Traffic Flow Fluctuation Analysis Based on Beijing Taxi GPS Data

Guo, Jingyi; Li, Xianghua; Zhang, Zili; Zhang, Junwei

doi:10.1007/978-3-319-99247-1_40

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Figure 3 shows the relationship between the change in total delay for different opening periods for each peak demand. From (a)(b)(c), it can be easily observed that if the lane is activated within [0,1] before the peak, stopping the opening of the bus lane at [1,2] will bring higher delays to the roadway vehicles, and the delays keep decreasing as the opening time increasing. When enabled between [1,2], such as in Figure 3(d), the total delay will have a rapid reduction in the process, because this time period is the peak demand, the lane will have queues before the use of the lane, after the enablement when the queue in the bus lane dissipates only the general lane will produce delays, the sum of the vehicle traffic rate of the left and right lanes is greater than the rate before the enablement, and as a result, the total delay will change significantly compared to before opening.…”

Section: Fixed Off-peak Demandmentioning

confidence: 99%

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Optimization study of bus lane opening time

Heng,

Wu,

Zhang

et al. 2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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Section: Fixed Off-peak Demandmentioning

confidence: 99%

“…In places where urban road space is scarce, it is common to convert general street lanes to bus-only lanes at specific times of the day [1] . However, a converted lane will reduce the capacity of the general lane, and the impact caused during the morning and evening rush hours is more obvious.…”

Section: Introductionmentioning

confidence: 99%

Optimization study of bus lane opening time

Heng,

Wu,

Zhang

et al. 2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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“…Nevertheless, GPS trajectories often contain lots of noisy GPS coordinates, owing to the inevitable errors of GPS positioning [1][2][3][4][5][6][7]. ese coordinates deviate from the true positions of mobile devices, calling for the important preprocessing task of map-matching [8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Map-Matching on Low Sampling Rate Trajectories through Frequent Pattern Mining

Zhang

Ding³

2022

Scientific Programming

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Map-matching, an important preprocessing task in many location-based services (LBS), projects each point of the global positioning system (GPS) within a trajectory dataset onto a digital map. The state-of-the-art map-matching algorithms typically employ Hidden Markov model (HMM) via shortest path computation. But the computation of the shortest path might not work well on low-sampling-rate trajectory data (e.g., one GPS point every 1–5 min), leading to low matching precision and high running time. To solve the problem, this paper firstly identifies frequent patterns (FPs) in historical trajectories to capture meaningful mobility behaviors, and then extracts mobile behavior criterion (MBC) of mobile users. Such a criterion generally represents the route choice of mobile users on road networks. Moreover, the temporal information within trajectory data was employed to estimate the speed of mobile users on road segments. The identified FPs, coupled with MBC and moving speed, help to improve the map-matching precision of low-sampling-rate trajectories. In addition, an FP-forest structure was proposed to index the identified FPs. The structure could greatly speed up the lookup of frequent paths for shorter running time. Furthermore, the FP-forest structure was pruned to reduce redundancy with smaller space cost. Finally, experiments were carried out on real-world datasets. The results confirm that our FP-matching method outperforms state-of-the-art in terms of effectiveness and efficiency.

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“…Only a few studies have considered statistical properties of Origin-Destination(OD) demand networks based on actual travel patterns [ 29 , 30 ]. In general, traffic demands are not consistent throughout a day or months, and the congestion occurs due to temporal inconsistencies between the designed properties of physical transportation network and desired properties of network structure perceived by road users.…”

Section: Introductionmentioning

confidence: 99%

Measuring health of highway network configuration against dynamic Origin-Destination demand network using weighted complex network analysis

et al. 2018

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Ideal configuration or layout of highways should resemble the actual demands for the roads represented by Origin-Destination (OD) information. It would be beneficial if existing highways can be evaluated for their configurational fitness against the current demands, and newly planned highways can carefully be designed in terms of their layouts and topologies that would reflect the demands. Analysis techniques used for complex networks in the matured field of network theory can be applied for the highway layout health monitoring against the current OD information. This paper proposes a methodology of measuring the fitness of existing highways by comparing their structural configuration against conceptual OD networks using well-established techniques in network theory for complex networks. In the first phase, this paper conducts an empirical analysis and finds that both structural highway network and OD network follow the “power law” distribution as they are weighted by capacity and traffic volume respectively. It is also found that the power law coefficient of the OD network dynamically changes throughout the day and week. In the second phase, a noble methodology of weighting and measuring the health, of structural highway networks against OD networks by means of comparing their power law coefficients is proposed. It is found that the proposed method is effective at detecting deviations from ideal structural configurations associated with actual demands.

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Traffic Flow Fluctuation Analysis Based on Beijing Taxi GPS Data

Cited by 5 publications

References 18 publications

Optimization study of bus lane opening time

Optimization study of bus lane opening time

Map-Matching on Low Sampling Rate Trajectories through Frequent Pattern Mining

Measuring health of highway network configuration against dynamic Origin-Destination demand network using weighted complex network analysis

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