Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning

Qin, Jiayu; Mei, Gang; Xiao, Lei

doi:10.3390/su13010266

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…Qin [35] built a traffic flow network model to investigate traffic congestion problems based on GPS trajectories. Though this method effectively identified the traffic congestion area and can help make decisions in traffic planning, it ultimately failed to provide specific quantitative results or parameters that could be directly used in traffic control, such as TOSI or SOSI.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Traffic Oversaturation Based on Multi-Objective Data

Huang

Zhang

2022

Sustainability

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With the increase in people’s travel demands, the air pollution generated by the means of transportation they take is also becoming more and more serious. Among them, in the process of people’s travel, the exhaust pollution caused by traffic congestion is particularly serious. Accurately identifying various regimes of oversaturation and taking effective control strategies play a key role in alleviating traffic congestion. There are three regimes of evolution during an oversaturated scenario: loading, oversaturated operation, and recovery. In the traffic signal control under the oversaturated scenario, the corresponding control targets and methods should be adopted based on the regime of oversaturation. In this paper, the multi-objective attributes and their trajectory data of each movement at the intersection are analyzed. Based on the oversaturation severity index, the traffic volume, and the queuing on the movement, the identification and cause analysis of each regime of the oversaturation are carried out. The examples and simulation results proved that the method proposed in this paper could effectively analyze the cause and degree of oversaturation and identify its regime. This has important implications for alleviating traffic congestion and reducing vehicle carbon emissions.

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Section: Discussionmentioning

confidence: 99%

Analysis of Traffic Oversaturation Based on Multi-Objective Data

Huang

Zhang

2022

Sustainability

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“…Furthermore, because this definition of traffic congestion is road behavior‐dependent, it is necessary to analyze each road independently to determine its appropriate threshold. In Reference 32, the authors proposed a model of a traffic flow network. The network model is edge (road) weighted, with the weights indicating the level of congestion.…”

Section: Related Workmentioning

confidence: 99%

Novel traffic congestion detection algorithms for smart city applications

Bawaneh

Simon

2022

Concurrency and Computation

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Traffic congestion detection (TCD) techniques are becoming a critical component of traffic management systems. They can be considered a pre-step to address traffic jam problems, providing useful input for traffic management systems to predict and avoid traffic congestions' unwanted effects. In this research article, two novel real-time congestion detection algorithms are proposed, the TCD algorithm and the ensemble based traffic congestion detection (EB-TCD) algorithm. TCD can detect the congestion based on one traffic feature (i.e., speed, occupancy, or flow). TCD first cleans and preprocesses the traffic data. After, it computes the absolute of the first derivative of each sample in the traffic feature to determine its anomaly score. Then, the anomaly likelihood of each sample is computed to classify it as an anomaly or normal sample. On the other hand, EB-TCD utilizes the information contained in multiple traffic features in parallel by proposing an ensemble technique to combine the anomaly scores coming out from the different traffic features in one unified score. EB-TCD computes an anomaly score based on each traffic feature separately. Then, it votes for each sample in each traffic feature as an anomaly or not (from the corresponding traffic feature's point of view). After, the weight for each traffic feature is determined by studying the dissimilarity between their anomaly scores. These weights show how much each traffic feature explains the traffic behavior. Finally, the results are combined to form a unified anomaly score for each sample. The proposed statistical-based techniques are able to detect changes in traffic patterns while mitigating the effect of noise in traffic data. Moreover, the proposed algorithms' parameters were tuned and tailored for fast detection, which is a crucial feature for traffic management systems since fast detection accelerates decision-making. The computational complexity analysis has shown the simplicity of the proposed algorithms. Furthermore, the evaluation results have shown that the two algorithms outperform other widespread methods from the literature in terms of false alarm rate and detection time while keeping the detection rate at the same high level.

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“…A traffic flow network model was investigated by [28] for congestion problems through taxi GPS data. For validation, an actual scenario was considered identifying the congestion areas.…”

Section: Related Workmentioning

confidence: 99%

Fuel Consumption Monitoring through COPERT Model—A Case Study for Urban Sustainability

et al. 2021

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Trackers installed in vehicles gives insights into many useful information and predict future mobility patterns and other aspects related to vehicles movement which can be used for smart and sustainable cities planning. A novel approach is used with the COPERT model to estimate fuel consumption on a huge dataset collected over a period of one year. Since the data size is enormous, Apache Spark, a big data analytical framework is used for performance gains while estimating vehicle fuel consumption with the lowest latency possible. The research presents peak and off-peak hours fuel consumption’s in three major cities, i.e., Karachi, Lahore and Islamabad. The results can assist smart city professionals to plan alternative trip routes, avoid traffic congestion in order to save fuel and time, and protect against urban pollution for effective smart city planning. The research will be a step towards Industry 5.0 by combining sustainable disruptive technologies.

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Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning

Cited by 12 publications

References 36 publications

Analysis of Traffic Oversaturation Based on Multi-Objective Data

Analysis of Traffic Oversaturation Based on Multi-Objective Data

Novel traffic congestion detection algorithms for smart city applications

Fuel Consumption Monitoring through COPERT Model—A Case Study for Urban Sustainability

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