嘉兴百乐路停车场MNT评价模型分析数据

随着城市化进程的加快，汽车保有量急剧增加，停车场作为城市交通系统的重要组成部分，其经营与管理显得尤为关键。通过建立停车场MNT模型看清停车场的经营情况，从而指定不同的策略，提升城市交通环节运行效率。
一、提升场地规划与建设的科学性：通过停车场MNT模型，可以快速识别出价值较高和需要扶持的停车场，长期价值较高的停车场可扩大场地，满足市民出行需求并提升收入。长期价值较低停车场可适当缩减场地压降成本、增加充电车位提升差异化竞争优势。
二、提升低收费停车场经营收入：通过分析停车场0元收费占比，可对预警停车场采取一定措施如推荐包月包年等优惠策略，吸引客流提升经营收入。一、数据采集：原始数据来自公司业务采集数据，包含停车场名称、车牌号、出入场时间、停车费实收金额、状态等原始数据字段，并对车牌号等敏感信息进行加密处理。
二、算法规则：
通过比较本停车场和公司旗下所有停车场一段时间内平均收费金额、停车次数、平均停车时长数据，建立停车场MNT评价模型，并根据停车场0元收费占比进行预警。
1、车辆单次停车时长=出场时间-入场时间；
2、该停车场平均收费金额=该停车场停车费实收金额合计/停车次数，高于公司旗下所有停车场平均收费金额的赋M，否则赋m；
3、该停车场停车次数高于高于公司旗下所有停车场平均停车次数的赋N，否则赋n；
4、该停车场平均停车时长=该停车场停车时长合计/停车次数，高于公司旗下所有停车场平均停车市场的赋T，否则赋t；
5、再根据MNT模型分层规则，将停车场分为4个层级，分别为重要价值（MNT）、重要发展（mNT、MnT、MNt）、一般价值（Mnt、mNt、mnT）、重点扶持（mnt），不同层级指定不同的经营策略；
6、0元收费占比=该停车场停车费0元订单数（去除支付类型为包期）/总订单数，占比超85%进行预警，标记为需经营优化停车场。

With the accelerating pace of urbanization, the number of motor vehicles has increased drastically. As a critical component of urban transportation systems, the operation and management of parking lots have become increasingly critical. Establishing a parking lot MNT model to gain insights into the operational conditions of parking lots and formulate corresponding strategies can improve the operational efficiency of urban transportation links.

1. Enhancing the scientificity of site planning and construction
The parking lot MNT model can quickly identify high-value parking lots and those requiring support. For parking lots with high long-term value, their site area can be expanded to meet citizens' travel demands and increase revenue. For parking lots with low long-term value, the site area can be appropriately reduced to cut costs, and more charging parking spaces can be added to enhance differentiated competitive advantages.

2. Improving operating revenue of low-fare parking lots
By analyzing the proportion of zero-fare parking orders in a parking lot, targeted measures such as recommending monthly or annual subscription preferential policies can be taken for early-warning parking lots to attract passenger flow and boost operating revenue.

### Data Collection
Original data is collected from the company's business systems, including original data fields such as parking lot name, license plate number, entry and exit time, actual collected parking fee amount, and status. Sensitive information such as license plate numbers has been encrypted.

### Algorithm Rules
An MNT evaluation model for parking lots is established by comparing the average charging amount, number of parking occurrences, and average parking duration of a single parking lot with those of all parking lots under the company over a specified period. Early warning is also conducted based on the proportion of zero-fare parking orders.
1. Single-vehicle parking duration = Exit time - Entry time;
2. Average charging amount of a parking lot = Total actual collected parking fees of the parking lot / Number of parking occurrences. If this value is higher than the average charging amount of all parking lots under the company, assign the label M; otherwise, assign m;
3. If the number of parking occurrences of a parking lot is higher than the average number of parking occurrences of all parking lots under the company, assign the label N; otherwise, assign n;
4. Average parking duration of a parking lot = Total parking duration of the parking lot / Number of parking occurrences. If this value is higher than the average parking duration of all parking lots under the company, assign the label T; otherwise, assign t;
5. According to the MNT model layering rules, parking lots are divided into four tiers: High Value (MNT), High Development Potential (mNT, MnT, MNt), General Value (Mnt, mNt, mnT), and Key Support (mnt). Different operational strategies are formulated for each tier;
6. Proportion of zero-fare orders = Number of zero-fare parking orders of the parking lot (excluding periodical payment orders) / Total number of orders. If the proportion exceeds 85%, issue an early warning and mark it as a parking lot requiring operational optimization.

搜集汇总

数据集介绍

特点

该数据集包含嘉兴百乐路停车场的经营数据，通过MNT评价模型分析停车场的经营情况，包括停车次数、收费金额、停车时长等指标，并根据模型结果对停车场进行分层和预警，以优化经营策略。数据集规模为765条，每周更新。