Adaptability evaluation of electronic vehicle identification in urban traffic: a case study of Beijing

Tehnologija elektroničke identifikacije vozila (EVI) često se uvodi kako bi se izvršila naplata cestarine u vrijeme zagušenja prometa. U radu je predstavljena metoda za procjenu prilagodljivosti EVI-a te razmatrana provedivost te tehnologije u nadzoru zagušenja na temelju procjene. Najprije je određena dinamika sustava u svrhu kvalitetne analize učinka EVI-a na sustav gradskog prometa s nizom povratnih informacija. Zatim je izgrađen model za procjenu prilagodljivosti EVI-a temeljen na analizi glavnih komponenti (principal component analysis - PCA) i analizi dobivenih podataka (data envelopment analysis – DEA). Zbog brojnih izlaznih varijabli sastavljen je PCA model kako bi se smanjile veličine varijabli. Nakon toga, predstavljena su dva scenarija primjene EVI-a za naplatu cestarine u vrijeme zagušenja prometa u Pekingu. Scenario 1 je obuhvatio 5 % ukupnog broja vozila, kao i naplatnu zonu unutar 2nd Ring Road. U međuvremenu, scenarijem 2 je obuhvaćeno 5 % više vozila i uključen je Zhongguancun West District baziran na scenariju 1. Prema dobivenim rezultatima procjene, scenarij 1 je označen kao u osnovi prilagodljiv, a scenarij 2 kao prilagodljiv, odnosno scenarij 2 je prepoznat kao prilagodljiviji i izvodljiviji nego scenarij 1. Osim toga, analizirali su se trendovi prilagodljivosti dvaju scenarija u periodu između 2003. i 2012. te se pokazalo da su bili u skladu s postojećom situacijom. Procjenjivanjem prilagodljivosti tih tehnologija prije njihove primjene u praksi, ti su rezultati značajno utjecali na određivanje područja od prioriteta u primjeni tehnologije interneta stvari (Internet of Things-IoT).Electronic vehicle identification (EVI) technology is often introduced to implement congestion-based toll. This paper presented an ex ante evaluation method for EVI adaptability and discussed the feasibility of this technology in congestion charge on the basis of assessment. First, system dynamics was introduced to qualitatively analyze the effect of EVI on urban traffic systems with feedback chains. An EVI adaptability evaluation model was then developed based on principal component analysis (PCA) and data envelopment analysis (DEA). Given numerous output variables, a PCA model was built to reduce variable dimensionalities. Subsequently, two scenarios of EVI application under a congestion-based toll in Beijing were presented and calculated according to field data. Scenario 1 covered 5 % of the total vehicles, as well as the toll zone within the 2nd Ring Road. Meanwhile, scenario 2 covered 5 % more vehicles and included Zhongguancun West District based on scenario 1. According to evaluation result, the adaptability classifications of scenarios 1 and 2 were identified as basic adaptive & adaptive respectively, and that scenario 2 was more adaptive and feasible than scenario 1. In addition, the adaptability trends of the two scenarios between 2003 and 2012 were analyzed and proved to be consistent with the practical situation. The findings had significant implications for policy makers who determined the priority domains of internet of things technology applications by assessing the adaptability of these technologies before deployment

Multi-level Safety Performance Functions For High Speed Facilities

High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors. In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data. iii At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized iv appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users