路段平均行程时间估计方法

张和生; 张毅; 胡东成

路段平均行程时间估计方法

1.
北京交通大学电气工程学院，北京 100084
2.
北京交通大学轨道交通控制与安全国家重点实验室，北京 100044
3.
清华大学自动化系，北京 100084

基金项目:

国家863计划项目 2006AAllZ231

国家973计划项目 2006CB705506

国家自然科学基金项目 60374059

轨道交通控制与安全国家重点实验室开放课题项目 SKL2007K011

北京交通大学科技发展基金项目 DOJ05007

详细信息

作者简介:
张和生(1970-), 男, 河北隆尧人, 北京交通大学副教授, 工学博士, 从事检测技术、控制工程与信息处理研究

中图分类号: U491.1
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- 被引次数: 0
出版历程
- 收稿日期: 2007-07-28
- 刊出日期: 2008-02-25

Estimation method of average travel time for road sections

1.
School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
2.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
3.
Department of Automation, Tsinghua University, Beijing 100084, China

More Information

Author Bio:
Zhang He-sheng(1970-), male, PhD, associate professor, +86-10-51684052, hszhang@bjtu.edu.cn

摘要

摘要: 为了有效利用线圈检测数据, 精确估计路段平均行程时间, 提出了一种路段平均行程时间估计方法。将路段平均行程时间分为平均行驶时间、平均排队时间和平均通过路口时间三部分。考虑线圈埋设的特点, 通过估计平均行驶速度得到平均行驶时间。用分段时齐Poisson过程描述车辆驶入路段过程和驶离过程, 用Markov排队模型描述车辆排队过程, 用生灭过程描述排队车辆数, 得到车辆排队模型, 计算了路段有、无初始排队的平均排队时间。基于选取与路口相关的饱和流率和平均车长, 计算了平均通过路口时间。计算结果表明: 平均行程时间估计值与实测值的误差小于12%, 说明路段平均行程时间估计方法可行。
- 智能交通系统 /
- 平均行程时间 /
- 线圈数据 /
- Markov排队模型 /
- Poisson过程
Abstract: In order to availably utilize loop data and estimate the average travel time of road sections accurately and quickly, an estimation method of average travel time was proposed. Average travel time was divided into average running time, average queue delay and average crossing intersection time. Average running time was calculated by using average speed based on taking account of the installation type differences of loops. Piece-wise time homogeneous Poisson process was used to describe the arrival and departure processes of vehicles, Markov model was used to describe the queue process of vehicles, birth and death process was used to model vehicles' number in queue, and two kinds of average queue times with initial queue and no initial queue were computed. Average crossing intersection time was computed based on average vehicle length and saturation volume rate at intersection. Computation result shows that the relative error between the measure value and computation value of average travel time is less than 12%, so the method is feasible.
- intelligent transportation systems /
- average travel time /
- loop data /
- Markov queue model /
- Poisson process

HTML全文

图 1 路段

Figure 1. Road sections

下载: 全尺寸图片幻灯片

图 2 选择路段

Figure 2. Selected links

下载: 全尺寸图片幻灯片

图 3 平均行驶速度

Figure 3. Average travel speed

下载: 全尺寸图片幻灯片

图 4 平均排队时间

Figure 4. Average queueing time

下载: 全尺寸图片幻灯片

图 5 平均行程时间比较

Figure 5. Comparison of average travel times

下载: 全尺寸图片幻灯片

图 6 车流量与平均排队时间

Figure 6. Flow volume and average travel time

下载: 全尺寸图片幻灯片

图 7 初始排队对排队时间的影响

Figure 7. Influence of initial queue on average travel time

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图 8 不同饱和流率下平均排队时间比较

Figure 8. Comparison of average queueing times under different saturation flow rates

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图 9 平均排队时间和平均行程时间比较

Figure 9. Comparison of average queueing time and average travel time

下载: 全尺寸图片幻灯片

表 1 计算结果

Table 1. Computation result

时段	行驶时间/s	排队时间/s	通过路口时间/s	平均行程时间/s
6:30~6:35	66.92	24.55	7.6	99.07
6:35~6:40	79.90	25.22	7.6	112.72
6:40~6:45	69.60	25.45	7.6	102.65
6:45~6:50	81.56	27.14	7.6	116.30
6:50~6:55	100.98	26.12	7.6	134.70
6:55~7:00	67.39	28.82	7.6	103.81
7:00~7:05	90.00	26.38	7.6	123.98
7:05~7:10	87.00	35.00	7.6	129.60
7:10~7:15	77.03	53.33	7.6	137.96
7:15~7:20	91.58	120.00	7.6	219.18
7:20~7:25	72.50	193.00	7.6	273.10
7:25~7:30	75.40	140.00	7.6	223.00
7:30~7:35	79.65	88.82	7.6	176.07
7:35~7:40	73.67	140.00	7.6	221.27
7:40~7:45	82.08	190.00	7.6	279.68
7:45~7:50	71.58	141.54	7.6	220.72
7:50~7:55	81.96	88.33	7.6	177.89
7:55~8:00	122.01	33.04	7.6	162.65
8:00~8:05	94.39	130.00	7.6	231.99
8:05~8:10	80.13	80.34	7.6	168.07
8:10~8:15	62.14	41.43	7.6	111.17
8:15~8:20	116.52	26.12	7.6	150.24
8:20~8:25	75.46	62.86	7.6	145.92
8:25~8:30	65.76	35.79	7.6	109.15
8:30~8:35	74.15	37.65	7.6	119.40
8:35~8:40	91.21	26.98	7.6	125.79
8:40~8:45	64.26	37.65	7.6	109.51
8:45~8:50	87.00	26.67	7.6	121.27
8:50~8:55	126.55	25.83	7.6	159.98
8:55~9:00	109.89	43.08	7.6	160.57
9:00~9:05	69.05	26.02	7.6	102.67
9:05~9:10	66.92	25.71	7.6	100.23
9:10~9:15	61.27	30.17	7.6	99.04
9:15~9:20	65.91	28.33	7.6	101.84
9:20~9:25	62.14	26.90	7.6	96.64
9:25~9:30	60.00	26.19	7.6	93.79
9:30~9:35	94.91	26.82	7.6	129.33

下载: 导出CSV

参考文献(14)

[1]	杨兆升. 关于智能运输系统的关键理论-综合路段行程时间预测的研究[J]. 交通运输工程学报, 2001, 1(1): 65-67. doi: 10.3321/j.issn:1671-1637.2001.01.017 Yang Zhao-sheng. Study on the synthetic link travel time prediction model of key theory of ITS[J]. Journal of Traffic and Transportation Engineering, 2001, 1(1): 65-67. (in Chinese) doi: 10.3321/j.issn:1671-1637.2001.01.017
[2]	Chien S I, Kuchipudi C M. Dynamic travel time prediction with real-time and historic data[J]. Journal of Transportation Engineering, 2003, 129(6): 608-616. doi: 10.1061/(ASCE)0733-947X(2003)129:6(608)
[3]	Dharia A, Adeli H. Neural network model for rapid forecasting of freeway link travel time[J]. Engineering Applications of Artificial Intelligence, 2003, 16(7/8): 607-613.
[4]	朱中, 杨兆升. 基于卡尔曼滤波理论的实时行程时间预测模型[J]. 系统工程理论与实践, 1999, 19(9): 74-78. https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL199909009.htm Zhu Zhong, Yang Zhao-sheng. Areal-time travel time estimation model based on BP neural network[J]. Systems Engineering—Theory & Practice, 1999, 19(9): 74-78. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL199909009.htm
[5]	Dailey D J. Travel-time estimation using cross-correlation techniques[J]. Transportation Research Part B, 1993, 27(2): 97-107. doi: 10.1016/0191-2615(93)90002-R
[6]	徐猛, 史忠科. 基于二次函数型路段行程时间模型描述的交通输出流特性[J]. 中国公路学报, 2006, 19(3): 77-81. doi: 10.3321/j.issn:1001-7372.2006.03.014 Xu Meng, Shi Zhong-ke. Behavior of traffic outflows under description of quadratic link travel time model[J]. China Journal of Highway and Transport, 2006, 19(3): 77-81. (in Chinese) doi: 10.3321/j.issn:1001-7372.2006.03.014
[7]	Nam D H, Drew D R. Traffic dynamics: method for estimating freeway travel times in real time from flow measurements[J]. Journal of Transportation Engineering, 1996, 122(3): 185-191. doi: 10.1061/(ASCE)0733-947X(1996)122:3(185)
[8]	Ran B, Rouphail N M, Tarko A, et al. Towards a class of link travel time functions for dynamic assignment models on signalized networks[J]. Transportation Research Part B, 1997, 31(4): 277-290. doi: 10.1016/S0191-2615(96)00036-7
[9]	荣建, 何民, 陈春妹. 信号交叉口排队长度动态计算方法研究[J]. 中国公路学报, 2002, 15(3): 101-103. doi: 10.3321/j.issn:1001-7372.2002.03.024 Rong Jian, He Min, Chen Chun-mei. Dynamic model of calculating queue length at signalized intersection[J]. China Journal of Highway and Transport, 2002, 15(3): 101-103. (in Chinese) doi: 10.3321/j.issn:1001-7372.2002.03.024
[10]	Athol P. Interdependence of certain operational characteristics within a moving traffic stream[J]. Highway Research Re-cord, 1965, 72: 58-87.
[11]	Dailey D J. A statistical algorithm for estimating speed from single loop volume and occupancy measurements[J]. Trans-portation Research Part B, 1999, 33(5): 313-322. doi: 10.1016/S0191-2615(98)00037-X
[12]	Wang Yin-hai, Nihan N L. Can single-loop detectors do the work of dual-loop detectors?[J]. Journal of Transportation Engineering, 2003, 129(2): 169-176. doi: 10.1061/(ASCE)0733-947X(2003)129:2(169)
[13]	张和生, 张毅, 温慧敏, 等. 利用GPS数据估计路段的平均行程时间[J]. 吉林大学学报: 工学版, 2007, 37(3): 533-537. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY200703010.htm Zhang He-sheng, Zhang Yi, Wen Hui-min, et al. Estimamtion approaches of average link travel time using GPS data[J]. Journal of Jilin University: Engineering and Technology Edition, 2007, 37(3): 533-537. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY200703010.htm
[14]	张和生. 基于多源数据的交通状态分析方法研究[D]. 北京: 清华大学, 2006.