Mitigating Bunching with Bus-following Models and Bus-to-Bus Cooperation

本地巴士 地址总线 总线网络 控制总线 背景(考古学) 公共交通 计算机科学 系统总线 地铁列车时刻表 直线(几何图形) 实时计算 工程类 运输工程 古生物学 几何学 数学 计算机硬件 生物 操作系统
作者
Konstantinos Ampountolas,Malcolm Kring
标识
DOI:10.1109/itsc.2015.18
摘要

Bus bunching is an instability problem where buses operating on high frequency public transport lines arrive at stops in bunches. In this work, we unveil that bus-following models can be used to design bus-to-bus cooperative control strategies and mitigate bunching. The use of bus-following models avoids the explicit modelling of bus-stops, which would render the resulting problem discrete, with events occurring at arbitrary time intervals. In a "follow-the-leader" two-bus system, bus-to-bus communication allows the driver of the following bus to observe (from a remote distance) the position and speed of a lead bus operating in the same transport line. The information transmitted from the lead bus is then used to control the speed of the follower to eliminate bunching. In this context, we first propose practical linear and nonlinear control laws to regulate space headways and speeds, which would lead to bunching cure. Then a combined state estimation and remote control scheme, which is based on the Linear-Quadratic Gaussian theory, is developed to capture the effect of bus stops, traffic disturbances, and randomness in passenger arrivals. To investigate the behaviour and performance of the developed approaches the 9-km 1-California line in San Francisco with about 50 arbitrary spaced bus stops is used. Simulations with real passenger data obtained from the San Francisco Municipal Transportation Agency are carried out. Results show bunching avoidance and significant improvements in terms of schedule reliability of bus services and delays. The proposed control is robust, scalable in terms of public transport network size, and thus easy to implement in real-world settings.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
平淡南霜发布了新的文献求助10
刚刚
2秒前
桐桐应助赵哈哈找文献采纳,获得10
2秒前
英俊的铭应助啊啊啊采纳,获得10
2秒前
充电宝应助啊啊啊采纳,获得10
2秒前
小二郎应助啊啊啊采纳,获得10
2秒前
kingwill应助柔弱的秋珊采纳,获得20
3秒前
3秒前
4秒前
田様应助lauuu采纳,获得10
6秒前
华仔应助吃脑花补脑花采纳,获得10
9秒前
阿童木完成签到,获得积分10
12秒前
13秒前
13秒前
xueshufengbujue完成签到,获得积分10
14秒前
Hello应助火星上的焦采纳,获得10
14秒前
17秒前
lauuu发布了新的文献求助10
18秒前
18秒前
19秒前
厚礼蟹发布了新的文献求助10
23秒前
24秒前
orixero应助1111采纳,获得10
26秒前
27秒前
vielate完成签到,获得积分10
27秒前
27秒前
27秒前
蒋依伶发布了新的文献求助10
29秒前
31秒前
TJway发布了新的文献求助10
32秒前
32秒前
搜集达人应助动听的笑南采纳,获得10
33秒前
FB发布了新的文献求助10
35秒前
36秒前
鲸落发布了新的文献求助10
37秒前
39秒前
39秒前
41秒前
TJway完成签到,获得积分10
41秒前
zhu发布了新的文献求助10
42秒前
高分求助中
Востребованный временем 2500
Agaricales of New Zealand 1: Pluteaceae - Entolomataceae 1040
지식생태학: 생태학, 죽은 지식을 깨우다 600
海南省蛇咬伤流行病学特征与预后影响因素分析 500
Neuromuscular and Electrodiagnostic Medicine Board Review 500
ランス多機能化技術による溶鋼脱ガス処理の高効率化の研究 500
Relativism, Conceptual Schemes, and Categorical Frameworks 500
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3462725
求助须知:如何正确求助?哪些是违规求助? 3056239
关于积分的说明 9051164
捐赠科研通 2745868
什么是DOI,文献DOI怎么找? 1506668
科研通“疑难数据库(出版商)”最低求助积分说明 696188
邀请新用户注册赠送积分活动 695720