Highway travel time estimation using multiple data sources

Pirc, Jure; Turk, Goran; Žura, Marijan

doi:10.1049/iet-its.2015.0211

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…Travel time Travel time on roads is a common indicator that can be estimated from the data received from these cameras [71][72][73], furthermore the effect of events on travel time can also be determined [74,75]. In order to filter out the travel time errors, [76] use the "overtaking" method, which compares the travel time to the travel time of consecutive vehicles.…”

Section: Floating-car Data (Fcd) Locating Vehicles In Real Timementioning

confidence: 99%

Analyzing passenger and freight vehicle movements from automatic-Number plate recognition camera data

Hadavi

Buldeo

Verlinde

et al. 2020

Eur. Transp. Res. Rev.

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Modern urban-transport planning requires evidence-based insights into current transport flows to better understand the needs and impacts of policymaking. Urban transport includes passenger and freight vehicles, which have different behavior, and the need for such a separation is often ignored in research and practice [1]. New digital data sources provide an opportunity to better understand urban transport and identify where policy interventions are required. We review the literature on digital counting techniques to monitor transport flows, including loops, Automatic-Number Plate Recognition (ANPR) cameras and floating car data. We further investigate the potential of ANPR cameras, which are widely deployed, and which can be augmented with vehicle category information. This article presents the methodology that we follow for transforming raw augmented ANPR camera data into practical knowledge for city planners. Our is aim is to provide a better understanding of passenger and freight vehicle movements and stops, identifying similarities and differences between vehicle categories. We demonstrate our methodology on a case study for the Mechelen-Willebroek district in Belgium, encompassing augmented data from 122 ANPR cameras for a period of two weeks. Additionally, we also look at the car-reduced zone and how time restrictions affect the different vehicle categories’ actions. The findings are validated with GPS data from heavy-good vehicles in the same period. The potential of augmented ANPR camera data and promising themes and applications of this data source are illustrated through the case study.

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Section: Floating-car Data (Fcd) Locating Vehicles In Real Timementioning

confidence: 99%

Analyzing passenger and freight vehicle movements from automatic-Number plate recognition camera data

Hadavi

Buldeo

Verlinde

et al. 2020

Eur. Transp. Res. Rev.

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“…Based on our observations from the video files, speed reduction in peak hours caused by various reasons, namely the abnormal and aggressive lane-changing behavior of main road drivers facing two merging flows, the shockwave resulting from the entrance of the Bosporus Bridge and later toll payment section. Like many highways around the world, the travel time and delay estimation for this segment is too complex [38,39].…”

Section: Existing Traffic Flow Characteristicsmentioning

confidence: 99%

An Integrated Variable Speed Limit and ALINEA Ramp Metering Model in the Presence of High Bus Volume

Dadashzadeh

Ergun

2019

Sustainability

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Under many circumstances, when providing full bus priority methods, urban transport officials have to operate buses in mixed traffic based on their road network limitations. In the case of Istanbul's Metrobus lane, for instance, when the route comes to the pre-designed Bosphorus Bridge, it has no choice but to merge with highway mixed traffic until it gets to the other side. Much has been written on the relative success of implementing Ramp Metering (RM), for example ALINEA (‘Asservissement line´ aire d’entre´ e autoroutie’) and Variable Speed Limits (VSL), two of the most widely-used “merging congestion” management strategies, in both a separate and combined manner. However, there has been no detailed study regarding the combination of these systems in the face of high bus volume. This being the case, the ultimate goal of this study is to bridge this gap by developing and proposing a combination of VSL and RM strategies in the presence of high bus volume (VSL+ALINEA/B). The proposed model has been coded using microscopic simulation software—VISSIM—and its vehicle actuated programming (VAP) feature; referred to as VisVAP. For current traffic conditions, the proposed model is able to improve total travel time by 9.0%, lower the number of average delays of mixed traffic and buses by 29.1% and 81.5% respectively, increase average speed by 12.7%, boost bottleneck throughout by 2.8%, and lower fuel consumption, Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Volatile Organic Compounds (VOC) emissions by 17.3% compared to the existing “VSL+ALINEA” model. The results of the scenario analysis confirmed that the proposed model is not only able to decrease delay times on the Metrobus system but is also able to improve the adverse effects of high bus volume when subject to adjacent mixed traffic flow along highway sections.

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“…Various ANNs topologies have been applied to estimate travel time, such as fuzzy neural networks, probabilistic networks, feedforward networks, recurrent neural network (RNN), and counter propagation neural network [13]. Among the various ANNs topologies, RNNs models are dynamic networks with internal feedbacks that enable the learning of complex temporal patterns.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Urban link travel time estimation using traffic states‐based data fusion

Zhu

Guo

Polak

et al. 2018

IET Intelligent Transport Systems

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Estimated travel time is a key input for many intelligent transport systems (ITS) applications and traffic management functions. There are numerous studies that show that fusing data from different sources such as global positioning system (GPS), Bluetooth, mobile phone network (MPN), and inductive loop detector (ILD) can result in more accurate travel time estimation. However, to date, there has been little research investigating the contribution of individual data sources to the quality of the final estimate or how this varies according to source-specific data quality under different traffic states. Here, three different data sources, namely bus-based GPS (bGPS) data, ILD data, and MPN data, of varying quality are combined using three different data fusion techniques of varying complexity. In order to quantify the accuracy of travel time estimation, travel time calculated using automatic number plate recognition (ANPR) data are used as the 'ground truth'. The final results indicate that fusing multiple data together does not necessarily enhance the accuracy of travel time estimation. The results also show that even in dense urban areas, bGPS data, when combined with ILD data, can provide reasonable travel time estimates of general traffic stream under different traffic states.

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Highway travel time estimation using multiple data sources

Cited by 15 publications

References 14 publications

Analyzing passenger and freight vehicle movements from automatic-Number plate recognition camera data

Analyzing passenger and freight vehicle movements from automatic-Number plate recognition camera data

An Integrated Variable Speed Limit and ALINEA Ramp Metering Model in the Presence of High Bus Volume

Urban link travel time estimation using traffic states‐based data fusion

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