嘉兴马厍停车场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。

Against the backdrop of accelerating urbanization and the sharp growth in vehicle ownership, parking lots have become a critical component of urban traffic systems, making their operation and management increasingly vital. By establishing the Parking Lot MNT Evaluation Model to gain insights into the operational status of parking lots, we can formulate targeted strategies to improve the operational efficiency of urban traffic links.

1. Enhancing the Scientificity of Site Planning and Construction: Using the Parking Lot MNT Model, we can quickly identify parking lots with high long-term value and those in need of support. For parking lots with high long-term value, their site areas can be expanded to meet citizens' travel demands and increase revenue. For parking lots with low long-term value, their site areas 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 can be taken for early-warning parking lots, such as recommending preferential policies like monthly or annual subscription packages, to attract more traffic and boost operating revenue.

### Data Collection
Raw data is sourced from the company's business collection system, including original data fields such as parking lot name, license plate number, entry/exit time, actual parking fee collected, and status. Sensitive information like license plate numbers is encrypted during data processing.

### Algorithm Rules
The Parking Lot MNT Evaluation Model is established by comparing the average charging amount, total parking times, and average parking duration of a target parking lot with those of all parking lots under the company over a specified period. Early warning is triggered based on the proportion of zero-fare parking orders in the target lot.
1. Single parking duration = Exit time - Entry time;
2. Average charging amount of the parking lot = Total actual parking fees collected in the lot / Total parking times. 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. Total parking times of the lot = Number of all parking instances in the lot. If this value is higher than the average parking times of all company-owned parking lots, assign the label N; otherwise, assign n;
4. Average parking duration of the lot = Total parking duration of the lot / Total parking times. If this value is higher than the average parking duration of all company-owned parking lots, assign the label T; otherwise, assign t;
5. Based on the hierarchical rules of the MNT model, parking lots are divided into four tiers: High Value (MNT), High Development Potential (mNT, MnT, MNt), General Value (Mnt, mNt, mnT), and Key Support Targets (mnt). Corresponding operational strategies are formulated for each tier;
6. Proportion of zero-fare orders = (Number of zero-fare parking orders in the lot, excluding periodic subscription orders) / Total number of orders. If this proportion exceeds 85%, issue an early warning and mark the lot as one requiring operational optimization.

搜集汇总

数据集介绍

特点

嘉兴马厍停车场MNT评价模型分析数据包含6797条记录，每周更新，主要用于评估停车场的经营效率和制定管理策略。数据通过MNT模型对停车场的收费金额、停车次数和停车时长进行分析，并根据0元收费占比进行预警，帮助提升停车场的经营收入和管理效率。