嘉兴德泰临时停车场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 acceleration of urbanization and the sharp growth in car ownership, parking lots, as an important component of the urban transportation system, their operation and management have become particularly critical. Establishing a parking lot MNT model to clarify the operating status of parking lots, thereby formulating targeted strategies to improve the operational efficiency of urban transportation links.

1. Improving the scientificity of site planning and construction:
Through the parking lot MNT model, high-value and supported parking lots can be quickly identified. For parking lots with high long-term value, their sites can be expanded to meet citizens' travel demands and increase revenue. For parking lots with low long-term value, the site can be appropriately reduced to cut costs, and charging parking spaces can be added to enhance differentiated competitive advantages.

2. Increasing operating revenue of low-fee parking lots:
By analyzing the proportion of $0 charging orders in parking lots, targeted measures such as recommending monthly or annual package preferential strategies can be taken for early-warning parking lots to attract passenger flow and improve operating revenue.

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

### Algorithm Rules
By comparing the average charging amount, parking times, and average parking duration of the target parking lot with all parking lots under the company over a specified period, a parking lot MNT evaluation model is established, and early warning is conducted based on the proportion of $0 charging orders.
1. Single parking duration of a vehicle = exit time minus entry time;
2. Average charging amount of the parking lot = total actual parking fee collected of the parking lot / number of parking times. If the 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 times of the parking lot is higher than the average parking times of all parking lots under the company, assign the label N; otherwise, assign n;
4. Average parking duration of the parking lot = total parking duration of the parking lot / number of parking times. If the 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 hierarchical rules of the MNT model, parking lots are divided into 4 tiers: Important Value (MNT), Important Development (mNT, MnT, MNt), General Value (Mnt, mNt, mnT), and Key Support (mnt). Different operating strategies are formulated for each tier;
6. Proportion of $0 charging orders = number of $0 charging orders of the parking lot (excluding period-package payment orders) / total number of orders. If the proportion exceeds 85%, conduct early warning and mark it as a parking lot requiring operational optimization.

搜集汇总

数据集介绍

特点

该数据集包含嘉兴德泰临时停车场的运营数据，通过MNT评价模型分析停车场的经营情况，用于优化城市交通管理。数据每周更新，包含583条记录，涉及停车场名称、车牌、出入场时间、收费金额等关键信息。