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Fusion of IoT, Blockchain and Artificial Intelligence for Developing Smart Cities
Published in Naveen Chilamkurti, T. Poongodi, Balamurugan Balusamy, Blockchain, Internet of Things, and Artificial Intelligence, 2021
M. Kiruthika, P. Priya Ponnuswamy
For instance, in smart transportation, blockchain can store details about the vehicle, traffic congestion places, parking areas, traffic lights, location of the vehicle, and many more. AI can be applied to derive analysis of transportation data and can be helpful when drivers need to make decisions on finding the shortest path to reach a destination, or the driver needs to make a decision on which parking lot the vehicle should be parked in. By gathering the vehicle details from the distributed ledger, AI can be efficiently applied to manage traffic and to make clear decisions. In order to enhance and modernize traffic management, AI plays a vital role. The ability to maintain and monitor huge data volumes stored in ledger and make intelligent decisions can have a major impact on congested cities.
Introduction to VANET
Published in Sonali P. Botkar, Sachin P. Godse, Parikshit N. Mahalle, Gitanjali R. Shinde, VANET, 2021
Sonali P. Botkar, Sachin P. Godse, Parikshit N. Mahalle, Gitanjali R. Shinde
Transportation is the backbone of economy in any country. Along with basic infrastructures like roads and vehicles, it is important to control the vehicles on road intelligently. Traditional traffic management systems used traffic signals for monitoring and controlling traffic on road. Traffic signals with three colors, i.e., red, green, and yellow, are time-based functioning device. Sometimes, it causes unnecessary waiting even there are no vehicles passing at the cross road. Waiting will cause congestion of vehicles and wastage of time. There is a need of some smart solution for traffic control where traffic information can be shared with other vehicles and the signal controller. VANET and vehicles can share important information about traffic jam, path, map, weather conditions, road conditions, drivers’ behaviors, information request, alternative paths, etc.
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
Transport emissions are a significant contributor to total emissions worldwide; in particular, road-traffic emissions have been increasing, and this trend will continue in the future. The substances PM10 and NOx (NO and NO2) present the greatest problems in transport, with the resultant CO2 contributing directly to global warming. Fuel-based vehicles are the majority, but electric vehicles gain market shares. Emissions arise from combustion processes, abrasion, resuspension, and evaporation. Either the emissions can be measured with test vehicles or the emissions can be modeled with traffic and emission models for large networks. Several methods exist on different spatial scales to validate emissions. Emissions can be reduced most effectively with an integrated approach consisting of vehicle-related, infrastructural, traffic-planning-related, traffic-management-related, and user-related measures. (Rebound effects must also be considered.) Traffic and emission models can be used for environmentally oriented traffic management. A realization of the true-cost principle will support a shift toward eco-conscious transportation.
Weather impact on macroscopic traffic stream variables prediction using recurrent learning approach
Published in Journal of Intelligent Transportation Systems, 2023
Archana Nigam, Sanjay Srivastava
The Intelligent Transportation System (ITS) technologies inform the traveler and the traffic authorities about the current and future traffic conditions (Lee et al., 2010). The traveler information system tells the driver about expected travel time, congestion and suggests the shortest and fastest routes as far as possible. The traffic management system helps traffic authorities to manage road traffic through surveillance strategies like real-time traffic monitoring and control strategies like traffic signal timing. These systems depend upon the short-term prediction of traffic stream variables such as flow, speed, and density on a road segment. The traffic stream variables are measured using sensor devices such as inductive loops, radar, camera, GPS, etc. Predicting traffic stream variables is a challenging problem. These variables are affected by several factors like time of the day, road condition, a non-recurrent event such as accident, road construction, weather events, social events, etc. Therefore traffic stream characteristic is non-linear and complex.
Combined flexible lane assignment and reservation-based intersection control in field-like traffic conditions
Published in Transportmetrica A: Transport Science, 2022
Farzaneh Azadi, Nikola Mitrovic, Aleksandar Z. Stevanovic
Traffic congestion, as one of the major issues in urban transportation, does not only result in loss of efficiency (by causing substantial delay) but also generates many critical safety events such as crashes and near-crashes. With the latest advancements in the Information Technology (IT) sector and automotive industries, Connected and Autonomous Vehicles (CAVs) are promised to revolutionise urban transportation and bring significant efficiency and safety benefits to transportation users. Vehicles with advanced driving assistance systems and low levels of autonomy are already on the market and new technologies are paving the way to novel traffic management approaches (Fagnant and Kockelman 2015), and some new technologies are also used for traffic management purposes (Tiaprasert et al. 2015; Lin et al. 2017). Since roadway intersections are often seen as some of the physical bottlenecks of current transportation systems, advancing intersection control mechanisms to better manage competing traffic flows is critical for the optimisation of complex urban networks.
On the modelling of speed–concentration curves for multi-class traffic lacking lane discipline using area occupancy
Published in Transportation Letters, 2022
The presence of varying sized vehicles and the behavior of lacking lane discipline makes traffic management plans different and difficult in LMICs compared to that in HICs. Traffic management requires accurate estimation of traffic state and relies on different types of data as well as different methods of data collection. Density is one of the fundamental variable for measuring traffic state. For traffic following perfect lane discipline, even in multi-class vehicles case, density is undoubtedly the correct variable to measure traffic congestion. However, for lanes with traffic in longitudinal direction but with frequent unpredicted lateral movements due to gap percolating behavior of smaller vehicles, number of vehicles per length varies continuously. In such case, density measurement cannot correctly reflect the traffic state and thus may lead to incorrect traffic management policies.