Decomposing travel times measured by probe-based traffic monitoring systems to individual road segments

Hellinga, Bruce; Izadpanah, Pedram; Takada, Hiroyuki; Fu, Liping

doi:10.1016/j.trc.2008.04.002

Cited by 99 publications

(73 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The inputs to the proposed procedure are link travel times and since they are not directly reported by probe vehicles, the estimation of link travel times from GPS data is of interest to integrate with our methodology. This problem is named as travel time allocation or decomposition in the literature and discussed more in Hellinga et al (2008), Hofleitner and Bayen (2011). The next crucial step for future studies is to investigate under what traffic conditions and route structures our method significantly outperforms convolution estimation, i.e.…”

Section: Discussionmentioning

confidence: 99%

On the estimation of arterial route travel time distribution with Markov chains

Ramezani

Geroliminis

2012

Transportation Research Part B: Methodological

168

View full text Add to dashboard Cite

a b s t r a c tRecent advances in the probe vehicle deployment offer an innovative prospect for research in arterial travel time estimation. Specifically, we focus on the estimation of probability distribution of arterial route travel time, which contains more information regarding arterial performance measurements and travel time reliability. One of the fundamental contributions of this work is the integration of travel time correlation of route's successive links within the methodology. In the proposed technique, given probe vehicles travel times of the traversing links, a two-dimensional (2D) diagram is established with data points representing travel times of a probe vehicle crossing two consecutive links. A heuristic grid clustering method is developed to cluster each 2D diagram to rectangular sub spaces (states) with regard to travel time homogeneity. By applying a Markov chain procedure, we integrate the correlation between states of 2D diagrams for successive links. We then compute the transition probabilities and link partial travel time distributions to obtain the arterial route travel time distribution. The procedure with various probe vehicle sample sizes is tested on two study sites with time dependent conditions, with field measurements and simulated data. The results are very close to the Markov chain procedure and more accurate once compared to the convolution of links travel time distributions for different levels of congestion, even for small penetration rates of probe vehicles.

show abstract

Section: Discussionmentioning

confidence: 99%

On the estimation of arterial route travel time distribution with Markov chains

Ramezani

Geroliminis

2012

Transportation Research Part B: Methodological

168

View full text Add to dashboard Cite

show abstract

“…In proposing solutions to these issues, Hellinga et al (2008) suggested a method to solve the problem of travel time allocation on individual links (o ne of the steps necessary to estimate the average travel time on link), which was evaluated using data from a simulated network and compared with the benchmark method. However, although the method was evaluated for different data sampling frequencies (i.e.…”

Section: Literaturementioning

confidence: 99%

Developing travel time estimation methods using sparse GPS data

Sanaullah

Quddus

Enoch

2016

Journal of Intelligent Transportation Systems

View full text Add to dashboard Cite

Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high frequency data sources from specialist data providers which can be expensive and are not always readily available.The study reported here therefore sought to better estimate travel time using 'readily available' vehicle trajectory data from moving sensors such as buses, taxis and logistical vehicles equipped with GPS in 'near' real-time.To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds and network connectivity information with a special focus o n sampling frequencies, vehicle penetration rates and time window lengths. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-2 Berkeley's Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were t h e n validated against reference travel time data collected from high resolution video cameras.The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5 minute time window length and for a GPS sampling frequency of 60 seconds.

show abstract

“…An example of vehicle tracking is reported in [19], in which this system is used to optimize a bus line. In some cases, probe vehicles can be used to measure the network characteristics [20]. Other approaches [21] consider sensors for short duration survey and sensors for permanent survey for collecting traffic load data.…”

Section: Data Survey and Transmissionmentioning

confidence: 99%

The role of ITS in evacuation route optimization for emergency vehicles

Polimeni¹,

Vitetta²

2012

Risk Analysis VIII

View full text Add to dashboard Cite

In this paper the application of ITS (Intelligent Transportation Systems) in road evacuation in the case of route optimization for emergency vehicles is reported. The ITS are usually used in transport to analyse the data and improve the system performances. In road evacuation, the ITS can be used at three levels: survey and transmission, control and user information. This structure allows managing an evacuation starting from the network monitoring and defining the optimal strategies to apply to speed up the evacuation procedure.

show abstract

Decomposing travel times measured by probe-based traffic monitoring systems to individual road segments

Cited by 99 publications

References 1 publication

On the estimation of arterial route travel time distribution with Markov chains

On the estimation of arterial route travel time distribution with Markov chains

Developing travel time estimation methods using sparse GPS data

The role of ITS in evacuation route optimization for emergency vehicles

Contact Info

Product

Resources

About