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Transportation Planning for Connected Autonomous Vehicles: How It All Fits Together
Abstract
As connected and autonomous vehicle (CAV) technology continues to evolve and rapidly develop new capabilities, it is becoming increasingly important for transportation planners to consider the effects that these vehicles will have on the transportation network. It is evident that this trend has already started; over 60% of long-range transportation plans in the largest urban areas now include some discussion of CAVs, up from just 6% in 2015. There are also numerous CAV pilot programs currently underway that entail testing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction in both isolated and real-world environments. In this review of the current assessments for CAV impacts, two primary trends are identified. First, there is a great deal of uncertainty that is not being explicitly considered and properly accounted for in the transportation-network planning process. Second, the predictions that are being made are not considering potential policy or planning actions that could shape or affect the impacts of CAVs. This paper provides a picture of how ongoing CAV research interacts with current transportation planning practices by examining how the methods, the ranges of predictions, and the different sources of uncertainty in each method impact the planning process and potential system outcomes. Finally, it will identify best practices from decision analysis to help plan the best possible future transportation networks.
... The design of transportation systems, be it private or shared AVs, necessitates legislation governing their operation and integration with existing modes [36]. Urban infrastructure must adapt or undergo a complete redesign to support AVs, emphasizing the need for regulatory measures to guide this transformation [36,37,[67][68][69][70]. ...
... 2024, 4 334 charging stations, and safety and comfort standards. These sub themes have been obtained with consideration to previous studies [44,70,73]. The development of AVs and road safety are coupled to infrastructure. ...
... Other major factors to consider are intelligent transportation systems (ITS), road infrastructure upgrades, parking infrastructure, communication infrastructure, and urban planning and development [49,73]. Developing and implementing ITS that enable AVs to interact with one another as well as with infrastructure features such as traffic lights, road sensors, and other vehicles can enhance overall traffic flow and safety [70]. To support their navigation and operations properly, AVs may require specific road infrastructure enhancements such as improved road markings, signage, and dedicated lanes. ...
Autonomous vehicles (AVs) aim to improve safety and comfort of road users while contributing to the reduction of traffic congestion, air pollution, fuel consumption, and enabling mobility and accessibility of disabled and older people. As AV technology is rapidly advancing, there is an urgent need to explore how those new mobility services will impact urban transport systems, including the users, the infrastructure, and the design of future urban areas. This paper applies a systematic review to assess the role of AVs in urban areas. It reviews 41 articles published between 2003 and 2023, and uses inductive and deductive coding approaches to identify seven themes and thirty sub-themes within the literature. The seven include: benefits, attitudes, and behaviours and user perception, climate adaptation, climate mitigation, legislation and regulations, sustainability, and infrastructure. Studies related to benefits accounted for 25% of the sample, followed by behaviours and user perception (24%) and sustainability (22%). The least amount of research has been undertaken on the role of AVs to support climate adaptation. Geographically, almost half (#22) of the papers originate within Europe, followed by America (#10) and Asia (#7). There is only limited research originating from the Global South. This systematic review sets the scene for considering how AVs in public transport can be implemented in urban areas by establishing the current state of knowledge on user attitudes, perceptions, and behaviour, the benefits of AVs, the infrastructure and legislation and regulations required for AVs, and the role AVs have in climate mitigation, adaptation, and sustainability.
... Travel costs are expected to decrease both economically and psychologically. However, the extent of these major changes may vary depending on uncertainties related to technology, policy and regulation, and preferred modes of transportation (Cottam 2018). For example, the use of SAVs may be affected by technological advancements in AVs as well as the public's perception of AVs and car-sharing (Yap, Correia and van Arem 2016). ...
... Second, there needs to be additional research into areas where the initial assumptions strongly influence the results of the investigation (see Soteropoulos, Berger and Ciari 2019;Harb et al. 2021;Wang and Zhang 2021), or where uncertainties are oversimplified (Cottam 2018). In particular, it is necessary to examine the uncertainties related to the level of technological advancement and public perception and attitudes toward AVs. ...
We conducted a comprehensive investigation into the relationship between autonomous vehicles (AVs) and the built environment (BE). We explored how various potential changes in transportation by AVs may influence the BE using the D-variables: density, diversity, design, and destination accessibility. Our review revealed that AVs could cause drastic changes to all aspects of the BE; however, the extent of these changes strongly depends on uncertainties in technology, policy, and transportation modes. Further research into AVs–BE relationships should focus on developing comprehensive scenarios, accounting for these uncertainties and regional and social characteristics.
... Google set up a company Waymo in 2009 to support the research and development of CAVs, and has already completed more than 2 million miles of road tests [5]. Another Internet company Uber, known for its taxi-hailing application, has also tested its own CAVs on public roads in the state of Arizona [6]. Except from Internet companies and electric vehicle companies, traditional vehicle manufactures such as Audi and Mercedes Benz have announced CAV initiatives as well. ...
... 5. Improvement: If the results from Step 4 are improved based on the proposed feature selection method, the feature subset will be chosen as the final set to detect attacks in CAVs. Otherwise, the process return to step 2 to process again.6. Final feature set output: The final feature set will be used to detect the attacks in CAVs and guide for real world CAV data collection. ...
... erefore, policymakers and practitioners should prepare strategies for the time that AVs and HDVs coexist and share the same transportation network. ese strategies need to recognize different sources of uncertainty in transportation planning for AVs such as technology, adoption rates, roadway capacity, land use changes, and travel demand [9]. ...
... e optimal solution at 40% includes alternatives 1, 7, 8, 10, 11, 12, and 14. We add alternatives 3,9,14, and 16 at 50%, and for 60%, 70%, and 80% alternative 2 is added to the previous set of alternatives. When the AV market size grows to 90%, fewer lanes need to be converted to exclusive AV lanes; therefore, alternatives 3, 7, and 8 were no longer selected to have an AV lane. ...
This paper proposes a model to find the optimal location of autonomous vehicle lanes in a transportation network consisting of both Autonomous Vehicles (AVs) and Human-Driven Vehicles (HDVs) while accounting for the roadway capacity variation. The main contribution of the model is considering a generalized definition of capacity as a function of AV proportion on a link and incorporating it into the network design problem. A bilevel optimization model is proposed with total travel time as the objective function to be minimized. At the upper-level problem, the optimal locations of AV lanes are determined, and at the lower level which is a multiclass equilibrium assignment, road users including both AVs and HDVs seek to minimize their individual travel times. It is shown that if capacity variation is ignored, the effect of AV lane deployment can be misleading. Since there will be a long transition period during which both AVs and HDVs will coexist in the network, this model can help the network managers to optimally reallocate the valuable road space and better understand the effects of AV lane deployment at the planning horizon as well as during the transition period. Employing this model as a planning tool presents how the proposed AV lane deployment plan could consider the AV market penetration growth during the transition period. Numerical analysis based on the Sioux Falls network is presented in two cases with and without variable capacity to illustrate the application of this model. At the 60% penetration rate of AVs, the improvement in total travel time was 3.85% with a fix capacity while this improvement was 9.88% with a variable capacity.
... Developing methods to assess the driving feasibility of autonomous vehicles in advance is essential for expanding service areas and ensuring safe operations [5,[46][47][48][49][50][51]. By evaluating road difficulty, it becomes possible to determine whether autonomous vehicles can safely navigate specific roads, aiding in selecting efficient service areas and planning safe operations [48]. ...
In addition to addressing the labor shortage due to an aging population, the transition to autonomous vehicle (AV)-based mobility services offers enhanced efficiency and operational flexibility for public transportation. However, much of the existing focus has been on improving AV safety without fully considering road conditions and real-world service demand. This study contributes to the literature by proposing a comprehensive framework for efficiently integrating AV-based mobility services at the network level, addressing these gaps. The framework analyzes and optimizes service networks by incorporating actual demand patterns, quantifying road segment difficulty from an AV perspective, and developing an optimization model based on these factors. The framework begins by quantifying the operational difficulty of road segments through an evaluation of Operational Design Domains (ODDs), providing a precise measure of AV suitability under varying road conditions. It then introduces a quantitative metric to assess operational feasibility, considering factors such as the service margin, costs, and safety risks. Using these metrics alongside Genetic Algorithms (GAs), the framework identifies an optimal service network that balances safety, efficiency, and profitability. By analyzing real-world data from different mobility services, such as taxis, Demand-Responsive Transport (DRT), and Special Transportation Services (STSs), this study highlights the need for service-specific strategies to optimize AV deployment. The findings show that optimal networks vary with demand patterns and road difficulty, demonstrating the importance of tailored network designs. This research provides a scalable, data-driven approach for integrating AV services into public transportation systems and lays the foundation for further improvements by incorporating dynamic factors and broader urban contexts.
... The introduction of new technologies, such as autonomous driving, has led to changes in the development of cities and surrounding territories [1]. Due to this, urbanization models have also found it necessary to change their vision for future transportation development and planning [2][3][4]. ...
The introduction of autonomous vehicles (AVs) has the potential to drastically change society, planning, design, and development strategies. This study uses the PRISMA protocol to carry out a systematic literature review, focusing on the backcasting method as an analytic tool. By examining. 21 studies published between 2003 and 2024, this paper highlights the phases of backcasting: visioning, policy packaging, and appraisal, and identifies critical factors necessary for the successful integration of AVs. Visioning for future driverless cities includes high-quality urban areas, active mobility, and innovative developments. Policies and Packaging suggested a focus on restricting vehicular access, transit-oriented development, and encouraging public transportation. Appraisal reveals skepticism about the positive impacts of AVs, urging policies that limit access to urban areas and promote sustainable modes of transportation. The main contribution of this study lies in its comprehensive application of backcasting to AV implementation, offering a structured approach to envisioning future urban scenarios, formulating supportive policies, and evaluating their impact. This analysis provides a solid foundation for future research, urging us to explore the intersection between AVs, citizen participation, and environmental sustainability to achieve more efficient and sustainable cities.
... In light of this, integrating multiple transportation modes can be a more practical approach to reducing overall trip costs and congestion, and minimizing transfer inconveniences (18). According to the National League of Cities, the inclusion of CAVs in the primary planning documents of metropolitan planning organizations (MPOs) saw a significant rise from 6% in 2015 to 61% in 2017 (20,21). State departments of transportation (DOTs) prioritize scenario-based planning programs when incorporating CAVs into transportation planning processes (22). ...
As technology advances, there is a growing demand for understanding the fundamental concepts of connected and autonomous vehicles (CAVs) and their relative impact on different aspects of transportation engineering. Various transportation courses are regularly offered in Civil Engineering programs, including but not limited to introduction to transportation engineering, transportation planning/modeling, highway design, transportation safety, traffic engineering, traffic simulation, and intelligent transportation system (ITS). Instructors often face challenges in identifying critical CAV topics for these courses, making it necessary to explore critical course content related to CAVs. The objectives of this study are 1) to identify critical CAV topics for different transportation engineering courses and 2) to emphasize upcoming CAV topics crucial for a successful transition into the CAV system. To achieve these objectives, a national survey was conducted among transportation educators and practitioners. The survey was distributed with the assistance of TRB standing committees to ensure broad participation. Among the 48 respondents from 33 states, faculty members who had already integrated CAV topics into their courses and practitioners were prioritized while estimating the weighted average of each topic. The findings of this research can serve as valuable resources for redesigning transportation engineering courses by incorporating CAVs. The identified high-priority CAV topics can play a crucial role in raising awareness among undergraduate and graduate students about the diversity of transportation engineering and motivating them to explore emerging CAV technologies and opportunities within the field.
... With these capabilities, CAVs have the potential to drastically transform the transportation systems and significantly impact the agencies responsible for the planning, design, construction, operation, and maintenance of the roadway infrastructure 1,2 . Numerous studies are conducted to assess the operational and safety benefits of CAVs 3,4 . Broadly speaking, these studies focus on transformative safety, mobility and transportation efficiency, and fuel consumption and energy saving of CAVs. ...
Connected autonomous vehicles (CAVs) have the potential to deal with the steady increase in road traffic while solving transportation related issues such as traffic congestion, pollution, and road safety. Therefore, CAVs are becoming increasingly popular and viewed as the next generation transportation solution. Although modular advancements have been achieved in the development of CAVs, these efforts are not fully integrated to operationalize CAVs in realistic driving scenarios. This paper surveys a wide range of efforts reported in the literature about the CAV developments, summarizes the CAV impacts from a statistical perspective, explores current state of practice in the field of CAVs in terms of autonomy technologies, communication backbone, and computation needs. Furthermore, this paper provides general guidance on how transportation infrastructures need to be prepared in order to effectively operationalize CAVs. The paper also identifies challenges that need to be addressed in near future for effective and reliable adoption of CAVs.
... Recently, various research has been conducted to study the potential impact of CAV on VMT (Auld, Verbas, Javanmardi, & Rousseau, 2018;Cottam, 2018;Shladover, Su, & Lu, 2012;Taiebat, Stolper, & Xu, 2019). These studies show that VMT is influenced by various factors that are most likely to be affected by CAV technologies, which are summarized as follows: ...
... The methods and findings of this study are of national and global importance as features of the travel demand model of the study area resembles most of the other travel demand models that are being used in other parts of the country and different regions of the world. Various statewide and regional planning organizations have already realized the needs for reshaping their models to incorporate AV-and CAV-related changes (Cottam 2018). As billions of dollars are expected to be spent in the development of AV and CAV technologies, it is critical for transportation planners and government agencies to ensure that the benefits of these technologies can be harnessed while minimizing any potential negative impacts. ...
... Its subcompany Waymo, set up in 2009, has been focusing on the research and development of CAVs and finished more than 2 million miles road test [4]. Taxi-hailing company Uber also tests their own CAVs on public roads in Arizona [5]. In Europe, traditional vehicle manufactures including Audi and Mercedes Benz also announce their initiatives on CAVs. ...
CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and communication components to make driving decisions. A large number of companies, research organizations, and governments have researched extensively on the development of CAVs. The increasing number of autonomous and connected functions however means that CAVs are exposed to more cyber security vulnerabilities. Unlike computer cyber security attacks, cyber attacks to CAVs could lead to not only information leakage but also physical damage. According to the UK CAV Cyber Security Principles, preventing CAVs from cyber security attacks need to be considered at the beginning of CAV development. In this paper, a large set of potential cyber attacks are collected and investigated from the aspects of target assets, risks, and consequences. Severity of each type of attacks is then analysed based on clearly defined new set of criteria. The levels of severity for the attacks can be categorized as critical, important, moderate, and minor. Mitigation methods including prevention, reduction, transference, acceptance, and contingency are then suggested. It is found that remote control, fake vision on cameras, hidden objects to LiDAR and Radar, spoofing attack to GNSS, and fake identity in cloud authority are the most dangerous and of the highest vulnerabilities in CAV cyber security.
Connected and Autonomous Vehicles (CAVs) are gaining more interest and are growing steadily in recent years. They will surely become the backbone of next generation intelligent vehicles offering safe travels, comfort, reduced pollution, with many other beneficial features. However, with CAVs being equipped with high levels of automation and connectivity also opens several attack points or vulnerable points for adversaries to conduct attacks. Such security issues need to be addressed before commercialising CAVs. In this research paper, the focus is to develop a few machine learning models using different machine learning algorithms and evaluate them using defined evaluation criterions to identify and recommend the best suitable model for detecting attacks in CAVs. In addition, this paper also defines different terms related to CAVs such as CAV, CAV cyber security, CAV architecture and different vulnerabilities and risks present in the CAN bus. The paper then describes the different attacks possible on CAVs and the corresponding mitigation methods and detection techniques.KeywordsAnomaly detectionConnected autonomous vehicleController area network busCyber-attacksMachine learning
As autonomous vehicles (AVs) become more prevalent on the roadways, accompanying driving technologies are being developed. V2I communication systems are one such development, allowing the dissemination of crucial information to drivers. As more AVs are introduced on the road, understanding driver perception of AVs is crucial not only for user acceptance, but non-user acceptance as well. The use of V2I systems may be helpful in bridging the gap between users and non-users by making all drivers more informed.
While a range of methods have been employed to quantify certain anticipated impacts of connected and autonomous vehicles (CAVs), a comprehensive framework for integrating CAVs into trip-based models, like those used by many metropolitan areas today, is lacking. Without real-world CAV usage data, integrating CAVs into trip-based models today requires speculative modeling assumptions; however, incorporating fundamental parameters into existing travel modeling frameworks is timely for two reasons. First, understanding the range of possible futures from scenario planning or exploratory modeling analysis can assist metropolitan areas anticipate and manage the potential risks and benefits of CAVs. Second, data on the travel behavior of early CAV adopters will become available during the lifespan of many models currently in use or development. This paper summarizes an enhanced trip-based modeling framework incorporating uncertainties related to CAVs initially developed in support of the Michigan Department of Transportation’s statewide model. This framework is now being applied in statewide and metropolitan scale models in Michigan, Illinois, Virginia, Indiana, and South Carolina. An important contribution of this framework is its typology of and methods for representing zero-occupant vehicle (ZOV) trips. Additionally, this paper details an exploratory analysis of CAV scenarios in Vermont using a trip-based model incorporating several elements of the framework. In this application, reasonable assumptions related to induced CAV demand, including ZOV trips, resulted in substantial increases in vehicle miles traveled, vehicle hours traveled, and delay despite capacity increases, demonstrating how relatively basic trip-based scenario modeling of CAVs can be a valuable tool for informing and encouraging public policy discussions.
Autonomous vehicles (AVs) are anticipated to improve traffic operations, but the magnitude of their effect on traffic flow is not yet known since the technology is still under development. However, transportation planners and engineers need to know how AVs will affect roadway capacity when developing projects to accommodate future transportation demand. The Highway Capacity Manual (HCM) provides the procedures to evaluate future conditions, but the methodologies are based on empirical research of existing facilities, which do not yet include AVs. To account for the capacity effect of AVs, the HCM procedure for calculating the demand flow rate under base conditions is proposed to be revised to add an adjustment factor for AVs. To develop the AV adjustment factor, traffic simulation analysis software can be applied since it provides the ability to model conditions that are not yet observable, such as a high percentage of AVs in a traffic stream. Given the complex nature of microscopic simulation tools, guidance for modeling AV capacity effects is needed. This paper presents a procedure for using traffic simulation software to develop capacity adjustment factors for AVs. A case study using a previously developed AV driving behavior is described to demonstrate the application of the procedure to estimate the effect of AVs on freeway capacity.
Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to our transportation system. This new technology has the potential to impact vehicle safety, congestion, and travel behavior. All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach 4000 when comprehensive crash costs are accounted for. Yet barriers to implementation and mass-market penetration remain. Initial costs will likely be unaffordable. Licensing and testing standards in the U.S. are being developed at the state level, rather than nationally, which may lead to inconsistencies across states. Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm. The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain. To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy.
This study used microscopic simulation to estimate the effect on highway capacity of varying market penetrations of vehicles with adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC). Because the simulation used the distribution of time gap settings that drivers from the general public used in a real field experiment, this study was the first on the effects of ACC and CACC on traffic to be based on real data on driver usage of these types of controls. The results showed that the use of ACC was unlikely to change lane capacity significantly. However, CACC was able to increase capacity greatly after its market penetration reached moderate to high percentages. The capacity increase could be accelerated by equipping non-ACC vehicles with vehicle awareness devices so that they could serve as the lead vehicles for CACC vehicles.
This paper describes the use of microscopic simulation to estimate the effect of varying market penetrations of adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) on highway capacity. The distribution of time gap settings that drivers from the general public used in a real field experiment were used in the simulation, making this the first study of the effects of ACC and CACC on traffic to be based on real data on driver usage of ACC and CACC. The results show that the use of ACC is unlikely to change lane capacity significantly. However, CACC is able to greatly increase capacity after its market penetration reaches moderate to high percentages. The capacity increase can be accelerated by equipping non-ACC vehicles with Vehicle Awareness Devices so that they can serve as the lead vehicles for CACC vehicles.
A ONE-OF-A-KIND GUIDE TO THE BEST PRACTICES IN DECISION ANALYSIS. Decision analysis provides powerful tools for addressing complex decisions that involve uncertainty and multiple objectives, yet most training materials on the subject overlook the soft skills that are essential for success in the field. This unique resource fills this gap in the decision analysis literature and features both soft personal/interpersonal skills and the hard technical skills involving mathematics and modeling. Readers will learn how to identify and overcome the numerous challenges of decision making, choose the appropriate decision process, lead and manage teams, and create value for their organization. Performing modeling analysis, assessing risk, and implementing decisions are also addressed throughout. Additional features include: Key insights gleaned from decision analysis applications and behavioral decision analysis research. Integrated coverage of the techniques of single- and multiple-objective decision analysis. Multiple qualitative and quantitative techniques presented for each key decision analysis task. Three substantive real-world case studies illustrating diverse strategies for dealing with the challenges of decision making. Extensive references for mathematical proofs and advanced topics. The Handbook of Decision Analysis is an essential reference for academics and practitioners in various fields including business, operations research, engineering, and science. The book also serves as a supplement for courses at the upper-undergraduate and graduate levels.
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