Data-Driven Approaches for Traffic State and Emission Estimation

Tsanakas, Nikolaos

doi:10.3384/diss.diva-175738

Cited by 2 publications

(2 citation statements)

References 76 publications

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“…Each link in the network is associated with a link cost, typically expressed as a function of the link volumes. In the simplest case, any propagation effects are neglected and the network loading is instantaneous, implying that all assigned vehicles to a route are present at all links of the route simultaneously (Tsanakas, 2021). The vehicles can also be propagated in the network.…”

Section: Chapter 3 Traffic Modelsmentioning

confidence: 99%

“…That can be used as input to monitoring and following up energy use of transportation. The proposed approach, described in more detail in (Tsanakas, 2021;Tsanakas et al, 2021), is based on large-scale traffic flow data, including GPS trajectories, mobile network data, and stationary vehicle count sensors. Estimates and predictions of demand and route choice are used as input to a vehicle flow propagation process resulting in linkflows over time.…”

Section: Summary Of the Included Papersmentioning

confidence: 99%

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Towards efficient urban road transport using multimodal traffic management

Danielsson

2024

Linköping Studies in Science and Technology. Licentiate Thesis

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I would like to express my deep gratitude to everyone who played a part in shaping this thesis. First of all, to my supervisors Clas Rydergren and David Gundlegård. Thank you for always making time for me, for your guidance and inspiration, and for your genuine and thorough feedback. To my colleagues at KTS for creating a fun and motivating workplace. It is inspiring to be in an environment with such passionate and talented people. A special thanks to Rasmus for never failing to share your wide knowledge, and to Vivvi for all your administrative assistance. I extend my gratitude to our colleagues from KTH and the Swedish Transport Administration (Trafikverket) in the MMTL project for sharing your insights on traffic modeling, data analysis, and traffic management, and to Nikolaos Tsanakas who co-authored one of the papers in this thesis. The work was funded by the Swedish Transport Administration via the Centre for Traffic Research, CTR.Lastly, I want to thank my family and friends for countless talks, lots of laughter, and never-ending encouragement. Thank you, Arvid, for your support, your patience, and your endless love.

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Section: Chapter 3 Traffic Modelsmentioning

confidence: 99%

Section: Summary Of the Included Papersmentioning

confidence: 99%

Towards efficient urban road transport using multimodal traffic management

Danielsson

2024

Linköping Studies in Science and Technology. Licentiate Thesis

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Urban Traffic Mobility Optimization Model: A Novel Mathematical Approach for Predictive Urban Traffic Analysis

Ulvi,

Yerlikaya,

Yildiz

2024

Applied Sciences

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This research introduces the Urban Traffic Mobility Optimization Model (UTMOM), a data-driven methodology for analyzing two distinctive urban traffic datasets through the integration of data mining and mathematical modeling. Designed to decode the complexities of urban mobility patterns, UTMOM meticulously evaluates daily traffic dynamics with a focus on reducing discrepancies and underscoring variations in traffic intensity, particularly during peak times. Our findings unveil pivotal insights into the differences across datasets, providing a substantial contribution to the realms of traffic management and urban planning. UTMOM delves into the intricacies of traffic flow variations, emphasizing the critical importance of comprehending fluctuations in traffic volume across diverse times and locations. By incorporating detailed graphical representations and statistical validations, including ANOVA analysis, our study delivers a comprehensive evaluation of UTMOM’s precision in reflecting real-world traffic scenarios. These insights affirm the value of data-informed strategies in optimizing traffic flow and alleviating congestion. Positioned as a valuable asset for traffic engineers, data scientists, and urban planners, UTMOM advocates for advanced modeling techniques to improve urban mobility. Beyond enriching academic discourse on traffic analysis, UTMOM offers actionable intelligence for enhancing the efficiency and sustainability of urban transportation systems. Through this in-depth investigation, our aim is to catalyze the development of innovative solutions to traffic challenges, steering towards smoother and more sustainable urban environments.

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Data-Driven Approaches for Traffic State and Emission Estimation

Cited by 2 publications

References 76 publications

Towards efficient urban road transport using multimodal traffic management

Towards efficient urban road transport using multimodal traffic management

Urban Traffic Mobility Optimization Model: A Novel Mathematical Approach for Predictive Urban Traffic Analysis

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