A method of path travel time estimation based on path splicing model

LI Yetao; HUANG Min; HUANG Chunting; ZHANG Xiaolan

doi:10.13471/j.cnki.acta.snus.2023D010

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A method of path travel time estimation based on path splicing model

Research articles | 更新时间：2023-12-08

- A method of path travel time estimation based on path splicing model
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 62, Issue 6, Pages: 80-88(2023)
- 作者机构：
  
  1.中山大学智能工程学院 / 广东省智能交通系统重点实验室，广东深圳 518107
  2.广东工贸职业技术学院，广东广州 510510
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.2023D010
  CLC： U491
- Published：25 November 2023，
  
  Published Online：26 July 2023，
  
  Received：13 February 2023，
  
  Accepted：21 April 2023
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李烨焘,黄敏,黄春婷等.基于路径拼接模型的路径行程时间估计方法[J].中山大学学报(自然科学版),2023,62(06):80-88.

LI Yetao,HUANG Min,HUANG Chunting,et al.A method of path travel time estimation based on path splicing model[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2023,62(06):80-88.
李烨焘,黄敏,黄春婷等.基于路径拼接模型的路径行程时间估计方法[J].中山大学学报(自然科学版),2023,62(06):80-88. DOI： 10.13471/j.cnki.acta.snus.2023D010.

LI Yetao,HUANG Min,HUANG Chunting,et al.A method of path travel time estimation based on path splicing model[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2023,62(06):80-88. DOI： 10.13471/j.cnki.acta.snus.2023D010.

摘要

依托卡口检测数据，提出路径拼接模型以利用大量短路径行程时间对城市道路中的长路径行程时间进行估计。该模型提出子路径进行拼接时要满足的两个条件：存在公共结点及交通状态相近；在此基础上将目标路径按照整体方差最小的原则拆分为由多个子路径组成的拼接方案。基于提出的路径交通流基本图划分子路径的交通状态，使用Burr分布拟合不同交通状态下的子路径行程时间得到行程时间分布函数；将给定的目标行程时间分配至拼接方案中的子路径使得子路径行程时间之和等于目标行程时间，累加所有时间分配方案对应的概率得到目标路径的行程时间概率，计算多个目标行程时间对应的概率得到目标路径的行程时间概率分布。实验结果显示：本文提出的路径行程时间估计方法得到的路径行程时间均值与真实轨迹均值的误差为3.04%；得到的行程时间分布与真实行程时间分布的JS散度为0.05。路径行程时间估计值真实可靠，可为后续研究提供数据基础。

Abstract

In this paper， we propose a path splicing model to estimate the long path travel time on urban roads using the automatic vehicle identification （AVI） data. The model requires two conditions to be met when splicing the sub-paths： public nodes exist and the traffic status is similar. A target path is split into a splicing scheme composed of several subpaths whose variance is the least. Based on the proposed traffic state， the travel time distributions are obtained by fitting the travel time under different states using the Burr distribution. Next， the method assigns target travel time to the sub-paths， thus， the travel time probability of the target path is the accumulation of the probability of all time assignments. The study shows that the error between the average path travel time of the proposed method and the real path is 3.04%， and the JS divergence between the travel time distribution of the method and the real path is 0.05. The estimated path travel time is reliable， which can provide a data basis for subsequent research.

关键词

路径行程时间路径拼接交通状态行程时间分布

Keywords

path travel timepath splicingtraffic statusdistribution of travel time

references

冯轩，2019. 稀疏轨迹数据下的行程时间估计方法研究［D］. 长沙：长沙理工大学.

靳文舟，朱子轩，巫威眺，等，2020. 联合配送外包策略的定位运输问题［J］. 中山大学学报（自然科学版），59（4）： 89-97.

李先通，全威，王华，等，2022. 基于时空特征深度学习模型的路径行程时间预测［J］. 吉林大学学报（工学版），52（3）： 557-563.

刘晓波，蒋阳升，唐优华，等，2019. 综合交通大数据应用技术的发展展望［J］. 大数据，5（3）： 55-68.

刘张雷，史忠科，2010. 一种基于路网变化的动态路径规划策略［J］. 交通运输系统工程与信息，10（3）： 147-152.

马闻博，2020. 基于车牌识别数据的城市道路行程时间估计及短时预测研究［D］. 成都：西南交通大学.

千梦晗，2020. 城市干道行程时间动态估计及可靠性计算方法［D］. 重庆：重庆交通大学.

CHEN Z, LIU X C, 2021. Statistical distance-based travel-time reliability measurement for freeway bottleneck identification and ranking[J]. Transp Res Rec, 2675(11): 424-438.

DAGANZO C F, 2007. Urban gridlock: Macroscopic modeling and mitigation approaches[J]. Transp Res B:Methodol, 41(1): 49-62.

EDIE L C, 1963. Discussion of traffic stream measurements and definitions[M].New York: Port of New York Authority.

GREENSHIELDS B D, CHANNING W, MILLER H, 1935. A study of traffic capacity[C]//Proceedings of the Fourteenth Annual Meeting of the Highway Research Board. Washington D C.

HAN L, DU B, LIN J, et al, 2022. Multi-semantic path representation learning for travel time estimation[J]. IEEE Trans Intell Transp Syst, 23(8): 13108-13117.

KULLBACK S, LEIBLER R A, 1951. On information and sufficiency[J]. Ann Math Statist, 22(1): 79-86.

LI X, CONG G, SUN A, et al, 2019. Learning travel time distributions with deep generative model[C]//The World Wide Web Conference. USA.

LIN X, 2019. A road network traffic state identification method based on macroscopic fundamental diagram and spectral clustering and support vector machine[J]. Math Probl Eng, DOI:10.1155/2019/6571237http://dx.doi.org/10.1155/2019/6571237.

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Related Author

LI Yetao

HUANG Min

HUANG Chunting

YAO Bofan

DENG Rufeng

XIONG Chen

CAI Ming

Bofan YAO

Related Institution

School of Intelligent Systems Engineering， Sun Yat-sen University / Guangdong Provincial Key Laboratory of Intelligent Transportation System

School of Intelligent Systems Engineering， Sun Yat-sen University

Key Laboratory of Intelligent Transportation System in Guangdong Province

Foshan Transportation Operations Monitor Centre

Key Laboratory of Intelligent Transportation System in Guangdong Province， Sun Yat-sen University

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