Smart Urban Mobility: When Mobility Systems Meet Smart Data

Abstract

Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the United States. These figures indicate the rapid rise of industrial cities and the urgent need to move from traditional cities to smart cities. This article provides a survey of different approaches and technologies such as intelligent transportation systems (ITS) that leverage communication technologies to help maintain road users safe while driving, as well as support autonomous mobility through the optimization of control systems. The role of ITS is strengthened when combined with accurate artificial intelligence models that are built to optimize urban planning, analyze crowd behavior and predict traffic conditions. AI-driven ITS is becoming possible thanks to the existence of a large volume of mobility data generated by billions of users through their use of new technologies and online social media. The optimization of urban planning enhances vehicle routing capabilities and solves traffic congestion problems, as discussed in this paper. From an ecological perspective, we discuss the measures and incentives provided to foster the use of mobility systems. We also underline the role of the political will in promoting open data in the transport sector, considered as an essential ingredient for developing technological solutions necessary for cities to become healthier and more sustainable.

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

“…The authors assert that autonomous ground vehicles can significantly enhance the efficiency of traffic management systems, thereby improving road safety by establishing a smart and collaborative transportation infrastructure. Similarly, Mahrez et al [5] confirm that real-time intelligent transportation systems (ITSs) safeguard vulnerable road users by employing collision warning systems, speeding alerts, safety indicators, and enhanced vision, radar and navigation systems. Moreover, they emphasize the potential of ITSs in predicting passenger, driver and traffic behaviour, which facilitates more efficient traffic management and routing algorithms.…”

Blending Human Ware with Software and Hardware in the Design of Smart Cities

“…Data generated by billions of individuals daily through their usage of modern technologies and social media had made artificial intelligence possible [119]. Singapore has structured its public and private transportation networks, installed smart traffic lights and sensors to measure traffic congestion, introduced smart parking throughout the city, and will soon see the widespread usage of autonomous cars.…”

Cooperative driving: A comprehensive perspective, the role of communications, and its potential development

“…Several authors discussed these scenarios and their potential, envisioning different techniques and proposing potential solutions [6,20,21,22], but clearly the topic is still wide open and in its infancy. As we and several others highlighted in previous works [9,23,24], the application of automatic inference techniques has to deal in this case with unprecedented requirements on latency, predictability (the outcome of the the inference is a decision that influences all the actors in the scenario) and dependability. This latter point is often disregarded, or not fully discussed in works on cooperative urban mobility, but it is clear that any algorithm, be it classic, fuzzy, based on centralized or distributed learning, or whatever other approach one may take, the fundamental constraint it must respect is dependability in face of safety: If a vehicle has to stop, brake, or steer to avoid a VRU the decision must be consistent and correct.…”