交通大数据分析算法

应对各类智能化交通业务分析需求，构建交通大数据分析算法库。提供面向城市交通运输、公交、出租、交管、海洋交通领域细分业务的16类分析算法模型，包括：道路流量、运力运量预测、车辆画像识别、地图匹配、轨迹纠偏、数据异常检测算法、节假日运力运量综合分析、公交上下车匹配算法、公交智能排班算法、公交充电智能排班、区域出租需求量预测、出租合乘召车匹配算法、区域运价调整、违法嫌疑车辆筛查、城市交通事故分析研判、交通警情感知预警及科学派警模型、海上渔船活动行为分析-生产活动分析、海上渔船活动行为分析-海上违法行为分析。可根据实际业务需求提供对应的算法分析接口。

序号
算法名称
算法简介

1
道路流量、运力运量预测
基于机器学习、深度学习算法实现对不同节假日、民航、铁路等不同运输方式的客流量及道路流量进行预测。

2
车辆画像识别
基于卡口过车数据，形成车辆出行特征，基于机器学习算法精准形成车辆画像，识别具有不同出行特征的车辆类别。

3
地图匹配、轨迹纠偏
构建面向低频定位数据的地图匹配和轨迹纠偏算法，纠正车辆定位数据的轨迹漂移、补充中断轨迹点。

4
数据异常检测算法
面向数据接入及监控场景，构建精细化、多场景的数据异常检测算法，及时识别和发现传输数据的异常，提升数据质量。

5
节假日运力运量综合分析
结合大数据、可视化、机器学习技术，生成重大活动及节假日期间的交通运力运量专项报告，赋能决策。形成重点区域的交通运行状态趋势预测。

6
公交上下车匹配算法
利用公交出行大数据，根据乘客上车刷卡信息，匹配乘客下车信息，精准获取乘客完整出行行为，为线网优化、排班、运力调配提供数据支撑。

7
公交智能排班算法
基于历史客流数据、天气数据构建客流量预测模型，结合优化算法构建智能排班模型，自动生成车辆排班建议。

8
公交充电智能排班
基于精准的车辆充放电能耗预估模型和优化模型，生成公交车辆日间和夜间充电排班方案，提升资源利用效率。

9
区域出租需求量预测
基于深度神经网络模型，利用历史天气、订单数据，构建需求出租需求预测模型，实现需求量实时和时段预测。

10
出租合乘召车匹配算法
根据合乘召车两种业务场景（已有拼车单再匹配拼车单、多个拼车单同时匹配），确定可行匹配约束条件，利用主客观赋权的模糊物元评价方法评估和确定各可行方案优劣，得到最优合乘匹配结果。

11
区域运价调整
基于运营数据，建立区域巡游车运价分析报告，通过比对分析、动态关系图等分析影响驾驶员收入的主要因素，为运价调整提供科学指导。

12
违法嫌疑车辆筛查
根据卡口、停车场设备数据，综合利用空间间隔排查法、状态排查法、数据碰撞分析、隐马尔可模型等，进行套牌车辆稽查、重点车辆提前布控。

13
城市交通事故分析研判
通过分析事故发生影响因素和特征，结合所在道路的道路情况对交通事故造成的影响程度进行等级划分。

14
交通警情感知预警及科学派警模型
通过多源数据融合分析，挖掘交通警情深度画像，实现重大突发事件的主动感知和预警，提升突发性事件应急处理能力，同时构建警情与警员的智能匹配算法，实现警力的最优配置，达到科学派警，提升警情处置效率。

15
海上渔船活动行为分析-生产活动分析
汇聚渔港、避风坞、航道等静态数据以及渔船的AIS数据、船舶定位数据等动态数据。分析船舶外出作业去向分析、出海时段及频次分析、停靠港区热度分析、渔区船舶来源分析等船舶活动行为。

16
海上渔船活动行为分析-海上违法行为分析
基于渔船活动轨迹数据，结合机器学习算法构建违法行为分析模型，根据预测轨迹精准识别违法采砂、非法捕鱼等海上违法行为。

To meet the requirements of various intelligent transportation business analysis, a transportation big data analysis algorithm library is constructed. It provides 16 types of analysis algorithm models for the segmented businesses in the fields of urban transportation, public transit, taxi, traffic management, and marine transportation, including: road traffic flow and transportation capacity/passenger volume prediction, vehicle profile recognition, map matching, trajectory correction, data anomaly detection algorithm, comprehensive analysis of transportation capacity and passenger volume during holidays, bus boarding and alighting matching algorithm, intelligent bus scheduling algorithm, intelligent bus charging scheduling, regional taxi demand prediction, taxi carpooling ride-hailing matching algorithm, regional fare adjustment, suspicious violation vehicle screening, urban traffic accident analysis and judgment, traffic police situation perception early warning and scientific police dispatching model, offshore fishing boat activity behavior analysis - production activity analysis, offshore fishing boat activity behavior analysis - offshore illegal activity analysis. Corresponding algorithm analysis interfaces can be provided according to actual business requirements.

Serial Number, Algorithm Name, Algorithm Introduction
1. Road Traffic Flow and Transportation Capacity/Passenger Volume Prediction: Based on machine learning and deep learning algorithms, it realizes the prediction of passenger flow and road traffic volume of different transportation modes such as holidays, civil aviation, and railways.
2. Vehicle Profile Recognition: Based on the passing vehicle data from surveillance cameras, vehicle travel characteristics are extracted, and accurate vehicle profiles are generated via machine learning algorithms to identify vehicle categories with different travel characteristics.
3. Map Matching and Trajectory Correction: A map matching and trajectory correction algorithm for low-frequency positioning data is developed to correct trajectory drift of vehicle positioning data and supplement interrupted trajectory points.
4. Data Anomaly Detection Algorithm: For data access and monitoring scenarios, refined and multi-scenario data anomaly detection algorithms are built to timely identify and detect anomalies in transmitted data, thereby improving data quality.
5. Comprehensive Analysis of Transportation Capacity and Passenger Volume During Holidays: Combining big data, visualization and machine learning technologies, it generates special reports on transportation capacity and passenger volume during major events and holidays to support decision-making, and forms trend predictions of traffic operation status in key areas.
6. Bus Boarding and Alighting Matching Algorithm: Using bus travel big data, it matches passenger alighting information with passenger boarding card swiping information to accurately obtain passengers' complete travel behaviors, providing data support for bus network optimization, scheduling and transportation capacity allocation.
7. Intelligent Bus Scheduling Algorithm: Based on historical passenger flow data and weather data, a passenger flow prediction model is constructed, and combined with optimization algorithms, an intelligent scheduling model is built to automatically generate vehicle scheduling suggestions.
8. Intelligent Bus Charging Scheduling: Based on accurate vehicle charging and discharging energy consumption estimation models and optimization models, it generates daytime and nighttime charging scheduling plans for buses to improve resource utilization efficiency.
9. Regional Taxi Demand Prediction: Based on deep neural network models, using historical weather and order data, a taxi demand prediction model is built to realize real-time and time-period demand prediction.
10. Taxi Carpooling Ride-Hailing Matching Algorithm: According to two carpooling ride-hailing business scenarios (matching new carpool orders with existing ones, matching multiple carpool orders simultaneously), feasible matching constraints are determined, and the fuzzy matter-element evaluation method with subjective and objective weighting is used to evaluate and determine the pros and cons of each feasible solution, obtaining the optimal carpool matching result.
11. Regional Fare Adjustment: Based on operation data, a regional cruising taxi fare analysis report is established, analyzing the main factors affecting drivers' income through comparative analysis, dynamic relationship diagrams and other methods, providing scientific guidance for fare adjustment.
12. Suspicious Violation Vehicle Screening: According to data from surveillance cameras and parking lot equipment, comprehensively using spatial interval detection method, state detection method, data collision analysis, Hidden Markov Model and other methods, to conduct counterfeit license plate vehicle investigation and key vehicle advance deployment.
13. Urban Traffic Accident Analysis and Judgment: By analyzing the influencing factors and characteristics of accidents, the impact degree of traffic accidents is graded in combination with the road conditions of the road where the accident occurs.
14. Traffic Police Situation Perception, Early Warning and Scientific Police Dispatching Model: Through multi-source data fusion analysis, it mines the in-depth profile of traffic police situations, realizes active perception and early warning of major emergencies, improves the emergency response capability of sudden events, and simultaneously constructs an intelligent matching algorithm between police situations and police officers to achieve optimal allocation of police force, realize scientific police dispatching and improve police situation disposal efficiency.
15. Offshore Fishing Boat Activity Behavior Analysis - Production Activity Analysis: Aggregates static data such as fishing ports, sheltered docks and waterways, as well as dynamic data such as fishing boat AIS data and ship positioning data. It analyzes ship activity behaviors including outbound operation destination analysis, departure time and frequency analysis, berthing port popularity analysis and ship origin analysis in fishing areas.
16. Offshore Fishing Boat Activity Behavior Analysis - Offshore Illegal Activity Analysis: Based on fishing boat activity trajectory data, an illegal activity analysis model is constructed using machine learning algorithms, and accurately identifies offshore illegal activities such as illegal sand mining and illegal fishing according to predicted trajectories.

搜集汇总

背景与挑战

背景概述

该数据集是一个交通大数据分析算法库，包含16类针对城市交通、公交、出租等领域的算法模型，如流量预测、车辆识别和智能排班等，适用于多种智能化交通业务需求。