China
Africa
Explore chapters and articles related to this topic
Traffic
Published in Robert M. Sanford, Environmental Site Plans and Development Review, 2017
The level of service (LOS) concept is one means of evaluating traffic conditions. The LOS provides a qualitative measure based on ranges in three critical variables: average travel speed, density, and maximum service flow rate. The relation between speed, flow, and density depends on the prevailing roadway segment and traffic conditions. LOS reads like a grade report, with A being the highest and F representing “failing.” It may not be possible to have a high level of service for all intersections and flow-through conditions; however, lower levels may still be safe and LOS is not quite the same thing as a safety evaluation or rating. The Transportation Research Board, in addition to providing standards and guidelines for design in its Highway Capacity Manual, provides a definition of the levels of service (2010).
Sensor Networking Software and Architectures
Published in John R. Vacca, Handbook of Sensor Networking, 2015
When a certain road segment presents signs of congestion, the service looks for nearby vehicles to reroute. Specifically, we select vehicles from incoming segments (i.e., segments that bring traffic into the congested one). To decide how far from congestion to look for candidates for rerouting, the service uses a parameter L (level). This parameter denotes the furthest distance (in number of segments) a candidate vehicle can be away from the congested segment. In practice, L could be computed as function of the severity of congestion; for example, we can use the "level of service" (LOS) defined in the Highway Capacity Manual [39]. L's value has to be large enough to mitigate congestion. If L is too high, however, more vehicles than necessary will be selected for rerouting, which can have undesired consequences (e.g., creating congestion in another spot). Since our focus is on the rerouting algorithms and the analysis of their performance, we decided to consider L a tuning parameter that is varied during our experiments.
Freight Transportation in Distributed Logistic Chains
Published in Javier Campos, Carla Seatzu, Xiaolan Xie, in Manufacturing, 2018
Angela Di Febbraro, Nicola Sacco
In this framework, it is evident that both passenger and freight travel demands are influenced by the performances and costs of the transportation supply provided by the available travel modes (such as private vehicles, transit and walking), as well as by the characteristics of the transportation infrastructures. These characteristics, synthetically known as Level of Service (LoS) or performance attributes, can include not only the already described travel times and monetary costs, but also the service reliability, riding comfort, and so on.
Road surface friction prediction using long short-term memory neural network based on historical data
Published in Journal of Intelligent Transportation Systems, 2021
Ziyuan Pu, Chenglong Liu, Xianming Shi, Zhiyong Cui, Yinhai Wang
Road surface condition has a great impact on road traffic mobility and safety (Chen et al., 2017; Pisano, 2017; Shi & Fu, 2018; Strong et al., 2010; Z. Ye et al., 2009). Especially in winter season, terrible road surface conditions could result in more traffic crashes and low level of service (LOS). The United States spends $2.3 billion annually to keep highways clear of snow and ice; in Canada, winter highway maintenance costs more than $1 billion (Shi, 2010). Improving road surface condition monitoring systems and operations could result in fewer crashes, higher LOS, improved mobility, better fuel economy and sustained economic productivity (Rita, 2018). As one of the direct measurements of road surface condition, road surface friction has strong correlation with traffic accident risk (Wallman & Åström, 2001). Thus, to mitigate the impact of road surface condition on traffic safety and mobility, an efficient and cost-effective road surface friction prediction methodology is needed for these concerns.
A simplified framework for sequencing of transportation projects considering user costs and benefits
Published in Transportmetrica A: Transport Science, 2018
Amit Kumar, Sabyasachee Mishra
State of good-repair is getting significant attention as the need to preserve the transportation infrastructure has become critical in the era of constrained budget. City, regional and state transportation planning authorities constantly undertake efforts to maintain the transportation infrastructure in good condition so that it meets the need of travelers and achieves the desired performance measures such as congestion, safety, mobility and air-quality among others (USDOT 2012). To achieve such objectives transportation network performance is constantly assessed and network improvements are planned ahead in time to maintain the desired level of service (LOS). Short-term planning periods range from 3 to 5 years, whereas long-term periods are of 20–30 years. In these planning periods, various projects are selected based on performance measures target set forth by the planning authorities. Typically, the project selection and its sequencing in practice are done based on engineering judgment or the needs set forth by the local citizens. Generally, planning authorities analyze one project at a time, and assess its economic benefits and justification of investment. However, in a transportation system framework, it may not be prudent to analyze one project at a time by cost–benefit analysis or by expert judgment. A more customized approach will entail a comprehensive approach in which project selection and its sequencing should be done through an optimization framework that simultaneously considers multiple competing projects in a given network.
Bi-objective evolutionary optimization of level of service in urban transportation based on traffic density
Published in Cogent Engineering, 2018
Rolando Armas, Hernán Aguirre, Kiyoshi Tanaka
The level of service (LoS) is a quality measure describing operational conditions within a traffic stream, generally in terms of service measures such as speed and travel time, freedom of maneuver, traffic interruptions, and comfort and convenience (Board, 2000). In this work, we define LoS in terms of traffic density, which is determined by the total number of vehicles that circulate in the city given by the proportions of the population that use public and private transportation.