城市公共交通公交到站时间预测方法研究

发布时间：2023-12-29 19:20

　　随着国民经济的不断发展,人均汽车拥有量不断提高,与之带来的是城市机动车保有量持续增长。然而现有的交通基础设施的建设速度和规模不能满足日益增长的城市交通的需要,供需矛盾突出,使得城市的交通日益拥堵。要解决城市交通问题,需要发展城市公共交通。现阶段,在很多中小城市,常规公共汽车是公共交通的主要组成部分。常规公共汽车是覆盖面最广、运行线路最多、乘车费用最低的一种解决城市居民出行的最好方式。然而现阶段在很多中小城市,常规公交的出行比例不高,公交对居民出行的吸引力较低。究其原因主要是由于常规公交车到站时间不确定,准时性较差,乘客需要等待未知的时间,需要时刻关注到站的车辆信息,公交信息发布较落后,出现“伸脖子”等公交的情况。乘客容易出现焦急的等待情绪,或者直接改换其他交通方式出行。因此,准确实时的公交车到站时间预测可以提高中小城市居民公交出行的比例,提高乘客乘车的满意度,提高城市公交的服务水平,对解决交通问题具有重要意义。本文首先分析公交车到站时间的运行特性及影响因素,把城市公交车辆到站时间分为三部分,分别为路段行驶时间、站点停靠时间、交叉口延误时间。针对这三部分的运行特性和影响因素进行分析,选取...

【文章页数】：93 页

【学位级别】：硕士

【文章目录】：
摘要
Abstract
Chapter 1 Introduction
    1.1 Research background and significance
    1.2 The current state of forecasting on arrival time of buses
        1.2.1 Application status of forecasting bus arrival time in abroad
        1.2.2 Status of theoretical research on bus arrival time
        1.2.3 Theoretical study on the prediction bus arrival time in China
    1.3 Research content and technical route
Chapter 2 Bus arrival time analysis and data preprocessing
    2.1 Analysis of the operating characteristics of public transport vehicles
        2.1.1 Operation characteristics of bus sections
        2.1.2 Operation characteristics of bus stop stations
        2.1.3 Deceleration pit stop
        2.1.4 Stop in the station
        2.1.5 Accelerating outbound
        2.1.6 Operation characteristics of bus intersections
    2.2 Analysis of the influencing factors of bus arrival time
        2.2.1 Analysis of road travel time factors
        2.2.2 Analysis of the influencing factors of bus stop time
        2.2.3 Intersection transit time analysis
    2.3 Data acquisition and processing
        2.3.1 Collection of basic data
        2.3.2 Pre-processing of bus GPS data
        2.3.3 Data error analysis
        2.3.4 Data processing
        2.3.5 Bus line information collection and preprocessing
        2.3.6 Discretization of bus lines
        2.3.7 Bus GPS data matching with bus line information
    2.4 Summary of this chapter
Chapter 3 Related prediction methods of bus arrival time
    3.1 Prediction methods of bus arrival time
        3.1.1 Forecast model based on historical data
    3.2 Regression prediction model
        3.2.1 Basic principles of time series forecasting methods
    3.3 Support vector machine algorithm principle
    3.4 Kalman filter model
        3.4.1 Principle of artificial neural network model
        3.4.2 Combined forecasting model
    3.5 Summary of this chapter
Chapter 4 Establishing the prediction model of bus arrival time
    4.1 Initial prediction model of bus arrival time
        4.1.1 Prediction model of bus journey time
        4.1.2 The model for predicting the stopping time of bus stops
        4.1.3 Prediction model of transit time at bus intersections
    4.2 Kalman filter-based bus arrival time prediction model
    4.3 Summary of this chapter
Chapter 5 Research on verification of predictive model examples
    5.1 Example verification
        5.1.1 Source of experimental data
        5.1.2 Example line and survey data description
        5.1.3 Forecast results and error analysis of bus arrival time
    5.2 Summary of this chapter
Conclus?on
References
Acknowledgements



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