Understanding the topological characteristics and flow complexity of urban traffic congestion

Wen, Tzai‐Hung; Chin, Wei Chien Benny; Lai, Pei-Chun

doi:10.1016/j.physa.2017.01.035

Cited by 38 publications

(19 citation statements)

References 31 publications

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“…The primary built environmental factors affecting traffic state of travel behaviors could be divided into traffic-related and land-use related factors [11,35,36]. The geographical detector [37] was introduced to assess the built environmental parameters that may be responsible for the road traffic state [24].…”

Section: Built Environmental Influence Factorsmentioning

confidence: 99%

“…Determining the network characteristics of structure and function is the key challenge of current research, and the ultimate goal of the network research is to better understand the behaviors of the transport systems [10]. Hence, the research involving the fusion of the two aspects is a more comprehensive research approach [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs

Wang

Ma³

et al. 2018

Eur. Transp. Res. Rev.

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Purpose: This paper aims to determine the urban traffic flow spatiotemporal characteristics and correlation with the built environment using SCATS (Sydney Coordinated Adaptive Traffic System) and POIs (Point of Interests) data of Shenyang, China. Methods: A standard analysis framework based on these data is proposed in the paper. The study analyzes the traffic volume spatiotemporal distributions and built environment influence factors determined by the geographical detector. An improved gravity model using simple structural parameters (lanes number and road length) is proposed to estimate the traffic flows of day and peak hour scales for specific flow ranges. Results: The results show that the peak hours of different intersections and roads are heterogeneous and reveal trip time flexibility. The correlation between peak hour flows and day flows is significant in the multidimensional analysis. Based on the investigation of lanes, more interesting conclusions are found. In this case, when the numbers of lanes of intersections and roads are more than 14 and 4 respectively, the lane resources are wasted to a great extent. There is also a certain correlation between these factors. Proposed gravity model establishes the connection between structure and function of urban roads. Conclusions: Flexible work time and places will be effective methods to reduce traffic congestion. The day flows could be estimated via a traffic survey on peak hour flows, especially in developing cities. The traffic flow mainly concentrates in a relatively small part of city roads. The maximum service traffic volumes exhibit segmentation, we should reconsider the maximum optimal lanes number of intersections and roads under better performance and utilization rate of the network. The effect of lanes number on the service traffic volumes is found to be more significant compared with the other factors. Our conclusions will be helpful for policy-makers and sustainable urban planning.

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Section: Built Environmental Influence Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs

Wang

Ma³

et al. 2018

Eur. Transp. Res. Rev.

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“…Hospital is the main service institution in urban areas, which is the potential congestion zones because of high traffic demand (Wen, Chin, & Lai, 2017).…”

Section: F7mentioning

confidence: 99%

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

Zhang

Sun

Shen

et al. 2017

JTLU

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Abstract:With the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship between traffic congestion and built environment, including traffic-related factors and land use. Fuzzy C-means clustering was used to conduct an exhaustive study on the 24-hour congestion pattern of road segments in an urban area, so that the spatial autoregressive moving average model (SARMA) could be introduced to analyze the output from the clustering analysis to establish the relationship between built environment and the 24-hour congestion pattern. The clustering result classified the road segments into four congestion levels, while the regression explained the impact of 12 traffic-related factors and land-use factors on the road congestion pattern. The continuous congestion was found to mainly occur in the city center, and the factors, such as road type, bus station in the vicinity, ramp nearby, commercial land use, and so on, had large impacts on congestion formation. The Fuzzy C-means clustering is proposed to be combined with quantitative spatial regression, and the overall evaluation process will assist to assess the spatial-temporal levels of service regarding traffic from the congestion perspective.Keywords: Congestion pattern, taxi GPS data, fuzzy C-means clustering, spatiotemporal regression, built environment factor IntroductionIn urban road network, the recurrent or current congestion of a certain road segment may largely impact the local network and reduce travel efficiency. Consequently, it is important to identify the Compared with mobile phone data, floating car data, cargo transport vehicle record and navigation system, taxi GPS trace data is one of the easiest available sources for accurate travel route and travel time records for a wider area with more road details. Data mining based on taxi trip can be traced back to the 1970s (Goddard, 1970), which has been applied to a wide range of studies, mainly including activity-based and infrastructure-based fields. The activity-based studies mostly focus on driver behavior, supply-demand pattern, and traffic state analysis, while the infrastructure-based studies mainly focused on lanes channelization (Tang, Yang, Kan, & Li, 2015) and signal-timing estimation (Yu & Lu, 2016).From driver behavior perspective, Zhang, Qiu, Duan, Du, and Lu (2015) proposed a space-time visualization method to demonstrate taxi daily trajectories by GIS-T to recognize working time, operating range, and residence location without time division. Qing, Parfenov, and Kim (2015) compared direct extracted datas like travel distance, speed, demand, and supply mismatch of taxi trip between fair weather and extreme storm using Manhattan GPS data, and discovered the reduction in trip distance and supply of drives during the extreme storm. Meanwhile, Hwang, Wu and Jian (2006) used structural equation modeling technique...

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“…Intersection delay is calculated by dividing the total additional travel time of the driver, passenger, or pedestrian by the effect of the control measures and of other road users by the flow through the corresponding cross section of the road. 59 It is a key performance index of intermittent traffic flow. This indicator determines the time loss caused by vehicles or pedestrians passing through intersections.…”

Section: Determining the Evaluation Index For Degree Of Traffic Congementioning

confidence: 99%

Evaluation of traffic congestion degree: An integrated approach

Hao

Feng

Zhang

et al. 2017

International Journal of Distributed Sensor Networks

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Intersection traffic congestion evaluation is essential for effective intelligent transportation system planning, and an objective and precise assessment of traffic congestion is vital to ensure the smooth circulation of traffic. Multiple criteria decision-making is a method for evaluating the degree of traffic congestion. A hybrid multiple criteria decision-making method integrating the fuzzy analytic hierarchy process, techniques for order preference by similarity to an ideal solution, and gray correlation techniques are presented in this work. The proposed method applied fuzzy analytic hierarchy process to determine the weight of the evaluation index; subsequently, gray correlation techniques for order preference by similarity to an ideal solution were integrated to construct the hybrid decision-making method. A case study of traffic congestion at intersections with five evaluation indexes verified the effectiveness of the hybrid method. The evaluation results of the different methods show that the proposed method overcomes the one-sidedness of analytical hierarchy process-techniques for order preference by similarity to an ideal solution and analytical hierarchy process-gray correlation. Thus, the proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the degree of traffic congestion.

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Understanding the topological characteristics and flow complexity of urban traffic congestion

Cited by 38 publications

References 31 publications

Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs

Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

Evaluation of traffic congestion degree: An integrated approach

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