💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
大数据的出行方式推荐系统介绍
本系统是一个基于Python+Django框架开发的智能出行方式推荐系统，专门针对现代城市复杂的交通环境和用户多样化的出行需求而设计。系统运用大数据分析技术，通过收集和处理用户的历史出行数据、实时交通状况、天气信息、个人偏好等多维度信息，为用户提供个性化的出行方案建议。系统核心功能包括用户出行行为分析、智能路线规划、多模式交通工具对比、实时交通预测等模块。通过机器学习算法对用户出行模式进行深度挖掘，结合当前交通状况和预测模型，系统能够在公交、地铁、打车、骑行、步行等多种出行方式中为用户推荐最优选择。系统界面采用响应式设计，支持Web端和移动端访问，用户可以便捷地查询出行建议、查看历史记录、设置个人偏好参数。整个系统架构采用前后端分离的设计模式，后端提供RESTful API接口，前端通过Ajax进行数据交互，保证了系统的可扩展性和维护性。
大数据的出行方式推荐系统演示视频
大数据的出行方式推荐系统演示图片
大数据的出行方式推荐系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, count, max, min, when, desc
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.clustering import KMeans
from pyspark.ml.regression import LinearRegression
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from datetime import datetime, timedelta
import json
import math
spark = SparkSession.builder.appName("TravelRecommendation").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
def user_behavior_analysis(request):
user_id = request.POST.get('user_id')
travel_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "user_travels").option("user", "root").option("password", "password").load()
user_travels = travel_data.filter(col("user_id") == user_id)
time_analysis = user_travels.groupBy("hour_of_day").agg(count("*").alias("travel_count"), avg("duration").alias("avg_duration"))
frequent_routes = user_travels.groupBy("start_location", "end_location").agg(count("*").alias("frequency")).orderBy(desc("frequency"))
transport_preference = user_travels.groupBy("transport_type").agg(count("*").alias("usage_count"), avg("cost").alias("avg_cost"), avg("duration").alias("avg_duration"))
weather_impact = user_travels.groupBy("weather_condition", "transport_type").agg(count("*").alias("count")).orderBy(desc("count"))
distance_analysis = user_travels.groupBy("distance_range").agg(count("*").alias("count"), avg("satisfaction").alias("avg_satisfaction"))
feature_cols = ["hour_of_day", "day_of_week", "weather_code", "distance", "cost", "duration"]
assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
feature_data = assembler.transform(user_travels.select(*feature_cols))
kmeans = KMeans(k=5, seed=1, featuresCol="features")
model = kmeans.fit(feature_data)
predictions = model.transform(feature_data)
cluster_analysis = predictions.groupBy("prediction").agg(count("*").alias("cluster_size"), avg("cost").alias("avg_cost"), avg("duration").alias("avg_duration"))
behavior_patterns = []
for row in time_analysis.collect():
patterns = {"hour": row["hour_of_day"], "frequency": row["travel_count"], "avg_duration": round(row["avg_duration"], 2)}
behavior_patterns.append(patterns)
transport_stats = []
for row in transport_preference.collect():
stats = {"type": row["transport_type"], "usage": row["usage_count"], "avg_cost": round(row["avg_cost"], 2), "avg_duration": round(row["avg_duration"], 2)}
transport_stats.append(stats)
result = {"behavior_patterns": behavior_patterns, "transport_preferences": transport_stats, "frequent_routes": [{"start": r["start_location"], "end": r["end_location"], "frequency": r["frequency"]} for r in frequent_routes.limit(10).collect()]}
return JsonResponse(result)
@csrf_exempt
def intelligent_route_planning(request):
start_lat = float(request.POST.get('start_lat'))
start_lng = float(request.POST.get('start_lng'))
end_lat = float(request.POST.get('end_lat'))
end_lng = float(request.POST.get('end_lng'))
departure_time = request.POST.get('departure_time', datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
user_preferences = json.loads(request.POST.get('preferences', '{}'))
traffic_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "real_time_traffic").option("user", "root").option("password", "password").load()
weather_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "weather_info").option("user", "root").option("password", "password").load()
historical_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "historical_travels").option("user", "root").option("password", "password").load()
distance = math.sqrt((end_lat - start_lat)**2 + (end_lng - start_lng)**2) * 111000
current_weather = weather_data.filter(col("timestamp") >= datetime.now().strftime('%Y-%m-%d %H:00:00')).first()
weather_factor = 1.0
if current_weather and current_weather["condition"] in ["rain", "snow"]:
weather_factor = 1.3
elif current_weather and current_weather["condition"] == "fog":
weather_factor = 1.2
routes = []
if distance <= 1000:
walking_time = distance / 83.33 * weather_factor
walking_cost = 0
walking_score = calculate_route_score(walking_time, walking_cost, "walking", user_preferences)
routes.append({"type": "walking", "duration": round(walking_time, 1), "cost": walking_cost, "score": walking_score, "description": "步行路线"})
if distance <= 5000:
cycling_time = distance / 250 * weather_factor
cycling_cost = 2.0
cycling_score = calculate_route_score(cycling_time, cycling_cost, "cycling", user_preferences)
routes.append({"type": "cycling", "duration": round(cycling_time, 1), "cost": cycling_cost, "score": cycling_score, "description": "骑行路线"})
current_traffic = traffic_data.filter((col("area_lat").between(min(start_lat, end_lat) - 0.01, max(start_lat, end_lat) + 0.01)) & (col("area_lng").between(min(start_lng, end_lng) - 0.01, max(start_lng, end_lng) + 0.01))).agg(avg("congestion_level")).first()
traffic_multiplier = 1.0
if current_traffic and current_traffic[0]:
traffic_multiplier = 1 + (current_traffic[0] - 1) * 0.5
driving_time = distance / 833.33 * traffic_multiplier * weather_factor
driving_cost = distance * 0.002 + 10
driving_score = calculate_route_score(driving_time, driving_cost, "driving", user_preferences)
routes.append({"type": "driving", "duration": round(driving_time, 1), "cost": round(driving_cost, 2), "score": driving_score, "description": "驾车路线"})
if distance > 2000:
subway_stations = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "subway_stations").option("user", "root").option("password", "password").load()
nearest_start = find_nearest_station(start_lat, start_lng, subway_stations)
nearest_end = find_nearest_station(end_lat, end_lng, subway_stations)
if nearest_start and nearest_end:
subway_time = calculate_subway_time(nearest_start, nearest_end) + 10
subway_cost = 4.0
subway_score = calculate_route_score(subway_time, subway_cost, "subway", user_preferences)
routes.append({"type": "subway", "duration": round(subway_time, 1), "cost": subway_cost, "score": subway_score, "description": "地铁路线"})
routes.sort(key=lambda x: x["score"], reverse=True)
return JsonResponse({"recommended_routes": routes[:3], "total_options": len(routes)})
def calculate_route_score(duration, cost, transport_type, preferences):
base_score = 100
time_weight = preferences.get("time_importance", 0.4)
cost_weight = preferences.get("cost_importance", 0.3)
comfort_weight = preferences.get("comfort_importance", 0.3)
time_penalty = (duration - 10) * time_weight * 2
cost_penalty = cost * cost_weight * 5
comfort_scores = {"walking": 60, "cycling": 70, "driving": 90, "subway": 85, "bus": 75}
comfort_bonus = comfort_scores.get(transport_type, 70) * comfort_weight * 0.5
final_score = max(0, base_score - time_penalty - cost_penalty + comfort_bonus)
return round(final_score, 1)
@csrf_exempt
def traffic_prediction_analysis(request):
location_lat = float(request.POST.get('latitude'))
location_lng = float(request.POST.get('longitude'))
prediction_hours = int(request.POST.get('hours', 6))
historical_traffic = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "traffic_history").option("user", "root").option("password", "password").load()
weather_forecast = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "weather_forecast").option("user", "root").option("password", "password").load()
area_traffic = historical_traffic.filter((col("latitude").between(location_lat - 0.005, location_lat + 0.005)) & (col("longitude").between(location_lng - 0.005, location_lng + 0.005)))
hourly_patterns = area_traffic.groupBy("hour_of_day", "day_of_week").agg(avg("traffic_density").alias("avg_density"), avg("average_speed").alias("avg_speed"), count("*").alias("sample_count"))
weather_impact = area_traffic.join(weather_forecast, ["date", "hour"], "inner").groupBy("weather_condition").agg(avg("traffic_density").alias("weather_avg_density"))
feature_data = area_traffic.select("hour_of_day", "day_of_week", "weather_code", "is_holiday", "traffic_density")
assembler = VectorAssembler(inputCols=["hour_of_day", "day_of_week", "weather_code", "is_holiday"], outputCol="features")
training_data = assembler.transform(feature_data)
lr = LinearRegression(featuresCol="features", labelCol="traffic_density")
model = lr.fit(training_data)
predictions = []
current_time = datetime.now()
for i in range(prediction_hours):
future_time = current_time + timedelta(hours=i)
hour = future_time.hour
day_of_week = future_time.weekday()
weather_code = get_weather_code_prediction(future_time)
is_holiday = check_holiday(future_time.date())
historical_pattern = hourly_patterns.filter((col("hour_of_day") == hour) & (col("day_of_week") == day_of_week)).first()
base_density = historical_pattern["avg_density"] if historical_pattern else 0.5
base_speed = historical_pattern["avg_speed"] if historical_pattern else 30.0
weather_adjustment = get_weather_traffic_impact(weather_code)
predicted_density = base_density * weather_adjustment
predicted_speed = base_speed / weather_adjustment
congestion_level = min(5, max(1, int(predicted_density * 5)))
prediction_item = {"time": future_time.strftime('%H:%M'), "predicted_density": round(predicted_density, 3), "predicted_speed": round(predicted_speed, 1), "congestion_level": congestion_level, "recommendation": get_traffic_recommendation(congestion_level)}
predictions.append(prediction_item)
traffic_trends = analyze_traffic_trends(area_traffic)
peak_hours = identify_peak_hours(hourly_patterns)
result = {"location": {"latitude": location_lat, "longitude": location_lng}, "predictions": predictions, "traffic_trends": traffic_trends, "peak_hours": peak_hours, "analysis_period": f"{prediction_hours} hours"}
return JsonResponse(result)
大数据的出行方式推荐系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
