Transportation Mobility in Smart Cities

Automated vehicles have to interact to make urban traffic safe. To handle many unplanned traffic issues, such as occlusions and system failures, automated vehicles need to cooperate with each other and the connected road infrastructure. A key component in a connected road infrastructure is the road vehicle control tower, where human operators can supervise and manage fleets of automated vehicles to ensure their safe and efficient operation. By introducing such operators, the transport system is able to mitigate a variety of complex scenarios, but at the expense of potentially introducing new human errors. In this chapter, we provide an overview of road vehicle control towers and how to guarantee safe interaction between automated driving systems and human operators. Specifically, we present our work on a behavior tree-based interaction framework that allows operators to intuitively create specifications for vehicle behavior. Using the human-defined behavior specification tree, we compute temporal logic trees, which allow us to both formally verify the feasibility of the specifications and to synthesize control sets that guarantee the specifications’ completion. Finally, we overview the implementation of a shared-autonomy approach to remote driving under the interaction framework to illustrate how it can be used for guaranteeing safety in practical use-cases.

Automated vehicles have the potential to transform transportation systems through enhanced safety, efficiency, and sustainability. This chapter first reviews cooperative control approaches for Connected Automated Vehicles (CAVs) at conflict areas such as merging roadways, intersections, and lane-changing maneuvers. Formulating and solving optimal control problems with hard safety constraints enables the derivation of cooperative trajectories. When solutions to such problems become computationally intractable, online control methods based on Control Barrier Functions (CBFs) can be used to still guarantee all constraint satisfaction at the expense of some possible performance loss. The chapter examines extending these approaches to mixed traffic environments, introducing new techniques to guarantee safety despite the presence of unpredictable Human-Driven Vehicles (HDVs). Finally, a cooperative compliance framework is proposed to incentivize HDVs to align their behavior with CAV objectives using virtual, refundable tokens, without any monetary transactions. The goal is to provide foundations and specific new techniques aimed at optimizing automated mobility in mixed traffic.

Connected and automated vehicles represent a technological breakthrough that can change the concept of mobility, paving the way for a new paradigm of traffic control in which the vehicles themselves can be used to implement traffic control strategies. Indeed, early field experiments have shown that even a small percentage of these vehicles traveling together in the form of platoons acts by limiting the occurrence of traffic instabilities that lead to congestion. In this context, the development of prediction models able to represent the mutual interactions between platoons and vehicular traffic is a key factor. It is then possible to develop model-based control schemes in which the speed of platoons is the adopted control action. The architectures of these control schemes can be of different types, with the control actions defined by a single controller with complete knowledge of the system state, giving rise to centralized control schemes, or with the control problem divided into subproblems, in decentralized control schemes, which are slightly less performant but more computationally efficient. In turn, these control schemes may involve the adoption of a hierarchical architecture in which different levels of control are defined, making the overall control framework more suitable for practical applications.

Inefficient mobility management leads to congestion with several socioeconomic and environmental adverse effects. Despite numerous research efforts and deployed technological solutions to address congestion, the problem still persists. One of the main reasons is attributed to the sparsity and low quality of traffic data. Recent advances in information and communication technologies have led to the emergence of new sensing devices with numerous capabilities for the collection of traffic data, that introduce new opportunities to traffic monitoring. This work provides an overview of such emerging sensing technologies and focuses on fine-grained traffic density estimation. A general streaming-data-driven probabilistic approach is designed to utilize different types of measurements. In this work, we examine how to utilize measurements from (i) fixed-location sensors, (ii) Connected and Automated Vehicles (CAVs), and (iii) Unmanned Aerial Vehicles (UAVs). The proposed methodology integrates the Gaussian process model within a Bayesian framework to effectively calculate traffic density estimates for different measurement types, even in cases where only a limited number of measurements are available within the designated time–space area being examined. This is achieved by using the available information of the time–space vehicle trajectory diagram that yields traffic density measurements which are extrapolated in time and space where no information has been obtained. A simulation study is performed to evaluate the estimation methodology utilizing measurements from the three different sensing technologies. Estimation results show that each sensing technology can be effective under different settings with their main difference being that CAVs and UAVs offer information with higher spatiotemporal resolutions compared to fixed-location sensors, while UAV-based sensing is effective even under low penetration rates of UAVs flying above the network and low percentages of network coverage.

Traffic congestion in modern cities inflicts substantial economic and societal burdens, manifesting in issues like reduced productivity, heightened driver stress, and fuel wastage. Traffic congestion emerges when road demand surpasses available road capacity. Despite numerous proposed traffic management solutions, congestion remains an unsolved problem. A notable limitation of existing solutions is their primary focus on redistributing traffic spatially across the network. While this might momentarily relieve specific congested points, it often merely redistributes or delays the emergence of congestion. This chapter introduces an innovative route reservation architecture aimed not only at alleviating congestion but also at enhancing the overall efficiency of the road network in terms of the average time vehicles spend within it. This architecture seamlessly integrates traffic management, steering drivers through the network, with demand management, influencing drivers’ departure timings. The architecture offers flexibility in application; it can provide direct routing and departure suggestions to drivers or, alternatively, utilize dynamic pricing mechanisms to influence their route and departure decisions. To ensure the robustness of the methodologies and algorithms derived from this architecture, this chapter provides extensive validation via detailed micro-simulations.

Connected Automated Vehicles (CAVs) have superb capabilities compared to human drivers, which calls for revisiting of established conventional road traffic principles. Recently, the TrafficFluid concept, a novel paradigm for vehicular traffic at high levels of vehicle automation, was proposed, relying on two combined principles: (a) Lane-free traffic, whereby vehicles are not bound to fixed traffic lanes and (b) Vehicle nudging, whereby vehicles influence other vehicles in front or aside of them. This chapter provides highlights of related research: three methodological approaches to lane-free vehicle movement strategies with nudging, namely, optimal control, nonlinear feedback control, multiagent reinforcement learning; a specialized microscopic simulation environment; macroscopic traffic flow models emerging from lane-free vehicle driving with nudging, using a theoretical and an empirical approach; a new, highly efficient traffic control measure for lane-free traffic; a challenging case study of a large-scale complex roundabout; and joint path optimization of multiple vehicles in a lane-free environment.

Electric mobility is one of the key components of the smart mobility transition that most of the cities worldwide are living nowadays. Besides the electrification of private cars, a great contribution in this direction is given by public transport, since a growing number of electric buses is appearing in most of the cities. At the same time, another important innovation is completely changing the mobility concept, that is, the technology allowing increasing levels of automation on board of vehicles. The mobility of public transport of the future smart cities will be then represented by fleets of electric automated vehicles. In order to correctly plan and control such vehicles, it is necessary, first of all, to develop appropriate models able to represent the dynamic behavior of buses when interacting with other vehicles on the road and able to estimate their energy consumption when traveling in the city roads. On the basis of such models, proper control algorithms can be designed in order to regulate the behavior of buses which have to follow a specific route and respect a fixed timetable. In this chapter, we provide an overview on electric buses, by highlighting the main modeling and control challenges, and by providing some possible traffic and consumption models, as well as some traffic control algorithms for optimizing bus speeds, dwell and charging times.

E-scooters are becoming increasingly popular as a convenient, fun, and environment-friendly micro-mobility option, especially among younger generations. As the numbers of e-scooter riders increase across cities and towns, related crashes and injuries increase at the same time. This chapter presents a summary of the major results and findings obtained from a multi-year, large-scale naturalistic study that addresses the following important issues: (1) Baseline moving patterns of e-scooters in diverse road environments and location; (2) interaction of e-scooter riders with vehicles and other road users in different scenarios; and (3) the common scenarios for crashes or near-misses involving e-scooter riders. The collected data and analytical results can be used to develop behavior prediction models for e-scooter riders which will help support the development of automated driving systems in challenging urban environments to improve road safety.

Connected vehicles can enhance safety by enabling real-time communication between vehicles and infrastructure, alerting drivers to potential hazards, and improving situational awareness. This technology also benefits vulnerable road users and reduces the economic burden of traffic incidents. In this chapter, we take a look at various challenges facing connected autonomous vehicles and proposed algorithms to address those challenges and enhance vehicle safety and mobility in smart cities. Specifically, cooperative adaptive cruise control subject to temporary communication or target detection loss; connected lane-change advance warning systems for improving traffic flow; probabilistic trajectory forecasting for vulnerable road users, especially pedestrians; and, finally, approaches to collision avoidance and mitigation for autonomous vehicles.

Beyond 5G/6G communication systems promise to significantly impact the development of a New Generation of CCAM. Reconfigurable Intelligent Metasurfaces (RIM) are by now established as a key enabling technology for 6G Systems. They have been extensively investigated the last few years, as they possess exotic properties allowing for precise control over any aspect of an impinging wave. As such, they can be harnessed for the realization of Programmable Wireless Environments (PWE). Despite their investigation for deployment in a number of applications, their integration in Connected Autonomous Driving Applications has not been considered in literature with respect to cooperative driving performance. In this chapter, we consider the HyperSurface (HSF) as the enabling technology for RIM and present recent results demonstrating the feasibility and the associated performance gains achieved from the integration of RIM in Connected Automated Driving Applications. We discuss HSF design considerations which can affect CCAM performance with respect to the system architecture, the controller network and the associated routing protocols, the workload characterization and the closed loop beam steering design. We envision the chapter to serve as a synopsis of tools and methodologies that can be used for RIM-enabled CCAM system design.

Curb management is critical for urban functionality, particularly in downtown areas, where various users compete for limited curb space. This chapter presents two Southern California curb management pilot projects funded by the US Department of Energy, focused on promoting zero-emission vehicles (ZEVs). The research employs semi-structured interviews with stakeholders involved in these projects to explore their characteristics, expectations, and limitations. The escalating demand for curb space, driven by ride-hailing services, goods delivery, and alternative transportation modes, is discussed, along with the ensuing challenges like congestion and conflicts between modes. The study highlights the complex relationship between optimizing curb efficiency and promoting ZEVs and the role of automation in enforcement. This chapter offers valuable insights into the intricate trade-offs inherent in managing curb space for a sustainable urban future.

This chapter proposes a smart, active, sustainable mobility framework model to promote accessibility, connectivity, and social inclusion among people in urban areas. In this context, Active Mobility, encompassing activities such as walking and cycling, could emerge as one of the most significant transport modes, sparking higher levels of well-being, quality of life, and sustainability in urban spaces. Historically, city planning authorities have often overlooked Active Mobility, heavily focusing instead on motorized traffic. This unjustified focus has resulted in severe traffic congestion and unbearable air pollution. In contrast, Active Mobility-friendly accessible, and connected urban spaces offer multiple health, economic, and environmental benefits to cities and their inhabitants. With support from the latest advances in Intelligent Transportation Systems (ITS), Information Communication Technology (ICT), Artificial Intelligence, and Augmented Reality, a viable Smart Active Mobility model may be established, representing a paradigm shift in how urban communities use and access transportation. In this envisioned model, active mobility is central and becomes the dominant transport mode for the development of revitalized sustainable smart cities.

The efficient routing and distribution of goods are vital to the survival and sustainability of any smart city. Currently, different truck companies route their trucks on a road network that is often congested and unbalanced in time and space with respect to traffic load distribution. The lack of coordination among truck operators often leads to long waiting times at pick-up and drop-off points and/or along popular routes that trucks usually follow due to overlaps. Due to connectivity and the Internet of Things new opportunities open for centrally coordinated solutions in order to achieve better load balancing in space and time for trucks across the road network. In this chapter, we present a centrally coordinated approach for routing freight in urban environments where truck traffic loads are balanced in time and space in an effort to improve mobility and reduce cost. We assume that freight is moved by trucks using the road network and truck fleets consist of a mix of diesel and electric trucks. The electric trucks add the constraint of charging time, charging station locations and driving range that is less than that of diesel. The routing problem is formulated as an optimization problem with several constraints. A co-simulation load-balancing approach is used to generate routes for trucks that reduce the overall cost. The co-simulation approach employs a traffic simulator in a feedback loop that replaces simple mathematical models often used to predict the states of the network. The simulation model captures the complexity and dynamics of the network and generates predicted states that are used to generate optimum routes and achieve load balancing. Numerical experiments are performed using a traffic simulator of the Los Angeles/Long Beach Metropolitan road traffic network that includes two major ports. The simulation results demonstrate strong potential for the proposed centralized truck routing system to reduce the impact of trucks on traffic and make their routing more efficient.

Connected and automated vehicles (CAVs) provide the most intriguing opportunity for enabling users to significantly improve safety and transportation efficiency by monitoring network conditions and making better operating decisions. CAVs, however, could alter the tendency to travel, eventually leading to a high traffic demand and causing rebound effects (e.g., increasing vehicle miles traveled). This chapter provides a control framework to distribute travel demand in a given transportation network, resulting in a socially optimal mobility system that travelers would be willing to accept. A “socially optimal mobility system” implies a mobility system that (1) is efficient (in terms of energy consumption and travel time), (2) mitigates rebound effects, and (3) ensures equity in transportation.

The efficient use of transportation infrastructure can greatly reduce travel times, fuel consumption, and environmental pollution. It is well known that a rational driver makes selfish routing decisions to lower their transportation cost when there is no coordination. These selfish route choices lead to the user equilibrium solution which can be far away from the actual social optimum solution. On the contrary, achieving the social optimum reduces the collective travel time, fuel cost, and pollution to the highest possible extent without requiring any additional infrastructure. However, this cannot be accomplished without a fair cost-sharing mechanism because, otherwise, some drivers would benefit at the expense of others. Therefore, the presence of a cost-sharing mechanism that distributes the overall benefits among the stakeholders in a fair manner is vital to establish a stable coordination. We review the working principles and the properties of the core cost-sharing mechanisms that are commonly used in transportation and logistics, as well as some of the most recent advancements in this field. Although there is a plethora of possible collaboration opportunities in transportation and logistics, we focus on vehicle sharing, ridesharing, shipping, and supply chain (SC) collaboration. We also identify possible future research directions in this area. To do so, we have surveyed 46 articles, most of which were published between 2016 and to date.

One of the promises of self-driving vehicles is equity to all social groups. Physically impaired people, people with disabilities, currently face unequal conditions in transportation that restrict their freedom of movement; unfavorably impact their medical, social, and economic life; and even accentuate conditions of social exclusion. Autonomous driving carries the potential to deliver accessibility to transportation and create more favorable living conditions for people with mobility impairments. However, there is still limited research, compared to the general population, addressing the challenges people with mobility disabilities face; the prospects autonomous driving can provide to them; and, more importantly, the attitudes and the perspectives of people with disabilities toward autonomous vehicles and autonomous driving. Nevertheless, such research is of importance since it can inform relevant research and policies and the design of transportation systems advancing social justice and equity. The current article, based on a recent survey, aims at filling the abovementioned gaps concentrating its focus on the Greek context from a user’s perspective.

Many cities across the world are looking to use technology and innovation to improve the overall efficiency and safety for their residents. At the heart of these smart-city plans, a variety of intelligent transportation system technologies can be used to improve safety, enhance mobility measures (e.g., traffic flow), and minimize environmental impacts of a city’s mobility ecosystem. Early implementations of these ITS technologies often take place in affluent cities, where there are many funding opportunities and suitable areas for deployment. However, it is critical that we also develop smart city solutions that are focused on improving conditions of disadvantaged and environmental justice communities, whose residents have suffered the most from unmitigated urban sprawl and its environmental and health impacts. As a leading example, Inland Southern California has grown to be one of the largest hubs of goods movement in the world. Numerous logistics facilities such as warehouses, rail facilities, and truck depots have rapidly spread throughout these communities, with the local residents bearing a disproportionate burden of truck traffic, poor air quality, and adverse health effects. Further, the majority of residents have lower-wage jobs and very few mobility options, other than low-end personal car ownership. To improve this situation, UC Riverside researchers have focused their smart city research on these impacted communities, finding innovative solutions to eco-friendly traffic management, developing better-shared (electric) mobility solutions for the community, improving freight movements, and enhancing the transition to vehicle electrification. Numerous research and development projects are currently underway in Inland Southern California, spanning advanced smart city modeling and impact analysis, community outreach events, and real-world technology demonstrations. This chapter describes several of these ITS solutions and their potential for improving many cities around the world.

Shared mobility services have shown that a continuum of solutions can be provided between traditional individual transport and mass transit by making use of an underlying cyber-physical substrate that provides advanced, distributed, and networked computational and communication support. Shared mobility services, also known as microtransit services, provide a way of solving the last-mile connectivity problem and can enable access to key services such as healthcare, jobs, and food for disadvantaged communities. Despite recent advances in optimizing energy efficiency in microtransit services, on-the-ground pilots have struggled to increase performance measures such as passenger miles per revenue mile or passengers per unit energy due to challenges in demand aggregation, understanding user needs, and human behavior. This chapter proposes a framework for multi-passenger transport that allows dynamic routing and behavioral dynamics of passengers and leads equitable microtransit services for members of disadvantaged communities. The implications of disadvantaged communities and how their needs may be met by the proposed framework are discussed. A numerical study based on a community in a Food Desert in the Western United States is presented.

This chapter examines the institutional environment in which new transportation technologies are being introduced within the United States. We focus on four broad technology categories: Shared mobility, integrated mobility, priced mobility, and autonomous mobility to illustrate the potential transformations to the transportation system and the institutional obstacles to adoption at scale. We identify two overarching themes to conceptualize how transportation innovations interact with the existing technology. First, the omnipresence of transportation in the public sphere and in people’s daily lives sets transportation technologies apart from other areas of rapid innovation that are more clearly in the private or public sector. Second, transportation technologies have the potential to significantly reshape land use, which greatly expands the scope of change to how cities function and develop. We use these themes to synthesize the literature and formulate a set of recommendations to steer innovations in the direction of equitably distributed benefits.