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Digital Twins for Assessing the Impact of Autonomous Vehicles on Built-Environment Changes
Abstract
This chapter explores the multifaceted impact of Autonomous Vehicles (AVs) on the built environment and identifies limitations in traditional methodologies for prediction. Proposing a transformative solution, the chapter advocates for integrating Digital Twin technology as a dynamic and data-driven assessment tool to evaluate the impact of AVs on urban density, diversity, design, distance to other transits, and destination accessibility (5Ds). In this case, Urban Digital Twins offer real-time data analysis, modeling, and simulation of AVs interaction with the users and built environment, leveraging artificial intelligence/machine learning, and immersive 3D visualization. The discussion underscores the importance of a comprehensive Urban Digital Twin architecture to monitor and simulate the AV’s impacts on 5Ds and provide robust safety and security measures to ensure reliability of deploying AVs to the city environment. The chapter envisions a future where Urban Digital Twins play a pivotal role in informed decision-making, proactive urban planning, and adaptive development amid the transformative integration of Autonomous Vehicles.
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This book reveals the power of digital twin technologies in terms of optimizing the performance and maintenance of infrastructure assets. From roads, bridges, and tunnels to airports and smart cities, it will guide you through the latest advances in and use cases on this cutting-edge technology. You will come to understand the challenges involved in the development of digital twins and learn about the initiatives and projects underway to overcome them. Explore the potential of this technology in terms of reducing costs, improving system performance, and enhancing the overall infrastructure experience for users. Get ready to embark on a journey of understanding the future of transportation infrastructure management with digital twin technologies.
The concept of Digital Twin (DT) has emerged as a trend in various industries development, enabling the creation of virtual models of physical objects. We conduct a systematic review of the digital twin technology in the field of transportation infrastructure management from the aspects of concept definition, whole life cycle application, advanced technology, and equipment utilization, as well as the challenges. We begin with an introduction that defines DT and its components, while also distinguishing it from Building Information Modeling (BIM) and Cyber-physical Systems (CPS). We explore the diverse applications of DT throughout its lifecycle and highlight the significance of DT in structural monitoring, infrastructure operation and maintenance, and dataset expansion. We further investigate the advanced techniques and equipment associated with DT components, focusing on the importance of virtual parts, data acquisition, transmission, multi-source data fusion processing, and data security as well as dynamic updating of models for effective integration and utilization of DT in transportation infrastructure management. We identify key challenges faced by DT in transportation infrastructure management and propose future trends in the study. This comprehensive review serves as a valuable resource for researchers, practitioners, and decision-makers in understanding the potential of DT technology in transportation infrastructure management.
The active participation of stakeholders is a crucial requirement for effective land-use planning (LUP). Involving stakeholders in LUP is a way of redistributing the decision-making power and ensuring social justice in land-management interventions. However, owing to the growing intricacy of sociopolitical and economic relations and the increasing number of competing claims on land, the choice of dynamic land use has become more complex, and the need to find balances between social, economic, and environmental claims and interests has become less urgent. These facts reflect a paradigm shift from top-down, noninteractive, and one-directional policymaking approaches to a more negotiable, bottom-up, deliberative, and responsible one. Geospatial industries claim that digital twin technology is a potential facilitator that improves the degree of stakeholder participation and influences land-use planning. The validity of this claim is, however, unknown. By adopting the integrative literature review, this study identifies where in LUP is stakeholder participation much needed and currently problematic, as well as how digital twin could potentially improve. The review shows that digital twins provide virtual visualisation opportunities for the identification of land-use problems and the assessment of the impacts of the proposed land uses. These offer the opportunity to improve stakeholder influence and collaboration in LUP, especially in the agenda-setting phase, where land-use issues could be identified and placed on the LUP agenda. This relies on the ability and willingness of local planning institutions to adopt digital twins, and stakeholders’ perception and willingness to use digital twins for various land-use goals. Despite the assertion that digital twins could improve the influence of stakeholders in LUP, the focus and the development of digital twins have not accomplished much for those features of the technology that could improve stakeholder influence in LUP. By adopting the principles of the social construction of technology, this study proposes a “technological fix” of digital twins to focus more on improving stakeholder influence on land-use planning.
This paper provides a state of the art of contemporary Digital Twins (DTs) projects for urban planning at an international level. The contribution investigates the evolution of the DT
concept and contextualises this tool within the scientific-cultural debate, highlighting the interconnection between global policies and local needs/wishes. Specifically, six case studies of DTs are compared, illustrating their application, content, technological infrastructure, and priority results. The projects presented provide an overview of the existing DT typologies, focusing on the evaluative/prefigurative use and the limits/potential of the tool in light of the socio-health, climate, and environmental crises. Reflections on DT reveal, on the one hand, its potential role in supporting decision-making and participatory processes and, on the other, the potential utopian trend of data-driven planning encouraged by public–private investments in the smart city/twin city sector. In conclusion, the study underlines the innovative role of DT as a cutting-edge scientific format in the disciplinary framework but highlights that the practical use of the tool is still in an experimental research-action phase. From this theoretical-critical review, it is possible to hypothesise new research paths to implement the realism and application potential of DTs for urban planning and urban governance.
Applications of Digital Twin technology have been growing at an exponential rate, and it is transforming the way businesses operate. In the past few years, Digital Twins leveraged vital business applications, and it is predicted that the technology will expand to more applications, use cases, and industries. The purpose of this paper is to do a literature review and explore how Digital Twins streamline intelligent automation in different industries. This paper defines the concept, highlights the evolution and development of Digital Twins, reviews its key enabling technologies, examines its trends and challenges, and explores its applications in different industries.
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Landscape architects and urban designers are often tasked with decision-making about implementation of flood moderating measures in urban renewal projects. These decisions require consideration of complex, interdependent existing and proposed infrastructure, and must be informed by data and modelling from multiple disciplines such as hydrologists, transport engineers and urban planners. Here we describe the challenges of integrating these data and modelling from both GIS and BIM sources, into a framework that could support flood moderation decision-making, embedded within a spatially enabled Digital Twin. Our findings outline some of the considerable adjustments to future data collection methods that will be required to enable such a decision-support framework. Furthermore, we outline the requirements of the framework for employability in stakeholders and community decision-making forums. We test this framework on a large-scale urban renewal precinct in Melbourne Australia, with well recognised current and future flooding issues.
This paper provides a state of the art of contemporary Digital Twins (DTs) projects for urban planning at an international level. The contribution investigates the evolution of the DT concept and contextualises this tool within the scientific-cultural debate, highlighting the interconnection between global policies and local needs/wishes. Specifically, six case studies of DTs are compared, illustrating their application, content, technological infrastructure, and priority results. The projects presented provide an overview of the existing DT typologies, focusing on the evaluative/prefigurative use and the limits/potential of the tool in light of the socio-health, climate, and environmental crises. Reflections on DT reveal, on the one hand, its potential role in supporting decision-making and participatory processes and, on the other, the potential utopian trend of data-driven planning encouraged by public–private investments in the smart city/twin city sector. In conclusion, the study underlines the innovative role of DT as a cutting-edge scientific format in the disciplinary framework but highlights that the practical use of the tool is still in an experimental research-action phase. From this theoretical-critical review, it is possible to hypothesise new research paths to implement the realism and application potential of DTs for urban planning and urban governance.
The goal of this study was to analyze the impact of private autonomous vehicles (PAVs), specifically their near-activity location travel patterns, on vehicle miles traveled (VMT). The study proposes an integrated mode choice and simulation-based parking assignment model, along with an iterative solution approach, to analyze the impacts of PAVs on VMT, mode choice, parking lot usage, and other system performance measures. The dynamic simulation-based parking assignment model determines the parking location choice of each traveler as a function of the spatial–temporal demand for parking from the mode choice model, whereas the multinomial logit mode choice model determines mode splits based on the costs and service quality of each travel mode coming, in part, from the parking assignment model. The paper presents a case study to illustrate the power of the modeling framework. The case study varies the percentage of persons with a private vehicle (PV) who own a PAV versus a private conventional vehicle (PCV). The results indicated that PAV owners traveled an extra 0.11 to 1.51 mi compared with PCV owners on average, and the PV mode share was significantly higher for PAV owners. Therefore, as PCVs are converted into PAVs in the future, the results indicate substantial increases in VMT near activity destinations. However, the results also indicated that adjusting parking fees and redistributing parking lot capacities could reduce VMT. The significant increase in VMT from PAVs implies that planners should develop policies to reduce PAV deadheading miles near activity locations, as the automated era comes closer.
This article develops an ontological description of land use and applies it to incorporate geospatial information describing land coverage into a knowledge-graph-based Universal Digital Twin. Sources of data relating to land use in the UK have been surveyed. The Crop Map of England (CROME) is produced annually by the UK Government and was identified as a valuable source of open data. Formal ontologies to represent land use and the geospatial data arising from such surveys have been developed. The ontologies have been deployed using a high-performance graph database. A customized vocabulary was developed to extend the geospatial capabilities of the graph database to support the CROME data. The integration of the CROME data into the Universal Digital Twin is demonstrated in two use cases that show the potential of the Universal Digital Twin to share data across sectors. The first use case combines data about land use with a geospatial analysis of scenarios for energy provision. The second illustrates how the Universal Digital Twin could use the land use data to support the cross-domain analysis of flood risk. Opportunities for the extension and enrichment of the ontologies, and further development of the Universal Digital Twin are discussed.
Autonomous vehicles (AVs) may significantly impact people’s choice of residential locations and spatial structures. The impact may vary across different countries, but few studies have focused on it. This study drew on China and the United States (US) as two cases to study car drivers’ knowledge of AVs and willingness to move farther if AVs were available by estimating ordered logistic regression models. The results showed that 42.3% of Chinese and 29.8% of US respondents were likely to consider moving farther away from the nearest city or the destination for the most frequent trip if they had an AV. The Chinese sample had less knowledge of AVs than the US sample, but they were more likely to consider a move. AVs may lead to a new round of urban sprawl, but the challenge may be greater for China. We captured the socio-economic and transport factors that affected this result.
This paper presents an efficient and practical approach for a car navigation system (CVM-Car) based on the velocity space optimization paradigm. The method calculates the velocity control commands to keep the car in the lane while avoiding the obstacles detected by the proximity sensors. The car has to follow a road path consisting of a sequence of lanelets. This approach is a lower-level reactive control that combines the pure pursuit method to obtain a reference curvature and a reactive control algorithm that keeps the vehicle in the center of the lane s free space while avoiding obstacles that can partially block it. CVM-Car formulates local obstacle avoidance as a constrained optimization problem in the velocity space of the car. In addition to the vehicle dynamics and obstacles constraints included by the curvature method, car-shape and non-holonomic restrictions are considered in the CVM-Car velocity space. The method has been applied to an autonomous vehicle prototype.
Car-following theory has received considerable attention as a core component of Intelligent Transportation Systems. However, its application to the emerging autonomous vehicles (AVs) remains an unexplored research area. AVs are designed to provide convenient and safe driving by avoiding accidents caused by human errors. They require advanced levels of recognition of other drivers’ driving-style. With car-following models, AVs can use their built-in technology to understand the environment surrounding them and make real-time decisions to follow other vehicles. In this paper, we design an end-to-end car-following framework for AVs using automated object detection and navigation decision modules. The objective is to allow an AV to follow another vehicle based on Red Green Blue Depth (RGB-D) frames. We propose to employ a joint solution involving the You Look Once version 3 (YOLOv3) object detector to identify the leader vehicle and other obstacles and a reinforcement learning (RL) algorithm to navigate the self-driving vehicle. Two RL algorithms, namely Q-learning and Deep Q-learning have been investigated. Simulation results show the convergence of the developed models and investigate their efficiency in following the leader. It is shown that, with video frames only, promising results are achieved and that AVs can adopt a reasonable car-following behavior.
The use of autonomous vehicles (AVs) has the potential to transform users’ behaviour and urban space management. Quantitative and qualitative analyses of impacts require a scenario building method. We considered the fleet size, modal share, car ownership, parking preferences, and urban space repurposing during the elaboration of a novel method. Existing scenarios and results of a questionnaire survey have been used as sources. The method was applied to build scenarios in a case study in Budapest, Hungary. The results were used to calculate the impacts on urban space management, including environmental savings. The key findings are: scenarios with significant shared AV use show that parking demand may be minimised (almost 83%) and urban space repurposing has the highest potential; furthermore, AV use and sharing acceptability may decrease the fleet size and alter the type of shared mode to multiple occupancies. The developed scenario building method serves as a base for future studies. The produced scenarios allow the researchers to focus on the analysis of the impacts caused.
Under the background of smart city construction, modern information technology has been continuously innovated and developed, which has effectively promoted the development of highway transportation in China, and GIS technology has effectively promoted the development speed of highway transportation informationization in China. As an important geographic information system, GIS technology has obvious technical advantages in traffic information data collection, information data analysis and processing, and promotes the road traffic to develop towards informationization gradually. In order to accelerate the mature development of road transportation, with the support of GIS technology, the road transportation digital twin system is gradually constructed. As a revolutionary technology in the development of highway transportation, digital technology promotes the development of highway transportation in a green, open, and shared direction, laying a solid foundation for the quality of highway engineering construction. Based on this, this article analyzes the overview of GIS technology, explores the digital twin technology, and specifically discusses the modeling process of the road traffic digital twin system, in order to verify the feasibility of the practical application of the road traffic digital twin system.
Autonomous vehicles (AVs) can generate major changes in urban systems due to their ability to use road infrastructures more efficiently and shorten trip times. However, there is great uncertainty about these effects and about whether the use of these vehicles will continue to be private, in continuity with the current paradigm, or whether they will become shared (carsharing/ridesharing). In order to try to shed light on these matters, the use of a scenario-based methodology and the evaluation of the scenarios using a land use–transport interaction model (LUTI model TRANSPACE) is proposed. This model allows simulating the impacts that changes in the transport system can generate on the location of households and companies oriented to local demand and accessibility conditions. The obtained results allow us to state that, if AVs would generate a significant increase in the capacity of urban and interurban road infrastructures, the impacts on mobility and on the location of activities could be positive, with a decrease in the distances traveled, trip times, and no evidence of significant urban sprawl processes. However, if these increases in capacity are accompanied by a large augment in the demand for shared journeys by new users (young, elderly) or empty journeys, the positive effects could disappear. Thus, this scenario would imply an increase in trip times, reduced accessibilities, and longer average distances traveled, all of which could cause the unwanted effect of expelling activities from the consolidated urban center.
Autonomous, or self-driving, cars are emerging as the solution to several problems primarily caused by humans on roads, such as accidents and traffic congestion. However, those benefits come with great challenges in the verification and validation (V&V) for safety assessment. In fact, due to the possibly unpredictable nature of Artificial Intelligence (AI), its use in autonomous cars creates concerns that need to be addressed using appropriate V&V processes that can address trustworthy AI and safe autonomy. In this study, the relevant research literature in recent years has been systematically reviewed and classified in order to investigate the state-of-the-art in the software V&V of autonomous cars. By appropriate criteria, a subset of primary studies has been selected for more in-depth analysis. The first part of the review addresses certification issues against reference standards, challenges in assessing machine learning, as well as general V&V methodologies. The second part investigates more specific approaches, including simulation environments and mutation testing, corner cases and adversarial examples, fault injection, software safety cages, techniques for cyber-physical systems, and formal methods. Relevant approaches and related tools have been discussed and compared in order to highlight open issues and opportunities.
With the anticipated introduction of self-driving vehicles, new challenges arise for urban transport- and planning authorities. This study contributes to the efforts of formulating the potential opportunities and threats stemming from the introduction of larger fleets of self-driving vehicles to our cities, and what action could be taken by transport authorities to shape this introduction beneficially. In particular, the focus is put on the impact different parking management strategies can have on the performance of a fleet of shared automated vehicles providing on-demand transport services. This analysis focuses on aspects of service efficiency, externalities and service provision equity.
The selected parking management strategies are tested in a large-scale activity-based simulation of a case study based on the city of Amsterdam, in which the parking facilities for SAV are digitally mapped throughout the city for different parking scenarios. The vehicles of the fleet aim at relocating to zones with high future demand, which can lead to bunching of vehicles at demand-hotspots. Parking management in the form of restricting parking facilities forces idle vehicles to spread out more evenly in the network. We show that this can reduce average passenger waiting times, increase service provision equity, cause less congestion and even can reduce the necessary fleet size. However, this comes at the cost of an increase in vehicle-kilometres-travelled, which reduces fleet efficiency and causes more undesired service externalities. Parking management is thus a simple, yet effective way for transport authorities to (a) determine where idle self-driving vehicles operating an on-demand transport service will be parked and (b) influence the performance of said transport service.
The widespread availability of connected and autonomous vehicles (CAVs) will likely affect social change in terms of how people travel. Traditional methods of travel demand and land use modeling require vast amounts of data that could be expensive to obtain. Such models use complex software that requires trained professionals to configure and hours to run a single scenario. Alternative closed-form models that can quickly assess trends in potential CAV impact on the regional demand for shopping, entertainment, or dining land use does not exist. This research developed a closed-form model that considers the potential mode shift towards CAVs, possible changes in the propensity to travel, shopping trip avoidance from e-commerce, and greater accessibility for non-drivers. Model parameter estimation based on statistics from the greater Toronto area found that population growth from 2017 to 2050 alone could increase the demand for shopping, entertainment, or dining land use by nearly 60%. However, CAVs could double or triple that demand—implicating dynamic planning and environmental considerations.
Most of the existing research on shared autonomous vehicles (SAVs) and road congestion pricing have studied the short-term impact on traffic flow. These types of studies focused on the influences on mobility and ignored the long-term impacts on regional job accessibility. Given this, from the perspective of land use and transportation integration, this study explored the long-term effects of SAVs and cordon-based congestion pricing on regional land use, transportation, and job accessibility. The contributions of this study have been summarized by the following three purposes. First, to the best of the authors’ knowledge, this study was the first attempt to identify the long-term impact of the combination of these two technologies on regional job accessibility. Second, compared to the previous research methodology, this study adopted the land use and transportation integrated model (TRANUS model) and scenario planning to ensure the comprehensiveness and validity of the results. Third, this study analyzed the spatial heterogeneity of the impact of the combination of the two technologies on regional job accessibility in different areas with different built-environment attributes. To realize this purpose, this study quantitatively classified traffic analysis zones (TAZs) using data mining technology, i.e., factor analysis and clustering analysis. Results showed that the introduction of SAVs will contribute to job and population development in the charging zone and reduce the negative effect of road congestion pricing. From the perspective of reducing the average travel time between TAZs, the best strategy will be to implement SAVs alone, followed by integrated use of SAVs and road congestion pricing, while the worst strategy will be to implement the cordon-based congestion pricing policy alone. By comparison, from the perspective of improving regional job accessibility, the effect of introducing SAVs was better than that of road congestion pricing, while the combination of these two technologies was not superior to implementing SAVs alone.
The paper describes experiments with a Digital Twin, which is intended to be used as a testbed for solutions in the field of centralized traffic control at airports. The data flow on spatial characteristics of vehicles is simulated using a special simulation model. A distinguishing feature of the model is the ability to describe and reproduce specific scenarios related to critical situations in the transport network that require the involvement of a centralized control system. It is assumed that in the early stages of using the Digital Twin, the role of the control system can be performed by a user who has the sufficient skills in traffic control. User decisions are stored in the Digital Twin's memory and can be used later to train a control system based on machine learning.
This paper used an implementation of the land-use model SILO in Austin, Texas, over a 27-year period with an aim to understand the impacts of the full adoption of self-driving vehicles on the region’s residential land use. SILO was integrated with MATSim for the Austin region. Land-use and travel results were generated for a business-as-usual case (BAU) of 0% self-driving or “autonomous” vehicles (AVs) over the model timeframe versus a scenario in which households’ value of travel time savings (VTTS) was reduced by 50% to reflect the travel-burden reductions of no longer having to drive. A third scenario was also compared and examined against BAU to understand the impacts of rising vehicle occupancy (VO) and/or higher roadway capacities due to dynamic ride-sharing (DRS) options in shared AV (SAV) fleets. Results suggested an 8.1% increase in average work-trip times when VTTS fell by 50% and VO remained unaffected (the 100% AV scenario) and a 33.3% increase in the number of households with “extreme work-trips” (over 1 hour, each way) in the final model year (versus BAU of 0% AVs). When VO was raised to 2.0 and VTTS fell instead by 25% (the “Hi-DRS” SAV scenario), average work-trip times increased by 3.5% and the number of households with “extreme work-trips” increased by 16.4% in the final model year (versus BAU of 0% AVs). The model also predicted 5.3% fewer households and 19.1% more available, developable land in the city of Austin in the 100% AV scenario in the final model year relative to the BAU scenario’s final year, with 5.6% more households and 10.2% less developable land outside the city. In addition, the model results predicted 5.6% fewer households and 62.9% more available developable land in the city of Austin in the Hi-DRS SAV scenario in the final model year relative to the BAU scenario’s final year, with 6.2% more households and 9.9% less developable land outside the city.
Autonomous driving is designed to enhance the overall performance of vehicular traffic. The primary objective of this study is to develop an improved car-following model for adaptive cruise control (ACC) by integrating conventional automated logic with human factors. Specifically, a modeling framework is proposed with a generalization of a classical action point paradigm describing drivers’ psychological reactions and expectations. The action points are determined from collected data, from which the unconscious and conscious reaction regimes can be identified. Based on this identification, the human-like driving mode is carried out, and its corresponding acceleration model is developed to reproduce empirical car-following spiral within the unconscious regime, whereas the conventional autonomous mode driven by the Intelligent Driver Model (IDM) is used to calculate changes in acceleration for the conscious regime. To integrate the two modes, a switching rule considering the random occurrence of action points is also determined. The results of numerical experiments reveal that simulated macroscopic traffic is compatible with the three-phase theory. Moreover, traffic mobility in terms of throughput and speed is significantly improved compared to that achieved by the classical IDM. Asymptotic stability is achieved in four typical fluctuation scenarios, as the amplitudes of fluctuations converge while travelling along the vehicular platoon. However, the classical IDM fails to maintain such stability. The proposed model enables ACC to drive in accordance with drivers’ psychological traits, and consequently has the potential to increase the acceptance of autonomous driving. It also has the ability to help ACC enhance traffic efficiency and safety.
Autonomous vehicle (AV) technologies are under constant improvement with pilot programs now underway in several urban areas worldwide. Modeling and field-testing efforts are demonstrating that shared mobility coupled with AV technology for automated mobility on-demand (AMoD) service may significantly impact levels of service and environmental outcomes in future cities. Given these rapidly emerging developments, there is an urgent need for methods to adequately quantify the economic impacts of new vehicle technologies and future urban mobility policy. In this paper, we show how broader user-centric impacts can be captured by the activity-based accessibility (ABA) measure, which takes advantage of the rich data and outcomes of utility-maximization activity-based models and its interaction with mesoscale agent-based traffic simulation frameworks. Using the SimMobility simulator, we evaluate shared AMoD strategies applied to a Singapore micromodel city testbed. A near-future strategy of exclusive availability of AMoD service in the central business district (CBD), and a further-horizon strategy of the full operation of AMoD city-wide in the absence of other on-demand services, were tested and evaluated. Our results provide insights into the income and accessibility effects on the population under the implementation of shared and automated mobility policies. The outcomes indicate that the city-wide deployment of AMoD results in greater accessibility and network performance. Moreover, the accessibility of low-income individuals is improved relative to that of mid- and high-income individuals. The restriction of AMoD to the CBD along with the operation of other on-demand services, however, provides a certain level of disbenefit to segments of the population in two exceptional cases. The first is to high-income individuals who live in a suburban zone and rely heavily on on-demand services; the second is to mid-income residents that have excellent public transportation coverage with close proximity to the CBD. We further establish the efficacy of the ABA measure, as these findings motivate the need for measuring socioeconomic impacts at the individual level. The work presented here serves as a foundation for policy evaluation in real-world urban models for future mobility paradigms.
New developments in the automotive world have the power to change mobility, but because of high uncertainties, municipalities are adopting a wait-and-see attitude. Nonetheless, autonomous, connected and shared vehicle technologies are in a far stage of development and it is only a matter of time before shared autonomous vehicles (SAVs) enter urban traffic. This research aims to provide insights into the congestion effects of SAVs on urban traffic, focusing on the differences in microscopic behaviour from conventional cars, and to investigate which easy-to-implement solutions a municipality could apply to facilitate the new mix of traffic. This was researched by performing a simulation study, using the traffic simulation package Vissim and a case study of a network in the city of The Hague during the morning peak in 2040. Several SAV market penetration scenarios were tested: 0%, 3%, 25%, 50% and 100% SAV usage by travellers. Additionally, two network designs were implemented: dedicated lanes for SAVs and kiss & ride (K&R)-facilities. From the results, it was clear that while the autonomous driving capabilities of SAVs help reduce traffic congestion, they also have a negative effect by stopping on the curbside to drop off passengers, forming bottlenecks for other road users, and by circulating on the network using low capacity links. Below the line, this adds up to an overall negative effect on urban traffic congestion according to our results. The dedicated lanes design was unsuccessful at reducing this congestion caused by SAVs. The K&R design, however, was successful at reducing delays, but only for SAV penetration rates higher than 25%. These exact effects are not generalizable due to limitations in network size and simulation software. However, the results can be seen as indicative for planning purposes. Similar effects could be expected in cities where transport network companies (TNCs) such as Uber become exceptionally popular with non-autonomous cars. The advice for municipalities is to closely monitor the situation and to account for SAVs (and TNCs) in each new infrastructural project.
Over the past years there have been many revelations that have made it clear autonomous technology will become increasingly prevalent in our communities. Despite optimism about the technology there is a high degree of uncertainty about how they will be deployed in urban environs, particularly with regard to street design. Based on the concepts of autonomy, livability and human scale design, this paper provides a dialogue of possible opportunities for street design in an autonomous future. This paper develops a framework for best practice, identifying policies including roadway design/thinning, curbside policy, and parking removal.
Combining cooperative vehicle driving behavior of Connected and Automated Vehicles with supporting information infrastructure, is expected to increase the capacity of roadway infrastructure, which in turn results in travel time savings and user benefits. Automated driving also relieves the driver from steering the car, allowing to conduct other activities during the trip, which is likely to generate further user benefits. In order to assess the magnitude of automated driving on travel time-related user benefits, a typical commuting relation is analyzed, considering three route options as well as level 4 and 5 vehicle automation. The impacts on travel times are estimated by microscopic traffic flow simulations. The simulations reveal that around 27% of the travel time can be saved on a commuting relation due to road automation according to level 5. For level 4 vehicles the travel time savings amount to up to 20%. User benefits that accrue from time savings and the passenger’s option of using travelling time for activities other than conducting the car, are expected at a relevant magnitude. Even under consideration of higher operating costs of an automated car, significant user benefits accrue: 1,310–2,240 € p.a. for level 4 and 2,770–3,440€ p.a. for level 5 vehicles during a passenger car’s typical depreciation period. Thus, automated driving will decrease the commuters’ generalized user costs for individual motorized mobility, which is likely to enhance the urban hinterland’s attractiveness as residential area. This pattern and inherent second-order effects pose challenges for transport, land use and urban planners. Furthermore, it represents a challenge for transport research: to elaborate appropriate concepts that allow for exploiting the benefits of use of automated vehicles while countervailing undesirable socio-economic effects, as well as strains on the transport system and land use.
Three main changes are currently taking place in the context of the automotive industry: electrification of vehicle propulsion, automated driving and a shift towards mobility as a service. While the first two represent opportunities for the industry growth, the later questions the private ownership of a car. Keeping the concept of a privately owned car will involve reducing the economical and environmental cost of such ownership. In this paper, we address the reduction of the parking footprint of cars, leveraging on electrification and low-level driving automation to more than double the density of cars parked in a given area, compared to conventional parking lots. We perform a complete evaluation of different strategies of vehicle coordination based on large-scale datasets of parking sessions in distinct scenarios and under varying demand patterns. Our results on the key metrics, namely area per vehicle, travel distance while parked, and removal time - clearly highlight the relevance and efficiency of this novel approach to parking. We also empirically derive guidelines for designing high-density parking systems (e.g. parking lot layout or capacity) and show the involved trade-offs.
In recent years, extensive research has been conducted to develop traffic surveillance and monitoring applications
using advanced technologies in a Smart-City context. One of the
fundamental challenges in an urban city scenario is developing
an efficient yet affordable parking lot management system. Such
systems play a crucial role in providing the necessary data to
local municipalities to make informed decisions to better manage
the traffic flow while saving valuable resources and time. In this
paper, we propose a novel end-to-end system architecture for
a real-time video-based parking management system that can
be easily scaled and enforced in a Smart-City context. Using
state-of-the-art object detection and tracking methods combined
with novel Static Object Detection techniques, we demonstrate
a localized strategy for identifying potential parking spaces. A
separate parking occupancy classification pipeline is discussed
which provides fast and accurate availability status of these
parking spaces. Furthermore, a 3D digital-twin application to
monitor and visualize the parking spaces is demonstrated. Our
experiments in a real-world scenario demonstrate the significant
potential of our proposed architecture to easily scale to a given
city with minimal manual efforts and lower implementation costs
Introducing autonomous vehicles (AVs) reduces generalized transportation costs and encourages people to relocate. Understanding the subsequent changes in urban structure can help predict the future development of urban economies and policies. Transportation connects multiple zones in cities, and by improving traffic flow and ease directly impacts the economies of various markets. Each direct impact then also indirectly affects related markets. Therefore, a spatial computable general equilibrium model that connects multiple zones via a transportation network is suitable for analyzing the impact of a new transport system. This study constructs a model to simulate the effects of AVs on residential location choice. The results show that increased prevalence of AVs steers people toward suburbs with poor public transportation. Thus, high-income workers react more to technological progress, while low-income workers react more to lowered ownership costs. Consumers’ location choices and budgets also affect residential zones’ development. Therefore, regional policy goals must be clarified and appropriate target consumer groups set when introducing AVs.
Autonomous vehicles (AVs) are pointed out as the technology that will reshape the concept of mobility, with significant implications for the economy, the environment, and society. This fact will bring new challenge to cities urban planning. Research to anticipate the AVs impacts, maximizing their benefits and reduce trade-offs are currently crucial. This work investigates the potential challenges and benefits of gradually replace internal combustion engine human driven vehicles with different penetration rates of AVs - 10%, 30%, 50%, 70%, 90%, and 100% - in urban roads of different characteristics, either in terms of traffic singularities or volumes, and its related implications on air quality. For that purpose, two urban areas with distinct features, Porto and Aveiro, were selected as case studies, and a modelling setup composed of a traffic model, an emission model, and a local air quality model was applied. The results revealed that the AVs benefits are directly linked with the urban design and the road characteristics. In the Aveiro case study, the AVs promoted positive changes with average reductions in daily NOx emissions (compared with the baseline scenario, without AVs) ranging between −2.1% (for C10%) and −7.7% (for C100%). In line with the emissions impacts, positive effects were found on air quality, with average reductions of NO2 concentrations up to −4% (for C100%). In Porto urban area, slight differences in NOx emissions were obtained (<2%), which implied no changes in the air quality levels. The distinct impact of AVs in the study areas is mostly explained by the traffic light coordination system and directional split distributions in the main roads. These results provide valuable insights to support decision-makers in the definition of strategies that allow the integration of these new emerging technologies in the road infrastructure, considering the features of the urban design, traffic profile and road characteristics.
Artificial Intelligence (AI) technology is extensively applied in all walks of life with continuous acceleration of the construction of smart cities. The current research status of intelligent development of transportation infrastructure is classified and predicted oriented to smart cities, which is mainly based on space type, function type, and facility use. The classification is mainly between residents and vehicles, between and vehicles, and between vehicles and transportation systems. The related scholars analyzed that Digital Twins (DTs) technology and AI technology show significant advantages in the classification of transportation infrastructure and the management of transportation spatial information network. The intelligent development of transportation infrastructure is analyzed, and it is predicted in functional design, intelligent development, and effective integration with new media, aiming to provide a reference for the subsequent intelligent development and construction of transportation infrastructure in smart cities.
The agent-based land use/transport model SILO/MITO/MATSim is adapted to simulate the impact of AVs on household relocation. The revised model accounts for the fact that households who own conventional cars are sensitive to parking availability at their dwelling. As AVs could park themselves anywhere, this sensitivity to parking is reduced for households who own AVs. Distance to work, which serves as a hard constraint for household relocation with conventional cars, becomes less critical for households who use an AV to commute as they may perform other activities while commuting. The induced demand of travel by AV is represented and leads to increased congestion.
Several scenarios were designed to analyze the effects of reduced value of time for AV travel, parking restrictions and increase of congestion. The simulation shows that AVs will compete with transit and reduce transit ridership by three quarters. The average commute distance is expected to double, and the vehicle-kilometers traveled will increase by one third. The impact of AVs on the distribution of population, however, is marginal. The urban sprawl caused by less burdensome commuting is largely compensated by the increased attractiveness of core cities in the absence of parking issues for AVs.
Implementing autonomous vehicles (AVs) can provide plenty of advantages, such as improving network capacity and reducing crashes. If morning commuters take AVs to the workplace, they can assign AVs an induced trip to a low-cost parking lot or even park AVs at their origins with no cost. This travel behavior might result in a more congested city network. This study firstly considers the equilibriums in route choice, mode choice, and parking lot choice in one optimization model. The solution algorithm is proposed to solve the optimization program iteratively. The Sioux Falls network is used to test the potential impacts of induced AV trips and the preference to choose an AV. The results using the test network find that scenarios with AVs increase the average travel time by approximately 50%, while 14.60% to 32.27% (scenario-based) of the area that is currently used as parking spaces can be re-purposed.
Shared mobility is a promising travel mode in the era of autonomous driving. Travelers may no longer own a vehicle, but use shared autonomous vehicle (SAV) services. This study investigates the effects and feasibility of SAV-based shared mobility, which includes ride-sharing and car-sharing strategies, by using a data-driven modeling approach. Ride-sharing indicates that two trips with similar origin–destination information can be combined into a new one, whereas car-sharing indicates that trips can be fulfilled by a single vehicle consecutively. On the basis of license plate recognition data of Langfang, China, this study extracts the urban-scale vehicle travel demand information. Models for ride-sharing and car-sharing are formulated to generate SAV assignment strategies for fulfilling travel demands. This study reveals the prospects and potential problems of SAV-supported shared mobility at different development stages by setting a variety of scenarios with different participation levels of ride-sharing and car-sharing. The minimum fleet size to fulfil the vehicle travel demand in the road network and the total vehicle stock in the urban area are compared under different scenarios, and the effects of shared mobility on vehicle kilometers traveled (VKT) and parking demand are evaluated. This study also reveals the impacts of SAVs in a practical scenario, which is constructed based on an online survey. Results show that ride-sharing and car-sharing with high participation will lead to considerable benefits, i.e., reductions in fleet size, vehicle stock, and parking demand. Under the shared mobility scenario with 100% ride-sharing and car-sharing participation levels, one SAV can potentially replace 3.80 private conventional vehicles in the road network. However, ride-sharing and car-sharing exhibit opposite effects on VKT. Car-sharing alone increases VKT whereas car-sharing and ride-sharing together have the potential to decrease VKT. This study provides insights for understanding the development of shared mobility and facilitating the efficient utilization of SAVs.
Sustainable urban road planning should endeavour to meet current and future traffic-related demands and achieve financial, environmental, and social benefits, which is a complex and interdisciplinary issue that needs to consider various factors and data. Multi-criteria decision making (MCDM) can provide reasonable solutions, and some existing studies integrated MCDM with geographic information system (GIS) technology. This paper presents an urban road planning approach based on digital twin (DT), MCDM, and GIS called DT-MCDM-GIS framework. DT can digitalize the physical world to provide various data for the whole process; MCDM can provide criteria and evaluation methods; GIS can provide an integrated environment for analysis. Building demolition and land use, traffic congestion, driving route selection habits, air quality, and noise are all considered in the framework for urban road planning. The proposed approach can provide a functional, economic, people-friendly, eco-friendly urban road planning scheme considering new road construction and existing old road widening to alleviate traffic congestion and provide an alternative route for drivers that conforms to their habits.
Shared autonomous vehicles (SAVs) can have significant impacts on the transport system and land use by replacing private vehicles. Sharing vehicles without drivers is expected to reduce parking demand, and as a side effect, increase congestion owing to the empty fleets made by SAVs picking up travelers and relocating. Although the impact may not be uniform over a region of interest owing to the heterogeneity of travel demand distribution and network configuration, few studies have debated such impact at a local scale, such as in transportation analysis zones (TAZs). To understand the impact in relation to geographical situations, this study aims to estimate the impacts of SAVs at the local scale by simulating their operation on a developed simulator. Using the mainland of Okinawa, Japan as a case study, it was found that parking demand was reduced the most in residence-dominant zones in terms of quantity and office-dominant zones in terms of proportion. As a side effect of replacing private vehicles with SAVs, empty fleets increase congestion, particularly at the periphery of the city. Overall, the results show the heterogeneous impacts of the SAVs at the TAZ level on both land use and traffic, thus suggesting the importance of developing appropriate strategies for urban and transport planning when considering the characteristics of the zones.
This study considers a closed core-suburb city connected by a highway. Two travel modes, namely on-demand and frequency-based autonomous vehicles (AVs), are used for commuting between the suburb and urban core. Each resident attempts to maximize his/her individual utility with consideration to environment quality by choosing a residential location, travel mode, departure time, and non-housing composite consumption good within an expenditure budget. The traffic equilibria are analytically derived for two right-of-way policies, namely (i) the two travel modes sharing the right-of-way and (ii) frequency-based AVs-priority, with consideration of the effect of AVs on travel and urban characteristics. It was found from the analytical results that at any level of AV automation, adopting the frequency-based AVs-priority policy can reduce travel cost for suburban residents while alleviating traffic congestion. Furthermore, an increase in AV automation level results in an increase in expected trip time if the highway capacity is less elastic than the value of travel time for on-demand AVs with respect to the automation level. In addition, if the environment quality depends solely on the residential density, with an increasing level of AV automation, the suburban population and the number of residents using on-demand AVs increase, the land rent in the urban core reduces, the suburb expands, and the utility for all residents increases. These findings help to provide a better understanding of the interactions between AVs, travel and urban characteristics, and serve as a valuable reference to transportation-urban planning in the era of driving automation.
Autonomous vehicle technology and its enabled mobility services are evolving at a more rapid pace than the understanding of the infrastructure required for them to be efficiently and safely implemented. This has not been systematically investigated in literature or practice. This research makes exploratory efforts to investigate this research area by examining and evaluating the infrastructure requirements needed to support autonomous vehicles. It formulates an infrastructure change guideline and an evaluation framework to prioritise the safety, efficiency and accessibility when integrating autonomous vehicles alongside conventional vehicles and multimodal users such as public transport commuters and pedestrians. The case study results show that for different type of regions, being a regional commercial and transportation hub in a residential area and a regional CBD street in a multimodal and spatially limited area, different arrangement and trade-offs can be made. Promisingly, the proposed guideline and framework work sufficiently, and serve as a first step towards a more systematic guideline for autonomous vehicle integration. The outcome of the research consists of a review of approaches that can guide urban planners and other users to understand and prioritise the implementation of autonomous vehicles.
Automated vehicles (AVs) have great potential to revolutionize the transportation sector and landscapes of future cities. The impacts of AVs on urban space, however, are far from clear. Mobility-on-Demand (MOD) services, on the other hand, are readily available in many places. This study seeks to explore (1) how Automated Mobility-on-Demand (AMOD) might affect urban residents' levels of accessibility and their residential relocation decisions; and (2) how these impacts might vary across space and socioeconomic groups. We use an agent-based microsimulation platform to assess two future AMOD scenarios in Singapore relative to a baseline. Results suggest that the addition of AMOD could enhance the overall accessibility of the population, but not if private transport modes, including private cars, taxis, and human-driven on-demand services, are prohibited. On the other hand, if private modes are eliminated, AMOD could alleviate inequality in accessibility as it appears to benefit the disadvantaged socioeconomic groups to a larger extent. We also find that AMOD deployment would not induce outward migration, nor would it increase home-work location imbalance. This study demonstrates how large-scale microsimulation can be leveraged to assess AMOD scenarios. The findings have some implications for preparing for the inevitable and potentially disruptive emergence of AVs.
Autonomous vehicles (AVs) hold great promise for increasing the capacity of existing roadways and intersections, providing more mobility to a wider range of people, and are likely to reduce vehicle crashes. However, AVs are also likely to increase travel demand which could diminish the potential for AVs to reduce congestion and cause emissions of greenhouse gases (GHG) and other air pollutants to increase. Therefore, understanding how AVs will affect travel demand is critical to understanding their potential benefits and impacts. We evaluate how adoption of AVs affects travel demand, congestion and vehicle emissions over several decades using an integrated travel demand, land-use and air quality modeling framework for the Albuquerque, New Mexico metropolitan area. We find that AVs are likely to increase demand and GHG emissions as development patterns shift to the region's periphery and trips become longer. Congestion declines along most roadways as expanded capacity from more efficient AV operation outpaces increasing demand. Most of the population can also expect a reduction in exposure to toxic vehicle emissions. Some locations will experience an increase in air pollution exposure and traffic congestion from changes in land-use and traffic patterns caused by the adoption of AVs.
Shared autonomous vehicles (SAVs) have been widely studied in the recent literature. Agent-based simulations and theoretical models have extensively explored the effects on travel service, fleet size, and congestion using heuristic dispatching strategies to match SAVs with on-demand passengers. A major question that simulations have sought to address is the service rate or replacement rate: the number of passengers each SAV can serve. Thus far, the service rate has mostly been estimated through simulation. This paper investigates an analytical max-pressure dispatch policy, which aims to maximize passenger throughput under any stochastic demand pattern, which takes the form of a model predictive control algorithm. An analytical proof using Lyapunov drift techniques is presented to show that the dispatch policy achieves maximum stability. The service rate and minimum fleet sizes are derived analytically in this paper and can be achieved with the proposed dispatch policy. Simulation results show that the maximum stable demand is linearly related to the fleet size given. Also, it demonstrates how asymmetric demand necessitates rebalancing trips that affect service rates. Even though decreasing average waiting time is not the primary goal of this paper, stability ensures bounded waiting times, which is demonstrated in simulation.
It is anticipated that Shared Autonomous Vehicles (SAV) may revolutionise the way people travel as it may lead to less ownership of vehicles and a gradual increase in demand responsive mobility. These vehicles are likely to able to park far away from popular destinations after they drop off their clients. It is, therefore, capable of releasing parking spaces around central business districts while cutting down parking search time and cost. But the magnitude at which these spaces will be released in relation to SAV fleet size is not currently known. This work therefore seeks to understand this relationship using the University of the West of England, Frenchay campus characterised by 23 carparks of varying size – as a case study – to quantitatively understand these impacts. This was done by simulating 2181 parking slot within the campus; motor traffic (SAVs and non-SAVs); and the route networks from the 3 entry stations – East, North and Longmead entrances – to each parking slot. Carpark preference data was obtained and analysed. It formed the context on which drivers choose a parking space within the system. These data alongside with the graphical features of the Frenchay campus were imported into ARENA simulation toolkit: a discrete event-based simulation software. Results indicated the obvious that as SAV fleet size increases, spaces released also increases but in addition, individual carpark reacted dynamically in response to the demand on neighbouring carparks. It also showed that there is currently an excess of parking supply compared to the number of vehicles entering the campus daily. It further discusses the potential of multiple occupancy SAVs to further release all parking spaces on the Frenchay campus. The work concludes with the potentials for this research to be extended to a citywide approach with a special interest in the ability for SAV to release kerb parking spaces.
The coming of automated vehicles (AVs) and Mobility-on-Demand (MoD services) is expected to reduce urban parking demand and correspondingly alter the urban parking landscape in a significant way. Multiple modeling efforts have already demonstrated that Shared AVs (SAVs) have promising potential to decrease urban parking demand. However, previous studies have only examined SAV parking demand at one point in time, with various market penetrations. It remains unclear what the demand reduction trajectory will be like during the transition period when there is a mix of SAVs, Privately-Owned AVs (PAVs), Shared Conventional Vehicles (SCVs), and Conventional Private Vehicles (CPVs). This study fills this gap by developing an agent-based simulation model to examine the spatially and temporally explicit parking reduction trends with mixed travel modes from 2020–2040. The results indicate that in the most optimal AV and MoD adoption scenario, the parking demand will decrease by over 20% after 2030, especially in core urban areas. Meanwhile, the parking demand in residential zones may double, which could lead to transportation equity concerns. Additionally, parking relocation may also induce environmental issues by generating a considerable amount of empty Vehicle Miles Traveled (VMT). To reap the benefits brought by AVs and MoD systems and to mitigate the accompanying social and environmental issues, our results suggest that proactive policymakers in the next decade will need to modify land use regulations for both new developments and existing parking infrastructure in commercial and residential zones, as well as update travel demand management policies.
The goal of this study is to assess and quantify the potential employment accessibility benefits of shared-use automated vehicle (AV) mobility service (SAMS) modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study proposes employing a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. The study further captures heterogeneity of workers using a latent class analysis (LCA) approach to account for different worker clusters valuing different types of employment opportunities differently, in which the socio-demographic characteristics of workers are the LCA model inputs. The accessibility analysis results in Southern California indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high-and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits.
Objective
Digitisation and automation are expected to change the transport system and settlement structures in a disruptive way. Due to new developments in sensor and communication technology different business models of automated vehicles (AV) - such as private AV, car-sharing-AV, ride-sharing-AV and public transport-AV – are likely to enter the transport market. Further, different penetration rates of AVs, extension of user groups (elderly and young, people without driving licenses etc.), different cost scenarios of AV veh-kms, parking regimes/fees, etc. will have significant impact on future transport demand. The objective of the work presented in this paper was to develop a simulation-based approach to analyse the potential impacts of different vehicle automation scenarios in Austria.
Method
A multitude of complex cause-effect-chains and feedback loops characterise the relationship between AV take up, travel demand and environmental effects. The method of System Dynamics (SD) is designed to deal with such complexities. As a first step the qualitative method of Causal Loop Diagrams (CLD) was used to identify all potential automation related factors influencing the attractiveness of car and public transport use. In a second step the identified factors were coded into an operational version of the Stock-Flow based land use and transport interaction model MARS (Metropolitan Activity Relocation Simulator). As a third step sensitivity tests and scenarios were defined and simulated. Finally the resulting quantitative impacts were assessed and discussed.
Results
In this paper we describe briefly how MARS was adapted to be able to simulate the effects of different AV-take up rates on the national level of Austria. Simulation results are presented for the scenario elements road capacity, remote parking, value of travel time, access for new user groups in the business models private car, car-sharing and ride-sharing and automation of the first and last mile in public transport. The results show that with a fleet share of 90% of private AVs in 2050, the nationwide changes in veh-kms travelled are between −2% (impact on road capacity) and +22 percent (new user groups in the case of AVs as private cars).
Policy implications
An uncontrolled market take-up of AVs into the transport system will very likely increase the dependency on motorised transport, veh-kms driven and related emissions. Compensating policies have to be implemented in parallel to avoid un-sustainable developments.
We adapt the classic monocentric city model to consider three main topics related to the possible widespread adoption of autonomous vehicles (AVs): sprawl, energy consumption, and housing affordability. AVs are modeled to reduce marginal commuting costs and in some cases, reduce demand for center-city and residential parking. This creates opposing forces that lead to sprawl in some models and increasing density in others. All models point to welfare increases, but also increases to energy consumption due to longer commutes, greater traffic congestion, and higher productivity, calling into question claims that autonomous vehicles will save energy. In most models, AVs lead to greater housing affordability by making suburban areas more accessible, and by reclaiming land that was previously used for parking. Effects of AVs on cities are substantial and depend on the manner in which this new technology is implemented.
