Non-parametric estimation of route travel time distributions from low-frequency floating car data

Rahmani, Mahmood; Jenelius, Erik; Koutsopoulos, Haris N.

doi:10.1016/j.trc.2015.01.015

Cited by 128 publications

(74 citation statements)

References 19 publications

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“…Two months of GPS data from more than 32,600 taxis with the average sampling rate of 96 sec per point in the time slot of 30 minutes was used for the assessment of proposed method, and was found to perform better than the baseline approaches in finding the correct values for missing data. Rahmani et al (2015) used low sampling frequency GPS data of 2 minutes to estimate the path travel time distribution by a non-parametric method. The focus of the study was to determine and correct the biases related to the floating car data for instance incomplete route coverage and traversal of the adjoining road network.…”

Section: Literaturementioning

confidence: 99%

“…Finally the issues with sampling frequency, vehicle penetration rate, time window length, link coverage and map-matched vehicle positions should be considered and analysed, whilst the accuracy of travel time estimation by developed methods should also be validated by using the reference (true) travel time data collected by ANPR cameras, which has been rarely done in literature (Rahmani et al 2015).…”

Section: Conceptual Frameworkmentioning

confidence: 99%

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Developing travel time estimation methods using sparse GPS data

Sanaullah

Quddus

Enoch

2016

Journal of Intelligent Transportation Systems

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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.

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Section: Literaturementioning

confidence: 99%

Section: Conceptual Frameworkmentioning

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

show abstract

“…In this vein, both parametric or non-parametric regression techniques have been already suggested. Second, part of the research has been inclined towards the case of the freeway, e.g., Li and Rose (2011) and some to urban Rahmani et al (2015); Zhan et al The primary goal of the above mentioned works was to inform travellers and hence in uence route planning. This means an indirect inclusion of the estimated, predicted travel time.…”

Section: Introductionmentioning

confidence: 99%

Network traffic flow optimization under performance constraints

Csikós

Charalambous

Farhadi

et al. 2017

Transportation Research Part C: Emerging Technologies

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In this paper, a model-based perimeter control policy for large-scale urban vehicular networks is proposed. Assuming a homogeneously loaded vehicle network and the existence of a well-posed Network Fundamental Diagram (NFD), we describe a protected network throughout its aggregated dynamics including nonlinear exit ow characteristics. Within this framework of constrained optimal boundary ow gating, two main performance metrics are considered: (a) rst, connected to the NFD, the concept of average network travel time and delay as a performance metric is de ned;(b) second, at boundaries, we take into account additional external network queue dynamics governed by uncontrolled in ow demands. External queue capacities in terms of nite-link lengths are used as the second performance metric. Hence, the corresponding performance requirement is an upper bound of external queues. While external queues represent vehicles waiting to enter the protected network, internal queue describes the protected network's aggregated behaviour.By controlling the number of vehicles joining the internal queue from the external ones, herewith a network tra c ow maximization solution subject to the internal and external dynamics and their performance constraints is developed. The originally non-convex optimization problem is transformed to a numerically e ciently convex one by relaxing the performance constraints into time-dependent state boundaries. The control solution can be 1 interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed performance requirements on travel time in the network and upper bound on the external queue are satis ed. Comparative numerical simulation studies on a microscopic tra c simulator are carried out to show the bene ts of the proposed method.

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“…The Mobile Century project [22] gives the results for 100 dedicated vehicles driving loops on a highway, giving an estimated 2-3% needed penetration rate. In Stockholm, a fleet of 1500 taxis has been used to estimate route travel times and travel time distributions in [23,24], focusing on eliminating inherent biases. Several comparable taxi FCD systems have also been deployed in Berlin (200 taxis), Vienna (400), and Nuremburg (500) in [25].…”

Section: Introductionmentioning

confidence: 99%

Automated Incident Detection Using Real-Time Floating Car Data

Houbraken

Logghe²,

Schreuder

et al. 2017

Journal of Advanced Transportation

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The aim of this paper is to demonstrate the feasibility of a live Automated Incident Detection (AID) system using only Floating Car Data (FCD) in one of the first large-scale FCD AID field trials. AID systems detect traffic events and alert upcoming drivers to improve traffic safety without human monitoring. These automated systems traditionally rely on traffic monitoring sensors embedded in the road. FCD allows for finer spatial granularity of traffic monitoring. However, low penetration rates of FCD probe vehicles and the data latency have historically hindered FCD AID deployment. We use a live country-wide FCD system monitoring an estimated 5.93% of all vehicles. An FCD AID system is presented and compared to the installed AID system (using loop sensor data) on 2 different highways in Netherlands. Our results show the FCD AID can adequately monitor changing traffic conditions and follow the AID benchmark. The presented FCD AID is integrated with the road operator systems as part of an innovation project, making this, to the best of our knowledge, the first full chain technical feasibility trial of an FCD-only AID system. Additionally, FCD allows for AID on roads without installed sensors, allowing road safety improvements at low cost.

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Non-parametric estimation of route travel time distributions from low-frequency floating car data

Cited by 128 publications

References 19 publications

Developing travel time estimation methods using sparse GPS data

Developing travel time estimation methods using sparse GPS data

Network traffic flow optimization under performance constraints

Automated Incident Detection Using Real-Time Floating Car Data

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