scholarly journals Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming

2021 ◽  
Vol 11 (22) ◽  
pp. 10573
Author(s):  
Federico Pigozzi ◽  
Eric Medvet ◽  
Laura Nenzi

Traffic systems, where human and autonomous drivers interact, are a very relevant instance of complex systems and produce behaviors that can be regarded as trajectories over time. Their monitoring can be achieved by means of carefully stated properties describing the expected behavior. Such properties can be expressed using Signal Temporal Logic (STL), a specification language for expressing temporal properties in a formal and human-readable way. However, manually authoring these properties is a hard task, since it requires mastering the language and knowing the system to be monitored. Moreover, in practical cases, the expected behavior is not known, but it has instead to be inferred from a set of trajectories obtained by observing the system. Often, those trajectories come devoid of human-assigned labels that can be used as an indication of compliance with expected behavior. As an alternative to manual authoring, automatic mining of STL specifications from unlabeled trajectories would enable the monitoring of autonomous agents without sacrificing human-readability. In this work, we propose a grammar-based evolutionary computation approach for mining the structure and the parameters of an STL specification from a set of unlabeled trajectories. We experimentally assess our approach on a real-world road traffic dataset consisting of thousands of vehicle trajectories. We show that our approach is effective at mining STL specifications that model the system at hand and are interpretable for humans. To the best of our knowledge, this is the first such study on a set of unlabeled real-world road traffic data. Being able to mine interpretable specifications from this kind of data may improve traffic safety, because mined specifications may be helpful for monitoring traffic and planning safety promotion strategies.

2020 ◽  
Vol 4 (3-4) ◽  
pp. 238-259 ◽  
Author(s):  
Marshall W. Meyer

Abstract Research Question What happened to US traffic safety during the first US COVID-19 lockdown, and why was the pattern the opposite of that observed in previous sudden declines of traffic volume? Data National and local statistics on US traffic volume, traffic fatalities, injury accidents, speeding violations, running of stop signs, and other indicators of vehicular driving behavior, both in 2020 and in previous US economic recessions affecting the volume of road traffic. Methods Comparative analysis of the similarities and differences between the data for the COVID-19 lockdown in parts of the USA in March 2020 and similar data for the 2008–2009 global economic crisis, as well as other US cases of major reductions in traffic volume. Findings The volume of traffic contracted sharply once a COVID-19 national emergency was declared and most states issued stay-at-home orders, but motor vehicle fatality rates, injury accidents, and speeding violations went up, and remained elevated even as traffic began returning toward normal. This pattern does not fit post-World War II recessions where fatality rates declined with the volume of traffic nor does the 2020 pattern match the pattern during World War II when traffic dropped substantially with little change in motor vehicle fatality rates. Conclusions The findings are consistent with a theory of social distancing on highways undermining compliance with social norms, a social cost of COVID which, if not corrected, poses potential long-term increases in non-compliance and dangerous driving.


Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Lin ◽  
Feng Shi ◽  
Weizi Li

AbstractCOVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for “Hispanic” and “Male” groups; (2) the “hot spots” of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic.


2021 ◽  
Vol 5 (12(81)) ◽  
pp. 26-32
Author(s):  
V. Volkov ◽  
E. Nabatnikova ◽  
E. Lebedev

The groups of participants of the pedestrian and automobile flows, whose actions cause the greatest danger to the occurrence of conflict situations in the zone of unregulated transition, are identified. The factors determining the likelihood of a traffic accident at an unregulated transition are systematized, for which probability estimates of the occurrence of road traffic accidents are calculated. As an estimated parameter, the hazard coefficient of a conflict point of an unregulated transition is proposed, which is determined by the ratio of the probability of a traffic accident in the real-time hourly interval to the average annual probability of a traffic accident reduced to the hourly interval. The dependences of the hazard ratio of an unregulated transition are established on the most significant factors: the speed mode of transport in the area before the transition and the state of the road surface.


2018 ◽  
Vol 170 ◽  
pp. 05009
Author(s):  
Artur Petrov ◽  
Daria Petrova

The article considers the results of research of accident rate heterogeneity in cities-administrative centers of subjects of Russian Federation (2015, 2016). Using methods of ranging, regression analysis and spatial differentiation these cities were classified into 5 classes on the basis of relative disadvantage in road traffic safety sphere. For each group of cities differentiated recommendations on financing regional road traffic safety programs were suggested.


2018 ◽  
Vol 7 (7) ◽  
pp. 287 ◽  
Author(s):  
Li Zheng ◽  
Meng Sun ◽  
Yuejun Luo ◽  
Xiangbo Song ◽  
Chaowei Yang ◽  
...  

With the rapidly increasing popularization of the automobile, challenges and greater demands have come to the fore, including traffic congestion, energy crises, traffic safety, and environmental pollution. To address these challenges and demands, enhanced data support and advanced data collection methods are crucial and highly in need. A probe-car serves as an important and effective way to obtain real-time urban road traffic status in the international Intelligent Transportation System (ITS), and probe-car technology provides the corresponding solution through advanced navigation data, offering more possibilities to address the above problems. In addition, massive spatial data-mining technologies associated with probe-car tracking data have emerged. This paper discusses the major problems of spatial data-mining technologies for probe-car tracking data, such as true path restoration and the close correlation of spatial data. To address the road-matching issue in massive probe-car tracking data caused by the strong correlation combining road topology with map matching, this paper presents a MapReduce-based technology in the second spatial data model. The experimental results demonstrate that by implementing the proposed spatial data-mining system on distributed parallel computing, the computational performance was effectively improved by five times and the hardware requirements were significantly reduced.


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