Analysis on Complaint Behavior of Electric Power Customers

Online Road Complaints Registration System

Analysis on Complaint Behavior of Electric Power Customers

Customer complaints refer to a kind of demand that customers express strong dissatisfaction with the services provided by the company [1]. Customer complaints’ purpose is to make the company take corresponding measures to improve the services and solve the problems reflected through complaints. Providing quality service is the commitment of State Grid Corporation of China (SGCC) to its customers. 95598 hotline is a public service telephone number for the national power system. 95598 hotline is an effective way for customers to reflect their real demands and solve service problems [2], [3].

It is also a powerful measure for SGCC to continuously improve its management level and service capability. In response to electric power customer complaints, SGCC has compiled “The handling regulation of power supply service complaints”, which clearly stipulates the matters about how to deal with electric power customer complaints for power supply company employees. Customer complaints contain a lot of intrinsic feature information, including customer demands, service defects and management weaknesses [4]-[6]. Analysis of customer complaint data can effectively identify service shortcomings and provide guidance for improving the weak points of power supply business [7], [8]. It will help power supply companies effectively reduce service problems and the number of customer complaints. This paper analyzes the complaint behaviors of electric power customers from three aspects, including complaint contents, complaint time and complaint areas. The related factors and risk index of customer complaints are studied. The suggestions to reduce complaints are proposed.

From January 2015 to December 2017, EZ power supply company received a total of 547 complaints from 95598 hotline. In this section, these 547 complaints are analyzed from time aspect. A.Monthly Distribution Fig. 4 shows the monthly distribution of 547 complaints from EZ power supply company. July and August are the two months with the most complaints in the year. February is the month with the least complaints in the year. The complaints in July and August are mainly due to frequent blackouts, which are 42 and 48 respectively, accounting for 44.7% and 49.0% of the total complaints in the month. The total number of complaints in January, July, August and December is 319, accounting for 51.29% of the complaints in the whole year.
B.Weekly Distribution Fig. 5 shows the weekly distribution of 547 complaints from EZ power supply company. Wednesday is the day with the largest number of complaints in a week. The complaints in Wednesday are mainly due to frequent blackouts, a total of 33 complaints, accounting for 30% of Wednesday’s complaints. The number of complaints drops over the weekend, and Sunday has the least complaints in the week.
C.Daily Distribution Fig. 6 shows the daily distribution of 547 complaints from EZ power supply company. The complaints peak in several days in a month.
D.Hourly Distribution The Hourly distribution of 547 complaints from EZ power supply company is shown as Fig. 7. 9:00 and 20:00 are the peak time. The complaints begin to show an obvious upward trend at 6:00 and reach a peak at 9:00, and then show a downward trend from 9:00 to 14:00. The complaints show an upward trend from 16:00 to 20:00, and then show a significant downward trend after 20:00. Code Shoppy
Fig. 8 shows complaint contents and its hourly distribution. Power supply reliability complaints mainly focus on 8:00 to 12:00 and 17:00 to 22:00, of which the night is more obvious, mainly caused by frequent blackouts due to line fault trips. The complaints of service behavior are mainly concentrated in 9:00 and 12:00. The complaints of business expansion are mainly concentrated in 9:00. The complaints of meter-reading and payment-collection rise from 7:00 to 10:00 and reach the peak at 10:00. The complaints of power construction are mainly concentrated in 8:00 to 10:00.
Customer complaints contain a lot of intrinsic feature information, such as customer demand, service defects, management weaknesses, etc. The data of electric power customer complaints from EZ power supply company in Hubei Province from January 2015 to May 2018 is analyzed from three aspects, including complaint contents, complaint time and complaint areas. The related factors and risk index of customer complaints are studied. The suggestions are proposed as follows: (1) Frequent blackouts complaints are the hotspot of current complaints from EZ power supply company. And fault blackouts are the main reason for frequent blackouts complaints. Firstly, lines with frequent faults should be strengthened and upgraded, especially for seven 10kV lines with 8 or more frequent blackouts complaints. Secondly, the daily operation and maintenance management of distribution networks should be reinforced, focusing on 39 10kV lines with 2 or more frequent blackouts complaints. Some measures can be taken, for example eliminating the defects of distribution network equipment, clearing the tree barricades, preventing external breakage, line lightning protection, and etc. Thirdly, power outages plan should be reasonably arranged to prevent complaints caused by unreasonable planned blackouts. Fourthly, power consumption inspection of high voltage users should be strengthened to reduce the outage of 10kV lines caused by user equipment failure. (2) The complaints about business, service and power grid construction mainly focus on service attitudes and personnel violations. Among them, there are 158 complaints about service attitudes and personnel violations, accounting for 25.40% of 622 complaints. Firstly, the training of employees should be strengthened to enhance their service awareness and attitude. Secondly, the reward and punishment mechanism for service work should be improved. Service behavior should be standardized. The behavior, such as barbaric payment-collection, illegal charges, unauthorized signing of electricity fee settlement agreements without the consent of customers, should be avoided. (3) The total number of complaints from five power supply stations is 256, accounting for 41.16% of 622 complaints. The capital investment in these five power supply stations should be increased to enhance the infrastructure construction and operation and maintenance of distribution network. The human resources management should be optimized to improve gridding services and enhance the quality of service capabilities.

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A STUDY OF MOTORWAY VEHICLE BREAKDOWN DURATION

A STUDY OF MOTORWAY VEHICLE BREAKDOWN DURATION

ABSTRACT

This paper presents an analysis of vehicle breakdown duration on motorways. The distribution of breakdown duration was shown to be statistically significantly different for three categories of vehicle type and were shown to conform to a Weibull distribution. A predictive vehicle breakdown duration model was developed, based on fuzzy logic theory. The variables used in this model were: vehicle type, breakdown time, breakdown location and reporting mechanism. The performance of. the model was tested with encouraging results. Clustering of data was shown to be due to rounding errors when the operator reported an incident duration of 60 and 120 minutes. The unexplained variation in the model was due to the limitations in the specification of the model parameters. This was because the incident data set available was incomplete. This paper highlights the need for standardisation in the recording of data used in incident management.

INTRODUCTION

Incident duration analysis has an important role to play in estimating the efficiency of incident management strategies. In particular, informing the drivers of the traffic condition can assist in alleviating congestion problems with consequential benefit to the environment. Recently, traffic incident has become one of the main causes of traffic congestion. Studies have shown that incident-induced congestion is between 50% and 75% of total traffic congestion in the urban area (Lindley. 1). Traffic incident is the event that is not planned, one about which there is no advance notice, for example emergencies, accidents, breakdowns, traffic crashes, etc (IEEE. 2). Simply, the traffic incident can be referred to as any non- recurring event that causes a reduction of road capacity or an abnormal increase in demand, (Farradyne, 3). Among all the incidents, breakdown is the most common. The incident data on the M4, collected by WS Atkins and made available for this study, demonstrated that 66% of all incidents were vehicle breakdowns during the period 1 May 2000 and 30 April 2001. Incident management is the systematic planned and co- ordinated use of human, institutional, mechanical and technical resources to reduce the duration and impact of incidents and improve the safety of motorists, crash victims and incident responders (Farradyne, 3). In the main, there are three different methods of analysing incident duration. These are regression (Sullivan, 4). hazard duration (Nam and Mannering, 5), and fuzzy logic (Kim and Choi, 6). The first two methods are statistical analyses that require a large volume of data. The advantage of the hazard duration method is that it allows the problem to be formulated in terms of the conditional probabilities of the entities of interest. Such a formulation can provide valuable insight into the empirical estimation of the model. However, often, there is insufficient data available to achieve statistical significance. The alternative approach, using fuzzy logic, can simulate the human mind in analysing the data as a complex decision making process. This paper presents the results of a preliminary study that has looked at the feasibility of using fuzzy logic theory as a method of predicting incident duration on motorways. The next section presents a description of the data and is followed by analysis of the characteristics of breakdown duration data to establish statistically significant differences. The next section presents the breakdown duration model based on fuzzy logic theory and the results. The final section provides a summary and recommendations for the future. 

CHARACTERISTICS OF VEHICLE BREAK- DOWN

 Vehicle breakdown, is a type of traffic incident that suggests that the vehicle is disabled on the road for a period of time. The main reasons for vehicle breakdown include: Low battery Flat tyre Mechanical failure Starter motor malfunction Engine fault Electrical failure Figure 1 Relationship between Vehicle Breakdown and Month of Year Normally, only one vehicle IS involved in this kind of incident, and there is no casualty. The duration of the vehicle breakdown consists of the time to report, verify, respond to and clear away the breakdown vehicle. After the vehicle breakdown is reported to the traffic control centre, by using the ETS or other communication media, the recovery company is informed to deploy staff to the incident scene to repair the vehicle or tow it away. Sometimes, the police may be involved to manage the traffic as appropriate, or offer help, especially if a female driver is involved. Figure 1 shows the frequency of the breakdowns occurring on the M4 according to the month of the year (starting in May 2000 and ending in April 2001). The figure shows that the number of vehicle breakdowns increases from May to August 2000 when it decreases, varying little up to April, 2001. It demonstrates that more breakdowns occur in the summer compared to the winter. Figure 2 shows the relationship between the number of breakdowns and time of the day. From the figure, as expected, most breakdowns occur in the day time. In contrast, few breakdowns occur at Number of Vehicles Breakdown vs Time

night and early morning. The number of breakdowns reaches its peak in the early afternoon. Unfortunately, trafk flow data was not available for stretches of roads along which vehicle breakdowns had been reported and therefore no direct relationship between the number of breakdowns occurring per hour as a function of the vehicle flows over the month and year could be explored. However, knowing the characteristics of the traffic along this road, it can be hypothesised that the highest number of vehicle breakdowns are coincident with the higher vehicle flows measured during the summer, reaching a peak during the month of August, and during the daytime hours reaching a ,peak early afternoon. The availability of appropriate traffic flow data is currently being explored. The next stage of the analysis studied the distribution of vehicle breakdown duration for all vehicles and then disaggregated according to vehicle type. The distribution of the vehicle breakdown duration for all vehicle types is given in Figure 3. This distribution was shown to conform to a Weibull distribution. A goodness-of-fit analysis was conducted, and the results showed that the Weibull distribution. It is interesting to note that there are two sharp peaks in the distribution that are coincident with 60 minutes and 120 minutes. This was believed due to rounding errors in the reported breakdown durations of one and two hours. A test was carried out to prove that this indeed was the case. This was achieved by randomly generating breakdown durations using the Weibull distribution fitted to the data. It was shown that these peaks could be reproduced by assuming the incident durations of 58. 59,'61 and 62 were also 60 and incident durations of 118, 119, 121 and 122 were also 120 minutes. This result was shown to be statistically significant at the 70% confidence level.

RE PLAN

This paper has presented the results of a statistical analysis of the duration of vehicle breakdowns on motorways. It has shown that the duration of vehicle breakdown conform to Weibull distribution of different parameters depending on vehicle size. The vehicle duration model, based on fuzzy logic theory, was specified and developed using the input variables vehicle size, breakdown time, location, and report mechanism. The performance of the model, although encouraging, illustrates a good deal of scatter. A standard of message set for incident management should be developed. Further analysis of the model results helped to identify shortcomings of the existing model. These were shown to include time of day when breakdown occurred, location and report mechanism. Additional work is needed to improve the performance by using more variables to modify the fuzzy set, membership, and fuzzy rules. This work will be conducted in the future.

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A Pattern Based Anti-Fraud Method in C2C Ecommerce Environment

A Pattern Based Anti-Fraud Method in C2C Ecommerce Environment

 

With the growing popularity of online trading sites, reputation systems are increasingly becoming an integral part of C2C ecommerce systems. Reputation systems can collect, aggregate and distribute participant feedback from past actions to encourage sellers' honest behaviors, and effectively avoid cheating behaviors of those dishonest sellers. In such a situation that neither buyers nor sellers are well informed of each other, the reputation system is able to help buyers determine which sellers are more credible. Such as eBay and Taobao[1][2][3][4], they all have their own reputation systems. The world's largest C2C online auction site eBay has a reputation system dealing with feedback information. Upon the completion of each transaction, buyers and sellers have rights to give an rating points(-1, 0, 1)of the other[5]. Each participant will have an identification name, and its evaluation will be given in connection with the transaction name on it. Nowadays, many trading sites are using reputation systems like eBay's, while some of them provide 1-5 rating range or use some other rating scales. Some of them calculate the average feedback rating points while others calculate the cumulative ones. These reputation-rating mechanisms can’t well deal with thereputation slander, the reputation speculation and other means of fraud generally. This leads to the reputation values given by reputation systems can’t effectively reflect the performance of sellers, eventually leading to the average benefit of buyers greatly reduced. In order to deal with the fraud patterns mentioned above, Based on TRUST[8] model, we proposed a new fraud pattern identification and filtering method. It is to find fraud pattern in Time Window Scope and filter out those fraud ratings, Such as plenty of newer buyers give higher ratings over threshold or lower ratings below threshold to a fixed number of sellers, higher ratings over threshold are given by a fixed number of sellers each other, etc. In this way, the reputation value that the buyer computed will show much more fully of the true reputation of the sellers. The experiment results in multi-agent system JADE prove that the method proposed by us can make the sellers get more profit. The organization of the paper is as follows. The second part introduces two kinds of fraud patterns that are very regular and very hard to be recognized in ecommerce system. The third part expounds the anti-fraud method we put forward based on TRUST; part four illuminates the simulation experiment which is based on multi-agent system JADE; part five is the summary of the paper and discusses the next step of our study.

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