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Data collection, processing, and database management
Published in Zongzhi Li, Transportation Asset Management, 2018
Traffic: Volume counts expressed by AADT or annual average weekday daily traffic (AAWDT) are basic data of congestion management. Specifically, the data set should include vehicle classification, land distribution, directional distribution, time-of-day counts, and turning movement counts in intersections. In network-level management, the raw traffic data can be aggregated into vehicle miles of travel (VMT) for further usage. Traffic volume counts can not only be obtained from traditional traffic volume data collection techniques including manual counts, test vehicle, and loop detector techniques, but can also be aggregated from trajectory data of each roadway user over time.
Road-Traffic Emissions
Published in Brian D. Fath, Sven E. Jørgensen, Megan Cole, Managing Air Quality and Energy Systems, 2020
Fabian Heidegger, Regine Gerike, Wolfram Schmidt, Udo Becker, Jens Borken-Kleefeld
The factors for the specification of driving dynamics (see Figure 2) are bundled in a traffic (demand) model. Traffic volume, traffic mileage (traffic volume multiplied by road section length), traffic flow, traffic composition, and traffic quality all serve as input variables for the energy demand of vehicles and emissions. Traffic volume is modeled either as Average Annual Daily Traffic (AADT) based on an average day of the year or in a higher temporal resolution with traffic flow by means of time series of a day, a week, or a year. Traffic flow is the number of vehicles which cross a specific road section over a certain period of time.
A risk assessment model for traffic crashes problem using fuzzy logic: a case study of Zonguldak, Turkey
Published in Transportation Letters, 2022
Xiao, Kulakowski, and EI-Gindy (1999) developed two FL models to predict the crash risk on wet pavements. One of those models was based on Mamdani inference and the other was based on Sugeno inference method. According to the results, the FL models proved to be better than both nonlinear regression and probabilistic models. Meng, Zheng, and Qing (2009) used FL to investigate the relationship between urban road crashes frequencies and some traffic and road conditions. They found that the annual average daily traffic (AADT) and traffic load are the major influence factors according to the model. Zheng Lai (2011) employed fuzzy neural network model (FNNM) for predicting crash frequencies in Harbin city of China. They found that AADT is the most significant factor, followed by speed limit, traffic load and lane width in this order according to their significance levels.
How weather and special events affect pedestrian activities: volume, space, and time
Published in International Journal of Sustainable Transportation, 2022
Sunyong Eom, Yasuhide Nishihori
Quantifying pedestrian activities is necessary to investigate the factors influencing walking. Annual average daily traffic (AADT) is a representative index in traffic engineering for planning and design purposes (El Esawey, 2017). To measure the degree of vitality and physical activity, it is necessary to monitor the spatial and temporal distribution of pedestrian counts at district levels that consist of multiple count locations. Even pedestrian counts across the districts, and skewed pedestrian counts in a specific location or time may have different effects on the vitality of a neighborhood. For example, Mumford et al. (2020) evaluated the dynamism and user demand of a city center based on the multiple count data of UK centers. AADT focuses on the traffic count of each location and is useful to evaluate the infrastructure, safety analyses, and level of service calculation of individual links. However, it cannot be applied to monitor the spatial and temporal distribution of pedestrian counts. Measuring the ‘pulse of a city’ where activity changes according to time and space is important for understanding human activity (Batty, 2010). Capturing human activities in total district and peak-times cannot give a comprehensive understanding of urban vitality.
A supervised learning approach to calibrating annual average daily traffic against highway roadworks: the impact of demographic and weather conditions
Published in Transportation Planning and Technology, 2021
Annual average daily traffic (AADT) is defined as the average number of daily traffic flows at a certain location over an entire year (Sfyridis and Agnolucci 2020). It is well-known that AADT plays a pivotal role in providing essential information to present a wide picture of traffic flows, evaluate traffic patterns, and predict the level-of-service of roads in the future (Eom et al. 2006; Sfyridis and Agnolucci 2020; Wang, Gan, and Alluri 2013; Gastaldi, Gecchele, and Rossi 2014; Xia et al. 1999; Gastaldi et al. 2013; Ha and Oh 2014; Khan et al. 2019; Jessberger et al. 2016). As a way to collect AADT data, the permanent traffic count method is intended to collect 24-hour basis traffic volume during 365 days a year, using automatic traffic counters (Sfyridis and Agnolucci 2020; Department for Transport 2020). Portable devices are also used to collect traffic data 1–5 times over a year, as the short-term traffic count method (Ha and Oh 2014).