The Bologna Ringway Dataset: Improving Road Network Conversion in SUMO and Validating Urban Mobility via Navigation Services

Bedogni, Luca; Gramaglia, Marco; Vesco, Andrea; Fiore, Marco; Härri, Jérôme; Ferrero, Francesco

doi:10.1109/tvt.2015.2475608

Cited by 73 publications

(36 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in their analysis, the authors of [27] only used 536 taxi nodes, which is far less than the number of nodes in our analysis. The Bologna dataset used in the study [28] is a good source of public dataset since the features of this dataset match well to those inferred from navigation services. However, there is no evidence that this dataset can be used to characterize the microscopic metrics, like dynamical connectivity in our study.…”

Section: Related Workmentioning

confidence: 91%

“…P (c k ; d, δ) −¯ P P (e) (c k ) −P (e) σ P (e) σ P (28) SM cor = 1 indicates that the two CDFs are completely similar, while SM cor = 0 indicates that the two CDFs are completely dissimilar. More specifically, let C Table 2 lists the correlation-based similarity measure values between the empirical CDF and the fitted CDF of the uniform distribution model for different network parameters d and δ.…”

Section: Uniform Distribution Approximationmentioning

confidence: 99%

See 1 more Smart Citation

On the Opportunistic Topology of Taxi Networks in Urban Mobility Environment

Chen

2020

IEEE Trans. Big Data

View full text Add to dashboard Cite

Understanding and characterizing the connectivity of vehicular networks has become increasingly important because of their wide applications and fast development. To address the dynamical links in vehicular networks, time-varying graph (TVG) is one of the most important models. Nowadays, due to the fact that lots of vehicular applications can tolerate a small amount of latency in communication, opportunistic reachability graph (ORG) characterizes the connectivity better by introducing delay tolerance to the model. However, people still do not have a high-level summarization, i.e. the topology, of the vehicular network on how nodes are clustered and isolated. In this paper, based on ORG model, we analyze the opportunistic topology of taxi networks in urban mobility environment by mainly focusing on the number, location and evolution of connected components and the size of the largest components to reveal the unique properties of the taxi networks instead of just links and hops. Our analysis is based on the real taxi traces of big cities and reflects the real urban mobility environment. We find that the opportunistic topology of the networks with delay tolerance is substantially different from the instantaneous topology without considering the delay. Moreover, we unveil the fundamental relationships and trade-offs between the dynamical topology and the key network parameters related to mobility, e.g., delay tolerance, transmission distance, etc. To the best of our knowledge, our study is the first work to reveal the characteristics of opportunistic topology models in the large-scale urban mobility environment with real traces.

show abstract

Section: Related Workmentioning

confidence: 91%

Section: Uniform Distribution Approximationmentioning

confidence: 99%

On the Opportunistic Topology of Taxi Networks in Urban Mobility Environment

Chen

2020

IEEE Trans. Big Data

View full text Add to dashboard Cite

show abstract

“…Additionally, the model freely operated the number of nodes, allowing us to evaluate NAC; we investigated the performance at the number of nodes from 60 nodes to 500 nodes. The Bologna models were based on Bologna data through the simulation of urban mobility (SUMO) simulator [28] [29].…”

Section: Requirementsmentioning

confidence: 99%

Performance Evaluation of NOMA for Sidelink Cellular-V2X Mode 4 in Driver Assistance System With Crash Warning

Hirai

Murase

2020

IEEE Access

View full text Add to dashboard Cite

This paper presents the performance characteristics of a potential extension of sidelink cellularvehicle-to-everything mode 4 (simply called mode 4) with uplink non-orthogonal multiple access (UL-NOMA), named SPS-NOMA, in driver assistance systems with a crash warning. In SPS-NOMA, multiple nodes (e.g., cars or pedestrians with communication equipment) simultaneously broadcast their data frames at a time-frequency resource selected by sensing-based semi-persistent scheduling (sensing-based SPS), the distributed random access protocol of mode 4. The transmitted signals are superposed at the receiver sides, and successive interference canceller (SIC) supports decoding the superposed signal. In SPS-NOMA, sensing-based SPS is expected to enhance the signal-to-interference-plus-noise ratio at each SIC iteration. The effect boosts the performance gains of the SIC in broadcast cases and mitigates the channel congestion. Our computer simulations highlighted that SPS-NOMA improved the performance of the ordinary mode 4 by 38% in the fundamental node distribution model. In conclusion, SPS-NOMA is expected to be the next generation sidelink C-V2X as the extended mode 4.

show abstract

“…SUMO has been used to simulate traffic in a number of studies ( [26], [27], [28], [29] and [30]). SUMO is a microscopic traffic simulation so it can describe every vehicles in detail such as departure and arrival time, lanes to use, velocity and positions.…”

Section: B Nsga-ii and Sumomentioning

confidence: 99%

Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search

Nguyen

Passow

Yang

2016

2016 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

Abstract-Multi-Objective Evolutionary Algorithms (MOEAs) and transport simulators have been widely utilized to optimise traffic signal timings with multiple objectives. However, traffic simulations require much processing time and need to be called repeatedly in iterations of MOEAs. As a result, traffic signal timing optimisation process is time-consuming. Anytime behaviour of an algorithm indicates its ability to return as good solutions as possible at any time during its implementation. Therefore, anytime behavior is desirable in traffic signal timing optimisation algorithms. In this study, we propose an optimisation strategy (NSGA-II-LS) to improve anytime behaviour based on NSGA-II and local search. To evaluate the validity of the proposed algorithm, the NSGA-II-LS, NSGA-II and MODEA are used to optimize signal durations of an intersection in Andrea Costa scenario. Results of the experiment show that the optimization method proposed in this study has good anytime behaviour in the traffic signal timings optimization problem.

show abstract

The Bologna Ringway Dataset: Improving Road Network Conversion in SUMO and Validating Urban Mobility via Navigation Services

Cited by 73 publications

References 27 publications

On the Opportunistic Topology of Taxi Networks in Urban Mobility Environment

On the Opportunistic Topology of Taxi Networks in Urban Mobility Environment

Performance Evaluation of NOMA for Sidelink Cellular-V2X Mode 4 in Driver Assistance System With Crash Warning

Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search

Contact Info

Product

Resources

About