Congestion Propagation Based Bottleneck Identification in Urban Road Networks

© 1967-2012 IEEE. Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method

A Review of Traffic Signal Control.

The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

DEVELOPMENT OF SPATIOTEMPORAL CONGESTION PATTERN OBSERVATION MODEL USING HISTORICAL AND NEAR REAL TIME DATA

Traffic congestion is not foreign to major metropolitan areas. Congestion in large cities often is associated with dense land developments and continued economic growth. In general, congestion can be classified into two categories: recurring and nonrecurring. Recurring congestion often occurs at certain parts of highway networks, referred to as bottleneck locations. Nonrecurring congestion, on the other hand, can be caused by different reasons, including work zones, special events, accidents, inclement weather, poor signal timing, etc. The work presented here demonstrates an approach to effectively identifying spatiotemporal patterns of traffic congestion at a network level. The Metro Atlanta highway network was used as a case study. Real time traffic data was acquired from the Georgia Department of Transportation (GDOT) Navigator system. For a qualitative analysis, speed data was categorized into three levels: low, median, and high. Cluster analysis was performed with respect to the categorized speed data in the spatiotemporal domain to identify where and when congestion has occurred and for how long, which indicate the severity of congestion. This qualitative analysis was performed by day of week to identify potential variation in congestion over weekdays and weekend. For a quantitative analysis, actual speed data was used to construct daily spatiotemporal maps to reveal congestion patterns at a more granular level, where congestion is represented as “cloud” in the spatiotemporal domain. Future work will be focusing on deep learning of congestion patterns using Convolutional Long Short Term Memory (ConvLSTM) networks

Modelling urban traffic dynamics based upon the variational formulation of kinematic waves

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What Is an Effective Way to Measure Arterial Demand When It Exceeds Capacity?

This project focused on developing and evaluating methods for estimating demand volume for oversaturated corridors. Measuring demand directly with vehicle sensors is not possible when demand is larger than capacity for an extended period, as the queue grows beyond the sensor, and the flow measurements at a given point cannot exceed the capacity of the section. The main objective of the study was to identify and develop methods that could be implemented in practice based on readily available data. To this end, two methods were proposed: an innovative method based on shockwave theory; and the volume delay function adapted from the Highway Capacity Manual. Both methods primarily rely on probe vehicle speeds (e.g., from INRIX) as the input data and the capacity of the segment or bottleneck being analyzed. The proposed methods were tested with simulation data and validated based on volume data from the field. The results show both methods are effective for estimating the demand volume and produce less than 4% error when tested with field data