City-Scale Holographic Traffic Flow Data based on Vehicular Trajectory Resampling

Wang, Yimin; Chen, Yixian; Li, Guilong; Lu, Yuhuan; Yu, Zhi; He, Zhaocheng

doi:10.48550/arxiv.2108.13376

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…The fields of the original data are shown in Table 4. We use the "traffic zone" proposed in Wang et al [2021] to describe the origin and destination of the trips. There are multiple points of interest(POI) inside of zones, and this information is publicly available on the Internet.…”

Section: Data Descriptionmentioning

confidence: 99%

Potential destination prediction for low predictability individuals based on knowledge graph

Li¹,

Chen²,

Liao³

et al. 2022

Preprint

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Destination prediction has been a critical topic in transportation research, and there are a large number of studies. However, almost all existing studies are based on high predictability data conditions while pay less attention to the data condition with low predictability, where the regularity of single individuals is not exposed. Based on a certain period of observation, there is a fact that individuals may choose destinations beyond observation, which we call "potential destinations". The number of potential destinations is very large and can't be ignored for the data condition with low predictability formed by short-term observation. To reveal the choice pattern of potential destination of individuals under the data condition with low predictability, we propose a global optimization method based on knowledge graph embedding. First, we joint the trip data of all individuals by constructing Trip Knowledge Graph(TKG). Next, we optimize the general algorithm of knowledge graph embedding for our data and task in training strategy and objective function, then implement it on TKG. It can achieve global optimization for association paths that exist between almost any two entities in TKG. On this basis, a method for potential destination prediction is proposed, giving the possible ranking of unobserved destinations for each individual. In addition, we improve the performance by fusing static statistical information that is not passed to TKG. Finally, we validate our method in a real-world dataset, and the prediction results are highly consistent with individuals' potential destination choice behaviour.

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Section: Data Descriptionmentioning

confidence: 99%

Potential destination prediction for low predictability individuals based on knowledge graph

Li¹,

Chen²,

Liao³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To speak in detail, a series of studies propose lane (lane group)-level estimation methods using the travel time pattern, for example, lane-based traffic demand estimation [29], lane-based saturation degree estimation [30] and lane groups-based maximum queue length estimation [31]. Another traffic scenario is vehicular trajectory reconstruction using the topology of the road network and some constraints of travel pattern [32,33], as well as the further applicationsroute choice modelling [34] and depicts city-scale holographic traffic flow data [35]. The rest of the traffic scenario using LPR data is TSC optimisation, such as dynamic TSC [36] and integrated TSC [37].…”

Section: Literature Reviewmentioning

confidence: 99%

Signal timing parameters inference method at intersections using license plate recognition data

Lin

Chen

et al. 2022

IET Intelligent Trans Sys

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Signal timing parameters are essential components in traffic signal control (TSC). It affects not only traffic management, but also traffic safety. However, due to the confidential issues of the traffic management department, or lack of data integration from different signal manufacturers, it is intractable to obtain the city‐scale signal timing data. In the previous studies, some existing estimation methods focused on a single parameter and fixed‐timing scheme. To tackle this issue, this study attempts to develop an integrated parameters inference method based on license plate recognition (LPR) data, considering phase weight, average phase duration information and the overall phases of the intersections. In particular, the proposed method includes phase sequence inference model, cycle length inference model and phase duration inference model. To testify the performance of the proposed method, a real‐world LPR dataset from Guangzhou, China, is applied. Numerical results show that the proposed method performs more efficiently on parameter inferences than the state‐of‐the‐art (SOTA) approach. For instance, in the given research time period, the mean absolute error (MAE) of each phase duration is 2 s averagely (6.32 s in the SOTA approach), and the mean relative error (MRE) of cycle length is 0.91% (11.67% in the SOTA approach).

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City-Scale Holographic Traffic Flow Data based on Vehicular Trajectory Resampling

Cited by 2 publications

References 14 publications

Potential destination prediction for low predictability individuals based on knowledge graph

Potential destination prediction for low predictability individuals based on knowledge graph

Signal timing parameters inference method at intersections using license plate recognition data

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