Semi-supervised route choice modeling with sparse Automatic vehicle identification data

Cao, Qi; Ren, Gang; Li, Dawei; Ma, Jiangshan; Li, Haojie

doi:10.1016/j.trc.2020.102857

Cited by 26 publications

(9 citation statements)

References 23 publications

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“…To speak in detail, a series of studies propose lane (lane group)-level estimation methods using the travel time pattern, for example, lane-based traffic demand estimation [29], lane-based saturation degree estimation [30] and lane groups-based maximum queue length estimation [31]. Another traffic scenario is vehicular trajectory reconstruction using the topology of the road network and some constraints of travel pattern [32,33], as well as the further applicationsroute choice modelling [34] and depicts city-scale holographic traffic flow data [35]. The rest of the traffic scenario using LPR data is TSC optimisation, such as dynamic TSC [36] and integrated TSC [37].…”

Section: Literature Reviewmentioning

confidence: 99%

Signal timing parameters inference method at intersections using license plate recognition data

Lin

Chen

et al. 2022

IET Intelligent Trans Sys

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Signal timing parameters are essential components in traffic signal control (TSC). It affects not only traffic management, but also traffic safety. However, due to the confidential issues of the traffic management department, or lack of data integration from different signal manufacturers, it is intractable to obtain the city‐scale signal timing data. In the previous studies, some existing estimation methods focused on a single parameter and fixed‐timing scheme. To tackle this issue, this study attempts to develop an integrated parameters inference method based on license plate recognition (LPR) data, considering phase weight, average phase duration information and the overall phases of the intersections. In particular, the proposed method includes phase sequence inference model, cycle length inference model and phase duration inference model. To testify the performance of the proposed method, a real‐world LPR dataset from Guangzhou, China, is applied. Numerical results show that the proposed method performs more efficiently on parameter inferences than the state‐of‐the‐art (SOTA) approach. For instance, in the given research time period, the mean absolute error (MAE) of each phase duration is 2 s averagely (6.32 s in the SOTA approach), and the mean relative error (MRE) of cycle length is 0.91% (11.67% in the SOTA approach).

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Section: Literature Reviewmentioning

confidence: 99%

Signal timing parameters inference method at intersections using license plate recognition data

Lin

Chen

et al. 2022

IET Intelligent Trans Sys

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“…We can use Bluetooth sensors along a road to estimate the travel time of vehicles (Haghani et al, 2010;Jaume et al, 2012). For the same purpose, we could also use Automatic number-plate recognition (ANPR) (Kazagli and Koutsopoulos, 2013;Cao et al, 2020). Wi-Fi access points can be used to understand pedestrian movements and the number of people at given places (Toch et al, 2018).…”

Section: Other Data Sourcesmentioning

confidence: 99%

Methods for Travel Pattern Analysis Using Large-Scale Passive Data

Breyer

2021

Linköping Studies in Science and Technology. Dissertations

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First, I would like to thank my main supervisor Clas Rydergren and my co-supervisor David Gundlegård. I very much appreciate that you have always been available and gave me guidance and inspiration. Thank you for sharing your knowledge in lengthy discussions and taking the time to give feedback that challenged me to continuously improve. I very much enjoyed working together with you! I would also like to thank Lars Sköld, Simon Moritz, Ida Kristofferson and Di Yuan, and all others who made this work possible with their engagement.The research in this thesis has been to a large extend funded by the projects Mobile Network Origin Destination Estimation (MODE) and Mobile Network Data in Future Transport Systems (MOFT), both financed by Vinnova and Demand model estimation based on combination of active and passive data collection (DEMOPAN) funded by Trafikverket.I also want to thank all my colleagues at the division of Communication and Transportation Systems (KTS). I really appreciated being part of a group this international, with different research fields and perspectives. The last year of working from home made it clear how much I miss the interaction and informal discussion with you! In particular, I want to thank the group of PhD students for the great company, fun times and all mutual support. Special thanks go to Niki and Mats for their commitment to represent the PhD students at KTS! A big thank also to Morten Eltved, Jesper Bláfoss Ingvardson and Otto Anker Nielsen at DTU in Copenhagen. I really enjoyed working with you, even though my research visit turned out to be two and a half only weeks instead of three months, due to the pandemic. Yet, two fredagsøl were enough to start appreciating the Danish culture.Finally, I would like to thank all my friends, my sister Annalena and my dear parents Marita and Gerd-Herbert, for their unconditional support. Thank you, Fanny, for all your love and for reminding me that there are other things in life than research.

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“…In order to define the recommended route, it could be calculated by using the route choice model approach. Route choice model tries to find the best path for drivers from an origin to a destination [36,37] among several alternative routes [38]. One of the most common methods in route choice model is shortest path algorithm [36,39].…”

Section: Route Recommendationmentioning

confidence: 99%

Contextual Route Recommendation System in Heterogeneous Traffic Flow

et al. 2021

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The traffic composition in developing countries comprises of variety of vehicles which include cars, buses, trucks, and motorcycles. Motorcycles dominate the road with 77.5% compared to other types. Meanwhile, route recommendation such as navigation and Advanced Driver Assistance Systems (ADAS) is limited to particular vehicles only. In this research, we propose a framework for a contextual route recommendation system that is compatible with traffic conditions and vehicle type, along with other relevant attributes (traffic prediction, weather, temperature, humidity, heterogeneity, current speed, and road length). The framework consists of two phases. First, it predicts the traffic conditions by using Knowledge-Growing Bayes Classifier on which the dataset is obtained from crawling the public CCTV feeds and TomTom digital map application for each observed road. The performances of the traffic prediction are around 60.78–73.69%, 63.64–77.39%, and 60.78–73.69%, for accuracy, precision, and recall respectively. Second, to accommodate the route recommendation, we simulate and utilize a new measure, called road capacity value, along with the Dijkstra algorithm. By adopting the compatibility, the simulation results could show alternative paths with the lowest RCV (road capacity value).

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Semi-supervised route choice modeling with sparse Automatic vehicle identification data

Cited by 26 publications

References 23 publications

Signal timing parameters inference method at intersections using license plate recognition data

Signal timing parameters inference method at intersections using license plate recognition data

Methods for Travel Pattern Analysis Using Large-Scale Passive Data

Contextual Route Recommendation System in Heterogeneous Traffic Flow

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