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Integrating Automated Vehicles (AVs) into existing traffic systems holds the promise of enhanced road safety, reduced congestion, and more sustainable travel. Effective integration of AVs requires understanding the interactions between AVs and Human-driving Vehicles (HVs), especially during the transition period in which AVs and HVs coexist in a mixed traffic environment. Numerous recent empirical studies find reduced headways of human drivers following an AV compared to following an HV, and attribute this reduction to behavioural changes of drivers when they follow AVs. However, more factors may be at play due to the inherent inconsistencies between the comparison conditions of HV-following-AV and HV-following-HV. This study scrutinises three alternative explanations for the observed reduction in headways: (1) systematic differences in car-following states during data collection, (2) systematic differences in driving variability between leading AVs and HVs, and (3) systematic differences in driving characteristics of leading AVs versus HVs. We use a large-scale dataset extracted from Lyft AV motion data and examine each of these explanations through data stratification and simulation. Our results show that all three mechanisms contribute to the observed reduction in headways of human drivers following AVs. In addition, our findings highlight the importance of driving homogeneity and stability in achieving reliably shorter headways. Thereby, this study offers a more comprehensive understanding on the difference between HV–AV and HV–HV interactions in mixed traffic, and is expected to promote more effective integration of AVs into human traffic. • Observation biases are inherent in data collected by automated vehicles (AVs). • 3 explanations for shorter headways of human drivers following AVs are validated. • Systematic differences in data collection and AV driving play remarkable roles. • Driving homogeneity and stability contribute to reliable reduction in time headway. • Behavioural insights from non-behavioural data require scrutiny. [ABSTRACT FROM AUTHOR]

• The unique parking characteristics of private autonomous vehicles are considered. • Perceived safety and usefulness strongly impact the choice of travel mode and parking. • Private autonomous vehicles can attract passengers from sustainable transportation. • Letting private autonomous vehicles self-park elsewhere can worsen road congestion. • Insights provided can be used to develop strategies for the autonomous driving era. Difficulty in finding parking spaces and high parking fees discourage private car usage. Fully autonomous vehicles (AVs) capable of self-parking away from destinations will likely remove this barrier. Despite extensive survey-based research on AVs in recent years, existing literature has not sufficiently addressed the potential impact of new parking options on the demand for these vehicles. This study explores commuters' joint choice of travel mode and parking for private autonomous vehicles (PAVs). To this end, a stated choice (SC) experiment was designed and deployed in the city of Beijing, China. Attitudinal statements were also designed to measure four latent variables: perceived ease of use, perceived usefulness, perceived safety, and attitude toward waiting. Using a hybrid choice model framework, the estimation results reveal that the choice of letting the PAV self-park at a non-destination location is significantly influenced by the location of such parking, the potential delay in re-taking the vehicle, and the fuel/energy consumption to and from the non-destination parking place. Attitudes toward AVs also play a crucial role, with perceived safety and perceived usefulness having the greatest impact. Our results can help managers and planners understand how PAVs affect people's travel mode choices and the corresponding parking options and assist them in developing strategies in preparation for the widespread use of AVs. [ABSTRACT FROM AUTHOR]

• An optimization model is formulated to maximize the automated taxi system's profit. • The effect of traffic congestion is considered in automated taxis' dial-a-ride problem. • A customized Lagrangian relaxation algorithm can solve the model for hard instances. • Ride-sharing contributes more served requests and less delay. • The automated taxis' service quality is sensitive to the delay penalty. In this paper, we study the dial-a-ride problem of ride-sharing automated taxis (ATs) in an urban road network, considering the traffic congestion caused by the ATs. This shared automated mobility system is expected to provide a seamless door-to-door service for urban travellers, much like what the existing transportation network companies (TNC) do, but with decreased labour cost and more flexible relocation operations due to the vehicles' automation. We propose an integer non-linear programming (INLP) model that optimizes the routing of the ATs to maximize the system profit, depending on dynamic travel times, which are a non-linear function of the ATs' flows. It is important to involve traffic congestion in such a routing problem since for a growing number of ATs circulating in the city their number will lead to delays. The model is embedded within a rolling horizon framework, which divides a typical day into several horizons to deal with the real-time travel demand. In each horizon, the routing model is solved with the demand at that interval and assuring the continuity of the trips between horizons. Nevertheless, each horizon model is hard to solve given its number of constraints and decision variables. Therefore, we propose a solution approach based on a customized Lagrangian relaxation algorithm, which allows identifying a near-optimal solution for this difficult problem. Numerical experiments for the city of Delft, The Netherlands, are used to demonstrate the solution quality of the proposed algorithm as well as obtaining insights about the AT system performance. Results show that the solution algorithm can solve the proposed model for hard instances. Ride-sharing makes the AT system more capable to provide better service regarding delay time and the number of requests that can be attended by the system. The delay penalty on the profit objective function is an effective control parameter on guaranteeing the service quality while maintaining system profitability. [ABSTRACT FROM AUTHOR]

• An analytical model of the expected saturation flow of mixed AV and NV traffic streams is derived. • The model explicitly considers the placement of AVs and NVs in the mixed stream. • Expected delay of an interrupted mixed flow is analytically modelled. • Analytical saturation flow and delay models are validated with microsimulation experiments. • An optimal lane management algorithm for a two-lane road is proposed. Presence of autonomous vehicles (AVs) affects traffic flow characteristics of a mixed traffic stream comprising human-driven vehicles. To model the impact of AVs on the saturation flow of arterials and highways, we propose analytical models to derive the expected value and variance of headway of a traffic stream with mixed AVs and conventional human-driven (or normal) vehicles (NVs), given the expected penetration rate of AVs. The proposed model considers the arrangement (order) of AVs and NVs in the mixed stream and the expected, the lowest, and the highest achievable headways and their variability are analytically derived. Moreover, the total delay of a two-lane road with interrupted traffic flow is analytically modeled for various possible lane allocation policies: (a) dedicated lanes, (b) mixed-mixed lanes, (c) mixed-NV lanes, and (d) mixed-AV lanes. Microsimulation experiments demonstrate the validity of the developed models for the average headway and its variability, as well as the delay formulas. [ABSTRACT FROM AUTHOR]

Highlights • We proposed and empirically tested an autonomous vehicle (AV) acceptance model. • Trust was the most critical factor in promoting AV acceptance. • Perceived safety risk had a negative effect on AV acceptance through trust. • Perceived ease of use (PEOU) and perceived usefulness (PU) were significant factors. • The effects of PEOU and PU were weaker compared to trust. Abstract The purpose of this study was to explore factors affecting users' acceptance of automated vehicles (AVs, Level 3). A theoretical acceptance model was proposed by extending the Technology Acceptance Model (TAM) with new constructs: initial trust and two types of perceived risk (i.e., perceived safety risk [PSR] and perceived privacy risk [PPR]). It was hypothesized that initial trust was built upon perception factors (i.e., perceived usefulness [PU], perceived ease of use [PEOU], PSR, and PPR) and was a key determinant of AV acceptance. The validity of the model was confirmed with a structure equation modeling analysis based on data collected from 216 survey samples. Results revealed that initial trust was the most critical factor in promoting a positive attitude towards AVs, which, together with PU, determined users' intention to use AVs. Initial trust could be enhanced by improving PU and reducing PSR associated with AVs. Theoretically, these findings suggest that initial trust offers another and probably more important pathway for other factors to impact consumers' adoption of systems with uncertainty. Practically, the findings provide guidance for designing interventions aimed at improving public's acceptance towards AVs. [ABSTRACT FROM AUTHOR]

Highlights • Studies impacts of automated vehicles on the performance of freeway weaving sections. • Proposes a multiclass hybrid model including lane change positions as exogenous input. • Finds optimal lane change positions using simulation-based optimization. • Results show 15% capacity increase by optimizing the spatial lane change distribution. • Considering vehicle class dependent reaction times reveals a combination effect. Abstract Weaving sections, where a merge and a diverge are in close proximity, are considered as crucial bottlenecks in the highway network. Lane changes happen frequently in such sections, leading to a reduced capacity and the traffic phenomenon known as capacity drop. This paper studies how the emerging automated vehicle technology can improve the operations and increase the capacity of weaving sections. We propose an efficient yet effective multiclass hybrid model that considers two aspects of this technology in scenarios with various penetration rates: (i) the potential to control the desired lane change decisions of automated vehicles, which is represented in a macroscopic manner as the distribution of lane change positions, and (ii) the lower reaction time associated with automated vehicles that can reduce headways and the required gaps for lane changing maneuvers. The proposed model is successfully calibrated and validated with empirical observations from conventional vehicles at a weaving section near the city of Basel, Switzerland. It is able to replicate traffic dynamics in weaving sections including the capacity drop. This model is then applied in a simulation-based optimization framework that searches for the optimal distribution of the desired lane change positions to maximize the capacity of weaving sections. Simulation results show that by optimizing the distribution of the desired lane change positions, the capacity of the studied weaving section can increase up to 15%. The results also indicate that if the reaction time is considered as well, there is an additional combined effect that can further increase the capacity. Overall, the results show the great potential of the automated vehicle technology for increasing the capacity of weaving sections. [ABSTRACT FROM AUTHOR]

While automated vehicles (AVs) are expected to bring a wide range of benefits to future transportation systems, emerging AV technologies open a door for cyberattacks, where a select number of AVs are compromised to drive in an adversarial manner. This could result in network-wide increases in traffic congestion and energy consumption, degrading the performance of transportation systems. Hence, it is necessary to develop attack mitigation strategies for AVs as AVs gradually become a reality. However, this is rather challenging due to the lack of knowledge of adversaries. Limited prior studies have assumed attacks with a given probability distribution without considering their malicious intention, which is far from realistic due to the adversarial nature of potential attacks. In this study, we derive an optimal feedback control law for AVs in the presence of cyberattacks. Notably, attacks are only assumed to have a bounded magnitude without being subject to any specific statistical distribution, which is not only of theoretical interest but also relaxes the assumptions seen in prior studies. More importantly, to deal with lack of knowledge of malicious attacks, we, for the first time, formulate a min–max control problem to minimize the worst-case disturbance to traffic flow. Specifically, we present a mathematical framework for describing mixed-autonomy traffic involving AVs and human-driven vehicles (HVs), considering two typical types of attacks, namely false data injection attack on sensor measurements and malicious attack on AV control commands. Based on the framework presented, we derive a set of necessary conditions of optimality for the min–max control problem, based on which an iterative computational algorithm is developed for determining the optimal control (driving) strategy of AVs. The effectiveness of the proposed approach is demonstrated via extensive numerical simulations. • Model malicious attacks on automated vehicles with mild assumptions • Formulate a min–max control problem capturing the maximum adverse impact of attacks • Derive analytical conditions to ensure optimality of the solution for vehicle control • Develop an iterative algorithm to compute the optimal driving strategy of vehicles [ABSTRACT FROM AUTHOR]

Highlights • An eco-driving model for mixed traffic flow including automated vehicles and human driven vehicles. • Platoon based cooperation between automated vehicles and human driven vehicles. • Dynamic platoon splitting and merging method in terms of cooperation. • Cooperation behaviour significantly smooth the traffic flow and reduce the travel time. Abstract The advancements in communication and sensing technologies can be exploited to assist the drivers in making better decisions. In this paper, we consider the design of a real-time cooperative eco-driving strategy for a group of vehicles with mixed automated vehicles (AVs) and human-driven vehicles (HVs). The lead vehicles in the platoon can receive the signal phase and timing information via vehicle-to-infrastructure (V2I) communication and the traffic states of both the preceding vehicle and current platoon via vehicle-to-vehicle (V2V) communication. We propose a receding horizon model predictive control (MPC) method to minimise the fuel consumption for platoons and drive the platoons to pass the intersection on a green phase. The method is then extended to dynamic platoon splitting and merging rules for cooperation among AVs and HVs in response to the high variation in urban traffic flow. Extensive simulation tests are also conducted to demonstrate the performance of the model in various conditions in the mixed traffic flow and different penetration rates of AVs. Our model shows that the cooperation between AVs and HVs can further smooth out the trajectory of the latter and reduce the fuel consumption of the entire traffic system, especially for the low penetration of AVs. It is noteworthy that the proposed model does not compromise the traffic efficiency and the driving comfort while achieving the eco-driving strategy. [ABSTRACT FROM AUTHOR]

Highlights • A dynamic lane-changing trajectory planning model is proposed. • Lane-changing vehicles can adapt to the real-time change of surrounding traffic. • The lane-changing trajectory curve is a time-independent cubic polynomial function. • Lane-changing can be finished smoothly, efficiently and safely using the new model. • The planned velocity and trajectory can be well-tracked by the automated vehicles. Abstract This paper focuses on the lane-changing trajectory planning (LTP) process in the automatic driving technologies. Existing studies on the LTP algorithms are primarily the static planning method in which the states of the surrounding vehicles of a lane-changing vehicle are assumed to keep unchanged in the whole lane-changing process. However, in real-world traffic, the velocities of the surrounding vehicles change dynamically, and the lane-changing vehicle needs to adjust its velocity and positions correspondingly in real-time to maintain safety. To address such limitations, the dynamic lane-changing trajectory planning (DLTP) model is proposed in the limited literature. This paper proposes a novel DLTP model consisting of the lane-changing starting-point determination module, trajectory decision module and trajectory generation module. The model adopts a time-independent polynomial trajectory curve to avoid the unrealistic assumptions on lane-changing velocities and accelerations in the existing DLTP model. Moreover, a rollover-avoidance algorithm and a collision-avoidance algorithm containing a reaction time are presented to guarantee the lane-changing safety of automated vehicles, even in an emergent braking situation. The field lane-changing data from NGSIM data are used to construct a real traffic environment for lane-changing vehicles and verify the effectiveness of the proposed model, and CarSim is applied to investigate the traceability of the planned lane-changing trajectories using the proposed model. The results indicate that an automated vehicle can complete the lane-changing process smoothly, efficiently and safely following the trajectory planned by the proposed model, and the planned velocity and trajectory can be well-tracked by automated vehicles. [ABSTRACT FROM AUTHOR]

Highlights • We highlight the energy saving potential of connected and automated vehicles. • Anticipative/Collaborative driving of CAVs can contribute to 3–20% energy saving. • With higher penetration, there will be system wide influences by such “eco-CAV”s. • CAVs offer opportunities for cooperative driving further improving energy efficiency. Abstract Connected and automated vehicles (CAV) are marketed for their increased safety, driving comfort, and time saving potential. With much easier access to information, increased processing power, and precision control, they also offer unprecedented opportunities for energy efficient driving. This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature. We explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and save energy. Opportunities for cooperative driving could further increase energy efficiency of a group of vehicles by allowing them to move in a coordinated manner. Energy efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles. [ABSTRACT FROM AUTHOR]

Highlights • A field study was conducted to explain public acceptance of automated vehicles (AVs). • AV experience increased trust, PU, and PEU related to self-driving vehicles (SDVs). • AV experience led PU and PEU to be more predictive of intentions to use SDVs. • PU, trust, and perceived safety predicted intentions to use SDVs and willingness to re-ride in our AV. • PEU predicted BI only after participants experienced the AV. Abstract This field study aims at understanding the influence of direct experience of an automated vehicle (AV, Level 3) and explaining and predicting public acceptance of AVs through a psychological model. The model includes behavioral intention (BI) to use self-driving vehicles (SDVs, Level 5), willingness to re-ride (WTR) in our AV (Level 3), and their four potential determinants, namely perceived usefulness (PU), perceived ease of use (PEU), trust related to SDVs, and perceived safety (PS) while riding in our AV. The last two determinants are largely ignored, but we consider them critical in the context of AVs. Three-hundred students were invited as participants (passengers) to experience the AV. The trust, PU, PEU, and BI of the participants were recorded prior to their experiencing the AV; after this experience, all the constructs of the psychological model were recorded. The participants’ experience with the AV was found to increase their trust, PU and PEU (but not BI), the consistency between PU/PEU and BI, and the explanatory power of BI. The model explained 55% of the variance in BI and 40% in WTR. PU, trust, and PS were found to be steady and direct predictors of both the acceptance measures; PEU predicted BI only after the participants’ AV experience. Mediation analysis showed that trust also can indirectly affect AV acceptance through other determinants. Out-of-sample prediction confirmed the model’s predictive capability for AV acceptance. The theoretical contributions and practical implications of the findings are discussed. [ABSTRACT FROM AUTHOR]

With the advent of connected and automated vehicle technology, in this paper, we propose an innovative intersection operation scheme named as MCross: M aximum C apacity inte R section O peration S cheme with S ignals. This new scheme maximizes intersection capacity by utilizing all lanes of a road simultaneously. Lane assignment and green durations are dynamically optimized by solving a multi-objective mixed-integer non-linear programming problem. The demand conditions under which full capacity can be achieved in MCross are derived analytically. Numerical examples show that MCross can almost double the intersection capacity (increase by as high as 99.51% in comparison to that in conventional signal operation scheme). [ABSTRACT FROM AUTHOR]

Automated Vehicles (AVs) offer their users a possibility to perform new non-driving activities while being on the way. The effects of this opportunity on travel choices and travel demand have mostly been conceptualised and modelled via a reduced penalty associated with (in-vehicle) travel time. This approach invariably leads to a prediction of more car-travel. However, we argue that reductions in the size of the travel time penalty are only a crude proxy for the variety of changes in time-use and travel patterns that are likely to occur at the advent of AVs. For example, performing activities in an AV can save time and in this way enable the execution of other activities within a day. Activities in an AV may also eliminate or generate a need for some other activities and travel. This may lead to an increase, or decrease in travel time, depending on the traveller’s preferences, schedule, and local accessibility. Neglecting these dynamics is likely to bias forecasts of travel demand and travel behaviour in the AV-era. In this paper, we present an optimisation model which rigorously captures the time-use effects of travellers’ ability to perform on-board activities. Using a series of worked out examples, we test the face validity of the model and demonstrate how it can be used to predict travel choices in the AV-era. [ABSTRACT FROM AUTHOR]

Literature has shown potentials of Connected/Cooperative Automated Vehicles (CAVs) in improving highway operations, especially on roadway capacity and flow stability. However, benefits were also shown to be negligible at low market penetration rates. This work develops a novel adaptive driving strategy for CAVs to stabilise heterogeneous vehicle strings by controlling one CAV under vehicle-to-infrastructure (V2I) communications. Assumed is a roadside system with V2I communications, which receives control parameters of the CAV in the string and estimates parameters imperfectly of non-connected automated vehicles. It determines the adaptive control parameters (e.g. desired time gap and feedback gains) of the CAV if a downstream disturbance is identified and sends them to the CAV. The CAV changes its behaviour based on the adaptive parameters commanded by the roadside system to suppress the disturbance. The proposed adaptive driving strategy is based on string stability analysis of heterogeneous vehicle strings. To this end, linearised vehicle dynamics model and control law are used in the controller parametrisation and Laplace transform of the speed and gap error dynamics in time domain to frequency domain enables the determination of sufficient string stability criteria of heterogeneous strings. The analytical string stability conditions give new insights into automated vehicular string stability properties in relation to the system properties of time delays and controller design parameters of feedback gains and desired time gap. It further allows the quantification of a stability margin , which is subsequently used to adapt the feedback control gains and desired time gap of the CAV to suppress the amplification of gap and speed errors through the string. Analytical results are verified via systematic simulation of both homogeneous and heterogeneous strings. Simulation demonstrates the predictive power of the analytical string stability conditions. The performance of the adaptive driving strategy under V2I cooperation is tested in simulation. Results show that even the estimation of control parameters of non-connected automated vehicles are imperfect and there is mismatch between the model used in analytical derivation and that in simulation, the proposed adaptive driving strategy suppresses disturbances in a wide range of situations. [ABSTRACT FROM AUTHOR]

Urban mobility is facing a paradigm shift towards providing more convenient, environmentally friendly, and on-demand services. Satisfying customer needs in a cost-efficient way has been the goal of many ridesharing systems. Capacitated ridesharing is considered as an effective service for reducing traffic congestion and pollution nowadays. Providing more operational strategies that can optimize on-demand ridesharing needs further investigation. In the current work, we focus on developing a matching algorithm for solving the on-demand ridesharing operation task in a real-time setting. We develop a simulation framework that can be used to propose a real-time shuttle ridesharing search algorithm. We propose a novel, computationally efficient, real-time ridesharing algorithm. We formulate the ridesharing assignment algorithm as a combinatorial optimization problem. The computational complexity of the proposed algorithm is reduced from exponential to linear, and the search space of the optimization problem is reduced by introducing heuristics. Our approach implements dynamic congestion by regularly updating the network's road segments' travel time during the simulation horizon to have more realistic results. We demonstrate how our algorithm, when applied to the New York City taxi dataset, provides a clear advantage over the current taxi fleet in terms of service rate. Furthermore, the developed simulation framework can provide valuable insights regarding cost functions and operational policies. • Development of a modular real-time simulation framework for the capacitated ridesharing problem. • Formulation of the ridesharing problem as a dynamic deterministic ondemand matching problem with tolerance times. • Implementation of dynamic congestion by regularly updating link travel times during the simulation horizon. • Solving the optimization problem in an online manner using both heuristics and commercial solvers. • Modeling of stakeholders' multiple objectives and design of policies that lead to efficient and mutually beneficial solutions. [ABSTRACT FROM AUTHOR]

This paper presents a longitudinal freeway merging control algorithm for maximizing the average travel speed of fully automated connected vehicles. Communication with a roadside unit allows the computation and transmission of optimized trajectories to the equipped vehicles. These vehicles then carry out the trajectories and resume normal operation once they cease communication with the roadside controller. A tool was developed to simulate and carry out the merging algorithm, while interfacing with the optimization software LINGO. A hypothetical merging segment was simulated to evaluate the effectiveness of the merging algorithm, and its performance is compared to conventional vehicle operation. During uncongested conditions the algorithm is able to reduce travel time, increase average travel speed and improve throughput. The capacity of the merge segment is directly related to the safe time gap selected to run the algorithm. Once capacity is reached, queuing forms on both the ramp and mainline segments upstream of the merge area. The algorithm provides safe merging operations during this congested traffic state. [ABSTRACT FROM AUTHOR]

This article addresses the problem of modeling and estimating traffic streams with mixed human operated and automated vehicles. A connection between the generalized Aw Rascle Zhang model and two class traffic flow motivates the choice to model mixed traffic streams with a second order traffic flow model. The traffic state is estimated via a fully nonlinear particle filtering approach, and results are compared to estimates obtained from a particle filter applied to a scalar conservation law. Numerical studies are conducted using the Aimsun micro simulation software to generate the true state to be estimated. The experiments indicate that when the penetration rate of automated vehicles in the traffic stream is variable, the second order model based estimator offers improved accuracy compared to a scalar modeling abstraction. When the variability of the penetration rate decreases, the first order model based filters offer similar performance. [ABSTRACT FROM AUTHOR]

Automated vehicles can increase network capacity by leveraging connectivity with other vehicles and the infrastructure, but come at a larger operating and maintenance cost than other human-driven vehicles. This study investigates the trade-off in cost and capacity effects of automated vehicles when a social planner decides the optimal fleet mix for serving a given network demand. We present a system optimal assignment problem that is a non-linear program with linear constraints. Using second-order analysis, we label each link of the network as either "convex" or "concave". The classification is done prior to any traffic assignment and is independent of demand patterns. According to the classification, a social planner benefits from increasing (decreasing) the automated vehicle ratio of traffic in convex (concave) links. We show that the proposed system optimal problem is non-convex and develop a Benders decomposition algorithm with a master problem that decides path flows and a sub-problem that finds the optimal automated vehicle ratio of each path. We further discuss the system optimal properties in three stylized networks with parallel, series, and mixed topology, and a larger Sioux Falls network. Results show that in parallel networks, the convex (concave) links have an automated vehicle ratio of one (zero), whereas in series networks, the optimal automated vehicle ratios depend on several network parameters. • We investigate the trade-off in cost and capacity effects of autonomous vehicles • We present a nonlinear system optimal assignment problem with linear constraints • We present a Benders decomposition algorithm to find optimal path flows and AV ratios • In parallel networks, the convex (concave) links have an AV ratio of one (zero) [ABSTRACT FROM AUTHOR]

Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios. [ABSTRACT FROM AUTHOR]

Connected vehicle technology can be beneficial for traffic operations at intersections. The information provided by cars equipped with this technology can be used to design a more efficient signal control strategy. Moreover, it can be possible to control the trajectory of automated vehicles with a centralized controller. This paper builds on a previous signal control algorithm developed for connected vehicles in a simple, single intersection. It improves the previous work by (1) integrating three different stages of technology development; (2) developing a heuristics to switch the signal controls depending on the stage of technology; (3) increasing the computational efficiency with a branch and bound solution method; (4) incorporating trajectory design for automated vehicles; (5) using a Kalman filter to reduce the impact of measurement errors on the final solution. Three categories of vehicles are considered in this paper to represent different stages of this technology: conventional vehicles, connected but non-automated vehicles (connected vehicles), and automated vehicles. The proposed algorithm finds the optimal departure sequence to minimize the total delay based on position information. Within each departure sequence, the algorithm finds the optimal trajectory of automated vehicles that reduces total delay. The optimal departure sequence and trajectories are obtained by a branch and bound method, which shows the potential of generalizing this algorithm to a complex intersection. Simulations are conducted for different total flows, demand ratios and penetration rates of each technology stage (i.e. proportion of each category of vehicles). This algorithm is compared to an actuated signal control algorithm to evaluate its performance. The simulation results show an evident decrease in the total number of stops and delay when using the connected vehicle algorithm for the tested scenarios with information level of as low as 50%. Robustness of this algorithm to different input parameters and measurement noises are also evaluated. Results show that the algorithm is more sensitive to the arrival pattern in high flow scenarios. Results also show that the algorithm works well with the measurement noises. Finally, the results are used to develop a heuristic to switch between the different control algorithms, according to the total demand and penetration rate of each technology. [ABSTRACT FROM AUTHOR]

One of the main triggers of traffic congestion on highways is vehicle merging at on-ramps. The development of automated procedures for cooperative vehicle merging is aimed to ensure safety and alleviate congestion problems. In this work, a longitudinal trajectory planning methodology is presented, developed to assist the merging of vehicles on highways; it achieves safe and traffic-efficient merging, while minimizing the engine effort and passenger discomfort through the minimization of acceleration and its first and second derivatives during the merging maneuver. The problem is formulated as a finite-horizon optimal control problem and is solved analytically. This enables the solution to be stored on-board, saving computational time and rendering the methodology suitable for practical applications. The tunable weights, used for taking into account the different optimization criteria, may serve as parameters to match the individual driver’s preferences. The proposed methodology is first developed for a pair of cooperating vehicles, a merging one and its putative leader. Moreover, an alternative solution procedure via a time-variant Linear-Quadratic Regulator approach is also presented. A Model Predictive Control (MPC) scheme is utilized to compensate possible disturbances in the trajectories of the cooperating vehicles, whereby the analytical optimal solution is applied repeatedly in real time, using updated measurements, until the merging procedure is actually finalized. Subsequently, the methodology is generalized for a set of vehicles inside the merging area. Various numerical simulations illustrate the validity and applicability of the method. [ABSTRACT FROM AUTHOR]

This study considers dynamic parking management in the era of driving automation that users of privately-owned automated vehicles (AVs) come to a downtown area to conduct various activities and choose between three parking options: outside parking, on-street parking, and cruising-as-substitution-for-parking. In the latter, AVs cruise in downtown until summoned by their users after concluding their activities. Given the distribution of users' activity time in downtown, we propose a system of ordinary differential equations to model AVs' parking choice and capture the impact of cruising-as-substitution-for-parking on traffic congestion. With the proposed model, we further investigate dynamic time-based tolling and parking provision to optimize the system performance. Results of our numerical experiments demonstrate the validity of our model and the potential of dynamic tolling and parking provision on managing downtown parking of AVs. • Investigate parking choices of privately owned AVs in the downtown. • Cruising-as-substitution-for-parking may create congestion. • Cruising-as-substitution-for-parking can be self-reinforcing. • Dynamic time-based tolling can effectively manage downtown parking of AVs. [ABSTRACT FROM AUTHOR]

• With the modified Swin Transformer, the MTPT precisely infers multimodal trajectories of vehicles considering surroundings. • The attention map provides model explainability to improve user acceptance by revealing the intrinsic mechanism of the MTPT. • The influencing factors agree with the human driving experience, which indicates the MTPT has learned how to predict. • The accessible grid is a useful prior knowledge of drivable areas that boosts the training process. Automated vehicles are expected to navigate complex urban environments safely along with several non-cooperating agents. Therefore, accurate trajectory prediction is crucial for safe navigation and receives much interest. However, most existing studies mainly focus on models' prediction accuracy rather than their explainability. In this paper, we propose a Multimodal Trajectory Prediction Transformer (MTPT) model to retrieve the influencing factors of prediction and help understand the intrinsic mechanism of prediction. Especially in the MTPT model, we use a modified Swin Transformer with multiple prediction heads to carry out multimodal trajectory prediction. Numerical experiments confirm the MTPT model can capture the most critical input factors with the help of the attention method and thus improve prediction accuracy. The proposed model obtains state-of-the-art results even with limited training data. Moreover, the identified input factors are also in agreement with the human driving experience. This agreement indicates that the proposed model appropriately learns how to predict. [ABSTRACT FROM AUTHOR]

The current approach to driving automation has been primarily vehicle-centric. However, a vehicle-infrastructure cooperative approach, in which infrastructure and vehicles cooperate to perform the different driving tasks, may prevail in enabling automated driving. This paper conducts an economic analysis of vehicle infrastructure cooperation for automated driving. In doing so, we present a model that captures investment decisions in vehicle automation and infrastructure digitalization and their effect on travelers' purchase and travel decisions. Our analysis shows that, under certain conditions, equipping both infrastructure and vehicles is socially optimal. However, by analyzing strategic interactions between infrastructure support service providers and automakers, we show that lack of coordination between these two actors results in suboptimal investment in vehicle automation and infrastructure digitalization. Especially, when these two technologies are complementary, service providers are reluctant to invest in digital infrastructure and vehicle manufacturers tend to over equip their vehicles so as to avoid relying on infrastructure technology. Thus, we conclude by showing that better coordination between automakers and service providers – under the form of profit sharing – is welfare-improving and could potentially yield the socially optimal levels of automation and digitalization. • Investment decision on vehicle infrastructure cooperation for driving automation. • Equipping both infrastructure and vehicles is socially optimal. • Lack of coordination results in suboptimal investment. • Profit sharing is welfare-improving. [ABSTRACT FROM AUTHOR]

• Novel joint behavioral model for AV carsharing and AV ride sourcing is presented. • Impacts of built environment & sustainable travel behaviors are analyzed. • Copula-based generalized additive discrete choice model is specified. • Identifies environmental & behavioral interventions to accelerate AV-C & AV-R adoption. • Important to consider systematic & random heterogeneity. Rapid advancements in vehicle automation and a shift towards collaborative consumption trends will disrupt future urban mobility systems. We present a joint model of consumers' affinity towards shared automated vehicles (SAVs) with two distinct yet related configurations: automated vehicle (AV) carsharing and AV ride sourcing. Compact and walkable built environment is known to support active travel and shared mobility but the potential impacts of built environment on adoption of automated shared mobility are largely unknown. Besides sociodemographic, sustainable travel, and technology-related factors, we examine the role of neighborhood-level walkability (composite measure of land-use, pedestrian-oriented urban design, and transit accessibility) in shaping public's opinion about AV carsharing and AV ride sourcing technologies. Detailed travel behavior and stated preference/taste data for over 2700 individuals from the most recent 2019 Puget Sound Travel Survey are complemented with neighborhood-level high-resolution data on built environment fabric. Two dependent variables of interest include individual's level of interest in AV carsharing and AV ride sourcing programs. A novel and comprehensive geo-additive heterogeneous copula-based joint behavioral model is developed - revealing complex stochastic dependence patterns between users' interest in AV ride sourcing and carsharing programs and different layers of systematic and random taste heterogeneity. Results show that improving neighborhood walkability can be an effective way to increase public's interest in using automated ride sourcing and carsharing services. Findings also highlight the opportunity to enable SAV adoption by harnessing the synergies between green (transit) travel, technology use for work/non-work purposes, use of existing mobility-on-demand (MOD) services, and affinity towards automated MOD services. Results are translated to potential policies for designing proactive behavioral and built environment interventions for accelerating SAV adoption with an enhanced sensitivity towards the needs of distinct and more vulnerable population groups (e.g., older, unemployed, and/or less educated individuals). The study methodologically contributes by presenting a comprehensive behavioral model for SAV adoption by differentiating ride sourcing and carsharing. [ABSTRACT FROM AUTHOR]

A path-planning algorithm for connected and non-connected automated road vehicles on multilane motorways is derived from the opportune formulation of an optimal control problem. In this framework, the objective function to be minimized contains appropriate respective terms to reflect: the goals of vehicle advancement; passenger comfort; and avoidance of collisions with other vehicles and of road departures. Connectivity implies, within the present work, that connected vehicles can exchange with each other (V2V) real-time information about their last generated short-term path. For the numerical solution of the optimal control problem, an efficient feasible direction algorithm (FDA) is used. To ensure high-quality local minima, a simplified Dynamic Programming (DP) algorithm is also conceived to deliver the initial guess trajectory for the start of the FDA iterations. Thanks to very low computation times, the approach is readily executable within a model predictive control (MPC) framework. The proposed MPC-based approach is embedded within the Aimsun microsimulation platform, which enables the evaluation of a plethora of realistic vehicle driving and advancement scenarios under different vehicles mixes. Results obtained on a multilane motorway stretch indicate higher efficiency of the optimally controlled vehicles in driving closer to their desired speed, compared to ordinary manually driven vehicles. Increased penetration rates of automated vehicles are found to increase the efficiency of the overall traffic flow, benefiting manual vehicles as well. Moreover, connected controlled vehicles appear to be more efficient in achieving their desired speed, compared also to the corresponding non-connected controlled vehicles, due to the improved real-time information and short-term prediction achieved via V2V communication. [ABSTRACT FROM AUTHOR]

Automated vehicles (AV) have the potential to provide cost-effective mobility options along with overall system-level benefits in terms of congestion and vehicular emissions. Additional resource allocation at the network level, such as AV-exclusive lanes, can further foster the usage of AVs rendering this mode of travel more attractive than legacy vehicles (LV). However, it is necessary to find the crucial locations in the network where providing these dedicated lanes would reap the maximum benefits. In this study, we propose an integrated mixed-integer programming framework for optimal AV-exclusive lane design on freeway networks which accounts for commuters' demand split among AVs and LVs via a logit model incorporating class-based utilities. We incorporate the link transmission model (LTM) as the underlying traffic flow model due to its computational efficiency for system optimum dynamic traffic assignment. The LTM is modified to integrate two vehicle classes namely, LVs and AVs with a lane-based approach. The presence of binary variables to represent lane design and the logit model for endogenous demand estimation results in a nonconvex mixed-integer nonlinear program formulation. We propose a Benders' decomposition approach to tackle this challenging optimization problem. Our approach iteratively explores possible lane designs in the Benders' master problem and, at each iteration, solves a sequence of system-optimum dynamic traffic assignment problems in an attempt to find fixed-points representative of logit-compatible demand splits. The proposed approach is implemented on three hypothetical freeway networks with single and multiple origins and destinations. Our numerical results reveal that the optimal lane design of freeway network is non-trivial while accounting for endogenous demand of each mode. • We address a freeway network design problem with connected and autonomous vehicles. • We consider legacy and automated vehicles and model class-based vehicle demand. • We use an analytical link transmission model to represent traffic dynamics. • We represent this problem as a MINLP and develop a Benders' decomposition approach. • Tests reveal that it is not systematically beneficial to increase AV-exclusive lanes. [ABSTRACT FROM AUTHOR]

The paper develops a simulation model and evaluates fixed versus on-demand operational designs of a station-based automated feeder service. The evaluation considers the operational cost and average passenger level-of-service trade-offs as well as distributional differences in waiting times. Two case studies are used to evaluate such trade-offs under different fleet compositions; (1) a simple circular network feeder service; (2) a case based on a real-world coordinated branched service in Stockholm, combining fixed-line services on the trunk portion with a flexible feeder service on the branches. Results for the circular network indicate that there are benefits in utilizing an on-demand operational policy for the lowest and highest demand levels tested. When fixed service capacity is exceeded, it is found that there are potential benefits in on-demand operations with respect to average level-of-service, as well as delivering a more even distribution of passenger waiting times. Results for the real-world case show that combining DRT on branches with fixed services on the trunk improves the overall median waiting times for all DRT scenarios and provides substantial improvements for passengers on the trunk, at the cost of more variable, and less equitable waiting times on the branches. For larger fleet sizes, generalized travel costs are reduced with and without rebalancing and level-of-service provided to branch-to-branch passengers is improved considerably by rebalancing idling vehicles to branch end-stops. The case studies demonstrate the usefulness of the simulation framework in evaluating trade-offs between fixed and on-demand service design variables and their effects on disaggregate level-of-service provided for stop-based feeder services. • A simulation for station-based DRT integrated with fixed public transit. • Evaluation of a circular feeder service and a real-world coordinated branched line. • Level-of-service and equity trade-offs are reported for substitute service designs. • DRT circular feeder design displays equity improvements in high demand settings. • DRT improves level-of-service with lower equity on branches in the real-world case. [ABSTRACT FROM AUTHOR]

• The effect of trust and travel experiences on VTTS for automated vehicles is assessed. • Privately-owned automated vehicles and shared automated vehicles were considered. • VTTS for different potential user groups were calculated. • Individual attitudes were found to influence significantly VTTS for automated vehicles. • Policy and practice implications are derived from the results. Autonomous driving is expected to strongly influence the value of travel time savings (VTTS) which is crucial for the assessment of the impact of automated vehicles (AVs). However, no consensus has been reached yet about the size and direction of the effect of AVs on VTTS. This high uncertainty around the VTTS is most likely due to high heterogeneity in preferences for travel time. Our hypothesis is that a key role in the heterogeneity of the VTTS for AVs is played by psychological factors. We focus in particular on Travel Experiences (besides individual preference for activities conducted during travelling) and Trust in travel time preferences for AVs. For this purpose, an online survey with a stated choice experiment and psychometric scales was conducted. Besides currently available transport modes, also a privately-owned AV (PAV) and a shared AV (SAV) were included in the choice sets. The data was analysed using a hybrid choice model. T-test and confidence intervals of the estimated VTTS are computed to assess if the VTTS are statistically significant and statistically different among user groups. Results confirm that both psychological factors have significant positive effect on VTTS for AVs. Gender, age, level of education and experience with similar systems were found to affect the VTTS directly and indirectly through their impact on the individual attitudes. Significant differences are found among some potential user groups, in particular in terms of trust to technology and anticipated travel experiences. For example, men are found to trust the technology more than women and also to have potentially higher technology affinity. However, our results show that they also perceive higher marginal disutility for travel time in both PAV and SAV. A comparison between a mixed logit model and the hybrid choice model reveals that capturing the indirect effect of the socio-economic characteristics of individuals through the effect of these factors on attitudes allows differentiating between the VTTS for different user groups. Lastly, implications for policy and technology deployment strategies are discussed based on the findings. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) promise to significantly improve road traffic. To a certain extent, this situation is similar to the expectations at the end of the last century about the positive effects that the introduction of Adaptive Cruise Control (ACC) systems would have had on motorway traffic. The parallelism is interesting because ACC equipped vehicles represent the first level of vehicle automation and are now widely available on the market. In this light, studying ACC impacts can help to anticipate potential problems related to its widespread application and to avoid that AVs and CAVs will lead to the same problems. Only a few test-campaigns had been carried out studying the ACC impacts under real-world driving conditions in quantitative terms. To bridge this gap, the Joint Research Centre of the European Commission has organized a number of experimental campaigns involving several ACC-equipped vehicles to study different implications of their widespread. In this context, the present paper summarizes the outcomes of a test campaign involving 10 commercially available ACC-equipped vehicles. The test campaign has been executed in two different test-tracks of the ZalaZONE proving ground, in Hungary. The tests have been carried out at low-speeds (30–60 km/h) and have involved platoons of vehicles of different brands and different powertrains, which were tested in a variety of vehicle orders and with different settings of their ACC systems. Test results have been used to derive information about the properties of the different ACC systems, to study their string stability, to study the effect of ACC systems on traffic flow, and to draw inference about the possible implications on energy consumption and traffic safety. Results confirm the previous findings in terms of string instability of the ACC and highlight that in the present form, ACC systems will possibly lead to higher energy consumption and introduce new safety risks when their penetration in the fleet increases. However, they also highlight that the materialization of the above findings for AVs depends on the operational logic that manufacturers will adopt during the implementation phase. Therefore, results suggest that functional requirements to guarantee string stability and in general to not disrupt the normal flow of traffic should be introduced both for ACC and for any automated system that will be placed on the market in the future. [ABSTRACT FROM AUTHOR]

• Joint behavioral model is presented for partially connected automated and fully automated vehicles adoption. • The role of preference heterogeneity at different geographic levels is highlighted. • Individuals are more willing to adopt automated vehicles with connectivity. • Vehicle attributes end users prioritize the most in buying or leasing vehicles are analyzed. • Role of safety concerns, sustainable travel behavior, & demographic determinants is quantified. • Findings can enable prediction of the future use and ownership patterns of CAVs and FAVs. An adequate understanding of consumer affinity towards automated vehicles (AVs) is necessary to more reliably forecast the potential impacts of AVs on transportation system. The road to fully automated driving systems pass through partially automated driving systems. This study contributes by simultaneously analyzing consumer affinity towards partially (Level 3/4 automation) connected automated vehicles (CAVs) and fully AVs (FAVs) (Level 5 automation), and how it relates to sustainable travel behavior, user valuation of different vehicle attributes, (electric) vehicle ownership, and safety concerns. Unique data for over 3500 households from the 2017 California Vehicle Survey are harnessed. With an on-average positive correlation between consumer affinity towards partially CAVs and FAVs (Kendall's τ b of 0.506), significant contrasts are also observed. Compared to around 64% of the households with positive affinity towards partially CAVs, only 35% were willing to consider buying a FAV in future. Around 30% of the households had negative affinity for FAVs but positive affinity for partially CAVs. A comprehensive simulation-assisted joint bivariate ordered discrete outcome modeling framework is used, considering preference heterogeneity at different geographic/ecological levels, from the region to household level, and further within and across the choices for partially CAVs and FAVs. Participation in carshare, rideshare programs, and more transit trips per member were independently associated with a greater affinity towards partially CAVs and FAVs. Ownership of a plug-in electric vehicle was associated with a 19.9% and 9.6% increase in the likelihood of highest affinity levels towards partially CAVs and FAVs, respectively. Some evidence is found that consumers may possibly use shared AVs, but not necessarily in combination with ride sharing. Simultaneous correlations between affinity towards partially CAVs and FAVs and most valued vehicle attributes, safety concerns, and sociodemographic factors are quantified. Methodologically, the results underscore the importance of considering preference heterogeneity at different geographic/ecological levels. Captured through random parameters, the associations of several factors with consumer affinity towards partially CAVs and FAVs varied in magnitude and direction. In addition, statistically significant interactive effects (opposite and counterbalancing) of the latent unobserved factors underlying several random parameters were observed. The presence of multilayered preference heterogeneity highlights the personalized and complex nature of behavioral phenomenon underlying user acceptance of AVs. By reflecting the users' preferences towards CAVs and FAVs and characterizing the taste/preference heterogeneity at multiple levels, the findings can be integrated in larger travel forecasting models to more accurately predict the future-use and ownership patterns of CAVs and FAVs by various sociodemographic cohorts. [ABSTRACT FROM AUTHOR]

• A vast literature review unveiled confusion and ambiguities in car-following calibration. • A methodology based on Pareto-efficiency is proposed to compare calibration settings. • Calibration settings which are to be avoided are outlined. • The sum of NRMSE or Theil's U on spacing, speed and acceleration is recommended. • To promote and enable transparent and reproducible research codes and data are shared. A comprehensive literature review reveals that there exist lots of ambiguities, confusion and even contradictions in setting a car-following calibration problem. In particular, confusion arises in the selection of measure of performances and goodness-of-fit functions. In this study, a methodology to compare and rank objective functions is thus proposed, which is based on Pareto-efficiency and on indifference curves. The methodology has been applied to all objective functions used in the field literature so far (and to new ones), in a vast set of calibration experiments. The experiments involved two car-following models and two adaptive cruise control (ACC) algorithms, and four different datasets, including both automated and human-driven vehicles trajectories. Since results are consistent among all the calibration experiments, a sound and robust guideline to calibrate car-following dynamics has been proposed. It includes recommendation about what calibration settings should be avoided and what are to be adopted. On the one hand, a general agreement on a sound calibration setting for car-following models is deemed necessary for comparing results from different studies which use different models and datasets. On the other hand, any new car-following model or objective function being developed in the future shall be compared with existing ones in a fair and impartial manner. For these reasons, and to promote and enable transparent and reproducible research, codes and data from this study are shared with the community. [ABSTRACT FROM AUTHOR]

• Collect trajectory data of commercial automated vehicles with different headway settings. • Estimate parameters on safety, mobility and stability of commercial automated vehicles. • Discover trade-offs between commercial automated vehicle following characteristics. • Validate several theoretical predictions in Li (2020). A recent study (Li, 2020) analytically predicted tradeoffs between automated vehicle (AV) following characteristics on safety, mobility, and stability using a parsimonious linear car-following model. This work aimed to verify the key theoretical findings in the above study with empirical experiments using commercial AVs, e.g., vehicles with adaptive cruise control (ACC) functions. We collect high-resolution trajectory data of multiple commercial AVs following one another in a platoon with different headway settings. Parsimonious linear AV-following models that capture the first-order parameters on safety, mobility, and stability aspects are estimated with the data. The estimation results of the key parameters validate several theoretical predictions predicted by Li (2020). Specifically, it was found that as the time lag setting increases, the corresponding safety buffer decreases, indicating that AV safety could be improved with less pursuit of AV mobility or, conversely, AV mobility improvement may come at a cost of more stringent safety requirements. Also, as the time lag setting increases, AV string stability increases, indicating that stop-and-go traffic potentially could be dampened by compromising AV mobility. With this, one possible explanation to the observed string instability of commercial AV following control (i.e., ACC function) is that automakers may prefer to ensure a relatively short headway (and thus better user experience on vehicle mobility) at a cost of compromising string stability. It was also found that as the time lag increases, the cycle period of traffic oscillations gets longer, and the oscillation amplification gets smaller, which supports the tradeoff between mobility and stability. On the other hand, field experiments revealed issues beyond the predictivity of a simple linear model. That is, vehicle control sensitivity factors vary across different speed and headway settings, and the model estimation results for key parameters are not consistent over different speed ranges. This opens future research needs for investigating nonlinearity and stochasticity in the AV following modeling. [ABSTRACT FROM AUTHOR]

• The general assumptions of automated vehicles in traffic flow studies are discussed. • The automated vehicle-involved traffic flow studies are systematically reviewed. • The review is conducted from the microscopic, mesoscopic and macroscopic perspectives. • The pros and cons of the existing models and approaches are critically discussed. Automated vehicles (AVs) are widely considered to play a crucial role in future transportation systems because of their speculated capabilities in improving road safety, saving energy consumption, reducing vehicle emissions, increasing road capacity, and stabilizing traffic. To materialize these widely expected potentials of AVs, a sound understanding of AVs' impacts on traffic flow is essential. Not surprisingly, despite the relatively short history of AVs, there have been numerous studies in the literature focusing on understanding and modeling various aspects of AV-involved traffic flow and significant progresses have already been made. To understand the recent development and ultimately inspire new research ideas on this important topic, this survey systematically and comprehensively reviews the existing AV-involved traffic flow models with different levels of details, and examines the relationship among the design of AV-based driving strategies, the management of transportation systems, and the resulting traffic dynamics. The pros and cons of the existing models and approaches are critically discussed, and future research directions are also provided. [ABSTRACT FROM AUTHOR]

• Investigate the parking decision of privately owned AVs in the downtown. • AVs parking choices may yield multiple equilibria with different social welfare. • AVs may choose to cruise instead of parking, which may worsen traffic congestion • A time-based congestion toll may be necessary to manage AVs parking in the downtown. Automated vehicles (AVs) eliminate the burden of finding a parking spot upon arrival to the destination, because they can park at a strategic location or cruise until summoned by their users. In this study, we investigate the parking choices of privately-owned AVs in a downtown area. Since each user's choice has impacts on another via cruising-incurred traffic congestion and parking competition, we model the parking choice problem of AVs as a Wardrop equilibrium in which each user cannot further reduce their parking cost by unilaterally changing their choice. The model considers all possible options for parking a private AV, and finds that these parking choices may yield multiple equilibria under which the congestion and social welfare are very different. We further develop an efficient solution algorithm to find all equilibrium solutions. Our analysis shows that even if AVs act in a non-cooperative manner, one possible outcome would involve many AVs choosing to cruise, which creates severe congestion to decrease the cost of cruising. However, this outcome can be avoided by a time-based congestion toll, which discourages AVs from cruising for a long period and increases the social welfare. Our analysis also shows parking pricing and provision may not reduce congestion induced by cruising AVs without the help of a congestion toll. [ABSTRACT FROM AUTHOR]

• People in the U.S. still have reservations about AVs, ones that are fading over time. • Some people believe they will multitask while riding AVs while others believe they will not. • The decrease in VOT will range from 5% to 90%. • Results on changes in residential location decisions are conflicting. • People prefer owning AVs over sharing them. Pooling is the least preferred AV option. • VMT increase will range from a low of 1% to a high of 90% depending on the scenario. While research on developing and testing automated vehicle (AVs) technologies is well underway, research on their implications on travel-related behavior is in its infancy. The aim of this paper is to summarize and analyze literature that focuses on travel-related behavior impacts of AVs, namely levels 4 and 5, as well as highlight important directions of research. We review five methods used to quantitatively investigate these implications and how each method contributes to this literature: 1) controlled testbeds, 2) driving simulators and virtual reality, 3) agent-based and travel-demand models, 4) surveys, and 5) field experiments. We also present five critical research questions regarding the implications of AVs on the demand side of transportation and summarize findings from the current literature on: 1) what is the willingness to adopt the technology? and what are the impacts of the technology on 2) in-vehicle behavior? 3) value of time? 4) travel-related behaviors (activity pattern, mode, destination, residential location)? and 5) vehicle miles traveled (VMT)? Results can be divided into four categories. The first category corresponds to results on research questions with numerous data points where the direction of the impact is consistent across the literature, albeit the magnitude varies considerably. For instance, surveys indicate 19% to 68% of people are unwilling to adopt AV technology, a sentiment that is fading over time. Moreover, people prefer owning AVs over sharing them and don't believe their car ownership will decrease. Regarding VMT, most studies predict an increase that varies from a low of 1% to a high of 90% depending on the scenario and assumptions under study. The second category of findings corresponds to research questions with limited and consistent, albeit highly variable data points. For example, a few stated preference survey studies indicate that reduced stress and multitasking during travel will reduce the value of time between 5% and 90%. The third category of results is on research questions with a few but conflicting data points. For instance, surveys indicate that people (80% to 85%) do not believe their residential location will be impacted by the adoption of AVs. Some simulation studies, however, indicate that lower travel costs will drive people away from cities and into suburbs while other studies report the opposite. The final category of results corresponds to research question with a single or no data points. For instance, one study explores how users will use vehicles to run errands while no studies investigate user preferences for vehicle types (e.g. mobile-homes vs. right-sized) or how they plan to use their vehicles when they are not needed (e.g. rent out vs. leave them idle). Moving forward, the goal is to shift all results into the first category while simultaneously tightening the prediction interval of the magnitude of the impacts. This can be achieved by: 1) focusing more efforts on research questions that fall under the three remaining categories to fill the holes in the literature, and 2) establishing consistency and clarity of assumptions used by researchers to enable comparisons and transferability of results. [ABSTRACT FROM AUTHOR]

• An innovative idea of implementing a mixed-use BRT/AV lane by allowing AVs to use dedicated BRT lanes. • A quantitative evaluation model for the performance on the BRT/AV lane under different traffic inputs. • An optimal assignment model of AVs on the entire corridor of general-purpose lanes and the mixed-use lane. • Both numerical experiments and simulation experiments to evaluate the effectiveness of the proposed methods. The establishment of dedicated automated vehicle (AV) lanes has been regarded as an effective approach for addressing heterogeneous traffic with both AVs and regular vehicles (RVs), promoting both traffic efficiency and safety. However, building new dedicated AV lanes in urban areas is not cost effective in the early stage of AV adoption. Fortunately, dedicated bus rapid transit (BRT) lanes can provide a separate right-of-way (ROW) for AVs, which is a practical and economical alternative for promoting AV development. In this paper, we propose an innovative idea of allowing AVs to use dedicated BRT lanes, and quantitatively analyze the stationary performance of mixed-use lanes. Specifically, the analysis is conducted through a cyclic space–time model for AVs on the mixed-use lane, and a sequential optimization method (SOM) is proposed to approximately solve the model. With the SOM providing a valid tool for performance evaluation, we then develop an assignment model for the routing of AVs on a traffic corridor with both mixed-use lane and general-purpose lanes to minimize the total travel time of both AVs and RVs. The model is formulated as a black-box nonlinear program without an explicit analytical form; a successive linear programming (SLP) algorithm with finite difference for gradient approximation is then utilized to solve the nonlinear program. Numerical experiments are conducted in different scenarios, which reveal that the establishment of the mixed-use lane can not only improve the efficiency of AVs but also alleviate the congestion on general-purpose lanes. [ABSTRACT FROM AUTHOR]

• Conflict-free routing algorithms for automated vehicles in dedicated zones. • Conflict-point networks with time occupations on conflict points. • Dynamic platoon strategy to reduce delay in heavy traffic. • Deep reinforcement learning for optimizing the platoon sizes. With the fast development of automated vehicle (AV) technologies, scholars have proposed various innovative local traffic control schemes for more effective management of AV traffic, especially at intersections. However, due to computational intractability, the investigation of network-level AV control is still at the initial stage. This study proposes a space-time routing framework applicable in dedicated AV zones. To relieve the computational load, we establish a node-based conflict point network to model realistic road networks, and at each conflict point, we record the space-time occupations of AVs in continuous timelines. Then, based on the conflict point network, we develop two space-time routing algorithms for each AV once it enters the dedicated AV zone to minimize its trip travel time while maintaining the non-collision insurances; these two algorithms can trade-off between solution quality and computational load. Furthermore, to enhance the network throughput for handling heavy traffic, we develop a "platoon strategy" that forces AVs to pass through conflict points in platoons, and we adopt Deep Q-learning (DQN) to optimize the platoon sizes at different spots dynamically. Numerical tests show that both proposed algorithms perform well in that they can execute the routing tasks with very limited computational time, and the average vehicle delay approaches zero when the traffic is relatively mild. Meanwhile, compared with the FCFS policy and the optimization-based approach, the platoon strategy can greatly reduce the average vehicle delay under congested scenarios and give a better balance between the optimality and real-time performance. [ABSTRACT FROM AUTHOR]