Empirical assessment of urban traffic congestion

Chow, Ahf; Santacreu, A; Tsapakis, Ioannis; Tanasaranond, Garavig; Cheng, Tao

doi:10.1002/atr.1241

Cited by 80 publications

(34 citation statements)

References 8 publications

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“…Previous researches highlight that the major source of travel time variability is NRC events (Noland and Polak 2002). Consequently, understanding how much of the total congestion is due to NRC has been studied thoroughly (Chow et al 2014;Dowling et al 2004;Skabardonis, Varaiya, and Petty 2003); however, research on detecting NRC events has only recently gained importance (Anbaroglu, Heydecker, and Cheng 2014).…”

Section: Introductionmentioning

confidence: 99%

Non-recurrent traffic congestion detection on heterogeneous urban road networks

Anbaroğlu

Cheng

Heydecker

2015

Transportmetrica A: Transport Science

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This paper proposes two novel methods for non-recurrent congestion (NRC) event detection on heterogeneous urban road networks based on link journey time (LJT) estimates. Heterogeneity exists on urban road networks in two main aspects: variation in link lengths and data quality. The proposed NRC detection methods are referred to as percentile-based NRC detection and space-time scan statistics (STSS) based NRC detection. Both of these methods capture the heterogeneity of an urban road network by modelling the LJTs with a lognormal distribution. Empirical analyses are conducted on London's urban road network consisting of 424 links for the 20 weekdays of October 2010. Various parameter settings are tested for both of the methods, and the results favour STSS-based NRC detection method over the percentile-based NRC detection method. Link-based analyses demonstrate the effectiveness of the proposed methods in capturing the heterogeneity of the analysed road network.

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Section: Introductionmentioning

confidence: 99%

Non-recurrent traffic congestion detection on heterogeneous urban road networks

Anbaroğlu

Cheng

Heydecker

2015

Transportmetrica A: Transport Science

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“…The following time patterns, which are well-known from earlier observations in different countries [35,36], can be observed by means of stationary road detectors (compare Fig. 3 and 4): At a small flow rate, vehicles move at free flow speed.…”

Section: Speed Breakdownmentioning

confidence: 66%

Impact of Synchronised Flow in Oversaturated City Traffic on Energy Efficiency of Conventional and Electrical Vehicles

Hemmerle¹,

Koller²,

Hermanns³

et al. 2016

Coll Dyn

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In this study of city traffic, we show that empirical synchronised flow patterns, which have been revealed recently in oversaturated traffic, exhibit considerable impact on the energy efficiency of vehicles. In particular, we have found out that energy consumption in oversaturated city traffic can decrease considerably when the oversaturated city traffic consists of synchronised flow patterns rather than consisting of moving queues of the classical traffic flow theory at traffic signals. Using empirical GNSS data measured by navigation devices on two different road sections in Düsseldorf, Germany, we show that synchronised flow patterns and moving queues differ in their cumulated vehicle acceleration (a sum of positive speed differences along a vehicle trajectory) despite similar mean speeds. Energy efficiency in return is dependent on the cumulated vehicle acceleration. We consider both the fuel consumption of conventional vehicles with combustion engines and the energy balance of electrical vehicles.

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“…They concluded that incidentrelated causes were responsible for 13 to 30 percent of total congestion during the peak period. (Chow et al, 2014) conducted an empirical study of urban traffic congestion in Central London, using an automatic plate number recognition system. The study proposed to interpret congestion and its causes through a linear regression model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The linear model was chosen based on the assumption that each of the explanatory variables contributes linearly to the delay. (Chow et al, 2014) and (Kwon et al, 2006) suggest linear regression decomposes delay in urban area and freeway. A more complex relationship could exist between variables, but it is not considered in this study to keep the number of model parameters small, (e.g.…”

Section: Regression Based Decomposition Modelmentioning

confidence: 99%

Development of Congestion Causal Pie Charts for Arterial Roadways

Soltani-Sobh¹,

Ostojic²,

Stevanović³

et al. 2017

IJTTE

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Urban congestion is being increased by a rapid growth in travel demand and a limited ability to expand physical infrastructure. If transportation agencies could accurately quantify the impacts of various congestion causes, they would be able to prioritize their strategies more efficiently. The Federal Highway Administration developed a well-known congestion causal pie chart in 2004, but this development process did not have extensive access to field data. Recent advancements in both traffic measurement technologies and data-driven analysis are making it possible to quantify congestion impacts more accurately. However, an assessment of congestion causes on signalized arterials presents many challenges, due to complexity of the required data and the interaction of traffic demand and control. The objective of this study is to create congestion pie charts which demonstrate the proportion of average experienced delay components on arterial corridors. A multivariate linear regression model of observed delay is used to demonstrate contributing factors to arterial street congestion. The methodology is explained using a section of Broward Boulevard in Fort Lauderdale, FL. The findings from the model demonstrate that a considerable part of arterial congestion can be attributed to travel demands and intersection signals.

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Empirical assessment of urban traffic congestion

Cited by 80 publications

References 8 publications

Non-recurrent traffic congestion detection on heterogeneous urban road networks

Non-recurrent traffic congestion detection on heterogeneous urban road networks

Impact of Synchronised Flow in Oversaturated City Traffic on Energy Efficiency of Conventional and Electrical Vehicles

Development of Congestion Causal Pie Charts for Arterial Roadways

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