本数据集由货源订单生成、候选承运司机生成、双方偏好生成与双边稳定匹配撮合程序共同构成。数据依据代码中的 Generate、GenerateData、ProcessMatch、GameMatch 和 LogGenerate 流程生成：在广东省广州市 11 个区县及 11 类街道名称范围内仿真生成货源、货主、装卸地点和订单流程时间，并为每个撮合批次生成等规模候选承运司机集合及双方偏好矩阵。现有数据包含 orderorigin_july.xlsx、orderorigin_august.xlsx、orderorigin_September.xlsx 三个订单场景文件，共 122928 条货源订单记录；每条记录包含货源id、货主id、出发/目的行政区县与街道、脱敏装卸地点、承运司机 id 以及货源创建、订单撮合、到达装货地、完成装货、到达卸货地、完成卸货、订单完成等流程时间。配套 JSON 文件记录 shipperMaId、carrierMaId、shipperMaPre 和 carrierMaPre，可用于复现延迟接受稳定匹配过程。文件名中的 july、august、september 为场景名称；根据现有 Excel 时间戳，货源创建时间实际覆盖 2024-09-01 06:01:53 至 2024-09-30 21:00:01，订单完成时间覆盖至 2024-10-01 10:25:50。
", "ds_source": "orderorigin_*.xlsx 为原始货源订单场景，字段包括：货源id、货主id、出发省份、出发省份id、出发城市、出发城市id、出发区县、出发区县id、出发街道、目的省份、目的省份id、目的城市、目的城市id、目的区县、目的区县id、目的街道、装货地点、卸货地点、承运司机 id、货源创建时间、订单撮合时间、订单取消时间、到达装货地时间、完成装货时间、到达卸货地时间、完成卸货时间、订单完成时间。shipperMaId_*.json 记录每一撮合批次对应的货源索引集合；carrierMaId_*.json 记录候选承运司机 id 集合；shipperMaPre_*.json 与 carrierMaPre_*.json 分别记录货源侧和承运司机侧偏好分值。三组场景对应撮合批次数分别为：july 3913 批、august 3926 批、september 3899 批，合计 11738 批。
", "ds_process_way": "本数据集由货源仿真、多阶段偏好建模与双边稳定匹配撮合程序共同构建。数据依据代码中的仿真、偏好生成、延迟接受匹配与日志记录流程生成：首先由 Generate 模块在广东省广州市 11 个区县及类街道范围内，随机仿真生成货源、货主、装卸地点和订单时序记录；随后由 GenerateData/GenerateMatch 将货源按 1—20 条规模切分为若干动态撮合批次，并为每个批次构建等规模的候选承运司机集合；再通过 GeneratePrefer 模块在预设值域内分别计算并生成货源侧与承运司机侧的双边偏好分值矩阵；最后由 ProcessMatch 读取结构化时序数据，调用 GameMatch 中的 Gale-Shapley 延迟接受算法实现双边稳定匹配，并可通过 LogGenerate 模块输出包含部分取消订单标记的完整订单场景文件。
", "ds_quality": "已根据现有 Excel 和 JSON 文件核查数据规模、字段和时间范围。orderorigin_july.xlsx 含 40973 条记录，orderorigin_august.xlsx 含 40697 条记录，orderorigin_September.xlsx 含 41258 条记录，合计 122928 条；所有原始订单文件均包含 27 个字段。出发地和目的地均覆盖广州市 11 个区县。原始 orderorigin 文件中的“订单取消时间”为空，属于匹配/取消标记生成前的原始状态；process.py 在输出 ordergenerate_*.xlsx 时通过 LogGenerate(cancel=True) 随机写入部分订单取消时间。由于数据为随机仿真生成，货主 id 可能重复，货源 id 由唯一生成函数控制。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "广东省广州市", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 103284431, "ds_files_count": 0, "ds_format": "xlsx、json", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "c1503fbf-4773-499f-9e85-82d1eca4a3e0.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据集可用于货源—承运司机双边匹配算法、稳定匹配机制、订单撮合效率、取消订单识别与物流调度仿真等研究。复现实验时，应将同一场景名称下的 orderorigin_*.xlsx、shipperMaId_*.json、carrierMaId_*.json、shipperMaPre_*.json 与 carrierMaPre_*.json 放在同一目录，并以 ProcessMatch(name) 读取和匹配。注意：july、august、september 在当前文件中是场景名称。", "ds_from_station": "", "organization_id": "9ecaaa78-39e9-411e-9f24-274e12aa643f", "ds_serv_man": "朱夏", "ds_serv_phone": "13851917681", "ds_serv_mail": "zhuxia1@huawei.com", "doi_value": "", "subject_codes": [ "410" ], "quality_level": 0, "publish_time": "2026-07-09 10:58:32", "last_updated": "2026-07-09 10:58:32", "protected": false, "protected_to": null, "lang": "zh", "cstr": "", "i18n": { "en": { "title": "Freight Source–Carrier Stable Matching Order Operation Scenario Dataset", "ds_format": "xlsx、json", "ds_source": "The `orderorigin_*.xlsx` files represent the raw shipment order scenario, containing fields such as: shipment ID, shipper ID, departure province (name and ID), departure city (name and ID), departure district/county (name and ID), departure street, destination province (name and ID), destination city (name and ID), destination district/county (name and ID), destination street, loading location, unloading location, carrier driver ID, shipment creation time, order matching time, order cancellation time, arrival time at loading location, loading completion time, arrival time at unloading location, unloading completion time, and order completion time. The `shipperMaId_*.json` files record the sets of shipment indices corresponding to each matching batch; `carrierMaId_*.json` records the sets of candidate carrier driver IDs; and `shipperMaPre_*.json` and `carrierMaPre_*.json` record the preference scores for the shipment side and the carrier driver side, respectively. The number of matching batches for the three scenarios is as follows: 3,913 batches in July, 3,926 batches in August, and 3,899 batches in September, totaling 11,738 batches.", "ds_quality": "The data volume, fields, and time ranges have been verified against the existing Excel and JSON files. The files `orderorigin_july.xlsx`, `orderorigin_august.xlsx`, and `orderorigin_September.xlsx` contain 40,973, 40,697, and 41,258 records respectively, totaling 122,928 records; all raw order files contain 27 fields. Both origins and destinations cover all 11 districts and counties of Guangzhou City. The \"Order Cancellation Time\" field in the raw `orderorigin` files is empty, reflecting the state prior to the generation of matching or cancellation markers; the `process.py` script randomly assigns cancellation times to some orders via `LogGenerate(cancel=True)` when generating the `ordergenerate_*.xlsx` output files. As the data is generated through random simulation, shipper IDs may be duplicated, whereas shipment source IDs are controlled by a unique generation function.", "ds_ref_way": "", "ds_abstract": "This dataset consists of simulated freight order generation, candidate carrier generation, two-sided preference generation, and stable matching. The data are generated by the code pipeline including Generate, GenerateData, ProcessMatch, GameMatch, and LogGenerate. Freight sources, shippers, loading/unloading locations, and order process timestamps are randomly generated within 11 districts and 11 street names in Guangzhou, Guangdong Province. For each matching batch, an equal-sized candidate carrier set and two-sided preference matrices are generated. The available data include three order scenario files, namely orderorigin_july.xlsx, orderorigin_august.xlsx, and orderorigin_September.xlsx, with 122928 freight source records in total. Each record contains freight source ID, shipper ID, origin/destination district and street, desensitized loading/unloading address, carrier driver ID, and process timestamps including source creation, order matching, arrival at loading location, loading completion, arrival at unloading location, unloading completion, and order completion. The accompanying JSON files store shipperMaId, carrierMaId, shipperMaPre, and carrierMaPre for reproducing the Gale-Shapley deferred-acceptance stable matching process. The names july, august, and september are scenario identifiers; according to the current Excel timestamps, freight source creation times range from 2024-09-01 06:01:53 to 2024-09-30 21:00:01, and order completion times extend to 2024-10-01 10:25:50.", "ds_time_res": "", "ds_acq_place": "Guangzhou, Guangdong Province, China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The processing workflow is as follows. First, the Generate class randomly samples origin and destination locations from preset Guangzhou districts, administrative codes, and street names, while generating freight source IDs, shipper IDs, goods IDs, carrier driver IDs, and order process timestamps. Then GenerateData/GenerateMatch divides freight sources into matching batches of size 1 to 20 and generates an equal number of candidate carriers for each batch. GeneratePrefer assigns preference scores in the range 1–6.5 for the shipper side and 1–5 for the carrier side. Finally, ProcessMatch reads the same-named Excel and JSON files, invokes the Gale-Shapley deferred-acceptance algorithm implemented in GameMatch, produces stable freight source–carrier matches, and can output ordergenerate_*.xlsx with partial cancellation labels through LogGenerate(cancel=True).", "ds_ref_instruction": "This dataset can be used for research on freight source–carrier two-sided matching, stable matching mechanisms, order matching efficiency, cancellation-order identification, and logistics dispatching simulation. To reproduce the experiments, place orderorigin_*.xlsx, shipperMaId_*.json, carrierMaId_*.json, shipperMaPre_*.json, and carrierMaPre_*.json with the same scenario name in the same directory and run ProcessMatch(name). Note that july, august, and september are scenario identifiers in the current files." } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "货运订单", "货源撮合", "承运司机", "双边匹配", "稳定匹配" ], "ds_subject_tags": [ "工程与技术科学基础学科" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "ds_contributors": [ { "true_name": "朱夏", "email": "zhuxia1@huawei.com", "work_for": "华为技术有限公司", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "朱夏", "email": "zhuxia1@huawei.com", "work_for": "华为技术有限公司", "country": "中国" } ], "ds_managers": [ { "true_name": "朱夏", "email": "zhuxia1@huawei.com", "work_for": "华为技术有限公司", "country": "中国" } ], "category": "其他" }