2 (high vs. low incentive for accuracy, between subjects) mixed design was used in which participants stated the specific amount they would be prepared to pay for 30 products using free-choice format. Results showed a Music × Type of Product interaction, such that preparedness to spend was higher in the classical music condition but only in the case of social identity products. This is more consistent with the attribute accessibility hypothesis than the normative behavior hypothesis, and various commercial and practical consequences of these findings are discussed.

Demand Characteristics of Electricity in Residential Sector of Kathmandu Valley

Planning for electricity demand is a vital as the characteristics of different electricity generation systems vary temporally – both hourly in a day as well as seasonally in a year. Thus, this study focuses on evaluating the demand characteristic of electricity in residential sector within urban boundaries of three districts in Kathmandu Valley. It has addressed variations in demand in form of load curve based on hourly peak demand during a day. The demand characteristics have been affected in recent years are primarily influenced by two factors – the trade debacle in 2015 and the end of load-shedding. A typical family with owned household would have highest demand with loads spread over various time of the day. While the one in rented family would have least demand level with most characteristic peaks. But in overall, there are peculiar morning and evening peaks, in addition to small early morning peak. The current technology interventions and electricity consumption pattern with reference in earlier years depicts the change in energy technology preference. The reduction in daily demand pattern as well as total electricity demand are majorly due to replacement of older technologies with more efficient appliances as well as reduced use of inverters for battery charging. Thus, it can be said that a stringent condition can enforce people to change to efficient technologies as well as proper supply can reduce unnecessary demand in battery charging. On other hand, similar trend, and hence the increase in electricity demand, can be anticipated in other flourishing urban areas of the country. Additionally, it is beneficial to have the demand characteristics of each sector separately - which can be useful to design the decentralized systems for specific sector.

Dealing with the Empty Vehicle Movements in Personal Rapid Transit System with Batteries Constraints in a Dynamic Context

The Personal Rapid Transit is a new emergent transportation tool. It relies on using a set of small driverless electric vehicles to transport people on demand. Because of the specific on-demand characteristic of the Personal Rapid Transit system, many Personal Rapid Transit vehicles would move empty which results in a high level of wasted transportation capacity. This is enhanced while using Personal Rapid Transit vehicles with limited electric battery capacity. This paper deals with this problem in a real time context while minimizing the set of empty vehicle movements. First, a mathematical formulation to benchmark waiting time of passengers in Personal Rapid Transit systems is proposed. Then, a simulation model that captures the main features of the Personal Rapid Transit system is developed. A decision support system which integrates several real time solution strategies as well as a simulation module is proposed. Our dispatching strategies are evaluated and compared based on our simulation model. The efficiency of our method is tested through extensive test studies.